03.05 Concatenation and merge in pandas.ipynb

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   "source": [
    "# Объединение нескольких наборов данных\n",
    "Pandas включает в себя несколько способов для объединения различных наборов данных. Данные могут быть объединены как посредством логики теории множеств так и реляционной алгебры, которая используется в реляционных базах данных.\n",
    "\n",
    "## [Конкатенация](https://pandas.pydata.org/pandas-docs/stable/merging.html#concatenating-objects)\n",
    "С помощью метода `pd.concat` можно сконкатенировать несколько наборов данных в один единый набор. В результате конкатенации данные складываются друг над другом или бок о бок. При этом данные выравниваются либо по колонкам либо по индексам.\n",
    "\n",
    "Для начала загрузим вспомоготальный модуль, который поможет наглядно отобразить наборы данных и результат их объединения"
   ]
  },
  {
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     "end_time": "2017-12-20T15:19:07.745220Z",
     "start_time": "2017-12-20T15:19:07.265220Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:07.751722Z",
     "start_time": "2017-12-20T15:19:07.747219Z"
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    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%run code/util.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь создадим три набора данных, которые будем затем объединять в один набор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "data": {
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       "<div style=\"float: left; padding-right: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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   ],
   "source": [
    "countries = ['Afghanistan', 'Kazakhstan', 'Kyrgyzstan', 'Tajikistan', 'Turkmenistan', 'Uzbekistan']\n",
    "area = [652864, 2724900, 199951, 143100, 491210, 448978]\n",
    "population = [34656032, 17987736, 6019480, 8734951, 5662544, 32979000]\n",
    "gdp = [21, 156.189, 7.061, 27.802, 42.355, 68.324]\n",
    "gini = [29, 26.4, 27.4, 30.8, 40.8, 36.7]\n",
    "\n",
    "df1 = pd.DataFrame(data={'country': countries[:2], 'area': area[:2], 'population': population[:2], \n",
    "                         'gdp': gdp[:2], 'gini': gini[:2]}, index=[1, 2])\n",
    "df2 = pd.DataFrame(data={'country': countries[2:4], 'area': area[2:4], 'population': population[2:4], \n",
    "                         'gdp': gdp[2:4], 'gini': gini[2:4]}, index=[3, 4])\n",
    "df3 = pd.DataFrame(data={'country': countries[4:], 'area': area[4:], 'population': population[4:], \n",
    "                         'gdp': gdp[4:], 'gini': gini[4:]}, index=[5, 6])\n",
    "\n",
    "frames = [df1, df2, df3]\n",
    "result = pd.concat(frames)\n",
    "display_stacked(frames, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если в метод `pd.concat` передать параметр `keys` со списком значений для каждого набора, то в результирующий набор данных получить иерархический индекс со значениями `keys` в качестве самого верхнего уровня"
   ]
  },
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   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">first</th>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">second</th>\n",
       "      <th>3</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">third</th>\n",
       "      <th>5</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = pd.concat(frames, keys=['first', 'second', 'third'])\n",
    "display_stacked(frames, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно выбрать нужный набор по этим ключам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:07.847720Z",
     "start_time": "2017-12-20T15:19:07.821720Z"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
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      "text/plain": [
       "     area     country     gdp  gini  population\n",
       "3  199951  Kyrgyzstan   7.061  27.4     6019480\n",
       "4  143100  Tajikistan  27.802  30.8     8734951"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "result.loc['second']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для горизонтальной конкатенации, когда данные объединяются бок о бок, нужно передать параметр `axis` со значением 1. Если при вертикальной конкатенации данные выравнивались по колонкам, то при горизонтальной конкатенации они выравниваются по индексам. Поэтому в нашем случае, чтобы в результирующем наборе были две строки, необходимо установить одинаковые индексы для каждого набора"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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     "start_time": "2017-12-20T15:19:07.849724Z"
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   },
   "outputs": [
    {
     "data": {
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       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
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       "      <th>area</th>\n",
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       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.index = [1, 2]\n",
    "df2.index = [1, 2]\n",
    "df3.index = [1, 2]\n",
    "result = pd.concat(frames, axis=1)\n",
    "display_horizontal(frames, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Все значения оси (колонки или индексы), по которой идет конкатенация, будут оставлены в результирующем наборе данных, даже если эти значения дублируются. Например, в предыдущем примере название каждой колонки встречается по три раза. При этом для другой оси, по которой не идет конкатенация, можно указать как поступить, если не все значения этой оси совпадают во всех наборах. Например, если в предыдущем примере у каждого набора были бы разные значения индексов, то их не получилось бы выровнить. С помощью параметра `join` можно указать как поступить в таком случае. По умолчанию параметр имеет значение `outer`, что соответствует сохранению всех значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:07.910720Z",
     "start_time": "2017-12-20T15:19:07.884220Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div padding-bottom: 20px;\">\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
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       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
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       "      <th>area</th>\n",
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       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
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       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
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       "      <th></th>\n",
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       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864.0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900.0</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736.0</td>\n",
       "      <td>199951.0</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143100.0</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951.0</td>\n",
       "      <td>491210.0</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448978.0</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.index = [1, 2]\n",
    "df2.index = [2, 3]\n",
    "df3.index = [3, 4]\n",
    "result = pd.concat(frames, axis=1, join='outer')\n",
