PythonDataScience-book-learning-Chapter3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第 3 章　Pandas数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% 3.2\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.50\n",
       "2    0.75\n",
       "3    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.50\n",
       "2    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "                index = ['a', 'b', 'c', 'd'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.25\n",
       "5    0.50\n",
       "3    0.75\n",
       "7    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "    index=[2, 5, 3, 7])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "New York      19651127\n",
       "Texas         26448193\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {'California': 38332521,\n",
    "'Texas': 26448193,\n",
    "'New York': 19651127,\n",
    "'Florida': 19552860,\n",
    "'Illinois': 12882135}\n",
    "population = pd.Series(population_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38332521"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['California':'Illinois']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([2,4,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    5\n",
       "200    5\n",
       "300    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(5, index=[100, 200, 300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    a\n",
       "3    b\n",
       "4    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({2:'a', 3:'b', 4:'c'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    a\n",
       "4    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({2:'a', 3:'b', 4:'c'}, index={4,2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %% 3.2.2\n",
    "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,\n",
    "'Florida': 170312, 'Illinois': 149995}\n",
    "area = pd.Series(area_dict)\n",
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>poplulation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  poplulation\n",
       "California  423967     38332521\n",
       "Florida     170312     19552860\n",
       "Illinois    149995     12882135\n",
       "New York    141297     19651127\n",
       "Texas       695662     26448193"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = pd.DataFrame({'poplulation': population, 'area': area})\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['California', 'Florida', 'Illinois', 'New York', 'Texas'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area', 'poplulation'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
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      "text/plain": [
       "            population\n",
       "California    38332521\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "New York      19651127\n",
       "Texas         26448193"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 通过单个 Series 对象创建\n",
    "pd.DataFrame(population, columns=['population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   a  b\n",
       "0  0  0\n",
       "1  1  2\n",
       "2  2  4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 通过字典列表创建\n",
    "data = [{'a':i, 'b':2*i} for i in range(3)]\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
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      "text/plain": [
       "     a  b    c\n",
       "0  1.0  2  NaN\n",
       "1  NaN  3  4.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([{'a':1, 'b':2}, {'b':3, 'c':4}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>poplulation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              area  poplulation\n",
       "California  423967     38332521\n",
       "Florida     170312     19552860\n",
       "Illinois    149995     12882135\n",
       "New York    141297     19651127\n",
       "Texas       695662     26448193"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (3) 通过 Series 对象字典创建\n",
    "pd.DataFrame({'poplulation': population, 'area': area})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "      <th>a</th>\n",
       "      <td>0.732568</td>\n",
       "      <td>0.661308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.164905</td>\n",
       "      <td>0.966081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.638873</td>\n",
       "      <td>0.428983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        foo       bar\n",
       "a  0.732568  0.661308\n",
       "b  0.164905  0.966081\n",
       "c  0.638873  0.428983"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (4) 通过 NumPy 二维数组创建\n",
    "pd.DataFrame(np.random.rand(3,2),\n",
    "            columns = ['foo', 'bar'],\n",
    "            index = ['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(0, 0.), (0, 0.), (0, 0.)], dtype=[('A', '<i8'), ('B', '<f8')])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (5) 通过 NumPy 结构化数组创建\n",
    "a = np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B\n",
       "0  0  0.0\n",
       "1  0  0.0\n",
       "2  0  0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 3, 5, 7, 11], dtype='int64')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.2.3\n",
    "ind = pd.Index([2,3,5,7,11])\n",
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 5, 11], dtype='int64')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, (5,), 1, dtype('int64'))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind.size, ind.shape, ind.ndim, ind.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 5, 7], dtype='int64')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indA = pd.Index([1,3,5,7,9])\n",
    "indB = pd.Index([2,3,5,7,11])\n",
    "indA & indB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indA | indB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 9, 11], dtype='int64')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indA ^ indB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 5, 7], dtype='int64')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indA.intersection(indB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% 3.3　数据取值与选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.3.1\n",
    "# 1. Series as dict\n",
    "import pandas as pd\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "index=['a', 'b', 'c', 'd'])\n",
    "data['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'a' in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a', 0.25), ('b', 0.5), ('c', 0.75), ('d', 1.0)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "e    1.23\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['e'] =1.23\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. series as one dimension array\n",
    "data['a':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ，当使用显式索引（即 data['a':'c']）作切片时，结果包含最后一个索引；\n",
    "#而当使用隐式索引（即 data[0:2]）作切片时，结果不包含最后一个索引\n",
    "data[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0.50\n",
       "c    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data [(data>0.3) & (data<0.8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "e    1.23\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['a', 'e']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. loc、iloc和ix\n",
    "data = pd.Series(['a', 'b', 'c'], index=[1,3,5])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:3] # 隐式索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('a', 1    a\n",
       " 3    b\n",
       " dtype: object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1], data.loc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Florida     170312  19552860\n",
       "Illinois    149995  12882135\n",
       "New York    141297  19651127\n",
       "Texas       695662  26448193"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.3.2\n",
    "area = pd.Series({'California': 423967, 'Texas': 695662,\n",
    "'New York': 141297, 'Florida': 170312,\n",
    "'Illinois': 149995})\n",
    "pop = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
    "'New York': 19651127, 'Florida': 19552860,\n",
    "'Illinois': 12882135})\n",
    "data = pd.DataFrame({'area':area,  'pop':pop})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.area is data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pop is data['pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop     density\n",
       "California  423967  38332521   90.413926\n",
       "Florida     170312  19552860  114.806121\n",
       "Illinois    149995  12882135   85.883763\n",
       "New York    141297  19651127  139.076746\n",
       "Texas       695662  26448193   38.018740"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['density'] = data['pop'] / data['area']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.23967000e+05, 3.83325210e+07, 9.04139261e+01],\n",
       "       [1.70312000e+05, 1.95528600e+07, 1.14806121e+02],\n",
       "       [1.49995000e+05, 1.28821350e+07, 8.58837628e+01],\n",
       "       [1.41297000e+05, 1.96511270e+07, 1.39076746e+02],\n",
       "       [6.95662000e+05, 2.64481930e+07, 3.80187404e+01]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th></th>\n",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>Illinois</th>\n",
       "      <th>New York</th>\n",
       "      <th>Texas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>4.239670e+05</td>\n",
       "      <td>1.703120e+05</td>\n",
       "      <td>1.499950e+05</td>\n",
       "      <td>1.412970e+05</td>\n",
       "      <td>6.956620e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop</th>\n",
       "      <td>3.833252e+07</td>\n",
       "      <td>1.955286e+07</td>\n",
       "      <td>1.288214e+07</td>\n",
       "      <td>1.965113e+07</td>\n",
       "      <td>2.644819e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>9.041393e+01</td>\n",
       "      <td>1.148061e+02</td>\n",
       "      <td>8.588376e+01</td>\n",
       "      <td>1.390767e+02</td>\n",
       "      <td>3.801874e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           California       Florida      Illinois      New York         Texas\n",
       "area     4.239670e+05  1.703120e+05  1.499950e+05  1.412970e+05  6.956620e+05\n",
       "pop      3.833252e+07  1.955286e+07  1.288214e+07  1.965113e+07  2.644819e+07\n",
       "density  9.041393e+01  1.148061e+02  8.588376e+01  1.390767e+02  3.801874e+01"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.23967000e+05, 3.83325210e+07, 9.04139261e+01])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <tbody>\n",
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       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
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       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
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      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Florida     170312  19552860\n",
       "Illinois    149995  12882135"
      ]
     },
     "execution_count": 70,
     "metadata": {},
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    }
   ],
   "source": [
    "data.iloc[:3, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Florida     170312  19552860\n",
       "Illinois    149995  12882135"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:'Illions', :'pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jinjia\\Anaconda3\\envs\\Python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
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       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
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       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
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      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Florida     170312  19552860\n",
       "Illinois    149995  12882135"
      ]
     },
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     "metadata": {},
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   "source": [
    "data.ix[:3, :'pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
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       "    <tr>\n",
       "      <th>New York</th>\n",
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      ],
      "text/plain": [
       "               pop     density\n",
       "Florida   19552860  114.806121\n",
       "New York  19651127  139.076746"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.loc[data.density>100, ['pop', 'density']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.000000</td>\n",
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       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop     density\n",
       "California  423967  38332521   90.000000\n",
       "Florida     170312  19552860  114.806121\n",
       "Illinois    149995  12882135   85.883763\n",
       "New York    141297  19651127  139.076746\n",
       "Texas       695662  26448193   38.018740"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0,2] = 90\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
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      ],
      "text/plain": [
       "            area       pop     density\n",
       "Florida   170312  19552860  114.806121\n",
       "Illinois  149995  12882135   85.883763"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Florida':'Illinois']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
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       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
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      "text/plain": [
