Pandas_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d366674",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88bae7e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    a\n",
       "4    b\n",
       "5    c\n",
       "6    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(['a','b','c','d'],index=[3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88934f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({'a': 1, 'b': 2, 'c': 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bee151b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.351536    0\n",
       "0.840264    0\n",
       "0.791356    0\n",
       "0.063820    0\n",
       "0.684914    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "pd.Series(np.random.randint(5),index=np.random.rand(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e32fa1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loc it does slicing both on rows and col\n",
    "\n",
    "# You can use only custome indexing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce9ce03",
   "metadata": {},
   "source": [
    "x.loc[startorw:stoprow:step,startcol:stopcol:step]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9375eb64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88ab35bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('datasets/citibike_tripdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aa873f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "\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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>15271</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "1          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "2           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "3           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "4           303  2018-05-01 04:27:12  2018-05-01 04:32:16              3207   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "0         Harborside            3199     Newport Pkwy   33558   \n",
       "1           Grand St            3185        City Hall   33593   \n",
       "2    McGinley Square            3193     Lincoln Park   29217   \n",
       "3          City Hall            3186    Grove St PATH   29662   \n",
       "4        Oakland Ave            3195          Sip Ave   15271   \n",
       "\n",
       "                       name_localizedValue    usertype  \n",
       "0                        Annual Membership  Subscriber  \n",
       "1                                  24 Hour    Customer  \n",
       "2  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "3                                  24 Hour    Customer  \n",
       "4                        Annual Membership  Subscriber  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use custome indexing only\n",
    "\n",
    "df.loc[0:4]\n",
    "\n",
    "# IN this 0 & 4 IS ICLUSIVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d62c43b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>15271</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>176</td>\n",
       "      <td>2018-05-01 04:37:05</td>\n",
       "      <td>2018-05-01 04:40:01</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>29298</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>577</td>\n",
       "      <td>2018-05-01 05:05:46</td>\n",
       "      <td>2018-05-01 05:15:23</td>\n",
       "      <td>3225</td>\n",
       "      <td>Baldwin at Montgomery</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>33619</td>\n",
       "      <td>Annual Membership from Citi Bike App</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "c           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "e           303  2018-05-01 04:27:12  2018-05-01 04:32:16              3207   \n",
       "f           176  2018-05-01 04:37:05  2018-05-01 04:40:01              3194   \n",
       "g           577  2018-05-01 05:05:46  2018-05-01 05:15:23              3225   \n",
       "\n",
       "      start station name  end station id end station name  bikeid  \\\n",
       "a             Harborside            3199     Newport Pkwy   33558   \n",
       "b               Grand St            3185        City Hall   33593   \n",
       "c        McGinley Square            3193     Lincoln Park   29217   \n",
       "d              City Hall            3186    Grove St PATH   29662   \n",
       "e            Oakland Ave            3195          Sip Ave   15271   \n",
       "f        McGinley Square            3195          Sip Ave   29298   \n",
       "g  Baldwin at Montgomery            3186    Grove St PATH   33619   \n",
       "\n",
       "                       name_localizedValue    usertype  \n",
       "a                        Annual Membership  Subscriber  \n",
       "b                                  24 Hour    Customer  \n",
       "c  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "d                                  24 Hour    Customer  \n",
       "e                        Annual Membership  Subscriber  \n",
       "f                        Annual Membership  Subscriber  \n",
       "g     Annual Membership from Citi Bike App  Subscriber  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data=df[0:7]\n",
    "new_data.index=['a','b','c','d','e','f','g']\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c224700",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
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       "    <tr>\n",
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       "      <th>c</th>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53\n",
       "c           232  2018-05-01 01:31:29  2018-05-01 01:35:22\n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40\n",
       "e           303  2018-05-01 04:27:12  2018-05-01 04:32:16"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.loc['a':'e',\"tripduration\":\"stoptime\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9aea360f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>stoptime</th>\n",
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       "      <th>e</th>\n",
       "      <td>303</td>\n",
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       "      <td>2018-05-01 04:32:16</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
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       "      <th>c</th>\n",
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       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
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       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
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      ],
      "text/plain": [
       "   tripduration            starttime             stoptime\n",
       "g           577  2018-05-01 05:05:46  2018-05-01 05:15:23\n",
       "f           176  2018-05-01 04:37:05  2018-05-01 04:40:01\n",
       "e           303  2018-05-01 04:27:12  2018-05-01 04:32:16\n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40\n",
       "c           232  2018-05-01 01:31:29  2018-05-01 01:35:22\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reverse the indexing\n",
    "# new_data.loc['e':'a',\"tripduration\":\"stoptime\"]\n",
    "new_data.loc[::-1,\"tripduration\":\"stoptime\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd2de162",
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starttime</th>\n",
       "      <th>bikeid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>33558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>29217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>29662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             starttime  bikeid\n",
       "a  2018-05-01 00:04:47   33558\n",
       "c  2018-05-01 01:31:29   29217\n",
       "d  2018-05-01 02:03:29   29662"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Complete customization \n",
