ch08.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling: Join, Combine, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.display.max_rows = 20\n",
    "np.random.seed(12345)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchical Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "等级划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1   -0.204708\n",
       "   2    0.478943\n",
       "   3   -0.519439\n",
       "b  1   -0.555730\n",
       "   3    1.965781\n",
       "c  1    1.393406\n",
       "   2    0.092908\n",
       "d  2    0.281746\n",
       "   3    0.769023\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series(np.random.randn(9),\n",
    "                 index=[['a', 'a', 'a', 'b', 'b', 'c', 'c', 'd', 'd'],\n",
    "                        [1, 2, 3, 1, 3, 1, 2, 2, 3]]) #important\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('a', 3),\n",
       "            ('b', 1),\n",
       "            ('b', 3),\n",
       "            ('c', 1),\n",
       "            ('c', 2),\n",
       "            ('d', 2),\n",
       "            ('d', 3)],\n",
       "           )"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   -0.555730\n",
       "3    1.965781\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "b  1   -0.555730\n",
       "   3    1.965781\n",
       "c  1    1.393406\n",
       "   2    0.092908\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "b  1   -0.555730\n",
       "   3    1.965781\n",
       "d  2    0.281746\n",
       "   3    0.769023\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['b']\n",
    "data['b':'c']\n",
    "data.loc[['b', 'd']]#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.519439\n",
       "b    1.965781\n",
       "d    0.769023\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:, 3]#2级序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-0.204708</td>\n",
       "      <td>0.478943</td>\n",
       "      <td>-0.519439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.555730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.965781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.393406</td>\n",
       "      <td>0.092908</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.281746</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3\n",
       "a -0.204708  0.478943 -0.519439\n",
       "b -0.555730       NaN  1.965781\n",
       "c  1.393406  0.092908       NaN\n",
       "d       NaN  0.281746  0.769023"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.unstack()#取消堆叠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1   -0.204708\n",
       "   2    0.478943\n",
       "   3   -0.519439\n",
       "b  1   -0.555730\n",
       "   3    1.965781\n",
       "c  1    1.393406\n",
       "   2    0.092908\n",
       "d  2    0.281746\n",
       "   3    0.769023\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.unstack().stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ohio     Colorado\n",
       "    Green Red    Green\n",
       "a 1     0   1        2\n",
       "  2     3   4        5\n",
       "b 1     6   7        8\n",
       "  2     9  10       11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.arange(12).reshape((4, 3)),\n",
    "                     index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\n",
    "                     columns=[['Ohio', 'Ohio', 'Colorado'],\n",
    "                              ['Green', 'Red', 'Green']])#important\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key1 key2                   \n",
       "a    1        0   1        2\n",
       "     2        3   4        5\n",
       "b    1        6   7        8\n",
       "     2        9  10       11"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.index.names = ['key1', 'key2']\n",
    "frame.columns.names = ['state', 'color']\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color      Green  Red\n",
       "key1 key2            \n",
       "a    1         0    1\n",
       "     2         3    4\n",
       "b    1         6    7\n",
       "     2         9   10"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['Ohio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MultiIndex.from_arrays([['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']],\n",
    "                       names=['state', 'color'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reordering and Sorting Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key2</th>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>b</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key2 key1                   \n",
       "1    a        0   1        2\n",
       "2    a        3   4        5\n",
       "1    b        6   7        8\n",
       "2    b        9  10       11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.swaplevel('key1', 'key2')#返回互换了级别i和j的新对象，不改变原数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead tr th {\n",
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       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key1 key2                   \n",
       "a    1        0   1        2\n",
       "b    1        6   7        8\n",
       "a    2        3   4        5\n",
       "b    2        9  10       11"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th>Colorado</th>\n",
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       "      <th>a</th>\n",
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       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key2 key1                   \n",
       "1    a        0   1        2\n",
       "     b        6   7        8\n",
       "2    a        3   4        5\n",
       "     b        9  10       11"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sort_index(level=1)\n",
    "frame.swaplevel(0, 1).sort_index(level=0)#important"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Statistics by Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
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       "    <tr>\n",
       "      <th>color</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
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      "text/plain": [
       "state  Ohio     Colorado\n",
       "color Green Red    Green\n",
       "key2                    \n",
       "1         6   8       10\n",
       "2        12  14       16"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
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       "      <th>2</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
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      "text/plain": [
       "color      Green  Red\n",
       "key1 key2            \n",
       "a    1         2    1\n",
       "     2         8    4\n",
       "b    1        14    7\n",
       "     2        20   10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sum(level='key2')\n",
    "frame.sum(level='color', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing with a DataFrame's columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b    c  d\n",
       "0  0  7  one  0\n",
       "1  1  6  one  1\n",
       "2  2  5  one  2\n",
       "3  3  4  two  0\n",
       "4  4  3  two  1\n",
       "5  5  2  two  2\n",
       "6  6  1  two  3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame({'a': range(7), 'b': range(7, 0, -1),\n",
    "                      'c': ['one', 'one', 'one', 'two', 'two',\n",