       "            area       pop     density\n",
       "Florida   170312  19552860  114.806121\n",
       "Illinois  149995  12882135   85.883763"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area       pop     density\n",
       "Florida   170312  19552860  114.806121\n",
       "New York  141297  19651127  139.076746"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.density > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% 3.4　Pandas数值运算方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    3\n",
       "2    7\n",
       "3    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.4.1\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "rng = np.random.RandomState(42)\n",
    "ser = pd.Series(rng.randint(0,10,4))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d\n",
       "0  6  9  2  6\n",
       "1  7  4  3  7\n",
       "2  7  2  5  4"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(rng.randint(0,10,(3,4)),columns = ['a','b', 'c', 'd'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     403.428793\n",
       "1      20.085537\n",
       "2    1096.633158\n",
       "3      54.598150\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.224647e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a             b         c             d\n",
       "0 -1.000000  7.071068e-01  1.000000 -1.000000e+00\n",
       "1 -0.707107  1.224647e-16  0.707107 -7.071068e-01\n",
       "2 -0.707107  1.000000e+00 -0.707107  1.224647e-16"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(df * np.pi / 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alaska              NaN\n",
       "California    90.413926\n",
       "New York            NaN\n",
       "Texas         38.018740\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.42\n",
    "# 01. Series 索引对齐\n",
    "area = pd.Series({'Alaska': 1723337, 'Texas': 695662,\n",
    "'California': 423967}, name='area')\n",
    "population = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
    "'New York': 19651127}, name = 'population')\n",
    "population / area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alaska', 'California', 'New York', 'Texas'], dtype='object')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.index | population.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    5.0\n",
       "2    9.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series([2,4,6], index=[0,1,2])\n",
    "b = pd.Series([1,3,5], index=[1,2,3])\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    5.0\n",
       "2    9.0\n",
       "3    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.add(b, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
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       "</div>"
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      "text/plain": [
       "   A   B\n",
       "0  1  11\n",
       "1  5   1"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 02. DataFrame索引对齐\n",
    "a = pd.DataFrame(rng.randint(0, 20, (2,2)),\n",
    "                columns = list('AB'))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   B  A  C\n",
       "0  3  8  2\n",
       "1  4  2  6\n",
       "2  4  8  6"
      ]
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     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "b = pd.DataFrame(rng.randint(0,10, (3, 3)),\n",
    "                columns = list('BAC'))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
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      "text/plain": [
       "     A     B   C\n",
       "0  9.0  14.0 NaN\n",
       "1  7.0   5.0 NaN\n",
       "2  NaN   NaN NaN"
      ]
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     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill = a.stack().mean()\n",
    "fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      A     B     C\n",
       "0   9.0  14.0   6.5\n",
       "1   7.0   5.0  10.5\n",
       "2  12.5   8.5  10.5"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "a.add(b, fill_value=fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 3, 1],\n",
       "       [7, 3, 1, 5],\n",
       "       [5, 9, 3, 5]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.4.3\n",
    "a = rng.randint(10, size=(3,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0],\n",
       "       [ 5,  3, -2,  4],\n",
       "       [ 3,  9,  0,  4]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a - a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "   Q  R  S  T\n",
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     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame(a, columns=list('QRST'))\n",
    "df - df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4</td>\n",
       "      <td>0</td>\n",
       "      <td>-6</td>\n",
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      "text/plain": [
       "   Q  R  S  T\n",
       "0  2  0  3  1\n",
       "1  4  0 -2  2\n",
       "2 -4  0 -6 -4"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.subtract(df['R'], axis=0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q    2\n",
       "S    3\n",
       "Name: 0, dtype: int32"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "halfrow = df.iloc[0, ::2]\n",
    "halfrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q   R    S   T\n",
       "0  0.0 NaN  0.0 NaN\n",
       "1  5.0 NaN -2.0 NaN\n",
       "2  3.0 NaN  0.0 NaN"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df - halfrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  R  S  T\n",
       "0  2  0  3  1\n",
       "1  7  3  1  5\n",
       "2  5  9  3  5"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% 3.5　处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, None, 3, 4], dtype=object)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.5.2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "vals1 = np.array([1, None, 3, 4])\n",
    "vals1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtype = object\n",
      "227 ms ± 21.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "\n",
      "dtype = int\n",
      "2.81 ms ± 149 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for dtype in ['object', 'int']:\n",
    "    print(\"dtype =\", dtype)\n",
    "    %timeit np.arange(1E6, dtype=dtype).sum()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-116-30a3fc8c6726>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvals1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\Python36\\lib\\site-packages\\numpy\\core\\_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[1;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
     ]
    }
   ],
   "source": [
    "vals1.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals2 = np.array([1, np.nan, 3, 4])\n",
    "vals2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals2.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.0, 1.0)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum(vals2), np.nanmin(vals2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    2.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1, np.nan, 2, None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, None, 3, nan], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, None, 3, np.nan])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "dtype: int32"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Series(range(2), dtype = int)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0] = None\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.5.3\n",
    "data = pd.Series([1, np.nan, 'hello', None])\n",
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
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       "     0    1  2\n",
       "0  1.0  NaN  2\n",
       "1  2.0  3.0  5\n",
       "2  NaN  4.0  6"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1, np.nan, 2],\n",
    "                    [2, 3, 5],\n",
    "                    [np.nan, 4, 6]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     0    1  2\n",
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   "source": [
    "df.dropna()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   2\n",
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     "execution_count": 132,
     "metadata": {},
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   "source": [
    "df.dropna(axis=1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2   3\n",
       "0  1.0  NaN  2 NaN\n",
       "1  2.0  3.0  5 NaN\n",
       "2  NaN  4.0  6 NaN"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2\n",
       "0  1.0  NaN  2\n",
       "1  2.0  3.0  5\n",
       "2  NaN  4.0  6"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 1, how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     0    1  2   3\n",
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     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0, thresh = 3) # 非缺失值的最少个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    NaN\n",
       "c    2.0\n",
       "d    NaN\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1, np.nan, 2, None, 3], index = list('abcde'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    0.0\n",
       "c    2.0\n",
       "d    0.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    2.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "     0    1  2   3\n",
       "0  1.0  NaN  2 NaN\n",
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       "2  NaN  4.0  6 NaN"
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     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "     0    1    2    3\n",
       "0  1.0  1.0  2.0  2.0\n",
       "1  2.0  3.0  5.0  5.0\n",
       "2  NaN  4.0  6.0  6.0"
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     "execution_count": 143,
     "metadata": {},
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   ],
   "source": [
    "df.fillna(method='ffill', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% 3.6　层级索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(California, 2000)    33871648\n",
       "(California, 2010)    37253956\n",
       "(New York, 2000)      18976457\n",
       "(New York, 2010)      19378102\n",
       "(Texas, 2000)         20851820\n",
       "(Texas, 2010)         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.6.1 \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "index = [('California', 2000), ('California', 2010),\n",
    "('New York', 2000), ('New York', 2010),\n",
    "('Texas', 2000), ('Texas', 2010)]\n",
    "populations = [33871648, 37253956,\n",
    "18976457, 19378102,\n",
    "20851820, 25145561]\n",
    "pop = pd.Series(populations, index = index)\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(California, 2010)    37253956\n",
       "(New York, 2000)      18976457\n",
       "(New York, 2010)      19378102\n",
       "(Texas, 2000)         20851820\n",
       "(Texas, 2010)         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[('California', 2010):('Texas', 2010)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(California, 2010)    37253956\n",
       "(New York, 2010)      19378102\n",
       "(Texas, 2010)         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[[i for i in pop.index if i[1] == 2010]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['California', 'New York', 'Texas'], [2000, 2010]],\n",
       "           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_tuples(index)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop=pop.reindex(index)\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    37253956\n",
       "New York      19378102\n",
       "Texas         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[:,2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>33871648</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>18976457</td>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>20851820</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                2000      2010\n",
       "California  33871648  37253956\n",
       "New York    18976457  19378102\n",
       "Texas       20851820  25145561"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = pop.unstack()\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>total</th>\n",
       "      <th>under18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">California</th>\n",
       "      <th>2000</th>\n",
       "      <td>33871648</td>\n",
       "      <td>9267089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>37253956</td>\n",
       "      <td>9284094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
       "      <th>2000</th>\n",
       "      <td>18976457</td>\n",
       "      <td>4687374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>19378102</td>\n",
       "      <td>4318033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
       "      <th>2000</th>\n",
       "      <td>20851820</td>\n",
       "      <td>5906301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>25145561</td>\n",
       "      <td>6879014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total  under18\n",
       "California 2000  33871648  9267089\n",
       "           2010  37253956  9284094\n",
       "New York   2000  18976457  4687374\n",
       "           2010  19378102  4318033\n",
       "Texas      2000  20851820  5906301\n",
       "           2010  25145561  6879014"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = pd.DataFrame({'total': pop,\n",
    "'under18': [9267089, 9284094,\n",
    "4687374, 4318033,\n",