    "new_data.loc[['a','c','d'],['starttime','bikeid']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e190f504",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__call__() takes from 1 to 2 positional arguments but 3 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/2079758417.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnew_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'e'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'd'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'g'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'stoptime'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'bikeid'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'start station id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# the difference is bracket\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __call__() takes from 1 to 2 positional arguments but 3 were given"
     ]
    }
   ],
   "source": [
    "new_data.loc(['e','c','d','g'],['stoptime','bikeid','start station id'])\n",
    "\n",
    "# the difference is bracket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b01ae84",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.loc[['e','c','d','g'],['stoptime','bikeid','start station id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43bec329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only row customized \n",
    "new_data.loc[['a','b','d'],'starttime':'bikeid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81d0927b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
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       "      <th>usertype</th>\n",
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       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>15271</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>577</td>\n",
       "      <td>2018-05-01 05:05:46</td>\n",
       "      <td>2018-05-01 05:15:23</td>\n",
       "      <td>3225</td>\n",
       "      <td>Baldwin at Montgomery</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>33619</td>\n",
       "      <td>Annual Membership from Citi Bike App</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "c           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "e           303  2018-05-01 04:27:12  2018-05-01 04:32:16              3207   \n",
       "g           577  2018-05-01 05:05:46  2018-05-01 05:15:23              3225   \n",
       "\n",
       "      start station name  end station id end station name  bikeid  \\\n",
       "a             Harborside            3199     Newport Pkwy   33558   \n",
       "b               Grand St            3185        City Hall   33593   \n",
       "c        McGinley Square            3193     Lincoln Park   29217   \n",
       "e            Oakland Ave            3195          Sip Ave   15271   \n",
       "g  Baldwin at Montgomery            3186    Grove St PATH   33619   \n",
       "\n",
       "                       name_localizedValue    usertype  \n",
       "a                        Annual Membership  Subscriber  \n",
       "b                                  24 Hour    Customer  \n",
       "c  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "e                        Annual Membership  Subscriber  \n",
       "g     Annual Membership from Citi Bike App  Subscriber  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select column\n",
    "\n",
    "new_data.loc[new_data[\"tripduration\"]>200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "72f7a704",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3681</td>\n",
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       "      <td>3185</td>\n",
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       "      <td>3225</td>\n",
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       "      <td>Grove St PATH</td>\n",
       "      <td>33619</td>\n",
       "      <td>Annual Membership from Citi Bike App</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "g           577  2018-05-01 05:05:46  2018-05-01 05:15:23              3225   \n",
       "\n",
       "      start station name  end station id end station name  bikeid  \\\n",
       "b               Grand St            3185        City Hall   33593   \n",
       "g  Baldwin at Montgomery            3186    Grove St PATH   33619   \n",
       "\n",
       "                    name_localizedValue    usertype  \n",
       "b                               24 Hour    Customer  \n",
       "g  Annual Membership from Citi Bike App  Subscriber  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[new_data['tripduration']>400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "860c369c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "new_data[new_data['start station id']==3681]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b6e50776",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
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       "      <td>Customer</td>\n",
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      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[new_data['start station name']=='Grand St']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd522757",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
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       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
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       "      <th>d</th>\n",
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       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "d          City Hall            3186    Grove St PATH   29662   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  \n",
       "d             24 Hour  Customer  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get all the Customer data from the 'usertype'\n",
    "new_data.loc[new_data[\"usertype\"]==\"Customer\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "50bf0c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "a         Harborside            3199     Newport Pkwy   33558   \n",
       "\n",
       "  name_localizedValue    usertype  \n",
       "a   Annual Membership  Subscriber  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[new_data['bikeid']==33558]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "abf9d9b0",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>577</td>\n",
       "      <td>2018-05-01 05:05:46</td>\n",
       "      <td>2018-05-01 05:15:23</td>\n",
       "      <td>3225</td>\n",
       "      <td>Baldwin at Montgomery</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>33619</td>\n",
       "      <td>Annual Membership from Citi Bike App</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "g           577  2018-05-01 05:05:46  2018-05-01 05:15:23              3225   \n",
       "\n",
       "      start station name  end station id end station name  bikeid  \\\n",
       "a             Harborside            3199     Newport Pkwy   33558   \n",
       "b               Grand St            3185        City Hall   33593   \n",
       "g  Baldwin at Montgomery            3186    Grove St PATH   33619   \n",
       "\n",
       "                    name_localizedValue    usertype  \n",
       "a                     Annual Membership  Subscriber  \n",
       "b                               24 Hour    Customer  \n",
       "g  Annual Membership from Citi Bike App  Subscriber  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.loc[new_data[\"bikeid\"]>30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "39d14f79",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_2260/1296192729.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# donot use and,or\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnew_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usertype\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'Customer'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"bikeid\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;36m30000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1535\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1536\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1537\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m   1538\u001b[0m             \u001b[1;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1539\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "# donot use and,or\n",
    "new_data.loc[new_data[\"usertype\"]=='Customer' and [\"bikeid\"]>30000]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d1b149",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "and -- &\n",
    "or -- |\n",