    "                            'two', 'two'],\n",
    "                      'd': [0, 1, 2, 0, 1, 2, 3]})\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "       a  b\n",
       "c   d      \n",
       "one 0  0  7\n",
       "    1  1  6\n",
       "    2  2  5\n",
       "two 0  3  4\n",
       "    1  4  3\n",
       "    2  5  2\n",
       "    3  6  1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = frame.set_index(['c', 'd'])#improtant\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>two</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b    c  d\n",
       "c   d              \n",
       "one 0  0  7  one  0\n",
       "    1  1  6  one  1\n",
       "    2  2  5  one  2\n",
       "two 0  3  4  two  0\n",
       "    1  4  3  two  1\n",
       "    2  5  2  two  2\n",
       "    3  6  1  two  3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.set_index(['c', 'd'], drop=False)#drop取消后面cd列的意思"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     c  d  a  b\n",
       "0  one  0  0  7\n",
       "1  one  1  1  6\n",
       "2  one  2  2  5\n",
       "3  two  0  3  4\n",
       "4  two  1  4  3\n",
       "5  two  2  5  2\n",
       "6  two  3  6  1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.reset_index()#重置索引。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining and Merging Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Database-Style DataFrame Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "  key  data1\n",
       "0   b      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   c      3\n",
       "4   a      4\n",
       "5   a      5\n",
       "6   b      6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  key  data2\n",
       "0   a      0\n",
       "1   b      1\n",
       "2   d      2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],\n",
    "                    'data1': range(7)})\n",
    "df2 = pd.DataFrame({'key': ['a', 'b', 'd'],\n",
    "                    'data2': range(3)})\n",
    "df1\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "  key  data1  data2\n",
       "0   b      0      1\n",
       "1   b      1      1\n",
       "2   b      6      1\n",
       "3   a      2      0\n",
       "4   a      4      0\n",
       "5   a      5      0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2)#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  key  data1  data2\n",
       "0   b      0      1\n",
       "1   b      1      1\n",
       "2   b      6      1\n",
       "3   a      2      0\n",
       "4   a      4      0\n",
       "5   a      5      0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  lkey  data1\n",
       "0    b      0\n",
       "1    b      1\n",
       "2    a      2\n",
       "3    c      3\n",
       "4    a      4\n",
       "5    a      5\n",
       "6    b      6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "  rkey  data2\n",
       "0    a      0\n",
       "1    b      1\n",
       "2    d      2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "  lkey  data1 rkey  data2\n",
       "0    b      0    b      1\n",
       "1    b      1    b      1\n",
       "2    b      6    b      1\n",
       "3    a      2    a      0\n",
       "4    a      4    a      0\n",
       "5    a      5    a      0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'lkey': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],\n",
    "                    'data1': range(7)})\n",
    "df3\n",
    "df4 = pd.DataFrame({'rkey': ['a', 'b', 'd'],\n",
    "                    'data2': range(3)})\n",
    "df4\n",
    "pd.merge(df3, df4, left_on='lkey', right_on='rkey')#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   b    0.0    1.0\n",
       "1   b    1.0    1.0\n",
       "2   b    6.0    1.0\n",
       "3   a    2.0    0.0\n",
       "4   a    4.0    0.0\n",
       "5   a    5.0    0.0\n",
       "6   c    3.0    NaN\n",
       "7   d    NaN    2.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='outer')#important 左右key合起来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  key  data1\n",
       "0   b      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   c      3\n",
       "4   a      4\n",
       "5   b      5"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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       "   key  data1  data2\n",
       "0    b      0    1.0\n",
       "1    b      0    3.0\n",
       "2    b      1    1.0\n",
       "3    b      1    3.0\n",
       "4    a      2    0.0\n",
       "5    a      2    2.0\n",
       "6    c      3    NaN\n",
       "7    a      4    0.0\n",
       "8    a      4    2.0\n",
       "9    b      5    1.0\n",
       "10   b      5    3.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],\n",
    "                    'data1': range(6)})\n",
    "df2 = pd.DataFrame({'key': ['a', 'b', 'a', 'b', 'd'],\n",
    "                    'data2': range(5)})\n",
    "df1\n",
    "df2\n",
    "pd.merge(df1, df2, on='key', how='left')#左边为基准"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "  key  data1  data2\n",
       "0   b      0      1\n",
       "1   b      0      3\n",
       "2   b      1      1\n",
       "3   b      1      3\n",
       "4   b      5      1\n",
       "5   b      5      3\n",
       "6   a      2      0\n",
       "7   a      2      2\n",
       "8   a      4      0\n",
       "9   a      4      2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='inner')#两边都有的值才行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "  key1 key2  lval  rval\n",
       "0  foo  one   1.0   4.0\n",
       "1  foo  one   1.0   5.0\n",
       "2  foo  two   2.0   NaN\n",
       "3  bar  one   3.0   6.0\n",
       "4  bar  two   NaN   7.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key1': ['foo', 'foo', 'bar'],\n",
    "                     'key2': ['one', 'two', 'one'],\n",
    "                     'lval': [1, 2, 3]})\n",
    "right = pd.DataFrame({'key1': ['foo', 'foo', 'bar', 'bar'],\n",
    "                      'key2': ['one', 'one', 'one', 'two'],\n",
    "                      'rval': [4, 5, 6, 7]})\n",
    "pd.merge(left, right, on=['key1', 'key2'], how='outer')#按序列次序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "  key1 key2_x  lval key2_y  rval\n",
       "0  foo    one     1    one     4\n",
       "1  foo    one     1    one     5\n",
       "2  foo    two     2    one     4\n",
       "3  foo    two     2    one     5\n",
       "4  bar    one     3    one     6\n",
       "5  bar    one     3    two     7"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "  key1 key2_left  lval key2_right  rval\n",
       "0  foo       one     1        one     4\n",
       "1  foo       one     1        one     5\n",
       "2  foo       two     2        one     4\n",
       "3  foo       two     2        one     5\n",
       "4  bar       one     3        one     6\n",
       "5  bar       one     3        two     7"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key1')\n",
    "pd.merge(left, right, on='key1', suffixes=('_left', '_right'))#重叠列加后缀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging on Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "0   a      0\n",
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       "2   a      2\n",
       "3   a      3\n",
       "4   b      4\n",
       "5   c      5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "   group_val\n",
