pd_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 库的导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入numpy库并重命名为np\n",
    "import numpy as np\n",
    "# 导入pandas 库并重命名为pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据的导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read_csv(filename)   # 从csv导入\n",
    "pd.read_table(filename) # 导入有分隔符的文本 (如TSV) 中的数据\n",
    "pd.read_excel(filename) # 从excel导入\n",
    "pd.read_sql(query, connection_object) # 导入SQL数据表/数据库中的数据\n",
    "pd.read_json(json_string) # 导入JSON格式的字符，URL地址或者文件中的数据\n",
    "pd.read_html(url) # 导入经过解析的URL地址中包含的数据框 (DataFrame) 数据\n",
    "pd.read_clipboard() # 导入系统粘贴板里面的数据\n",
    "pd.DataFrame(dict)  # 导入Python字典 (dict) 里面的数据，其中key是数据框的表头，value是数据框的内容。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据的导出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv(filename) # 将数据框 (DataFrame)中的数据导入csv格式的文件中\n",
    "df.to_excel(filename) # 将数据框 (DataFrame)中的数据导入Excel格式的文件中\n",
    "df.to_sql(table_name,connection_object) # 将数据框 (DataFrame)中的数据导入SQL数据表/数据库中\n",
    "df.to_json(filename) # 将数据框 (DataFrame)中的数据导入JSON格式的文件中"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建测试对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.016860</td>\n",
       "      <td>0.855994</td>\n",
       "      <td>0.992872</td>\n",
       "      <td>0.652278</td>\n",
       "      <td>0.517510</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.190823</td>\n",
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       "      <td>0.197766</td>\n",
       "      <td>0.042523</td>\n",
       "      <td>0.580323</td>\n",
       "      <td>0.498982</td>\n",
       "      <td>0.473128</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.029820</td>\n",
       "      <td>0.886500</td>\n",
       "      <td>0.902864</td>\n",
       "      <td>0.465084</td>\n",
       "      <td>0.380933</td>\n",
       "      <td>0.033583</td>\n",
       "      <td>0.928827</td>\n",
       "      <td>0.501687</td>\n",
       "      <td>0.857512</td>\n",
       "      <td>0.671840</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.897254</td>\n",
       "      <td>0.413717</td>\n",
       "      <td>0.991061</td>\n",
       "      <td>0.393033</td>\n",
       "      <td>0.388630</td>\n",
       "      <td>0.661025</td>\n",
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       "      <td>0.305378</td>\n",
       "      <td>0.147508</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.573882</td>\n",
       "      <td>0.786888</td>\n",
       "      <td>0.177782</td>\n",
       "      <td>0.864474</td>\n",
       "      <td>0.594416</td>\n",
       "      <td>0.765678</td>\n",
       "      <td>0.217279</td>\n",
       "      <td>0.446570</td>\n",
       "      <td>0.930604</td>\n",
       "      <td>0.686823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "          0         1         2         3         4         5         6  \\\n",
       "0  0.016860  0.855994  0.992872  0.652278  0.517510  0.742986  0.452981   \n",
       "1  0.476801  0.190823  0.450436  0.912401  0.335651  0.197766  0.042523   \n",
       "2  0.029820  0.886500  0.902864  0.465084  0.380933  0.033583  0.928827   \n",
       "3  0.897254  0.413717  0.991061  0.393033  0.388630  0.661025  0.635417   \n",
       "4  0.573882  0.786888  0.177782  0.864474  0.594416  0.765678  0.217279   \n",
       "\n",
       "          7         8         9  \n",
       "0  0.568701  0.795436  0.622609  \n",
       "1  0.580323  0.498982  0.473128  \n",
       "2  0.501687  0.857512  0.671840  \n",
       "3  0.695609  0.305378  0.147508  \n",
       "4  0.446570  0.930604  0.686823  "
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.rand(5, 10)) # 创建一个5列10行的由随机浮点数组成的数据框 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.Series(my_list) # 从一个可迭代的对象 my_list 中创建一个数据组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           abc\n",
       "1           123\n",
       "2    HelloWorld\n",
       "3           5.7\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = ['abc',123,'HelloWorld', 5.7]\n",
    "pd.Series(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0.011762</td>\n",
       "      <td>0.634116</td>\n",
       "      <td>0.045220</td>\n",
       "      <td>0.452117</td>\n",
       "      <td>0.879969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>0.802262</td>\n",
       "      <td>0.661908</td>\n",
       "      <td>0.214822</td>\n",
       "      <td>0.444259</td>\n",
       "      <td>0.200370</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>0.301050</td>\n",
       "      <td>0.004534</td>\n",
       "      <td>0.881042</td>\n",
       "      <td>0.825632</td>\n",
       "      <td>0.331118</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>0.095324</td>\n",
       "      <td>0.916430</td>\n",
       "      <td>0.177795</td>\n",
       "      <td>0.191502</td>\n",
       "      <td>0.546973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>0.482868</td>\n",
       "      <td>0.953719</td>\n",
       "      <td>0.615461</td>\n",
       "      <td>0.868984</td>\n",