    "\n",
    "\"\"\"\n",
    "use it in the multiple conditioin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "d88ecada",
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[(new_data['usertype']==\"Customer\") & (new_data['start station id']>3680)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "2f09b0f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
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       "      <th>usertype</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "d          City Hall            3186    Grove St PATH   29662   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  \n",
       "d             24 Hour  Customer  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.loc[(new_data['usertype']=='Customer') | (new_data['start station id']>3680)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "054b2109",
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use paranthesis with logic bitwise operation\n",
    "\n",
    "new_data.loc[(new_data[\"usertype\"]=='Customer') & (new_data[\"bikeid\"]>30000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "bfc40e29",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
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       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[(new_data[\"usertype\"]=='Customer') & (new_data[\"bikeid\"]>30000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "4d87eb46",
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "b           Grand St            3185        City Hall   33593   \n",
       "\n",
       "  name_localizedValue  usertype  \n",
       "b             24 Hour  Customer  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[(new_data['starttime']== '2018-05-01 01:31:10') | (new_data['end station id']<3080)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd54157f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "new_data.loc[(new_data['tripduration']>=200) & (new_data[\"tripduration\"]<=1000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bb4f2c0",
   "metadata": {},
   "source": [
    "### iloc is opp of loc\n",
    "* rows as well as  col\n",
    "* only indexing - zero indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52124a58",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot do positional indexing on Index with these indexers [a] of type str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/1623218892.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# custom indexing fails in iloc[]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mnew_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 931\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    932\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1541\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1542\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1543\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1544\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1545\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m   1576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1577\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1578\u001b[1;33m         \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_positional_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1579\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1580\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_validate_positional_slice\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3681\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0meach\u001b[0m \u001b[0mof\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3682\u001b[0m         \"\"\"\n\u001b[1;32m-> 3683\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3684\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3685\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_validate_indexer\u001b[1;34m(self, form, key, kind)\u001b[0m\n\u001b[0;32m   5717\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5718\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5719\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5720\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5721\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_maybe_cast_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mside\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr_t\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mno_default\u001b[0m\u001b[1;33m)\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: cannot do positional indexing on Index with these indexers [a] of type str"
     ]
    }
   ],
   "source": [
    "# custom indexing fails in iloc[]\n",
    "\n",
    "new_data.iloc['a':'d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf43660a",
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "1          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "2           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "3           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "0         Harborside            3199     Newport Pkwy   33558   \n",
       "1           Grand St            3185        City Hall   33593   \n",
       "2    McGinley Square            3193     Lincoln Park   29217   \n",
       "3          City Hall            3186    Grove St PATH   29662   \n",
       "\n",
       "                       name_localizedValue    usertype  \n",
       "0                        Annual Membership  Subscriber  \n",
       "1                                  24 Hour    Customer  \n",
       "2  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "3                                  24 Hour    Customer  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prints 0 to 3 rows\n",
    "df.iloc[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c1b2453f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "c           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "\n",
       "  start station name  \n",
       "a         Harborside  \n",
       "b           Grand St  \n",
       "c    McGinley Square  \n",
       "d          City Hall  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prints 4 rows and 5 col\n",
    "\n",
    "new_data.iloc[:4,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7b55493",
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can  only use zero indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "773bc967",
   "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",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "c           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "a         Harborside            3199     Newport Pkwy   33558   \n",
       "b           Grand St            3185        City Hall   33593   \n",
       "c    McGinley Square            3193     Lincoln Park   29217   \n",
       "d          City Hall            3186    Grove St PATH   29662   \n",
       "\n",
       "                       name_localizedValue    usertype  \n",
       "a                        Annual Membership  Subscriber  \n",
       "b                                  24 Hour    Customer  \n",
       "c  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "d                                  24 Hour    Customer  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.iloc[0:4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "30f2e939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id\n",
       "a           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639\n",
       "b          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681\n",
       "d           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185\n",
       "e           303  2018-05-01 04:27:12  2018-05-01 04:32:16              3207"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no filtering available\n",
    "\n",
    "new_data.iloc[[0,1,3,4],:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "adbc5720",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bikeid</th>\n",
       "      <th>stoptime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>33558</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>29662</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bikeid             stoptime\n",
       "a   33558  2018-05-01 00:10:25\n",