       "a        3.5\n",
       "b        7.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "1   b      1        7.0\n",
       "4   b      4        7.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
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    }
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   "source": [
    "left1 = pd.DataFrame({'key': ['a', 'b', 'a', 'a', 'b', 'c'],\n",
    "                      'value': range(6)})\n",
    "right1 = pd.DataFrame({'group_val': [3.5, 7]}, index=['a', 'b'])\n",
    "left1\n",
    "right1\n",
    "pd.merge(left1, right1, left_on='key', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "1   b      1        7.0\n",
       "4   b      4        7.0\n",
       "5   c      5        NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     key1  key2  data\n",
       "0    Ohio  2000   0.0\n",
       "1    Ohio  2001   1.0\n",
       "2    Ohio  2002   2.0\n",
       "3  Nevada  2001   3.0\n",
       "4  Nevada  2002   4.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "      <th rowspan=\"4\" valign=\"top\">Ohio</th>\n",
       "      <th>2000</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
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       "      <th>2001</th>\n",
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      ],
      "text/plain": [
       "             event1  event2\n",
       "Nevada 2001       0       1\n",
       "       2000       2       3\n",
       "Ohio   2000       4       5\n",
       "       2000       6       7\n",
       "       2001       8       9\n",
       "       2002      10      11"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefth = pd.DataFrame({'key1': ['Ohio', 'Ohio', 'Ohio',\n",
    "                               'Nevada', 'Nevada'],\n",
    "                      'key2': [2000, 2001, 2002, 2001, 2002],\n",
    "                      'data': np.arange(5.)})\n",
    "righth = pd.DataFrame(np.arange(12).reshape((6, 2)),\n",
    "                      index=[['Nevada', 'Nevada', 'Ohio', 'Ohio',\n",
    "                              'Ohio', 'Ohio'],\n",
    "                             [2001, 2000, 2000, 2000, 2001, 2002]],\n",
    "                      columns=['event1', 'event2'])\n",
    "lefth\n",
    "righth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Nevada</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "     key1  key2  data  event1  event2\n",
       "0    Ohio  2000   0.0       4       5\n",
       "0    Ohio  2000   0.0       6       7\n",
       "1    Ohio  2001   1.0       8       9\n",
       "2    Ohio  2002   2.0      10      11\n",
       "3  Nevada  2001   3.0       0       1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "      <td>Ohio</td>\n",
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       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     key1  key2  data  event1  event2\n",
       "0    Ohio  2000   0.0     4.0     5.0\n",
       "0    Ohio  2000   0.0     6.0     7.0\n",
       "1    Ohio  2001   1.0     8.0     9.0\n",
       "2    Ohio  2002   2.0    10.0    11.0\n",
       "3  Nevada  2001   3.0     0.0     1.0\n",
       "4  Nevada  2002   4.0     NaN     NaN\n",
       "4  Nevada  2000   NaN     2.0     3.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(lefth, righth, left_on=['key1', 'key2'], right_index=True)\n",
    "pd.merge(lefth, righth, left_on=['key1', 'key2'],\n",
    "         right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada\n",
       "a   1.0     2.0\n",
       "c   3.0     4.0\n",
       "e   5.0     6.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Missouri  Alabama\n",
       "b       7.0      8.0\n",
       "c       9.0     10.0\n",
       "d      11.0     12.0\n",
       "e      13.0     14.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama\n",
       "a   1.0     2.0       NaN      NaN\n",
       "b   NaN     NaN       7.0      8.0\n",
       "c   3.0     4.0       9.0     10.0\n",
       "d   NaN     NaN      11.0     12.0\n",
       "e   5.0     6.0      13.0     14.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left2 = pd.DataFrame([[1., 2.], [3., 4.], [5., 6.]],\n",
    "                     index=['a', 'c', 'e'],\n",
    "                     columns=['Ohio', 'Nevada'])\n",
    "right2 = pd.DataFrame([[7., 8.], [9., 10.], [11., 12.], [13, 14]],\n",
    "                      index=['b', 'c', 'd', 'e'],\n",
    "                      columns=['Missouri', 'Alabama'])\n",
    "left2\n",
    "right2\n",
    "pd.merge(left2, right2, how='outer', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama\n",
       "a   1.0     2.0       NaN      NaN\n",
       "b   NaN     NaN       7.0      8.0\n",
       "c   3.0     4.0       9.0     10.0\n",
       "d   NaN     NaN      11.0     12.0\n",
       "e   5.0     6.0      13.0     14.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left2.join(right2, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
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       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "1   b      1        7.0\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "4   b      4        7.0\n",
       "5   c      5        NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left1.join(right1, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>New York</th>\n",
       "      <th>Oregon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   New York  Oregon\n",
       "a       7.0     8.0\n",
       "c       9.0    10.0\n",
       "e      11.0    12.0\n",
       "f      16.0    17.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "      <th>New York</th>\n",
       "      <th>Oregon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama  New York  Oregon\n",
       "a   1.0     2.0       NaN      NaN       7.0     8.0\n",
       "c   3.0     4.0       9.0     10.0       9.0    10.0\n",
       "e   5.0     6.0      13.0     14.0      11.0    12.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "      <th>New York</th>\n",
       "      <th>Oregon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama  New York  Oregon\n",
       "a   1.0     2.0       NaN      NaN       7.0     8.0\n",
       "c   3.0     4.0       9.0     10.0       9.0    10.0\n",
       "e   5.0     6.0      13.0     14.0      11.0    12.0\n",
       "b   NaN     NaN       7.0      8.0       NaN     NaN\n",
       "d   NaN     NaN      11.0     12.0       NaN     NaN\n",
       "f   NaN     NaN       NaN      NaN      16.0    17.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "another = pd.DataFrame([[7., 8.], [9., 10.], [11., 12.], [16., 17.]],\n",
    "                       index=['a', 'c', 'e', 'f'],\n",
    "                       columns=['New York', 'Oregon'])\n",
    "another\n",
    "left2.join([right2, another])#important\n",
    "left2.join([right2, another], how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenating Along an Axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(12).reshape((3, 4))\n",
    "arr\n",
    "np.concatenate([arr, arr], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([0, 1], index=['a', 'b'])\n",
    "s2 = pd.Series([2, 3, 4], index=['c', 'd', 'e'])\n",
    "s3 = pd.Series([5, 6], index=['f', 'g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "f    5\n",
       "g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3])#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "      <th>g</th>\n",