    "5906301, 6879014]})\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    0.273594\n",
       "            2010    0.249211\n",
       "New York    2000    0.247010\n",
       "            2010    0.222831\n",
       "Texas       2000    0.283251\n",
       "            2010    0.273568\n",
       "dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_u18 = pop_df['under18'] / pop_df['total']\n",
    "f_u18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>0.273594</td>\n",
       "      <td>0.249211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>0.247010</td>\n",
       "      <td>0.222831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.283251</td>\n",
       "      <td>0.273568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2000      2010\n",
       "California  0.273594  0.249211\n",
       "New York    0.247010  0.222831\n",
       "Texas       0.283251  0.273568"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_u18.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0.105402</td>\n",
       "      <td>0.917690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.959530</td>\n",
       "      <td>0.527222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>0.201880</td>\n",
       "      <td>0.413224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.887327</td>\n",
       "      <td>0.315086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        data1     data2\n",
       "a 1  0.105402  0.917690\n",
       "  2  0.959530  0.527222\n",
       "b 1  0.201880  0.413224\n",
       "  2  0.887327  0.315086"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.6.2\n",
    "df = pd.DataFrame(np.random.rand(4, 2),\n",
    "index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\n",
    "columns=['data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {('California', 2000): 33871648,\n",
    "('California', 2010): 37253956,\n",
    "('Texas', 2000): 20851820,\n",
    "('Texas', 2010): 25145561,\n",
    "('New York', 2000): 18976457,\n",
    "('New York', 2010): 19378102}\n",
    "pd.Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_product([['a', 'b'], [1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
    "labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state       year\n",
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.index.names = ['state', 'year']\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>33871648</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>18976457</td>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>20851820</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year            2000      2010\n",
       "state                         \n",
       "California  33871648  37253956\n",
       "New York    18976457  19378102\n",
       "Texas       20851820  25145561"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[58. , 36.5, 50. , 37.7, 37. , 37. ],\n",
       "       [22. , 38. , 21. , 35.3, 47. , 36.2],\n",
       "       [42. , 35.7, 38. , 37.4, 30. , 37.7],\n",
       "       [37. , 37.1, 33. , 36.2, 61. , 38.3]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],\n",
    "                            names=['year', 'visit'])\n",
    "columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],\n",
    "                    names=['subject', 'type'])\n",
    "data = np.round(np.random.randn(4,6), 1)\n",
    "data[:, ::2] *= 10\n",
    "data += 37\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>58.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      58.0  36.5  50.0  37.7  37.0  37.0\n",
       "     2      22.0  38.0  21.0  35.3  47.0  36.2\n",
       "2014 1      42.0  35.7  38.0  37.4  30.0  37.7\n",
       "     2      37.0  37.1  33.0  36.2  61.0  38.3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data = pd.DataFrame(data, index = index, columns = columns)\n",
    "health_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>52.0</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.0</td>\n",
       "      <td>36.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>45.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.0</td>\n",
       "      <td>36.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type          HR  Temp\n",
       "year visit            \n",
       "2013 1      52.0  36.8\n",
       "     2      16.0  36.6\n",
       "2014 1      45.0  34.0\n",
       "     2      41.0  36.4"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data['Guido']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(California, 2000)    33871648\n",
       "(California, 2010)    37253956\n",
       "(New York, 2000)      18976457\n",
       "(New York, 2010)      19378102\n",
       "(Texas, 2000)         20851820\n",
       "(Texas, 2010)         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.6.3\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_tuples(index)\n",
    "pop=pop.reindex(index)\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33871648"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop['California', 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000    33871648\n",
       "2010    37253956\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop['California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop['California':'New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    33871648\n",
       "New York      18976457\n",
       "Texas         20851820\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[:,2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "Texas       2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[pop>22000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[['California', 'Texas']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>58.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      58.0  36.5  50.0  37.7  37.0  37.0\n",
       "     2      22.0  38.0  21.0  35.3  47.0  36.2\n",
       "2014 1      42.0  35.7  38.0  37.4  30.0  37.7\n",
       "     2      37.0  37.1  33.0  36.2  61.0  38.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  visit\n",
       "2013  1        50.0\n",
       "      2        21.0\n",
       "2014  1        38.0\n",
       "      2        33.0\n",
       "Name: (Guido, HR), dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data['Guido', 'HR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>58.0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob      \n",
       "type          HR  Temp\n",
       "year visit            \n",
       "2013 1      58.0  36.5\n",
       "     2      22.0  38.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data.iloc[:2, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  visit\n",
       "2013  1        58.0\n",
       "      2        22.0\n",
       "2014  1        42.0\n",
       "      2        37.0\n",
       "Name: (Bob, HR), dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data.loc[:, ('Bob', 'HR')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>Bob</th>\n",
       "      <th>Guido</th>\n",
       "      <th>Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>HR</th>\n",
       "      <th>HR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>1</th>\n",
       "      <td>58.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <th>1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob Guido   Sue\n",
       "type          HR    HR    HR\n",
       "year visit                  \n",
       "2013 1      58.0  50.0  37.0\n",
       "2014 1      42.0  38.0  30.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.IndexSlice\n",
    "health_data.loc[idx[:,1], idx[:,'HR']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(slice(None, None, None), slice(None, None, None))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "char  int\n",
       "a     1      0.535642\n",
       "      2      0.855558\n",
       "c     1      0.002988\n",
       "      2      0.550488\n",
       "b     1      0.930810\n",
       "      2      0.762377\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.6.4\n",
    "index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]])\n",
    "data = pd.Series(np.random.rand(6), index=index)\n",
    "data.index.names = ['char', 'int']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.errors.UnsortedIndexError'>\n",
      "'Key length (1) was greater than MultiIndex lexsort depth (0)'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    data['a':'b']\n",
    "except KeyError as e:\n",
    "    print(type(e))\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "char  int\n",
       "a     1      0.535642\n",
       "      2      0.855558\n",
       "b     1      0.930810\n",
       "      2      0.762377\n",
       "c     1      0.002988\n",
       "      2      0.550488\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.sort_index()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "char  int\n",
       "a     1      0.535642\n",
       "      2      0.855558\n",
       "b     1      0.930810\n",
       "      2      0.762377\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['a':'b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>California</th>\n",
       "      <th>New York</th>\n",
       "      <th>Texas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>33871648</td>\n",
       "      <td>18976457</td>\n",
       "      <td>20851820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>37253956</td>\n",
       "      <td>19378102</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      California  New York     Texas\n",
       "2000    33871648  18976457  20851820\n",
       "2010    37253956  19378102  25145561"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>33871648</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>18976457</td>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>20851820</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2000      2010\n",
       "California  33871648  37253956\n",
       "New York    18976457  19378102\n",
       "Texas       20851820  25145561"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.unstack(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000  California    33871648\n",
       "      New York      18976457\n",
       "      Texas         20851820\n",
       "2010  California    37253956\n",
       "      New York      19378102\n",
       "      Texas         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.unstack(level=0).stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>2000</td>\n",
       "      <td>33871648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>California</td>\n",
       "      <td>2010</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York</td>\n",
       "      <td>2000</td>\n",
       "      <td>18976457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>2010</td>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas</td>\n",
       "      <td>2000</td>\n",
       "      <td>20851820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Texas</td>\n",
       "      <td>2010</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      level_0  level_1  population\n",
       "0  California     2000    33871648\n",
       "1  California     2010    37253956\n",
       "2    New York     2000    18976457\n",
       "3    New York     2010    19378102\n",
       "4       Texas     2000    20851820\n",
       "5       Texas     2010    25145561"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_flat = pop.reset_index(name = 'population')\n",
    "pop_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>2000</td>\n",
       "      <td>33871648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>California</td>\n",
       "      <td>2010</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York</td>\n",
       "      <td>2000</td>\n",
       "      <td>18976457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>2010</td>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas</td>\n",
       "      <td>2000</td>\n",
       "      <td>20851820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Texas</td>\n",
       "      <td>2010</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state  year  population\n",
       "0  California  2000    33871648\n",
       "1  California  2010    37253956\n",
       "2    New York  2000    18976457\n",
       "3    New York  2010    19378102\n",
       "4       Texas  2000    20851820\n",
       "5       Texas  2010    25145561"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_flat.columns = (['state', 'year', 'population'])\n",
    "pop_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">California</th>\n",
       "      <th>2000</th>\n",
       "      <td>33871648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
       "      <th>2000</th>\n",
       "      <td>18976457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
       "      <th>2000</th>\n",
       "      <td>20851820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 population\n",
       "state      year            \n",
       "California 2000    33871648\n",
       "           2010    37253956\n",
       "New York   2000    18976457\n",
       "           2010    19378102\n",
       "Texas      2000    20851820\n",
       "           2010    25145561"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_flat.set_index(['state', 'year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>58.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      58.0  36.5  50.0  37.7  37.0  37.0\n",
       "     2      22.0  38.0  21.0  35.3  47.0  36.2\n",