       "d   29662  2018-05-01 02:06:40"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.loc[['a','d'],['bikeid','stoptime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "e161cbad",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             starttime             stoptime  start station id  \\\n",
       "b  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "c  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "\n",
       "  start station name  \n",
       "b           Grand St  \n",
       "c    McGinley Square  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.iloc[1:3,1:5]\n",
    "# iloco neeed numeric indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "9a29ea1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             starttime             stoptime  start station id  \\\n",
       "a  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "b  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "c  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "\n",
       "  start station name  \n",
       "a         Harborside  \n",
       "b           Grand St  \n",
       "c    McGinley Square  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.loc[['a','b','c'],'starttime':'start station name']\n",
    "# loc need names of row and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "862fd28d",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": ".iloc requires numeric indexers, got ['starttime' 'stoptime']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/1089551754.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#  custome indexiing is not posible\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mnew_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"starttime\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"stoptime\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    923\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 925\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    926\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    927\u001b[0m             \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1504\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1506\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1507\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIndexingError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1508\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    752\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    753\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 754\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    755\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    756\u001b[0m                 raise ValueError(\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1418\u001b[0m             \u001b[1;31m# check that the key has a numeric dtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1419\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_numeric_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1420\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\".iloc requires numeric indexers, got {arr}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1422\u001b[0m             \u001b[1;31m# check that the key does not exceed the maximum size of the index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: .iloc requires numeric indexers, got ['starttime' 'stoptime']"
     ]
    }
   ],
   "source": [
    "# for column also use zero indexing\n",
    "\n",
    "#  custome indexiing is not posible\n",
    "\n",
    "new_data.iloc[[1,3,4],[\"starttime\",\"stoptime\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "32ec2a6b",
   "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",
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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>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             starttime             stoptime  start station id\n",
       "a  2018-05-01 00:04:47  2018-05-01 00:10:25              3639\n",
       "b  2018-05-01 01:31:10  2018-05-01 01:55:53              3681\n",
       "d  2018-05-01 02:03:29  2018-05-01 02:06:40              3185\n",
       "e  2018-05-01 04:27:12  2018-05-01 04:32:16              3207"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "new_data.iloc[[0,1,3,4],1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8d2d7a63",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Organization Name</th>\n",
       "      <th>Headquater</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>Rome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>The Hague</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNO</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name  Headquater\n",
       "0               FAO        Rome\n",
       "1               ICJ   The Hague\n",
       "2               IMF  Washington\n",
       "3            UNESCO       Paris\n",
       "4               UNO    New York\n",
       "5        World Bank  Washington"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how to add and delete the daata\n",
    "\n",
    "import pandas as pd\n",
    "df=pd.read_csv('datasets/international_org.csv')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "af83fddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Rome\n",
       "1     The Hague\n",
       "2    Washington\n",
       "3         Paris\n",
       "4      New York\n",
       "5    Washington\n",
       "Name: Headquater, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Headquater\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0f6bf873",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Location']='Bengaluru'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aff63649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Organization Name</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>The Hague</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNO</td>\n",
       "      <td>New York</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name  Headquater   Location\n",
       "0               FAO        Rome  Bengaluru\n",
       "1               ICJ   The Hague  Bengaluru\n",
       "2               IMF  Washington  Bengaluru\n",
       "3            UNESCO       Paris  Bengaluru\n",
       "4               UNO    New York  Bengaluru\n",
       "5        World Bank  Washington  Bengaluru"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Location\"]=='Bengaluru']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "91889085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Organization Name</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>The Hague</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNO</td>\n",
       "      <td>New York</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Bengaluru</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name  Headquater   Location\n",
       "0               FAO        Rome  Bengaluru\n",
       "1               ICJ   The Hague  Bengaluru\n",
       "2               IMF  Washington  Bengaluru\n",
       "3            UNESCO       Paris  Bengaluru\n",
       "4               UNO    New York  Bengaluru\n",
       "5        World Bank  Washington  Bengaluru"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "beadc48d",
   "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>Organization Name</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>kera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>dfg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>er</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>asd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name Headquater Location\n",
       "0               FAO   Banglore     Banh\n",
       "1               ICJ   Banglore     kera\n",
       "2               IMF   Banglore      dfg\n",
       "3            UNESCO   Banglore      Mjm\n",
       "4               UNO   Banglore       er\n",
       "5        World Bank   Banglore      asd"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Headquater\"]=\"Banglore\"\n",
    "df\n",
    "\n",
    "# all the dat od=f Headquater changed to given value\n",
    "\n",