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       "     0    1    2\n",
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       "e  NaN  4.0  NaN\n",
       "f  NaN  NaN  5.0\n",
       "g  NaN  NaN  6.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3], axis=1)#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "f    5\n",
       "g    6\n",
       "dtype: int64"
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     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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     "execution_count": 40,
     "metadata": {},
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   ],
   "source": [
    "s4 = pd.concat([s1, s3])\n",
    "s4\n",
    "pd.concat([s1, s4], axis=1)\n",
    "pd.concat([s1, s4], axis=1, join='inner')#要都有的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "concat() got an unexpected keyword argument 'join_axes'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-28e446bc353b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ms1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoin_axes\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'c'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'e'\u001b[0m\u001b[1;33m]\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;31mTypeError\u001b[0m: concat() got an unexpected keyword argument 'join_axes'"
     ]
    }
   ],
   "source": [
    "pd.concat([s1, s4], axis=1, join_axes=[['a', 'c', 'b', 'e']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    a    0\n",
       "       b    1\n",
       "two    a    0\n",
       "       b    1\n",
       "three  f    5\n",
       "       g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "         a    b    f    g\n",
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     "execution_count": 42,
     "metadata": {},
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    "result = pd.concat([s1, s1, s3], keys=['one', 'two', 'three'])\n",
    "result\n",
    "result.unstack()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>e</th>\n",
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       "      <th>f</th>\n",
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       "      <th>g</th>\n",
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      "text/plain": [
       "   one  two  three\n",
       "a  0.0  NaN    NaN\n",
       "b  1.0  NaN    NaN\n",
       "c  NaN  2.0    NaN\n",
       "d  NaN  3.0    NaN\n",
       "e  NaN  4.0    NaN\n",
       "f  NaN  NaN    5.0\n",
       "g  NaN  NaN    6.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3], axis=1, keys=['one', 'two', 'three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   one  two\n",
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       "c    4    5"
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     "execution_count": 44,
     "metadata": {},
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       "   three  four\n",
       "a      5     6\n",
       "c      7     8"
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     "execution_count": 44,
     "metadata": {},
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       "    <tr>\n",
       "      <th>b</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>c</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "  level1     level2     \n",
       "     one two  three four\n",
       "a      0   1    5.0  6.0\n",
       "b      2   3    NaN  NaN\n",
       "c      4   5    7.0  8.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "df1 = pd.DataFrame(np.arange(6).reshape(3, 2), index=['a', 'b', 'c'],\n",
    "                   columns=['one', 'two'])\n",
    "df2 = pd.DataFrame(5 + np.arange(4).reshape(2, 2), index=['a', 'c'],\n",
    "                   columns=['three', 'four'])#注意5+\n",
    "df1\n",
    "df2\n",
    "pd.concat([df1, df2], axis=1, keys=['level1', 'level2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">level1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">level2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  level1     level2     \n",
       "     one two  three four\n",
       "a      0   1    5.0  6.0\n",
       "b      2   3    NaN  NaN\n",
       "c      4   5    7.0  8.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat({'level1': df1, 'level2': df2}, axis=1)#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>upper</th>\n",
       "      <th colspan=\"2\" halign=\"left\">level1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">level2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lower</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "upper level1     level2     \n",
       "lower    one two  three four\n",
       "a          0   1    5.0  6.0\n",
       "b          2   3    NaN  NaN\n",
       "c          4   5    7.0  8.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], axis=1, keys=['level1', 'level2'],\n",
    "          names=['upper', 'lower'])#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0.274992</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>0.228913</td>\n",
       "      <td>1.352917</td>\n",
       "      <td>0.886429</td>\n",
       "      <td>-2.001637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.371843</td>\n",
       "      <td>1.669025</td>\n",
       "      <td>-0.438570</td>\n",
       "      <td>-0.539741</td>\n",
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      "text/plain": [
       "          a         b         c         d\n",
       "0  1.246435  1.007189 -1.296221  0.274992\n",
       "1  0.228913  1.352917  0.886429 -2.001637\n",
       "2 -0.371843  1.669025 -0.438570 -0.539741"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>a</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.476985</td>\n",
       "      <td>3.248944</td>\n",
       "      <td>-1.021228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.577087</td>\n",
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       "      <td>0.302614</td>\n",
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      "text/plain": [
       "          b         d         a\n",
       "0  0.476985  3.248944 -1.021228\n",
       "1 -0.577087  0.124121  0.302614"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(3, 4), columns=['a', 'b', 'c', 'd'])\n",
    "df2 = pd.DataFrame(np.random.randn(2, 3), columns=['b', 'd', 'a'])\n",
    "df1\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>0.228913</td>\n",
       "      <td>1.352917</td>\n",
       "      <td>0.886429</td>\n",
       "      <td>-2.001637</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.371843</td>\n",
       "      <td>1.669025</td>\n",
       "      <td>-0.438570</td>\n",
       "      <td>-0.539741</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.021228</td>\n",
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       "      <td>3.248944</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.302614</td>\n",
       "      <td>-0.577087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.124121</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0  1.246435  1.007189 -1.296221  0.274992\n",
       "1  0.228913  1.352917  0.886429 -2.001637\n",
       "2 -0.371843  1.669025 -0.438570 -0.539741\n",
       "3 -1.021228  0.476985       NaN  3.248944\n",
