pandas/concat_and_merge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concat and Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\r\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>col1</th>\n      <th>col2</th>\n      <th>col3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>6</td>\n      <td>7</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td>9</td>\n      <td>10</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>e</th>\n      <td>12</td>\n      <td>13</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>f</th>\n      <td>15</td>\n      <td>16</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>g</th>\n      <td>18</td>\n      <td>19</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>h</th>\n      <td>21</td>\n      <td>22</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   col1  col2  col3\na     0     1     2\nb     3     4     5\nc     6     7     8\nd     9    10    11\ne    12    13    14\nf    15    16    17\ng    18    19    20\nh    21    22    23"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.arange(24).reshape(8, 3), \r\n",
    "                  index=list(\"abcdefgh\"),\r\n",
    "                  columns=[f\"col{i}\" for i in range(1, 4)])\r\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead 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>col1</th>\n      <th>col2</th>\n      <th>col3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>6</td>\n      <td>7</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   col1  col2  col3\na     0     1     2\nb     3     4     5\nc     6     7     8"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[:3]\r\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>col1</th>\n      <th>col2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>g</th>\n      <td>18</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>h</th>\n      <td>21</td>\n      <td>22</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   col1  col2\ng    18    19\nh    21    22"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = df.loc[\"g\":, :\"col2\"]\r\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>col1</th>\n      <th>col2</th>\n      <th>col3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>3</td>\n      <td>4</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>6</td>\n      <td>7</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>g</th>\n      <td>18</td>\n      <td>19</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>h</th>\n      <td>21</td>\n      <td>22</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   col1  col2  col3\na     0     1   2.0\nb     3     4   5.0\nc     6     7   8.0\ng    18    19   NaN\nh    21    22   NaN"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([a, b])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>height</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Jake</td>\n      <td>165</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Kyle</td>\n      <td>155</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   name  height\n0  Jake     165\n1  Kyle     155"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\r\n",
    "    [\r\n",
    "        [\"Jake\", 165],\r\n",
    "        [\"Kyle\", 155],\r\n",
    "    ],\r\n",
    "    columns=[\"name\", \"height\"],\r\n",
    ")\r\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Jake</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Kyle</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Annie</td>\n      <td>45</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "    name  weight\n0   Jake      60\n1   Kyle      50\n2  Annie      45"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(\r\n",
    "    [\r\n",
    "        [\"Jake\", 60],\r\n",
    "        [\"Kyle\", 50],\r\n",
    "        [\"Annie\", 45],\r\n",
    "    ],\r\n",
    "    columns=[\"name\", \"weight\"],\r\n",
    ")\r\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>height</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Jake</td>\n      <td>165.0</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Kyle</td>\n      <td>155.0</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Annie</td>\n      <td>NaN</td>\n      <td>45</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "    name  height  weight\n0   Jake   165.0      60\n1   Kyle   155.0      50\n2  Annie     NaN      45"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df, df2, on=\"name\", how=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference\r\n",
    "\r\n",
    "- https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#merge\r\n",
    "- https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#concatenating-objects"
   ]
  }
 ],
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