    "# make sure the column is not present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "61d9c5be",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length of values (5) does not match length of index (6)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/535232838.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# len must same\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Location\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Banh\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"kera\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"dfg\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Mjm\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"er\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3610\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3611\u001b[0m             \u001b[1;31m# set column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3612\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3613\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3614\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3782\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3783\u001b[0m         \"\"\"\n\u001b[1;32m-> 3784\u001b[1;33m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3785\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3786\u001b[0m         if (\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m   4507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4508\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4509\u001b[1;33m             \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4510\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m    529\u001b[0m     \"\"\"\n\u001b[0;32m    530\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m    532\u001b[0m             \u001b[1;34m\"Length of values \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m             \u001b[1;34mf\"({len(data)}) \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Length of values (5) does not match length of index (6)"
     ]
    }
   ],
   "source": [
    "# len must same\n",
    "df[\"Location\"]=[\"Banh\",\"kera\",\"dfg\",\"Mjm\",\"er\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2f633d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>kera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>dfg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>er</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name Headquater Location\n",
       "0               FAO   Banglore     Banh\n",
       "1               ICJ   Banglore     kera\n",
       "2               IMF   Banglore      dfg\n",
       "3            UNESCO   Banglore      Mjm\n",
       "4               UNO   Banglore       qw\n",
       "5        World Bank   Banglore       er"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df[\"Location\"]=[\"Banh\",\"kera\",\"dfg\",\"Mjm\",\"qw\",\"er\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3591f870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\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>Organization Name</th>\n",
       "      <th>asd</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>kera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>dfg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>er</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name       asd Location\n",
       "0               FAO  Banglore     Banh\n",
       "1               ICJ  Banglore     kera\n",
       "2               IMF  Banglore      dfg\n",
       "3            UNESCO  Banglore      Mjm\n",
       "4               UNO  Banglore       qw\n",
       "5        World Bank  Banglore       er"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'Headquater':'asd'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "98f39a3e",
   "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>Organization Name</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>abc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>kera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>dfg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>as</td>\n",
       "      <td>as</td>\n",
       "      <td>as</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>er</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name Headquater   abc\n",
       "0               FAO   Banglore  Banh\n",
       "1               ICJ   Banglore  kera\n",
       "2               IMF   Banglore   dfg\n",
       "3            UNESCO   Banglore   Mjm\n",
       "4                as         as    as\n",
       "5        World Bank   Banglore    er"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename\n",
    "df.rename(columns={\"Location\":\"abc\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "3be10d83",
   "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>Organization Name</th>\n",
       "      <th>abcd</th>\n",
       "      <th>asdf</th>\n",
       "      <th>abc</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name     abcd     asdf      abc Headquater Location\n",
       "0               FAO  default  default  default   Banglore     Banh"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Organization Name']=='FAO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "8bda2e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Location'][4]='gas'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d28800fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Headquater']='Banglore'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e08a5bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Headquater'][2]=12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "aa563331",
   "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>Organization Name</th>\n",
       "      <th>abcd</th>\n",
       "      <th>asdf</th>\n",
       "      <th>abc</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAO</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>kera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>default</td>\n",
       "      <td>Bang</td>\n",
       "      <td>default</td>\n",
       "      <td>12</td>\n",
       "      <td>dfg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>default</td>\n",
       "      <td>lore</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>as</td>\n",
       "      <td>default</td>\n",
       "      <td>ke</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>gas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>default</td>\n",
       "      <td>ra</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name     abcd     asdf      abc Headquater Location\n",
       "0               FAO  default  default  default   Banglore     Banh\n",
       "1               ICJ  default  default  default   Banglore     kera\n",
       "2               IMF  default     Bang  default         12      dfg\n",
       "3            UNESCO  default     lore  default   Banglore      Mjm\n",
       "4                as  default       ke  default   Banglore      gas\n",
       "5        World Bank  default       ra  default   Banglore      qas"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "15d33948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Headquater',\n",
       " 'Location',\n",
       " 'T',\n",
       " '_AXIS_LEN',\n",
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       " '_AXIS_REVERSED',\n",
       " '_AXIS_TO_AXIS_NUMBER',\n",
       " '_HANDLED_TYPES',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__annotations__',\n",
       " '__array__',\n",
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       " '__deepcopy__',\n",
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       " '__getattribute__',\n",
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       " '__getstate__',\n",
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       " '__iand__',\n",
       " '__ifloordiv__',\n",
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       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
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       " '_agg_by_level',\n",
       " '_agg_examples_doc',\n",
       " '_agg_summary_and_see_also_doc',\n",
       " '_align_frame',\n",
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       " '_arith_method',\n",
       " '_as_manager',\n",
       " '_attrs',\n",
       " '_box_col_values',\n",
       " '_can_fast_transpose',\n",
       " '_check_inplace_and_allows_duplicate_labels',\n",