       "4  0.302614 -0.577087       NaN  0.124121"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], ignore_index=True)#注意ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining Data with Overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name '混合数据带重叠' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-ada1bfceb803>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0m混合数据带重叠\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name '混合数据带重叠' is not defined"
     ]
    }
   ],
   "source": [
    "混合数据带重叠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    NaN\n",
       "e    2.5\n",
       "d    NaN\n",
       "c    3.5\n",
       "b    4.5\n",
       "a    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "f    0.0\n",
       "e    1.0\n",
       "d    2.0\n",
       "c    3.0\n",
       "b    4.0\n",
       "a    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([0. , 2.5, 2. , 3.5, 4.5, nan])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series([np.nan, 2.5, np.nan, 3.5, 4.5, np.nan],\n",
    "              index=['f', 'e', 'd', 'c', 'b', 'a'])\n",
    "b = pd.Series(np.arange(len(a), dtype=np.float64),\n",
    "              index=['f', 'e', 'd', 'c', 'b', 'a'])\n",
    "b[-1] = np.nan\n",
    "a\n",
    "b\n",
    "np.where(pd.isnull(a), b, a)#important pd.isnull(age)用来判断 ，数据a中某列数据是否有空值，如果有空值返回True，否则返回False\n",
    "#1. np.where(condition, x, y) 满足条件(condition)，输出x，不满足输出y。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    0.0\n",
       "e    1.0\n",
       "d    2.0\n",
       "c    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "d    NaN\n",
       "c    3.5\n",
       "b    4.5\n",
       "a    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    NaN\n",
       "b    4.5\n",
       "c    3.0\n",
       "d    2.0\n",
       "e    1.0\n",
       "f    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:-2]\n",
    "a[2:]\n",
    "b[:-2].combine_first(a[2:])\n",
    "#因为df3为调用方，所以combine_first返回df4对df3的修补，若相同位置都有值，以df3主调方位置 print(df3.combine_first(df4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14</td>\n",
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      "text/plain": [
       "     a    b   c\n",
       "0  1.0  NaN   2\n",
       "1  NaN  2.0   6\n",
       "2  5.0  NaN  10\n",
       "3  NaN  6.0  14"
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     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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      "text/plain": [
       "     a    b\n",
       "0  5.0  NaN\n",
       "1  4.0  3.0\n",
       "2  NaN  4.0\n",
       "3  3.0  6.0\n",
       "4  7.0  8.0"
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     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b     c\n",
       "0  1.0  NaN   2.0\n",
       "1  4.0  2.0   6.0\n",
       "2  5.0  4.0  10.0\n",
       "3  3.0  6.0  14.0\n",
       "4  7.0  8.0   NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'a': [1., np.nan, 5., np.nan],\n",
    "                    'b': [np.nan, 2., np.nan, 6.],\n",
    "                    'c': range(2, 18, 4)})\n",
    "df2 = pd.DataFrame({'a': [5., 4., np.nan, 3., 7.],\n",
    "                    'b': [np.nan, 3., 4., 6., 8.]})\n",
    "df1\n",
    "df2\n",
    "df1.combine_first(df2)#看这个即可"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping and Pivoting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-53-5c6c5a70ae9d>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-53-5c6c5a70ae9d>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    重塑 支点旋转\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "重塑 支点旋转"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping with Hierarchical Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>number</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "number    one  two  three\n",
       "state                    \n",
       "Ohio        0    1      2\n",
       "Colorado    3    4      5"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.arange(6).reshape((2, 3)),\n",
    "                    index=pd.Index(['Ohio', 'Colorado'], name='state'),\n",
    "                    columns=pd.Index(['one', 'two', 'three'],\n",
    "                    name='number'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state     number\n",
       "Ohio      one       0\n",
       "          two       1\n",
       "          three     2\n",
       "Colorado  one       3\n",
       "          two       4\n",
       "          three     5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = data.stack()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>number</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "number    one  two  three\n",
       "state                    \n",
       "Ohio        0    1      2\n",
       "Colorado    3    4      5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state   Ohio  Colorado\n",
       "number                \n",
       "one        0         3\n",
       "two        1         4\n",
       "three      2         5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state   Ohio  Colorado\n",
       "number                \n",
       "one        0         3\n",
       "two        1         4\n",
       "three      2         5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack(0)#注意 转置了\n",
    "result.unstack('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one  a    0\n",
       "     b    1\n",
       "     c    2\n",
       "     d    3\n",
       "two  c    4\n",
       "     d    5\n",
       "     e    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a    b    c    d    e\n",
       "one  0.0  1.0  2.0  3.0  NaN\n",
       "two  NaN  NaN  4.0  5.0  6.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([0, 1, 2, 3], index=['a', 'b', 'c', 'd'])\n",
    "s2 = pd.Series([4, 5, 6], index=['c', 'd', 'e'])\n",
    "data2 = pd.concat([s1, s2], keys=['one', 'two'])\n",
    "data2\n",
    "data2.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a    b    c    d    e\n",
       "one  0.0  1.0  2.0  3.0  NaN\n",
       "two  NaN  NaN  4.0  5.0  6.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "one  a    0.0\n",
       "     b    1.0\n",
       "     c    2.0\n",
       "     d    3.0\n",
       "two  c    4.0\n",
       "     d    5.0\n",
       "     e    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "one  a    0.0\n",
       "     b    1.0\n",
       "     c    2.0\n",
       "     d    3.0\n",
       "     e    NaN\n",
       "two  a    NaN\n",
       "     b    NaN\n",
       "     c    4.0\n",
       "     d    5.0\n",
       "     e    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.unstack()\n",
    "data2.unstack().stack()\n",
    "data2.unstack().stack(dropna=False) #除去nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>side</th>\n",
       "      <th>left</th>\n",
       "      <th>right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Ohio</th>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Colorado</th>\n",
       "      <th>one</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "side             left  right\n",
       "state    number             \n",
       "Ohio     one        0      5\n",
       "         two        1      6\n",
       "         three      2      7\n",
       "Colorado one        3      8\n",
       "         two        4      9\n",
       "         three      5     10"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th>side</th>\n",