    "display_horizontal(frames, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В итоге данные сложились как лесенькой и с пустыми значениями в строках, в которых не бло значений во всех наборах данных. Если же передать в параметр `join` значение `inner`, то в итоге останутся лишь те записи, значения индексов которых есть во всех наборах данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:07.937220Z",
     "start_time": "2017-12-20T15:19:07.913220Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.index = [1, 2]\n",
    "df2.index = [1, 3]\n",
    "df3.index = [1, 4]\n",
    "result = pd.concat(frames, axis=1, join='inner')\n",
    "display_horizontal(frames, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И наконец можно передать отдельный индекс в параметр `join_axes`, который будет использован при конкатенации. Все значения, индексы которых есть в переданном в `join_axes` индексе, будут оставлены в результирующем наборе данных. В качестве такого индекса можно использовать индексы нескольких объединяемых наборов данных. В результате все значения выбранных наборов будут оставлены, а из остальных наборов останутся лишь те значения, индексы которых есть в выбранном индексе. В следующем примере в `join_axes` передается объединение индексов первых двух наборов данных `df1.index | df2.index`. В результате все значения этих двух наборов остаются, а запись с индексом 4 в третьем наборе исключается из результата"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:07.968719Z",
     "start_time": "2017-12-20T15:19:07.938719Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "      <td>32979000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>652864.0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34656032.0</td>\n",
       "      <td>199951.0</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6019480.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900.0</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17987736.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>491210.0</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "      <td>5662544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143100.0</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "      <td>8734951.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.index = [1, 2]\n",
    "df2.index = [1, 3]\n",
    "df3.index = [2, 4]\n",
    "result = pd.concat(frames, axis=1, join_axes=[df1.index | df2.index])\n",
    "display_horizontal(frames, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Слияние в стиле реляционных СУБД](https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging)\n",
    "Pandas включает в себя возможность для слияния (merge) данных в стиле SQL (соответствует оператору `join`). Для этого используется метод `pd.merge`. Стоит отметить, что слияние в pandas реализовано очень эффективно и в некоторых случаях может выполняться на порядок быстрее, чем в аналогичных библиотеках (например, в R).\n",
    "\n",
    "SQL включает в себя несколько типов объединения данных\n",
    "1. Один к одному - одна запись в таблице соответствует одной записи в другой таблице (one-to-one)\n",
    "2. Один ко многим - одна запись в таблице соответствует многим записям в другой таблице (one-to-many)\n",
    "3. Многое ко многим - множество записей в таблице соответствуют множеству записей в другой таблице (many-to-many)\n",
    "\n",
    "Если название колонок, по которым нужно объеденить два набора данных, совпадают, то можно их объеденить, указав ключ в параметре `on`. В следующем примере строки в двух наборах данных объединяются по ключу `country` и они совпадают один к одному"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:07.995721Z",
     "start_time": "2017-12-20T15:19:07.970720Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
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       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "    </tr>\n",
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       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gini</th>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_left = pd.DataFrame(data={'country': countries, 'area': area})\n",
    "df_right = pd.DataFrame(data={'country': countries, 'gdp': gdp, 'gini': gini})\n",
    "\n",
    "result = pd.merge(df_left, df_right, on='country')\n",
    "display_horizontal([df_left, df_right], result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Точно так же можно объединить наборы данных, строки которых соотносятся как один ко многим. В следующем примере используется данные, которые содержат внешний долг страны за 2015 и 2016 годы (согласно базе Всемирного Банка)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:08.014719Z",
     "start_time": "2017-12-20T15:19:07.997720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\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>country</th>\n",
       "      <th>external_debt</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>153381212000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>14854025000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>163757713000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>16282526000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  external_debt  year\n",
       "0  Kazakhstan   153381212000  2015\n",
       "1  Uzbekistan    14854025000  2015\n",
       "2  Kazakhstan   163757713000  2016\n",
       "3  Uzbekistan    16282526000  2016"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "external_debt_2015_2016 = [153381212000, 14854025000, \n",
    "                           163757713000, 16282526000]\n",
    "year = [2015] * 2 + [2016] * 2\n",
    "key = ['Kazakhstan', 'Uzbekistan'] * 2\n",
    "\n",
    "df_debt = pd.DataFrame(data={'country': key, 'year': year, \n",
    "                             'external_debt': external_debt_2015_2016})\n",
    "df_debt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При слиянии данные из левого набора продублируются, так как каждой строке соответствуют две строки из правого набора. Страны, данные по которым отсутствуют в правом наборе, будут исключены из результата"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:08.033726Z",
     "start_time": "2017-12-20T15:19:08.016220Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>163757713000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>16282526000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>external_debt</th>\n",
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       "      <th>2</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>14854025000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>16282526000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = pd.merge(df_left, df_debt, on='country')\n",
    "display_horizontal([df_left, df_debt], result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы можем оставить все данные из левого набора указав параметр `how=left` (по умолчанию `how=inner`). При этом внешний долг в тех строках, для которых отсутствуют соответствующие строки в правом наборе, будут иметь пустые значения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:08.056219Z",
     "start_time": "2017-12-20T15:19:08.035220Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>external_debt</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>153381212000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>14854025000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>163757713000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>16282526000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>external_debt</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>1.533812e+11</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>1.637577e+11</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>1.485402e+10</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>1.628253e+10</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = pd.merge(df_left, df_debt, on='country', how='left')\n",