       "2014 1      42.0  35.7  38.0  37.4  30.0  37.7\n",
       "     2      37.0  37.1  33.0  36.2  61.0  38.3"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.6.5\n",
    "health_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>40.0</td>\n",
       "      <td>37.25</td>\n",
       "      <td>35.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>36.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>39.5</td>\n",
       "      <td>36.40</td>\n",
       "      <td>35.5</td>\n",
       "      <td>36.8</td>\n",
       "      <td>45.5</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject   Bob        Guido         Sue      \n",
       "type       HR   Temp    HR  Temp    HR  Temp\n",
       "year                                        \n",
       "2013     40.0  37.25  35.5  36.5  42.0  36.6\n",
       "2014     39.5  36.40  35.5  36.8  45.5  38.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mean = health_data.mean(level='year')\n",
    "data_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>39.166667</td>\n",
       "      <td>36.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>40.166667</td>\n",
       "      <td>37.066667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type         HR       Temp\n",
       "year                      \n",
       "2013  39.166667  36.783333\n",
       "2014  40.166667  37.066667"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mean.mean(axis =1, level='type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.7　合并数据集：Concat与Append操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a0</td>\n",
       "      <td>b0</td>\n",
       "      <td>c0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a1</td>\n",
       "      <td>b1</td>\n",
       "      <td>c1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "      <td>c2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c\n",
       "0  a0  b0  c0\n",
       "1  a1  b1  c1\n",
       "2  a2  b2  c2"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_df(cols, ind):\n",
    "    data = {c: [str(c) + str(i) for i in ind]\n",
    "           for c in cols}\n",
    "    #print(type(data))\n",
    "    return pd.DataFrame(data, ind)\n",
    "make_df('abc', range(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.7.1\n",
    "x = [1, 2, 3]\n",
    "y = [4, 5, 6]\n",
    "z = [7, 8, 9]\n",
    "np.concatenate([x, y, z])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 1, 2],\n",
       "       [3, 4, 3, 4]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [[1, 2],\n",
    "[3, 4]]\n",
    "np.concatenate([x, x], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([x, x], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': ['a0', 'a1', 'a2'], 'b': ['b0', 'b1', 'b2'], 'c': ['c0', 'c1', 'c2']}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {c: [str(c) + str(i) for i in range(3)]\n",
    "           for c in 'abc'}\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a0', 'a1', 'a2']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.7.2\n",
    "ser1 = pd.Series(['a', 'b', 'c'], index=[1,2,3])\n",
    "ser2 = pd.Series(['d', 'e', 'f'], index=[4,5,6])\n",
    "pd.concat([ser1, ser2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "1  A1  B1\n",
      "2  A2  B2\n",
      "    A   B\n",
      "3  A3  B3\n",
      "4  A4  B4\n",
      "    A   B\n",
      "1  A1  B1\n",
      "2  A2  B2\n",
      "3  A3  B3\n",
      "4  A4  B4\n"
     ]
    }
   ],
   "source": [
    "df1 = make_df('AB', [1, 2])\n",
    "df2 = make_df('AB', [3, 4])\n",
    "print(df1); print(df2); print(pd.concat([df1, df2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "0  A0  B0\n",
      "1  A1  B1\n",
      "    C   D\n",
      "0  C0  D0\n",
      "1  C1  D1\n",
      "    A   B   C   D\n",
      "0  A0  B0  C0  D0\n",
      "1  A1  B1  C1  D1\n"
     ]
    }
   ],
   "source": [
    "# df3 = make_df('AB', [0, 1])\n",
    "df4 = make_df('CD', [0, 1])\n",
    "print(df3); print(df4); print(pd.concat([df3, df4], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a0</td>\n",
       "      <td>b0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a1</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a3</td>\n",
       "      <td>b3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b\n",
       "0  a0  b0\n",
       "1  a1  b1\n",
       "0  a2  b2\n",
       "1  a3  b3"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = make_df('ab', [0,1])\n",
    "y = make_df('ab', [2,3])\n",
    "y.index = x.index\n",
    "pd.concat([x, y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError: Indexes have overlapping values: [0, 1]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    pd.concat([x,y], verify_integrity=True)\n",
    "except ValueError as e:\n",
    "    print('ValueError:', e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    a   b\n",
      "0  a0  b0\n",
      "1  a1  b1\n",
      "    a   b\n",
      "0  a2  b2\n",
      "1  a3  b3\n",
      "    a   b\n",
      "0  a0  b0\n",
      "1  a1  b1\n",
      "2  a2  b2\n",
      "3  a3  b3\n"
     ]
    }
   ],
   "source": [
    "print(x); print(y); print(pd.concat([x,y], ignore_index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    a   b\n",
      "0  a0  b0\n",
      "1  a1  b1\n",
      "    a   b\n",
      "0  a2  b2\n",
      "1  a3  b3\n",
      "      a   b\n",
      "x 0  a0  b0\n",
      "  1  a1  b1\n",
      "y 0  a2  b2\n",
      "  1  a3  b3\n"
     ]
    }
   ],
   "source": [
    "print(x); print(y); print(pd.concat([x,y], keys=['x', 'y']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B   C\n",
      "1  A1  B1  C1\n",
      "2  A2  B2  C2\n",
      "    B   C   D\n",
      "3  B3  C3  D3\n",
      "4  B4  C4  D4\n",
      "     A   B   C    D\n",
      "1   A1  B1  C1  NaN\n",
      "2   A2  B2  C2  NaN\n",
      "3  NaN  B3  C3   D3\n",
      "4  NaN  B4  C4   D4\n"
     ]
    }
   ],
   "source": [
    "df5 = make_df('ABC', [1, 2])\n",
    "df6 = make_df('BCD', [3, 4])\n",
    "print(df5); print(df6); print(pd.concat([df5, df6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B   C\n",
      "1  A1  B1  C1\n",
      "2  A2  B2  C2\n",
      "    B   C   D\n",
      "3  B3  C3  D3\n",
      "4  B4  C4  D4\n",
      "    B   C\n",
      "1  B1  C1\n",
      "2  B2  C2\n",
      "3  B3  C3\n",
      "4  B4  C4\n"
     ]
    }
   ],
   "source": [
    "print(df5); print(df6); print(pd.concat([df5, df6], join='inner'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.8　合并数据集：合并与连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  employee        group\n",
      "0      Bob   Accounting\n",
      "1     Jake  Engineering\n",
      "2     Lisa  Engineering\n",
      "3      Sue           HR\n",
      "  employee  hire_date\n",
      "0     Lisa       2004\n",
      "1      Bob       2008\n",
      "2     Jake       2012\n",
      "3      Sue       2014\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "'hire_date': [2004, 2008, 2012, 2014]})\n",
    "print(df1); print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df1, df2)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>supervisor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>Carly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HR</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group supervisor\n",
       "0   Accounting      Carly\n",
       "1  Engineering      Guido\n",
       "2           HR      Steve"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],\n",
    "'supervisor': ['Carly', 'Guido', 'Steve']})\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>supervisor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "      <td>Carly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date supervisor\n",
       "0      Bob   Accounting       2008      Carly\n",
       "1     Jake  Engineering       2012      Guido\n",
       "2     Lisa  Engineering       2004      Guido\n",
       "3      Sue           HR       2014      Steve"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df3, df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HR</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HR</td>\n",
       "      <td>organization</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group        skills\n",
       "0   Accounting          math\n",
       "1   Accounting  spreadsheets\n",
       "2  Engineering        coding\n",
       "3  Engineering         linux\n",
       "4           HR  spreadsheets\n",
       "5           HR  organization"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',\n",
    "'Engineering', 'Engineering', 'HR', 'HR'],\n",
    "'skills': ['math', 'spreadsheets', 'coding', 'linux',\n",
    "'spreadsheets', 'organization']})\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>organization</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group        skills\n",
       "0      Bob   Accounting          math\n",
       "1      Bob   Accounting  spreadsheets\n",
       "2     Jake  Engineering        coding\n",
       "3     Jake  Engineering         linux\n",
       "4     Lisa  Engineering        coding\n",
       "5     Lisa  Engineering         linux\n",
       "6      Sue           HR  spreadsheets\n",
       "7      Sue           HR  organization"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  employee        group\n",
      "0      Bob   Accounting\n",
      "1     Jake  Engineering\n",
      "2     Lisa  Engineering\n",
      "3      Sue           HR\n",
      "  employee  hire_date\n",
      "0     Lisa       2004\n",
      "1      Bob       2008\n",
      "2     Jake       2012\n",
      "3      Sue       2014\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.3.3\n",
    "print(df1); print(df2); pd.merge(df1,df2, on='employee')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  employee        group\n",
      "0      Bob   Accounting\n",
      "1     Jake  Engineering\n",
      "2     Lisa  Engineering\n",
      "3      Sue           HR\n",
      "   name  salary\n",
      "0   Bob   70000\n",
      "1  Jake   80000\n",
      "2  Lisa  120000\n",
      "3   Sue   90000\n"
     ]
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "'salary': [70000, 80000, 120000, 90000]})\n",
    "print(df1); print(df3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  name  salary\n",
       "0      Bob   Accounting   Bob   70000\n",
       "1     Jake  Engineering  Jake   80000\n",
       "2     Lisa  Engineering  Lisa  120000\n",
       "3      Sue           HR   Sue   90000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df3, left_on='employee', right_on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  salary\n",
       "0      Bob   Accounting   70000\n",
       "1     Jake  Engineering   80000\n",
       "2     Lisa  Engineering  120000\n",
       "3      Sue           HR   90000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df3, left_on='employee', right_on='name').drop('name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                group\n",
      "employee             \n",
      "Bob        Accounting\n",
      "Jake      Engineering\n",
      "Lisa      Engineering\n",
      "Sue                HR\n",
      "          hire_date\n",
      "employee           \n",
      "Lisa           2004\n",
      "Bob            2008\n",
      "Jake           2012\n",
      "Sue            2014\n"
     ]
    }
   ],
   "source": [
    "df1a = df1.set_index('employee')\n",
    "df2a = df2.set_index('employee')\n",
    "print(df1a); print(df2a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lisa</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group  hire_date\n",
       "employee                        \n",
       "Bob        Accounting       2008\n",
       "Jake      Engineering       2012\n",
       "Lisa      Engineering       2004\n",
       "Sue                HR       2014"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1a, df2a, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lisa</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group  hire_date\n",
       "employee                        \n",
       "Bob        Accounting       2008\n",
       "Jake      Engineering       2012\n",
       "Lisa      Engineering       2004\n",
       "Sue                HR       2014"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1a.join(df2a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                group\n",
      "employee             \n",
      "Bob        Accounting\n",
      "Jake      Engineering\n",
      "Lisa      Engineering\n",
      "Sue                HR\n",
      "   name  salary\n",
      "0   Bob   70000\n",
      "1  Jake   80000\n",
      "2  Lisa  120000\n",
      "3   Sue   90000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HR</td>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group  name  salary\n",
       "0   Accounting   Bob   70000\n",
       "1  Engineering  Jake   80000\n",
       "2  Engineering  Lisa  120000\n",
       "3           HR   Sue   90000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df1a); print(df3); pd.merge(df1a, df3, left_index=True, right_on = 'name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    name   food\n",
      "0  Peter   fish\n",
      "1   Paul  beans\n",
      "2   Mary  bread\n",
      "     name drink\n",
      "0    Mary  wine\n",
      "1  Joseph  beer\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name   food drink\n",
       "0  Mary  bread  wine"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.8.4\n",