       "      <th colspan=\"2\" halign=\"left\">left</th>\n",
       "      <th colspan=\"2\" halign=\"left\">right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "side   left          right         \n",
       "state  Ohio Colorado  Ohio Colorado\n",
       "number                             \n",
       "one       0        3     5        8\n",
       "two       1        4     6        9\n",
       "three     2        5     7       10"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'left': result, 'right': result + 5},\n",
    "                  columns=pd.Index(['left', 'right'], name='side'))\n",
    "df\n",
    "df.unstack('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th>side</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>left</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>left</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">three</th>\n",
       "      <th>left</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state         Colorado  Ohio\n",
       "number side                 \n",
       "one    left          3     0\n",
       "       right         8     5\n",
       "two    left          4     1\n",
       "       right         9     6\n",
       "three  left          5     2\n",
       "       right        10     7"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.unstack('state').stack('side')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivoting “Long” to “Wide” Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',\n",
       "             '1960Q3', '1960Q4', '1961Q1', '1961Q2',\n",
       "             ...\n",
       "             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',\n",
       "             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],\n",
       "            dtype='period[Q-DEC]', name='date', length=203, freq='Q-DEC')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['realgdp', 'infl', 'unemp'], dtype='object', name='item')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>item</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>infl</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2710.349</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2778.801</td>\n",
       "      <td>2.34</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2775.488</td>\n",
       "      <td>2.74</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2785.204</td>\n",
       "      <td>0.27</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2847.699</td>\n",
       "      <td>2.31</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "item   realgdp  infl  unemp\n",
       "0     2710.349  0.00    5.8\n",
       "1     2778.801  2.34    5.1\n",
       "2     2775.488  2.74    5.3\n",
       "3     2785.204  0.27    5.6\n",
       "4     2847.699  2.31    5.2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1959-03-31 23:59:59.999999999',\n",
       "               '1959-06-30 23:59:59.999999999',\n",
       "               '1959-09-30 23:59:59.999999999',\n",
       "               '1959-12-31 23:59:59.999999999',\n",
       "               '1960-03-31 23:59:59.999999999',\n",
       "               '1960-06-30 23:59:59.999999999',\n",
       "               '1960-09-30 23:59:59.999999999',\n",
       "               '1960-12-31 23:59:59.999999999',\n",
       "               '1961-03-31 23:59:59.999999999',\n",
       "               '1961-06-30 23:59:59.999999999',\n",
       "               ...\n",
       "               '2007-06-30 23:59:59.999999999',\n",
       "               '2007-09-30 23:59:59.999999999',\n",
       "               '2007-12-31 23:59:59.999999999',\n",
       "               '2008-03-31 23:59:59.999999999',\n",
       "               '2008-06-30 23:59:59.999999999',\n",
       "               '2008-09-30 23:59:59.999999999',\n",
       "               '2008-12-31 23:59:59.999999999',\n",
       "               '2009-03-31 23:59:59.999999999',\n",
       "               '2009-06-30 23:59:59.999999999',\n",
       "               '2009-09-30 23:59:59.999999999'],\n",
       "              dtype='datetime64[ns]', name='date', length=203, freq=None)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>item</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2710.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2778.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2009-06-30 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>3.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2009-06-30 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>9.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2009-09-30 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>12990.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>2009-09-30 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>3.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2009-09-30 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>9.600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>609 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             date     item      value\n",
       "0   1959-03-31 23:59:59.999999999  realgdp   2710.349\n",
       "1   1959-03-31 23:59:59.999999999     infl      0.000\n",
       "2   1959-03-31 23:59:59.999999999    unemp      5.800\n",
       "3   1959-06-30 23:59:59.999999999  realgdp   2778.801\n",
       "4   1959-06-30 23:59:59.999999999     infl      2.340\n",
       "..                            ...      ...        ...\n",
       "604 2009-06-30 23:59:59.999999999     infl      3.370\n",
       "605 2009-06-30 23:59:59.999999999    unemp      9.200\n",
       "606 2009-09-30 23:59:59.999999999  realgdp  12990.341\n",
       "607 2009-09-30 23:59:59.999999999     infl      3.560\n",
       "608 2009-09-30 23:59:59.999999999    unemp      9.600\n",
       "\n",
       "[609 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('examples/macrodata.csv')\n",
    "#data.head()\n",
    "periods = pd.PeriodIndex(year=data.year, quarter=data.quarter,\n",
    "                         name='date')#重组为时间序列\n",
    "periods\n",
    "columns = pd.Index(['realgdp', 'infl', 'unemp'], name='item')\n",
    "columns\n",
    "data = data.reindex(columns=columns)\n",
    "data.head()\n",
    "data.index = periods.to_timestamp('D', 'end')#to_timestamp() sql中的字符到数据库中的时间戳的操作\n",
    "data.index\n",
    "ldata = data.stack().reset_index().rename(columns={0: 'value'})\n",
    "ldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>value</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2710.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2778.801</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1959-09-30 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2775.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1959-09-30 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959-09-30 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1959-12-31 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2785.204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           date     item     value\n",
       "0 1959-03-31 23:59:59.999999999  realgdp  2710.349\n",
       "1 1959-03-31 23:59:59.999999999     infl     0.000\n",
       "2 1959-03-31 23:59:59.999999999    unemp     5.800\n",
       "3 1959-06-30 23:59:59.999999999  realgdp  2778.801\n",
       "4 1959-06-30 23:59:59.999999999     infl     2.340\n",
       "5 1959-06-30 23:59:59.999999999    unemp     5.100\n",