       " '_check_inplace_setting',\n",
       " '_check_is_chained_assignment_possible',\n",
       " '_check_label_or_level_ambiguity',\n",
       " '_check_setitem_copy',\n",
       " '_clear_item_cache',\n",
       " '_clip_with_one_bound',\n",
       " '_clip_with_scalar',\n",
       " '_cmp_method',\n",
       " '_combine_frame',\n",
       " '_consolidate',\n",
       " '_consolidate_inplace',\n",
       " '_construct_axes_dict',\n",
       " '_construct_axes_from_arguments',\n",
       " '_construct_result',\n",
       " '_constructor',\n",
       " '_constructor_sliced',\n",
       " '_convert',\n",
       " '_count_level',\n",
       " '_data',\n",
       " '_dir_additions',\n",
       " '_dir_deletions',\n",
       " '_dispatch_frame_op',\n",
       " '_drop_axis',\n",
       " '_drop_labels_or_levels',\n",
       " '_ensure_valid_index',\n",
       " '_find_valid_index',\n",
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       " '_get_index_resolvers',\n",
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       " '_get_value',\n",
       " '_getitem_bool_array',\n",
       " '_getitem_multilevel',\n",
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       " '_init_mgr',\n",
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       " '_is_label_or_level_reference',\n",
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       " '_is_level_reference',\n",
       " '_is_mixed_type',\n",
       " '_is_view',\n",
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       " '_reindex_multi',\n",
       " '_reindex_with_indexers',\n",
       " '_replace_columnwise',\n",
       " '_repr_data_resource_',\n",
       " '_repr_fits_horizontal_',\n",
       " '_repr_fits_vertical_',\n",
       " '_repr_html_',\n",
       " '_repr_latex_',\n",
       " '_reset_cache',\n",
       " '_reset_cacher',\n",
       " '_sanitize_column',\n",
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       " '_set_axis_name',\n",
       " '_set_axis_nocheck',\n",
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       " '_set_item',\n",
       " '_set_item_frame_value',\n",
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       " '_setitem_array',\n",
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       " '_stat_axis_name',\n",
       " '_stat_axis_number',\n",
       " '_stat_function',\n",
       " '_stat_function_ddof',\n",
       " '_take_with_is_copy',\n",
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       " '_typ',\n",
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       " 'abc',\n",
       " 'abcd',\n",
       " 'abs',\n",
       " 'add',\n",
       " 'add_prefix',\n",
       " 'add_suffix',\n",
       " 'agg',\n",
       " 'aggregate',\n",
       " 'align',\n",
       " 'all',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply',\n",
       " 'applymap',\n",
       " 'asdf',\n",
       " 'asfreq',\n",
       " 'asof',\n",
       " 'assign',\n",
       " 'astype',\n",
       " 'at',\n",
       " 'at_time',\n",
       " 'attrs',\n",
       " 'axes',\n",
       " 'backfill',\n",
       " 'between_time',\n",
       " 'bfill',\n",
       " 'bool',\n",
       " 'boxplot',\n",
       " 'clip',\n",
       " 'columns',\n",
       " 'combine',\n",
       " 'combine_first',\n",
       " 'compare',\n",
       " 'convert_dtypes',\n",
       " 'copy',\n",
       " 'corr',\n",
       " 'corrwith',\n",
       " 'count',\n",
       " 'cov',\n",
       " 'cummax',\n",
       " 'cummin',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'describe',\n",
       " 'diff',\n",
       " 'div',\n",
       " 'divide',\n",
       " 'dot',\n",
       " 'drop',\n",
       " 'drop_duplicates',\n",
       " 'droplevel',\n",
       " 'dropna',\n",
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       " 'duplicated',\n",
       " 'empty',\n",
       " 'eq',\n",
       " 'equals',\n",
       " 'eval',\n",
       " 'ewm',\n",
       " 'expanding',\n",
       " 'explode',\n",
       " 'ffill',\n",
       " 'fillna',\n",
       " 'filter',\n",
       " 'first',\n",
       " 'first_valid_index',\n",
       " 'flags',\n",
       " 'floordiv',\n",
       " 'from_dict',\n",
       " 'from_records',\n",
       " 'ge',\n",
       " 'get',\n",
       " 'groupby',\n",
       " 'gt',\n",
       " 'head',\n",
       " 'hist',\n",
       " 'iat',\n",
       " 'idxmax',\n",
       " 'idxmin',\n",
       " 'iloc',\n",
       " 'index',\n",
       " 'infer_objects',\n",
       " 'info',\n",
       " 'insert',\n",
       " 'interpolate',\n",
       " 'isin',\n",
       " 'isna',\n",
       " 'isnull',\n",
       " 'items',\n",
       " 'iteritems',\n",
       " 'iterrows',\n",
       " 'itertuples',\n",
       " 'join',\n",
       " 'keys',\n",
       " 'kurt',\n",
       " 'kurtosis',\n",
       " 'last',\n",
       " 'last_valid_index',\n",
       " 'le',\n",
       " 'loc',\n",
       " 'lookup',\n",
       " 'lt',\n",
       " 'mad',\n",
       " 'mask',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'melt',\n",
       " 'memory_usage',\n",
       " 'merge',\n",
       " 'min',\n",
       " 'mod',\n",
       " 'mode',\n",
       " 'mul',\n",
       " 'multiply',\n",
       " 'ndim',\n",
       " 'ne',\n",
       " 'nlargest',\n",
       " 'notna',\n",
       " 'notnull',\n",
       " 'nsmallest',\n",
       " 'nunique',\n",
       " 'pad',\n",
       " 'pct_change',\n",
       " 'pipe',\n",
       " 'pivot',\n",
       " 'pivot_table',\n",
       " 'plot',\n",
       " 'pop',\n",
       " 'pow',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'quantile',\n",
       " 'query',\n",
       " 'radd',\n",
       " 'rank',\n",
       " 'rdiv',\n",
       " 'reindex',\n",
       " 'reindex_like',\n",
       " 'rename',\n",
       " 'rename_axis',\n",
       " 'reorder_levels',\n",
       " 'replace',\n",
       " 'resample',\n",
       " 'reset_index',\n",
       " 'rfloordiv',\n",
       " 'rmod',\n",
       " 'rmul',\n",
       " 'rolling',\n",
       " 'round',\n",
       " 'rpow',\n",
       " 'rsub',\n",
       " 'rtruediv',\n",
       " 'sample',\n",
       " 'select_dtypes',\n",
       " 'sem',\n",
       " 'set_axis',\n",
       " 'set_flags',\n",
       " 'set_index',\n",
       " 'shape',\n",
       " 'shift',\n",
       " 'size',\n",
       " 'skew',\n",
       " 'slice_shift',\n",
       " 'sort_index',\n",
       " 'sort_values',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'style',\n",
       " 'sub',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'swaplevel',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'to_clipboard',\n",
       " 'to_csv',\n",
       " 'to_dict',\n",
       " 'to_excel',\n",
       " 'to_feather',\n",
       " 'to_gbq',\n",
       " 'to_hdf',\n",
       " 'to_html',\n",
       " 'to_json',\n",
       " 'to_latex',\n",
       " 'to_markdown',\n",
       " 'to_numpy',\n",
       " 'to_parquet',\n",
       " 'to_period',\n",
       " 'to_pickle',\n",
       " 'to_records',\n",
       " 'to_sql',\n",
       " 'to_stata',\n",
       " 'to_string',\n",
       " 'to_timestamp',\n",
       " 'to_xarray',\n",
       " 'to_xml',\n",
       " 'transform',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncate',\n",
       " 'tz_convert',\n",
       " 'tz_localize',\n",
       " 'unstack',\n",
       " 'update',\n",
       " 'value_counts',\n",
       " 'values',\n",
       " 'var',\n",
       " 'where',\n",
       " 'xs']"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "2b6566e9",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot insert abcd, already exists",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/4059360867.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# add a column at specific location\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"abcd\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"default\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36minsert\u001b[1;34m(self, loc, column, value, allow_duplicates)\u001b[0m\n\u001b[0;32m   4412\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_duplicates\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4413\u001b[0m             \u001b[1;31m# Should this be a different kind of error??\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4414\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"cannot insert {column}, already exists\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4415\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4416\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loc must be int\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot insert abcd, already exists"