    "df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'],\n",
    "'food': ['fish', 'beans', 'bread']},\n",
    "columns=['name', 'food'])\n",
    "df7 = pd.DataFrame({'name': ['Mary', 'Joseph'],\n",
    "'drink': ['wine', 'beer']},\n",
    "columns=['name', 'drink'])\n",
    "print(df6); print(df7); pd.merge(df6, df7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>beans</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name   food drink\n",
       "0   Peter   fish   NaN\n",
       "1    Paul  beans   NaN\n",
       "2    Mary  bread  wine\n",
       "3  Joseph    NaN  beer"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df6, df7, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>beans</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name   food drink\n",
       "0  Peter   fish   NaN\n",
       "1   Paul  beans   NaN\n",
       "2   Mary  bread  wine"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df6, df7, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   name  rank\n",
      "0   Bob     1\n",
      "1  Jake     2\n",
      "2  Lisa     3\n",
      "3   Sue     4\n",
      "   name  rank\n",
      "0   Bob     3\n",
      "1  Jake     1\n",
      "2  Lisa     4\n",
      "3   Sue     2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rank_x</th>\n",
       "      <th>rank_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  rank_x  rank_y\n",
       "0   Bob       1       3\n",
       "1  Jake       2       1\n",
       "2  Lisa       3       4\n",
       "3   Sue       4       2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.8.5\n",
    "df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "'rank': [1, 2, 3, 4]})\n",
    "df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "'rank': [3, 1, 4, 2]})\n",
    "print(df8); print(df9); pd.merge(df8, df9, on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rank_L</th>\n",
       "      <th>rank_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  rank_L  rank_R\n",
       "0   Bob       1       3\n",
       "1  Jake       2       1\n",
       "2  Lisa       3       4\n",
       "3   Sue       4       2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df8, df9, on='name', suffixes=['_L', '_R'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  state/region     ages  year  population\n",
      "0           AL  under18  2012   1117489.0\n",
      "1           AL    total  2012   4817528.0\n",
      "2           AL  under18  2010   1130966.0\n",
      "3           AL    total  2010   4785570.0\n",
      "4           AL  under18  2011   1125763.0\n",
      "        state  area (sq. mi)\n",
      "0     Alabama          52423\n",
      "1      Alaska         656425\n",
      "2     Arizona         114006\n",
      "3    Arkansas          53182\n",
      "4  California         163707\n",
      "        state abbreviation\n",
      "0     Alabama           AL\n",
      "1      Alaska           AK\n",
      "2     Arizona           AZ\n",
      "3    Arkansas           AR\n",
      "4  California           CA\n"
     ]
    }
   ],
   "source": [
    "# 3.8.6\n",
    "fdir = r'c:\\tmp\\data'\n",
    "pop = pd.read_csv( fdir + '\\state-population.csv')\n",
    "areas = pd.read_csv( fdir + '\\state-areas.csv')\n",
    "abbrevs = pd.read_csv( fdir + '\\state-abbrevs.csv')\n",
    "print(pop.head()); print(areas.head()); print(abbrevs.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ages</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "      <th>abbreviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>under18</td>\n",
       "      <td>2012</td>\n",
       "      <td>1117489.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>total</td>\n",
       "      <td>2012</td>\n",
       "      <td>4817528.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>under18</td>\n",
       "      <td>2010</td>\n",
       "      <td>1130966.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total</td>\n",
       "      <td>2010</td>\n",
       "      <td>4785570.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>under18</td>\n",
       "      <td>2011</td>\n",
       "      <td>1125763.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ages  year  population    state abbreviation\n",
       "0  under18  2012   1117489.0  Alabama           AL\n",
       "1    total  2012   4817528.0  Alabama           AL\n",
       "2  under18  2010   1130966.0  Alabama           AL\n",
       "3    total  2010   4785570.0  Alabama           AL\n",
       "4  under18  2011   1125763.0  Alabama           AL"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfpa = pd.merge(pop, abbrevs, how='outer', left_on='state/region', right_on='abbreviation').drop('state/region', axis=1)\n",
    "dfpa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>ages</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>under18</td>\n",
       "      <td>2012</td>\n",
       "      <td>1117489.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>total</td>\n",
       "      <td>2012</td>\n",
       "      <td>4817528.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>under18</td>\n",
       "      <td>2010</td>\n",
       "      <td>1130966.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total</td>\n",
       "      <td>2010</td>\n",
       "      <td>4785570.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>under18</td>\n",
       "      <td>2011</td>\n",
       "      <td>1125763.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ages  year  population    state abbreviation   area\n",
       "0  under18  2012   1117489.0  Alabama           AL  52423\n",
       "1    total  2012   4817528.0  Alabama           AL  52423\n",
       "2  under18  2010   1130966.0  Alabama           AL  52423\n",
       "3    total  2010   4785570.0  Alabama           AL  52423\n",
       "4  under18  2011   1125763.0  Alabama           AL  52423"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfall = pd.merge(dfpa, areas)\n",
    "dfall.columns = ['ages', 'year', 'population', 'state', 'abbreviation', 'area']\n",
    "dfall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "      <th>abbreviation</th>\n",
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       "  <tbody>\n",
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       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "      <td>21.316769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>total</td>\n",
       "      <td>2012</td>\n",
       "      <td>4817528.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "      <td>91.897221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>under18</td>\n",
       "      <td>2010</td>\n",
       "      <td>1130966.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "      <td>21.573851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total</td>\n",
       "      <td>2010</td>\n",
       "      <td>4785570.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "      <td>91.287603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>under18</td>\n",
       "      <td>2011</td>\n",
       "      <td>1125763.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>52423</td>\n",
       "      <td>21.474601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ages  year  population    state abbreviation   area    density\n",
       "0  under18  2012   1117489.0  Alabama           AL  52423  21.316769\n",
       "1    total  2012   4817528.0  Alabama           AL  52423  91.897221\n",
       "2  under18  2010   1130966.0  Alabama           AL  52423  21.573851\n",
       "3    total  2010   4785570.0  Alabama           AL  52423  91.287603\n",
       "4  under18  2011   1125763.0  Alabama           AL  52423  21.474601"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfall['density'] = dfall['population'] / dfall['area']\n",
    "dfall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state/region</th>\n",
       "      <th>ages</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2012</td>\n",
       "      <td>1117489.0</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>total</td>\n",
       "      <td>2012</td>\n",
       "      <td>4817528.0</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2010</td>\n",
       "      <td>1130966.0</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>total</td>\n",
       "      <td>2010</td>\n",
       "      <td>4785570.0</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2011</td>\n",
       "      <td>1125763.0</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state/region     ages  year  population    state\n",
       "0           AL  under18  2012   1117489.0  Alabama\n",
       "1           AL    total  2012   4817528.0  Alabama\n",
       "2           AL  under18  2010   1130966.0  Alabama\n",
       "3           AL    total  2010   4785570.0  Alabama\n",
       "4           AL  under18  2011   1125763.0  Alabama"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(pop, abbrevs, how='outer', left_on='state/region', right_on='abbreviation')\n",
    "merged = merged.drop('abbreviation', 1)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state/region    False\n",
       "ages            False\n",
       "year            False\n",
       "population       True\n",
       "state            True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>state/region</th>\n",
       "      <th>ages</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2448</th>\n",
       "      <td>PR</td>\n",
       "      <td>under18</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2449</th>\n",
       "      <td>PR</td>\n",
       "      <td>total</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>PR</td>\n",
       "      <td>total</td>\n",
       "      <td>1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>PR</td>\n",
       "      <td>under18</td>\n",
       "      <td>1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2452</th>\n",
       "      <td>PR</td>\n",
       "      <td>total</td>\n",
       "      <td>1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state/region     ages  year  population state\n",
       "2448           PR  under18  1990         NaN   NaN\n",
       "2449           PR    total  1990         NaN   NaN\n",
       "2450           PR    total  1991         NaN   NaN\n",
       "2451           PR  under18  1991         NaN   NaN\n",
       "2452           PR    total  1993         NaN   NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged['population'].isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PR', 'USA'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.loc[merged['state'].isnull(), 'state/region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state/region    False\n",
       "ages            False\n",
       "year            False\n",
       "population       True\n",
       "state           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico'\n",
    "merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States'\n",
    "merged.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state/region</th>\n",
       "      <th>ages</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "      <th>area (sq. mi)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2012</td>\n",
       "      <td>1117489.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>total</td>\n",
       "      <td>2012</td>\n",
       "      <td>4817528.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2010</td>\n",
       "      <td>1130966.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>total</td>\n",
       "      <td>2010</td>\n",
       "      <td>4785570.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2011</td>\n",
       "      <td>1125763.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state/region     ages  year  population    state  area (sq. mi)\n",
       "0           AL  under18  2012   1117489.0  Alabama        52423.0\n",
       "1           AL    total  2012   4817528.0  Alabama        52423.0\n",
       "2           AL  under18  2010   1130966.0  Alabama        52423.0\n",
       "3           AL    total  2010   4785570.0  Alabama        52423.0\n",
       "4           AL  under18  2011   1125763.0  Alabama        52423.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = pd.merge(merged, areas, on='state', how='left')\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state/region     False\n",
       "ages             False\n",
       "year             False\n",
       "population        True\n",
       "state            False\n",
       "area (sq. mi)     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['state'][final['area (sq. mi)'].isnull()].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.loc[final['area (sq. mi)'].isnull(), 'state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state/region</th>\n",
       "      <th>ages</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "      <th>area (sq. mi)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2012</td>\n",
       "      <td>1117489.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>total</td>\n",
       "      <td>2012</td>\n",
       "      <td>4817528.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2010</td>\n",
       "      <td>1130966.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>total</td>\n",
       "      <td>2010</td>\n",
       "      <td>4785570.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>under18</td>\n",
       "      <td>2011</td>\n",