       "6 1959-09-30 23:59:59.999999999  realgdp  2775.488\n",
       "7 1959-09-30 23:59:59.999999999     infl     2.740\n",
       "8 1959-09-30 23:59:59.999999999    unemp     5.300\n",
       "9 1959-12-31 23:59:59.999999999  realgdp  2785.204"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>item</th>\n",
       "      <th>infl</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-03-31 23:59:59.999999999</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-30 23:59:59.999999999</th>\n",
       "      <td>2.34</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-30 23:59:59.999999999</th>\n",
       "      <td>2.74</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-31 23:59:59.999999999</th>\n",
       "      <td>0.27</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31 23:59:59.999999999</th>\n",
       "      <td>2.31</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09-30 23:59:59.999999999</th>\n",
       "      <td>-3.16</td>\n",
       "      <td>13324.600</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31 23:59:59.999999999</th>\n",
       "      <td>-8.79</td>\n",
       "      <td>13141.920</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-31 23:59:59.999999999</th>\n",
       "      <td>0.94</td>\n",
       "      <td>12925.410</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30 23:59:59.999999999</th>\n",
       "      <td>3.37</td>\n",
       "      <td>12901.504</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-30 23:59:59.999999999</th>\n",
       "      <td>3.56</td>\n",
       "      <td>12990.341</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "item                           infl    realgdp  unemp\n",
       "date                                                 \n",
       "1959-03-31 23:59:59.999999999  0.00   2710.349    5.8\n",
       "1959-06-30 23:59:59.999999999  2.34   2778.801    5.1\n",
       "1959-09-30 23:59:59.999999999  2.74   2775.488    5.3\n",
       "1959-12-31 23:59:59.999999999  0.27   2785.204    5.6\n",
       "1960-03-31 23:59:59.999999999  2.31   2847.699    5.2\n",
       "...                             ...        ...    ...\n",
       "2008-09-30 23:59:59.999999999 -3.16  13324.600    6.0\n",
       "2008-12-31 23:59:59.999999999 -8.79  13141.920    6.9\n",
       "2009-03-31 23:59:59.999999999  0.94  12925.410    8.1\n",
       "2009-06-30 23:59:59.999999999  3.37  12901.504    9.2\n",
       "2009-09-30 23:59:59.999999999  3.56  12990.341    9.6\n",
       "\n",
       "[203 rows x 3 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted = ldata.pivot('date', 'item', 'value')\n",
    "pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>0.523772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-03-31 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.800</td>\n",
       "      <td>1.343810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>-0.713544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.340</td>\n",
       "      <td>-0.831154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1959-06-30 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.100</td>\n",
       "      <td>-2.370232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1959-09-30 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>-1.860761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1959-09-30 23:59:59.999999999</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.740</td>\n",
       "      <td>-0.860757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959-09-30 23:59:59.999999999</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.300</td>\n",
       "      <td>0.560145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1959-12-31 23:59:59.999999999</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>-1.265934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           date     item     value    value2\n",
       "0 1959-03-31 23:59:59.999999999  realgdp  2710.349  0.523772\n",
       "1 1959-03-31 23:59:59.999999999     infl     0.000  0.000940\n",
       "2 1959-03-31 23:59:59.999999999    unemp     5.800  1.343810\n",
       "3 1959-06-30 23:59:59.999999999  realgdp  2778.801 -0.713544\n",
       "4 1959-06-30 23:59:59.999999999     infl     2.340 -0.831154\n",
       "5 1959-06-30 23:59:59.999999999    unemp     5.100 -2.370232\n",
       "6 1959-09-30 23:59:59.999999999  realgdp  2775.488 -1.860761\n",
       "7 1959-09-30 23:59:59.999999999     infl     2.740 -0.860757\n",
       "8 1959-09-30 23:59:59.999999999    unemp     5.300  0.560145\n",
       "9 1959-12-31 23:59:59.999999999  realgdp  2785.204 -1.265934"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldata['value2'] = np.random.randn(len(ldata))\n",
    "ldata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1959-03-31 23:59:59.999999999</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1959-06-30 23:59:59.999999999</th>\n",
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       "      <td>-0.713544</td>\n",
       "      <td>-2.370232</td>\n",
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       "    <tr>\n",
       "      <th>1959-09-30 23:59:59.999999999</th>\n",
       "      <td>2.74</td>\n",
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       "    <tr>\n",
       "      <th>1959-12-31 23:59:59.999999999</th>\n",
       "      <td>0.27</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.119827</td>\n",
       "      <td>-1.265934</td>\n",
       "      <td>-1.063512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31 23:59:59.999999999</th>\n",
       "      <td>2.31</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>5.2</td>\n",
       "      <td>-2.359419</td>\n",
       "      <td>0.332883</td>\n",
       "      <td>-0.199543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              value                    value2            \\\n",
       "item                           infl   realgdp unemp      infl   realgdp   \n",
       "date                                                                      \n",
       "1959-03-31 23:59:59.999999999  0.00  2710.349   5.8  0.000940  0.523772   \n",
       "1959-06-30 23:59:59.999999999  2.34  2778.801   5.1 -0.831154 -0.713544   \n",
       "1959-09-30 23:59:59.999999999  2.74  2775.488   5.3 -0.860757 -1.860761   \n",
       "1959-12-31 23:59:59.999999999  0.27  2785.204   5.6  0.119827 -1.265934   \n",
       "1960-03-31 23:59:59.999999999  2.31  2847.699   5.2 -2.359419  0.332883   \n",
       "\n",
       "                                         \n",
       "item                              unemp  \n",
       "date                                     \n",
       "1959-03-31 23:59:59.999999999  1.343810  \n",
       "1959-06-30 23:59:59.999999999 -2.370232  \n",
       "1959-09-30 23:59:59.999999999  0.560145  \n",
       "1959-12-31 23:59:59.999999999 -1.063512  \n",
       "1960-03-31 23:59:59.999999999 -0.199543  "
      ]
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     "execution_count": 66,
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     "output_type": "execute_result"
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       "      <th>1959-03-31 23:59:59.999999999</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1959-06-30 23:59:59.999999999</th>\n",
       "      <td>2.34</td>\n",
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       "      <th>1959-09-30 23:59:59.999999999</th>\n",