     ]
    }
   ],
   "source": [
    "# add a column at specific location\n",
    "df.insert(1,\"abcd\",\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "66a27b8f",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot insert asdf, already exists",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/295628715.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'asdf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'default'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"default\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Bang\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"lore\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"ke\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"ra\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36minsert\u001b[1;34m(self, loc, column, value, allow_duplicates)\u001b[0m\n\u001b[0;32m   4412\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_duplicates\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4413\u001b[0m             \u001b[1;31m# Should this be a different kind of error??\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4414\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"cannot insert {column}, already exists\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4415\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4416\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loc must be int\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot insert asdf, already exists"
     ]
    }
   ],
   "source": [
    "df.insert(2,'asdf',['default',\"default\",\"Bang\",\"lore\",\"ke\",\"ra\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "8da34e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
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       "      <td>as</td>\n",
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       "      <td>default</td>\n",
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       "      <td>default</td>\n",
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       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "  Organization Name     abcd     asdf      abc Headquater Location\n",
       "0               FAO  default  default  default   Banglore     Banh\n",
       "1               ICJ  default  default  default   Banglore     kera\n",
       "2               IMF  default     Bang  default         12      dfg\n",
       "3            UNESCO  default     lore  default   Banglore      Mjm\n",
       "4                as  default       ke  default   Banglore      gas\n",
       "5        World Bank  default       ra  default   Banglore      qas"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f59ede52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>ICJ</td>\n",
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       "      <td>default</td>\n",
       "      <td>fcs</td>\n",
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       "      <td>Mjm</td>\n",
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       "      <td>as</td>\n",
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       "      <td>default</td>\n",
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       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>default</td>\n",
       "      <td>ra</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name     abcd     asdf      abc Headquater Location\n",
       "0               FAO  default  default  default   Banglore     Banh\n",
       "1               ICJ  default  default  default        fcs     kera\n",
       "2               IMF  default     Bang  default        qwe      dfg\n",
       "3            UNESCO  default     lore  default   Banglore      Mjm\n",
       "4                as  default       ke  default   Banglore      gas\n",
       "5        World Bank  default       ra  default   Banglore      qas"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Accessing the single value from the col\n",
    "\n",
    "df[\"Headquater\"][1]='fcs'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "c4c76fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'qwe'"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Headquater\"][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "5118410f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=\"abc\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e538189e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns new obj\n",
    "# df.drop(columns=[\"Headquater\",\"sad\",\"asdd\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6d88797",
   "metadata": {},
   "outputs": [],
   "source": [
    "# it do not save untill you use var assign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "7e646822",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you want to change in existing the \n",
    "# inplace=True\n",
    "df.drop(columns=[\"abcd\"],inplace=True)\n",
    "\n",
    "# Changes will stored in same obj\n",
    "# to save memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "372c1667",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>fcs</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Bang</td>\n",
       "      <td>qwe</td>\n",
       "      <td>dfg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>lore</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>as</td>\n",
       "      <td>ke</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>gas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>ra</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name     asdf Headquater Location\n",
       "0               FAO  default   Banglore     Banh\n",
       "1               ICJ  default        fcs     kera\n",
       "2               IMF     Bang        qwe      dfg\n",
       "3            UNESCO     lore   Banglore      Mjm\n",
       "4                as       ke   Banglore      gas\n",
       "5        World Bank       ra   Banglore      qas"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fc0e46c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>FAO</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "      <td>abcd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>default</td>\n",
       "      <td>fcs</td>\n",
       "      <td>kera</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Bang</td>\n",
       "      <td>qwe</td>\n",
       "      <td>dfg</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>lore</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>as</td>\n",
       "      <td>ke</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>gas</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>ra</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name     asdf Headquater Location     0\n",
       "0               FAO  default   Banglore     Banh  abcd\n",
       "1               ICJ  default        fcs     kera     s\n",
       "2               IMF     Bang        qwe      dfg     d\n",
       "3            UNESCO     lore   Banglore      Mjm     g\n",
       "4                as       ke   Banglore      gas     e\n",
       "5        World Bank       ra   Banglore      qas     w"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# want to add new data\n",
    "\n",
    "df[0]=[\"abcd\",'s','d','g','e','w']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e042c37",
   "metadata": {},
   "source": [