       "      <td>1125763.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state/region     ages  year  population    state  area (sq. mi)\n",
       "0           AL  under18  2012   1117489.0  Alabama        52423.0\n",
       "1           AL    total  2012   4817528.0  Alabama        52423.0\n",
       "2           AL  under18  2010   1130966.0  Alabama        52423.0\n",
       "3           AL    total  2010   4785570.0  Alabama        52423.0\n",
       "4           AL  under18  2011   1125763.0  Alabama        52423.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.dropna(inplace=True)\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state/region</th>\n",
       "      <th>ages</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "      <th>area (sq. mi)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AL</td>\n",
       "      <td>total</td>\n",
       "      <td>2000</td>\n",
       "      <td>4452173.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>AK</td>\n",
       "      <td>total</td>\n",
       "      <td>2000</td>\n",
       "      <td>627963.0</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>656425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>AZ</td>\n",
       "      <td>total</td>\n",
       "      <td>2000</td>\n",
       "      <td>5160586.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>114006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>AR</td>\n",
       "      <td>total</td>\n",
       "      <td>2000</td>\n",
       "      <td>2678588.0</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>53182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>CA</td>\n",
       "      <td>total</td>\n",
       "      <td>2000</td>\n",
       "      <td>33987977.0</td>\n",
       "      <td>California</td>\n",
       "      <td>163707.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state/region   ages  year  population       state  area (sq. mi)\n",
       "28            AL  total  2000   4452173.0     Alabama        52423.0\n",
       "68            AK  total  2000    627963.0      Alaska       656425.0\n",
       "124           AZ  total  2000   5160586.0     Arizona       114006.0\n",
       "162           AR  total  2000   2678588.0    Arkansas        53182.0\n",
       "220           CA  total  2000  33987977.0  California       163707.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2010 = final.query(\"year == 2000 & ages == 'total'\")\n",
    "data2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "District of Columbia    8412.441176\n",
       "Puerto Rico             1084.098151\n",
       "New Jersey               966.592639\n",
       "Rhode Island             679.785113\n",
       "Connecticut              615.399892\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2010.set_index('state', inplace=True)\n",
    "density = data2010['population'] / data2010['area (sq. mi)']\n",
    "density.sort_values(ascending=False, inplace=True)\n",
    "density.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "South Dakota    9.800755\n",
       "North Dakota    9.080434\n",
       "Montana         6.146192\n",
       "Wyoming         5.053262\n",
       "Alaska          0.956641\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "density.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.9　累计与分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1035, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.9.1\n",
    "import seaborn as sns\n",
    "planets = sns.load_dataset('planets')\n",
    "planets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1035"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(planets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.374540\n",
       "1    0.950714\n",
       "2    0.731994\n",
       "3    0.598658\n",
       "4    0.156019\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.9.2\n",
    "import numpy as np\n",
    "rng = np.random.RandomState(42)\n",
    "ser = pd.Series(rng.rand(5))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.811925491708157"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5623850983416314"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155995</td>\n",
       "      <td>0.020584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058084</td>\n",
       "      <td>0.969910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.866176</td>\n",
       "      <td>0.832443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.601115</td>\n",
       "      <td>0.212339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.708073</td>\n",
       "      <td>0.181825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b\n",
       "0  0.155995  0.020584\n",
       "1  0.058084  0.969910\n",
       "2  0.866176  0.832443\n",
       "3  0.601115  0.212339\n",
       "4  0.708073  0.181825"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'a':rng.rand(5), 'b':rng.rand(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.477888\n",
       "b    0.443420\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.088290\n",
       "1    0.513997\n",
       "2    0.849309\n",
       "3    0.406727\n",
       "4    0.444949\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>498.00000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.73494</td>\n",
       "      <td>835.778671</td>\n",
       "      <td>2.509320</td>\n",
       "      <td>52.068213</td>\n",
       "      <td>2007.377510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.17572</td>\n",
       "      <td>1469.128259</td>\n",
       "      <td>3.636274</td>\n",
       "      <td>46.596041</td>\n",
       "      <td>4.167284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.328300</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>38.272250</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>24.497500</td>\n",
       "      <td>2005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>1.245000</td>\n",
       "      <td>39.940000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>999.600000</td>\n",
       "      <td>2.867500</td>\n",
       "      <td>59.332500</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.00000</td>\n",
       "      <td>17337.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          number  orbital_period        mass    distance         year\n",
       "count  498.00000      498.000000  498.000000  498.000000   498.000000\n",
       "mean     1.73494      835.778671    2.509320   52.068213  2007.377510\n",
       "std      1.17572     1469.128259    3.636274   46.596041     4.167284\n",
       "min      1.00000        1.328300    0.003600    1.350000  1989.000000\n",
       "25%      1.00000       38.272250    0.212500   24.497500  2005.000000\n",
       "50%      1.00000      357.000000    1.245000   39.940000  2009.000000\n",
       "75%      2.00000      999.600000    2.867500   59.332500  2011.000000\n",
       "max      6.00000    17337.500000   25.000000  354.000000  2014.000000"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.dropna().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     1\n",
       "2   C     2\n",
       "3   A     3\n",
       "4   B     4\n",
       "5   C     5"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.9.3\n",
    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "'data': range(6)}, columns=['key', 'data'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A       3\n",
       "B       5\n",
       "C       7"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                         631.180000\n",
       "Eclipse Timing Variations         4343.500000\n",
       "Imaging                          27500.000000\n",
       "Microlensing                      3300.000000\n",
       "Orbital Brightness Modulation        0.342887\n",
       "Pulsar Timing                       66.541900\n",
       "Pulsation Timing Variations       1170.000000\n",
       "Radial Velocity                    360.200000\n",
       "Transit                              5.714932\n",
       "Transit Timing Variations           57.011000\n",
       "Name: orbital_period, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry                     shape=(2, 6)\n",
      "Eclipse Timing Variations      shape=(9, 6)\n",
      "Imaging                        shape=(38, 6)\n",
      "Microlensing                   shape=(23, 6)\n",
      "Orbital Brightness Modulation  shape=(3, 6)\n",
      "Pulsar Timing                  shape=(5, 6)\n",
      "Pulsation Timing Variations    shape=(1, 6)\n",
      "Radial Velocity                shape=(553, 6)\n",
      "Transit                        shape=(397, 6)\n",
      "Transit Timing Variations      shape=(4, 6)\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print(\"{a:30s} shape={b}\".format(a=method, b=group.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       method                       \n",
       "count  Astrometry                          2.000000\n",
       "       Eclipse Timing Variations           9.000000\n",
       "       Imaging                            38.000000\n",
       "       Microlensing                       23.000000\n",
       "       Orbital Brightness Modulation       3.000000\n",
       "       Pulsar Timing                       5.000000\n",
       "       Pulsation Timing Variations         1.000000\n",
       "       Radial Velocity                   553.000000\n",
       "       Transit                           397.000000\n",
       "       Transit Timing Variations           4.000000\n",
       "mean   Astrometry                       2011.500000\n",
       "       Eclipse Timing Variations        2010.000000\n",
       "       Imaging                          2009.131579\n",
       "       Microlensing                     2009.782609\n",
       "       Orbital Brightness Modulation    2011.666667\n",
       "       Pulsar Timing                    1998.400000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2007.518987\n",
       "       Transit                          2011.236776\n",
       "       Transit Timing Variations        2012.500000\n",
       "std    Astrometry                          2.121320\n",
       "       Eclipse Timing Variations           1.414214\n",
       "       Imaging                             2.781901\n",
       "       Microlensing                        2.859697\n",
       "       Orbital Brightness Modulation       1.154701\n",
       "       Pulsar Timing                       8.384510\n",
       "       Pulsation Timing Variations              NaN\n",
       "       Radial Velocity                     4.249052\n",
       "       Transit                             2.077867\n",
       "       Transit Timing Variations           1.290994\n",
       "                                           ...     \n",
       "50%    Astrometry                       2011.500000\n",
       "       Eclipse Timing Variations        2010.000000\n",
       "       Imaging                          2009.000000\n",
       "       Microlensing                     2010.000000\n",
       "       Orbital Brightness Modulation    2011.000000\n",
       "       Pulsar Timing                    1994.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2009.000000\n",
       "       Transit                          2012.000000\n",
       "       Transit Timing Variations        2012.500000\n",
       "75%    Astrometry                       2012.250000\n",
       "       Eclipse Timing Variations        2011.000000\n",
       "       Imaging                          2011.000000\n",
       "       Microlensing                     2012.000000\n",
       "       Orbital Brightness Modulation    2012.000000\n",
       "       Pulsar Timing                    2003.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2011.000000\n",
       "       Transit                          2013.000000\n",
       "       Transit Timing Variations        2013.250000\n",
       "max    Astrometry                       2013.000000\n",
       "       Eclipse Timing Variations        2012.000000\n",
       "       Imaging                          2013.000000\n",
       "       Microlensing                     2013.000000\n",
       "       Orbital Brightness Modulation    2013.000000\n",
       "       Pulsar Timing                    2011.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2014.000000\n",
       "       Transit                          2014.000000\n",
       "       Transit Timing Variations        2014.000000\n",
       "Length: 80, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['year'].describe().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "rng = np.random.RandomState(0)\n",
    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "'data1': range(6),\n",
    "'data2': rng.randint(0, 10, 6)},\n",
    "columns = ['key', 'data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">data1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data1            data2           \n",
       "      min median max   min median max\n",
       "key                                  \n",
       "A       0    1.5   3     3    4.0   5\n",
       "B       1    2.5   4     0    3.5   7\n",
       "C       2    3.5   5     3    6.0   9"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate(['min', np.median, max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        0      5\n",
       "B        1      7\n",
       "C        2      9"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate({'data1':'min', 'data2':'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filter_func(x):\n",