    "display_horizontal([df_left, df_debt], result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При слиянии можно указать более чем одну колонку, по которым нужно объединить наборы. Создадим еще один набор с данными по численности населения за 2015 и 2016 год"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:08.073220Z",
     "start_time": "2017-12-20T15:19:08.057720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>17544126</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>31298900</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>17797032</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>31848200</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  population  year\n",
       "0  Kazakhstan    17544126  2015\n",
       "1  Uzbekistan    31298900  2015\n",
       "2  Kazakhstan    17797032  2016\n",
       "3  Uzbekistan    31848200  2016"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_2015_2016 = [17544126, 31298900, \n",
    "                        17797032, 31848200]\n",
    "df_pop = pd.DataFrame(data={'country': key, 'year': year, \n",
    "                            'population': population_2015_2016})\n",
    "df_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь можем объединить данные по внешнему долгу и численности населения связав их по двум ключам `country` и `year`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:08.093718Z",
     "start_time": "2017-12-20T15:19:08.075218Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>external_debt</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>153381212000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>14854025000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>163757713000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>16282526000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>17544126</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>31298900</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>17797032</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>31848200</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>external_debt</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>153381212000</td>\n",
       "      <td>2015</td>\n",
       "      <td>17544126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>14854025000</td>\n",
       "      <td>2015</td>\n",
       "      <td>31298900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>163757713000</td>\n",
       "      <td>2016</td>\n",
       "      <td>17797032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>16282526000</td>\n",
       "      <td>2016</td>\n",
       "      <td>31848200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_debt_pop = pd.merge(df_debt, df_pop, on=['country', 'year'])\n",
    "display_horizontal([df_debt, df_pop], df_debt_pop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если названия колонок в двух наборах не совпадают, то можно использовать параметры `left_on` и `right_on` для указания колонок в каждом наборе по отдельности. При этом обе колонки останутся в результирующем наборе"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:19:08.113220Z",
     "start_time": "2017-12-20T15:19:08.096221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div padding-bottom: 20px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>left_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdp</th>\n",
       "      <th>right_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.000</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>156.189</td>\n",
       "      <td>Kazakhstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.061</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.802</td>\n",
       "      <td>Tajikistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.355</td>\n",
       "      <td>Turkmenistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68.324</td>\n",
       "      <td>Uzbekistan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"></p>\n",
       "    </div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>left_key</th>\n",
       "      <th>gdp</th>\n",
       "      <th>right_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>652864</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.000</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2724900</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>156.189</td>\n",
       "      <td>Kazakhstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199951</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>7.061</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143100</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>27.802</td>\n",
       "      <td>Tajikistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491210</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>42.355</td>\n",
       "      <td>Turkmenistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448978</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.324</td>\n",
       "      <td>Uzbekistan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_left = pd.DataFrame(data={'left_key': countries, 'area': area})\n",
    "df_right = pd.DataFrame(data={'right_key': countries, 'gdp': gdp})\n",
    "\n",
    "result = pd.merge(df_left, df_right, left_on='left_key', right_on='right_key')\n",
    "display_horizontal([df_left, df_right], result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:12:03.250964Z",
     "start_time": "2017-12-20T15:12:03.246460Z"
    }
   },
   "source": [
    "Мы можем передать суффиксы, чтобы в объединенном наборе различать колонки с одинаковым названием. Кроме того, данные могут быть объединены по индексу. В следующем примере демонстрируются обе этих возможностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-20T15:20:58.756288Z",
     "start_time": "2017-12-20T15:20:58.742788Z"
    }
   },
   "outputs": [
    {
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   "source": [
    "df_left = pd.DataFrame(data={'param_name': ['area', 'population'], \n",
    "                             'value': [area[-1], population[-1]]})\n",
    "df_right = pd.DataFrame(data={'param_name': ['gdp', 'gini'], \n",
    "                             'value': [gdp[-1], gini[-1]]})\n",
    "result = pd.merge(df_left, df_right, left_index=True, right_index=True, suffixes=('_left', '_right'))\n",
    "display_horizontal([df_left, df_right], result)"
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   "cell_type": "markdown",
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   "source": [
    "Если данные объединяются по индексу, как в предыдущем примере, то можно использовать метод [`join`](https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index), который является оберткой метода `merge` и предоставляет несколько сокращенный способ написания кода"
   ]
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     "end_time": "2017-12-20T15:21:06.706514Z",
     "start_time": "2017-12-20T15:21:06.695512Z"
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   "outputs": [
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    "result = df_left.join(df_right, lsuffix='_left', rsuffix='_right')\n",
    "display_horizontal([df_left, df_right], result)"
   ]
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