    "if you want to add new row make sure row not present\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c7e9ba24",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot set a row with mismatched columns",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/3300575542.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m's'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'd'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'g'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'e'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    722\u001b[0m         \u001b[0miloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 723\u001b[1;33m         \u001b[0miloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    724\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    725\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1722\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1723\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1724\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer_missing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1725\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1726\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer_missing\u001b[1;34m(self, indexer, value)\u001b[0m\n\u001b[0;32m   2025\u001b[0m                     \u001b[1;31m# must have conforming columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2026\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2027\u001b[1;33m                         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot set a row with mismatched columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2028\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2029\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot set a row with mismatched columns"
     ]
    }
   ],
   "source": [
    "df.loc[-1]=[\"a\",'s','d','g','e','w']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "9f440d7d",
   "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>Organization Name</th>\n",
       "      <th>asdf</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FAO</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "      <td>abcd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>default</td>\n",
       "      <td>fcs</td>\n",
       "      <td>kera</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Bang</td>\n",
       "      <td>qwe</td>\n",
       "      <td>dfg</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>lore</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>as</td>\n",
       "      <td>ke</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>gas</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>ra</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name     asdf Headquater Location     0\n",
       "2               FAO  default   Banglore     Banh  abcd\n",
       "3               ICJ  default        fcs     kera     s\n",
       "4               IMF     Bang        qwe      dfg     d\n",
       "5            UNESCO     lore   Banglore      Mjm     g\n",
       "6                as       ke   Banglore      gas     e\n",
       "7        World Bank       ra   Banglore      qas     w"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how to make -1 index to 6th index\n",
    "\n",
    "df.index= df.index+1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "20c583a2",
   "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>Organization Name</th>\n",
       "      <th>asdf</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FAO</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "      <td>abcd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICJ</td>\n",
       "      <td>default</td>\n",
       "      <td>fcs</td>\n",
       "      <td>kera</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Bang</td>\n",
       "      <td>qwe</td>\n",
       "      <td>dfg</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>lore</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>as</td>\n",
       "      <td>ke</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>gas</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>ra</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name     asdf Headquater Location     0\n",
       "2               FAO  default   Banglore     Banh  abcd\n",
       "3               ICJ  default        fcs     kera     s\n",
       "4               IMF     Bang        qwe      dfg     d\n",
       "5            UNESCO     lore   Banglore      Mjm     g\n",
       "6                as       ke   Banglore      gas     e\n",
       "7        World Bank       ra   Banglore      qas     w"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "c645b0cf",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must have equal len keys and value when setting with an iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_7900/3562294957.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'xyz'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    722\u001b[0m         \u001b[0miloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 723\u001b[1;33m         \u001b[0miloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    724\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    725\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1728\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtake_split_path\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1729\u001b[0m             \u001b[1;31m# We have to operate column-wise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1730\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer_split_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1731\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1732\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_single_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer_split_path\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1806\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1807\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1808\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m   1809\u001b[0m                     \u001b[1;34m\"Must have equal len keys and value \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1810\u001b[0m                     \u001b[1;34m\"when setting with an iterable\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Must have equal len keys and value when setting with an iterable"
     ]
    }
   ],
   "source": [
    "df.loc[len(df)+1]=['xyz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "940b28b8",
   "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>Organization Name</th>\n",
       "      <th>asdf</th>\n",
       "      <th>Headquater</th>\n",
       "      <th>Location</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FAO</td>\n",
       "      <td>default</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Banh</td>\n",
       "      <td>abcd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IMF</td>\n",
       "      <td>Bang</td>\n",
       "      <td>qwe</td>\n",
       "      <td>dfg</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UNESCO</td>\n",
       "      <td>lore</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Mjm</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>as</td>\n",
       "      <td>ke</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>gas</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>World Bank</td>\n",
       "      <td>ra</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>qas</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Organization Name     asdf Headquater Location     0\n",
       "2               FAO  default   Banglore     Banh  abcd\n",
       "4               IMF     Bang        qwe      dfg     d\n",
       "5            UNESCO     lore   Banglore      Mjm     g\n",
       "6                as       ke   Banglore      gas     e\n",
       "7        World Bank       ra   Banglore      qas     w"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(index=[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "135f7309",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abd21c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c813f231",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by=['Headquater'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1acc066b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by=['Headquater','Organization Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db5a0a16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0552c125",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8201d0f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07eb7a67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed8d3d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d85970f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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