    "    return x['data2'].std()>4\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2.12132</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>4.949747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>4.242641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       data1     data2\n",
       "key                   \n",
       "A    2.12132  1.414214\n",
       "B    2.12132  4.949747\n",
       "C    2.12132  4.242641"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
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      ],
      "text/plain": [
       "  key  data1  data2\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').filter(filter_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>-1.5</td>\n",
       "      <td>-3.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   data1  data2\n",
       "0   -1.5    1.0\n",
       "1   -1.5   -3.5\n",
       "2   -1.5   -3.0\n",
       "3    1.5   -1.0\n",
       "4    1.5    3.5\n",
       "5    1.5    3.0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').transform(lambda x: x-x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key     data1  data2\n",
       "0   A  0.000000      5\n",
       "1   B  0.142857      0\n",
       "2   C  0.166667      3\n",
       "3   A  0.375000      3\n",
       "4   B  0.571429      7\n",
       "5   C  0.416667      9"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def norm_by_data2(x):\n",
    "    x['data1'] /= x['data2'].sum()\n",
    "    return x\n",
    "df.groupby('key').apply(norm_by_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>7</td>\n",
       "      <td>17</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  data2\n",
       "0      7     17\n",
       "1      4      3\n",
       "2      4      7"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = [0, 1, 0, 1, 2, 0]\n",
    "df.groupby(L).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <td>8</td>\n",
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       "      <th>B</th>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        3      8\n",
       "B        5      7\n",
       "C        7     12"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df['key']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        0      5\n",
       "B        1      0\n",
       "C        2      3\n",
       "A        3      3\n",
       "B        4      7\n",
       "C        5      9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index('key')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>consonant</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vowel</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           data1  data2\n",
       "consonant     12     19\n",
       "vowel          3      8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {'A': 'vowel', 'B': 'consonant', 'C': 'consonant'}\n",
    "df2.groupby(mapping).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  data2\n",
       "a    1.5    4.0\n",
       "b    2.5    3.5\n",
       "c    3.5    6.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(str.lower).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>vowel</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>consonant</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>consonant</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             data1  data2\n",
       "a vowel        1.5    4.0\n",
       "b consonant    2.5    3.5\n",
       "c consonant    3.5    6.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby([str.lower, mapping]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>decade</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "      <th>2010s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "decade                         1980s  1990s  2000s  2010s\n",
       "method                                                   \n",
       "Astrometry                       0.0    0.0    0.0    2.0\n",
       "Eclipse Timing Variations        0.0    0.0    5.0   10.0\n",
       "Imaging                          0.0    0.0   29.0   21.0\n",
       "Microlensing                     0.0    0.0   12.0   15.0\n",
       "Orbital Brightness Modulation    0.0    0.0    0.0    5.0\n",
       "Pulsar Timing                    0.0    9.0    1.0    1.0\n",
       "Pulsation Timing Variations      0.0    0.0    1.0    0.0\n",
       "Radial Velocity                  1.0   52.0  475.0  424.0\n",
       "Transit                          0.0    0.0   64.0  712.0\n",
       "Transit Timing Variations        0.0    0.0    0.0    9.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade = 10 * (planets['year'] // 10 )\n",
    "decade = decade.astype(str) + 's'\n",
    "decade.name = 'decade'\n",
    "planets.groupby(['method', decade])['number'].sum().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.10　数据透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.10.1\n",
    "import numpy as np\n",
    "import pandas as sns\n",
    "import seaborn as sns\n",
    "titanic = sns.load_dataset('titanic')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.10.2\n",
    "titanic.groupby('sex')[['survived']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.10.3 \n",
    "titanic.pivot_table('survived', index='sex', columns='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 55]</th>\n",
       "      <td>0.970149</td>\n",
       "      <td>0.915254</td>\n",
       "      <td>0.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55, 80]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 55]</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>0.137755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55, 80]</th>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 55]  0.970149  0.915254  0.413793\n",
       "       (55, 80]  1.000000  0.000000  1.000000\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 55]  0.428571  0.064103  0.137755\n",
       "       (55, 80]  0.157895  0.166667  0.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 55, 80])\n",
    "titanic.pivot_table('survived', ['sex', age], 'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th colspan=\"2\" halign=\"left\">(-0.001, 8.662]</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(8.662, 26.0]</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(26.0, 512.329]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 55]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.895833</td>\n",
       "      <td>0.413793</td>\n",
       "      <td>0.969231</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55, 80]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 55]</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55, 80]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fare            (-0.001, 8.662]           (8.662, 26.0]                      \\\n",
       "class                     First     Third         First    Second     Third   \n",
       "sex    age                                                                    \n",
       "female (0, 18]              NaN  0.700000           NaN  1.000000  0.583333   \n",
       "       (18, 55]             NaN  0.523810           1.0  0.895833  0.413793   \n",
       "       (55, 80]             NaN       NaN           NaN  0.000000  1.000000   \n",
       "male   (0, 18]              NaN  0.166667           NaN  0.500000  0.500000   \n",
       "       (18, 55]             0.0  0.132450           0.0  0.078125  0.102564   \n",
       "       (55, 80]             NaN  0.000000           0.0  0.200000       NaN   \n",
       "\n",
       "fare            (26.0, 512.329]                   \n",
       "class                     First Second     Third  \n",
       "sex    age                                        \n",
       "female (0, 18]         0.909091    1.0  0.111111  \n",
       "       (18, 55]        0.969231    1.0  0.125000  \n",
       "       (55, 80]        1.000000    NaN       NaN  \n",
       "male   (0, 18]         0.800000    0.8  0.052632  \n",
       "       (18, 55]        0.458333    0.0  0.500000  \n",
       "       (55, 80]        0.166667    0.0       NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare = pd.qcut(titanic['fare'], 3)\n",
    "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='sex', columns='class', \n",
    "                   aggfunc = {'survived':sum, 'fare':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', \n",
    "                    columns='class', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day gender  births\n",
       "0  1969      1  1.0      F    4046\n",
       "1  1969      1  1.0      M    4440\n",
       "2  1969      1  2.0      F    4454\n",
       "3  1969      1  2.0      M    4548\n",
       "4  1969      1  3.0      F    4548"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.10.4\n",
    "csv = r'C:\\tmp\\data\\births.csv'\n",
    "births = pd.read_csv(csv)\n",
    "births.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>1753634</td>\n",
       "      <td>1846572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>16263075</td>\n",
       "      <td>17121550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>18310351</td>\n",
       "      <td>19243452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>19479454</td>\n",
       "      <td>20420553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>18229309</td>\n",
       "      <td>19106428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender         F         M\n",
       "decade                    \n",
       "1960     1753634   1846572\n",
       "1970    16263075  17121550\n",
       "1980    18310351  19243452\n",
       "1990    19479454  20420553\n",
       "2000    18229309  19106428"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births['decade'] = 10 * (births['year'] // 10)\n",
    "births.pivot_table('births', index ='decade', columns='gender', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'total births per year')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as  plt\n",
    "sns.set()\n",
    "births.pivot_table('births', index ='year', columns='gender', \n",
    "                   aggfunc='sum').plot()\n",
    "plt.ylabel('total births per year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4814.0, 689.31)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
    "mu = quartiles[1]\n",
    "sig = 0.74 * (quartiles[2] - quartiles[0])\n",
    "mu, sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\n",
    "births['day'] = births['day'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "births.index = pd.to_datetime(10000 * births.year + 100 * births.month +\n",
    "                births.day, format='%Y%m%d')\n",
    "births['dayofweek'] = births.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "births.pivot_table('births', index='dayofweek', columns='decade',\n",
    "                  aggfunc = 'mean').plot()\n",
    "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', \n",
    "                           'Sat', 'Sun'])\n",
    "plt.ylabel('mean births by day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       births\n",
       "1 1  4009.225\n",
       "  2  4247.400\n",
       "  3  4500.900\n",
       "  4  4571.350\n",
       "  5  4603.625"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births_by_date = births.pivot_table('births', [births.index.month, \n",
    "                                    births.index.day])\n",
    "births_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              births\n",
       "2012-01-01  4009.225\n",
       "2012-01-02  4247.400\n",
       "2012-01-03  4500.900\n",
       "2012-01-04  4571.350\n",
       "2012-01-05  4603.625"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births_by_date.index=[pd.datetime(2012, month, day)\n",
    "                     for (month, day) in births_by_date.index]\n",
    "births_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xecb2130>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12,4))\n",
    "births_by_date.plot(ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.11　向量化字符串操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  6, 10, 14, 22, 26])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.11.1\n",
    "import numpy as np\n",
    "x = np.array([2,3,5,7,11,13])\n",
    "x * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Peter', 'Paul', 'Mary', 'Guido']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ['peter', 'Paul', 'MARY', 'gUIDO']\n",
    "[s.capitalize() for s in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PETER', 'PAUL', 'MARY', 'GUIDO']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[s.upper() for s in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'capitalize'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-64-3b0264c38d59>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'peter'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Paul'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'MARY'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gUIDO'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-64-3b0264c38d59>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'peter'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Paul'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'MARY'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gUIDO'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'capitalize'"
     ]
    }
   ],
   "source": [
    "data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\n",