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       "      <th>1959-12-31 23:59:59.999999999</th>\n",
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       "    <tr>\n",
       "      <th>1960-03-31 23:59:59.999999999</th>\n",
       "      <td>2.31</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>5.2</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "item                           infl   realgdp  unemp\n",
       "date                                                \n",
       "1959-03-31 23:59:59.999999999  0.00  2710.349    5.8\n",
       "1959-06-30 23:59:59.999999999  2.34  2778.801    5.1\n",
       "1959-09-30 23:59:59.999999999  2.74  2775.488    5.3\n",
       "1959-12-31 23:59:59.999999999  0.27  2785.204    5.6\n",
       "1960-03-31 23:59:59.999999999  2.31  2847.699    5.2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted = ldata.pivot('date', 'item')\n",
    "pivoted[:5]\n",
    "pivoted['value'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1959-03-31 23:59:59.999999999</th>\n",
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       "      <td>2710.349</td>\n",
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       "      <th>1959-06-30 23:59:59.999999999</th>\n",
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       "    <tr>\n",
       "      <th>1959-09-30 23:59:59.999999999</th>\n",
       "      <td>2.74</td>\n",
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       "    <tr>\n",
       "      <th>1959-12-31 23:59:59.999999999</th>\n",
       "      <td>0.27</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>5.6</td>\n",
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       "      <td>-1.265934</td>\n",
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       "    <tr>\n",
       "      <th>1960-03-31 23:59:59.999999999</th>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>1960-06-30 23:59:59.999999999</th>\n",
       "      <td>0.14</td>\n",
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       "      <td>-1.541996</td>\n",
       "      <td>-1.307030</td>\n",
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       "    <tr>\n",
       "      <th>1960-09-30 23:59:59.999999999</th>\n",
       "      <td>2.70</td>\n",
       "      <td>2839.022</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.377984</td>\n",
       "      <td>0.286350</td>\n",
       "      <td>-0.753887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              value                    value2            \\\n",
       "item                           infl   realgdp unemp      infl   realgdp   \n",
       "date                                                                      \n",
       "1959-03-31 23:59:59.999999999  0.00  2710.349   5.8  0.000940  0.523772   \n",
       "1959-06-30 23:59:59.999999999  2.34  2778.801   5.1 -0.831154 -0.713544   \n",
       "1959-09-30 23:59:59.999999999  2.74  2775.488   5.3 -0.860757 -1.860761   \n",
       "1959-12-31 23:59:59.999999999  0.27  2785.204   5.6  0.119827 -1.265934   \n",
       "1960-03-31 23:59:59.999999999  2.31  2847.699   5.2 -2.359419  0.332883   \n",
       "1960-06-30 23:59:59.999999999  0.14  2834.390   5.2 -0.970736 -1.541996   \n",
       "1960-09-30 23:59:59.999999999  2.70  2839.022   5.6  0.377984  0.286350   \n",
       "\n",
       "                                         \n",
       "item                              unemp  \n",
       "date                                     \n",
       "1959-03-31 23:59:59.999999999  1.343810  \n",
       "1959-06-30 23:59:59.999999999 -2.370232  \n",
       "1959-09-30 23:59:59.999999999  0.560145  \n",
       "1959-12-31 23:59:59.999999999 -1.063512  \n",
       "1960-03-31 23:59:59.999999999 -0.199543  \n",
       "1960-06-30 23:59:59.999999999 -1.307030  \n",
       "1960-09-30 23:59:59.999999999 -0.753887  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unstacked = ldata.set_index(['date', 'item']).unstack('item')\n",
    "unstacked[:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivoting “Wide” to “Long” Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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      "text/plain": [
       "   key  A  B  C\n",
       "0  foo  1  4  7\n",
       "1  bar  2  5  8\n",
       "2  baz  3  6  9"
      ]
     },
     "execution_count": 68,
     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame({'key': ['foo', 'bar', 'baz'],\n",
    "                   'A': [1, 2, 3],\n",
    "                   'B': [4, 5, 6],\n",
    "                   'C': [7, 8, 9]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   key variable  value\n",
       "0  foo        A      1\n",
       "1  bar        A      2\n",
       "2  baz        A      3\n",
       "3  foo        B      4\n",
       "4  bar        B      5\n",
       "5  baz        B      6\n",
       "6  foo        C      7\n",
       "7  bar        C      8\n",
       "8  baz        C      9"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "melted = pd.melt(df, ['key'])\n",
    "melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "variable  A  B  C\n",
       "key              \n",
       "bar       2  5  8\n",
       "baz       3  6  9\n",
       "foo       1  4  7"
      ]
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     "execution_count": 70,
     "metadata": {},
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   "source": [
    "reshaped = melted.pivot('key', 'variable', 'value')\n",
    "reshaped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "variable  key  A  B  C\n",
       "0         bar  2  5  8\n",
       "1         baz  3  6  9\n",
       "2         foo  1  4  7"
      ]
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   "source": [
    "reshaped.reset_index()#important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key variable  value\n",
       "0  foo        A      1\n",
       "1  bar        A      2\n",
       "2  baz        A      3\n",
       "3  foo        B      4\n",
       "4  bar        B      5\n",
       "5  baz        B      6"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(df, id_vars=['key'], value_vars=['A', 'B'])#important 列转行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
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       "  variable  value\n",
       "0        A      1\n",
       "1        A      2\n",
       "2        A      3\n",
       "3        B      4\n",
       "4        B      5\n",
       "5        B      6\n",
       "6        C      7\n",
       "7        C      8\n",
       "8        C      9"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <td>A</td>\n",
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       "      <th>6</th>\n",
       "      <td>B</td>\n",
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       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "  variable value\n",
       "0      key   foo\n",
       "1      key   bar\n",
       "2      key   baz\n",
       "3        A     1\n",
       "4        A     2\n",
       "5        A     3\n",
       "6        B     4\n",
       "7        B     5\n",
       "8        B     6"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(df, value_vars=['A', 'B', 'C'])\n",
    "pd.melt(df, value_vars=['key', 'A', 'B'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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