    "[s.capitalize() for s in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     MARY\n",
       "4    gUIDO\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names = pd.Series(data)\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     Mary\n",
       "4    Guido\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    peter\n",
       "1     paul\n",
       "2     None\n",
       "3     mary\n",
       "4    guido\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14\n",
       "1    11\n",
       "2    13\n",
       "3     9\n",
       "4    11\n",
       "5    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.11.2\n",
    "monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam',\n",
    "'Eric Idle', 'Terry Jones', 'Michael Palin'])\n",
    "monte.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.startswith('T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham, Chapman]\n",
       "1       [John, Cleese]\n",
       "2     [Terry, Gilliam]\n",
       "3         [Eric, Idle]\n",
       "4       [Terry, Jones]\n",
       "5     [Michael, Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jinjia\\Anaconda3\\envs\\Python36\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     Graham\n",
       "1       John\n",
       "2      Terry\n",
       "3       Eric\n",
       "4      Terry\n",
       "5    Michael\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.extract('([A-Za-z]+)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham Chapman]\n",
       "1                  []\n",
       "2     [Terry Gilliam]\n",
       "3                  []\n",
       "4       [Terry Jones]\n",
       "5     [Michael Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.findall(r'^[^AEIOU].*[^aeiou]$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Gra\n",
       "1    Joh\n",
       "2    Ter\n",
       "3    Eri\n",
       "4    Ter\n",
       "5    Mic\n",
       "dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.slice(0,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Gra\n",
       "1    Joh\n",
       "2    Ter\n",
       "3    Eri\n",
       "4    Ter\n",
       "5    Mic\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chapman\n",
       "1     Cleese\n",
       "2    Gilliam\n",
       "3       Idle\n",
       "4      Jones\n",
       "5      Palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split().str.get(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chapman\n",
       "1     Cleese\n",
       "2    Gilliam\n",
       "3       Idle\n",
       "4      Jones\n",
       "5      Palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split().str[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Graham\n",
       "1       John\n",
       "2      Terry\n",
       "3       Eric\n",
       "4      Terry\n",
       "5    Michael\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.rsplit().str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>info</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B|C|D</td>\n",
       "      <td>Graham Chapman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B|D</td>\n",
       "      <td>John Cleese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A|C</td>\n",
       "      <td>Terry Gilliam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B|D</td>\n",
       "      <td>Eric Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B|C</td>\n",
       "      <td>Terry Jones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B|C|D</td>\n",
       "      <td>Michael Palin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    info            name\n",
       "0  B|C|D  Graham Chapman\n",
       "1    B|D     John Cleese\n",
       "2    A|C   Terry Gilliam\n",
       "3    B|D       Eric Idle\n",
       "4    B|C     Terry Jones\n",
       "5  B|C|D   Michael Palin"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_monte = pd.DataFrame({'name': monte,\n",
    "'info': ['B|C|D', 'B|D', 'A|C', 'B|D', 'B|C',\n",
    "'B|C|D']})\n",
    "full_monte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C  D\n",
       "0  0  1  1  1\n",
       "1  0  1  0  1\n",
       "2  1  0  1  0\n",
       "3  0  1  0  1\n",
       "4  0  1  1  0\n",
       "5  0  1  1  1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_monte['info'].str.get_dummies('|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError: Could not reserve memory block\n"
     ]
    }
   ],
   "source": [
    "# 3.11.3\n",
    "try:\n",
    "    recipes = pd.read_json(r'c:\\tmp\\data\\recipeitems-latest.json')\n",
    "except ValueError as e:\n",
    "    print(\"ValueError:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 12)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(r'c:\\tmp\\data\\recipeitems-latest.json') as f:\n",
    "    line = f.readline()\n",
    "pd.read_json(line).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import codecs as co\n",
    "import pandas as pd\n",
    "with co.open(r'c:\\tmp\\data\\recipeitems-latest.json', 'r', 'utf-8') as f:\n",
    "    data = (line.strip() for line in f)\n",
    "    data_jason = \"[{0}]\".format(','.join(data))\n",
    "recipes = pd.read_json(data_jason)    \n",
    "#recipes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 17)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                                {'$oid': '5160756b96cc62079cc2db15'}\n",
       "cookTime                                                          PT30M\n",
       "creator                                                             NaN\n",
       "dateModified                                                        NaN\n",
       "datePublished                                                2013-03-11\n",
       "description           Late Saturday afternoon, after Marlboro Man ha...\n",
       "image                 http://static.thepioneerwoman.com/cooking/file...\n",
       "ingredients           Biscuits\\n3 cups All-purpose Flour\\n2 Tablespo...\n",
       "name                                    Drop Biscuits and Sausage Gravy\n",
       "prepTime                                                          PT10M\n",
       "recipeCategory                                                      NaN\n",
       "recipeInstructions                                                  NaN\n",
       "recipeYield                                                          12\n",
       "source                                                  thepioneerwoman\n",
       "totalTime                                                           NaN\n",
       "ts                                             {'$date': 1365276011104}\n",
       "url                   http://thepioneerwoman.com/cooking/2013/03/dro...\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50000.000000\n",
       "mean       218.946400\n",
       "std        142.018828\n",
       "min          0.000000\n",
       "25%        125.000000\n",
       "50%        187.000000\n",
       "75%        269.000000\n",
       "max       3247.000000\n",
       "Name: ingredients, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.ingredients.str.len().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1117"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.description.str.contains('[Bb]reakfast').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1126"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.ingredients.str.contains('[Cc]innamon').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.ingredients.str.contains('[Cc]inamon').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "spice_list = ['salt', 'pepper', 'oregano', 'sage', 'parsley',\n",
    "'rosemary', 'tarragon', 'thyme', 'paprika', 'cumin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumin</th>\n",
       "      <th>oregano</th>\n",
       "      <th>paprika</th>\n",
       "      <th>parsley</th>\n",
       "      <th>pepper</th>\n",
       "      <th>rosemary</th>\n",
       "      <th>sage</th>\n",
       "      <th>salt</th>\n",
       "      <th>tarragon</th>\n",
       "      <th>thyme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cumin  oregano  paprika  parsley  pepper  rosemary   sage   salt  tarragon  \\\n",
       "0  False    False    False    False   False     False   True  False     False   \n",
       "1  False    False    False    False   False     False  False  False     False   \n",
       "2   True    False    False    False    True     False  False   True     False   \n",
       "3  False    False    False    False   False     False  False  False     False   \n",
       "4  False    False    False    False   False     False  False  False     False   \n",
       "\n",
       "   thyme  \n",
       "0  False  \n",
       "1  False  \n",
       "2  False  \n",
       "3  False  \n",
       "4  False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "spice_df = pd.DataFrame(dict((spice, recipes.ingredients.str.contains(spice, re.IGNORECASE))\n",
    "                            for spice in spice_list))\n",
    "spice_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection = spice_df.query('parsley & paprika & tarragon')\n",
    "len(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2069    All cremat with a Little Gem, dandelion and wa...\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.name[selection.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  3.12　处理时间序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2005, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.12.1\n",
    "from datetime import datetime\n",
    "datetime(year=2005, month=7, day=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "date = parser.parse('4th of july, 2015')\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Sat', 'Saturday')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%a'), date.strftime('%A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('2015-07-04', dtype='datetime64[D]')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "date = np.array('2015-07-04', dtype=np.datetime64)\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
       "       '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
       "       '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
       "      dtype='datetime64[D]')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + np.arange(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.datetime64('2015-07-04'), numpy.datetime64('2015-07-04T12:00:00'))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04'), np.datetime64('2015-07-04 12:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-07-04T12:59:59.500000000')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04 12:59:59.50', 'ns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2015-07-04 00:00:00'), 'Saturday')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = pd.to_datetime(\"4th of July, 2015\")\n",
    "date, date.strftime('%A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
       "               '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
       "               '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + pd.to_timedelta(np.arange(12), 'D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-04    0\n",
       "2014-08-04    1\n",
       "2015-07-04    2\n",
       "2015-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.12.2\n",
    "index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',\n",
    "'2015-07-04', '2015-08-04'])\n",
    "data = pd.Series([0,1,2,3], index=index)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-04    0\n",
       "2014-08-04    1\n",
       "2015-07-04    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2014-07-04':'2015-07-04']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-07-04    2\n",
       "2015-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2015']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
       "               '2015-07-08'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.12.3\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "dates = pd.to_datetime([datetime(2015,7,3),'4th of July, 2015',\n",
    "'2015-Jul-6', '07-07-2015', '20150708'])\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
       "             '2015-07-08'],\n",
       "            dtype='period[D]', freq='D')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.to_period('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates - dates[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-7-3', '2015-7-10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-7-3', periods=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\n",
       "               '2015-07-03 02:00:00', '2015-07-03 03:00:00',\n",
       "               '2015-07-03 04:00:00', '2015-07-03 05:00:00',\n",
       "               '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-7-3', periods=8, freq='H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-31', '2015-08-31', '2015-09-30', '2015-10-31',\n",
       "               '2015-11-30', '2015-12-31', '2016-01-31', '2016-02-29'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-7-3', periods=8, freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',\n",
       "                '05:00:00', '06:00:00', '07:00:00', '08:00:00', '09:00:00'],\n",
       "               dtype='timedelta64[ns]', freq='H')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.timedelta_range(0, periods=10, freq='H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.12.4\n",
    "# Merget github test"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}