       "      <td>0.639286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>0.958404</td>\n",
       "      <td>0.155357</td>\n",
       "      <td>0.293012</td>\n",
       "      <td>0.115218</td>\n",
       "      <td>0.177846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>0.915488</td>\n",
       "      <td>0.486922</td>\n",
       "      <td>0.440474</td>\n",
       "      <td>0.584764</td>\n",
       "      <td>0.271243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>0.480413</td>\n",
       "      <td>0.600622</td>\n",
       "      <td>0.325212</td>\n",
       "      <td>0.532259</td>\n",
       "      <td>0.687718</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>0.859887</td>\n",
       "      <td>0.236677</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.811840</td>\n",
       "      <td>0.497289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>0.024623</td>\n",
       "      <td>0.635122</td>\n",
       "      <td>0.346393</td>\n",
       "      <td>0.860260</td>\n",
       "      <td>0.325502</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                   0         1         2         3         4\n",
       "2017-01-01  0.011762  0.634116  0.045220  0.452117  0.879969\n",
       "2017-01-02  0.802262  0.661908  0.214822  0.444259  0.200370\n",
       "2017-01-03  0.301050  0.004534  0.881042  0.825632  0.331118\n",
       "2017-01-04  0.095324  0.916430  0.177795  0.191502  0.546973\n",
       "2017-01-05  0.482868  0.953719  0.615461  0.868984  0.639286\n",
       "2017-01-06  0.958404  0.155357  0.293012  0.115218  0.177846\n",
       "2017-01-07  0.915488  0.486922  0.440474  0.584764  0.271243\n",
       "2017-01-08  0.480413  0.600622  0.325212  0.532259  0.687718\n",
       "2017-01-09  0.859887  0.236677  0.635073  0.811840  0.497289\n",
       "2017-01-10  0.024623  0.635122  0.346393  0.860260  0.325502"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10, 5))\n",
    "df.index = pd.date_range('2017/1/1', periods=df.shape[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 数据的查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.head(n)  # 查看前n行的数据\n",
    "df.tail(n)  # 查看后n行的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>4</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.996381</td>\n",
       "      <td>0.440502</td>\n",
       "      <td>0.583701</td>\n",
       "      <td>0.120444</td>\n",
       "      <td>0.241775</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.126877</td>\n",
       "      <td>0.646841</td>\n",
       "      <td>0.740163</td>\n",
       "      <td>0.764182</td>\n",
       "      <td>0.810129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.254386</td>\n",
       "      <td>0.451341</td>\n",
       "      <td>0.288513</td>\n",
       "      <td>0.515995</td>\n",
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       "          0         1         2         3         4\n",
       "0  0.996381  0.440502  0.583701  0.120444  0.241775\n",
       "1  0.126877  0.646841  0.740163  0.764182  0.810129\n",
       "2  0.254386  0.451341  0.288513  0.515995  0.146529"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10, 5))\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
       "      <td>0.466316</td>\n",
       "      <td>0.747013</td>\n",
       "      <td>0.568442</td>\n",
       "      <td>0.562552</td>\n",
       "      <td>0.949529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.243633</td>\n",
       "      <td>0.605133</td>\n",
       "      <td>0.114011</td>\n",
       "      <td>0.898604</td>\n",
       "      <td>0.024648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.155605</td>\n",
       "      <td>0.799580</td>\n",
       "      <td>0.160883</td>\n",
       "      <td>0.986743</td>\n",
       "      <td>0.446114</td>\n",
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      "text/plain": [
       "          0         1         2         3         4\n",
       "7  0.466316  0.747013  0.568442  0.562552  0.949529\n",
       "8  0.243633  0.605133  0.114011  0.898604  0.024648\n",
       "9  0.155605  0.799580  0.160883  0.986743  0.446114"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10, 5))\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看数据的形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.shape # 查看数据的形状（行和宽）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看数据的相关信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      "0    10 non-null float64\n",
      "1    10 non-null float64\n",
      "2    10 non-null float64\n",
      "3    10 non-null float64\n",
      "4    10 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 480.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info() # 查看数据的索引、数据类型及内存信息"
   ]
  },
  {
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.432296</td>\n",
       "      <td>0.601318</td>\n",
       "      <td>0.444123</td>\n",
       "      <td>0.543053</td>\n",
       "      <td>0.413944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.268632</td>\n",
       "      <td>0.216483</td>\n",
       "      <td>0.298076</td>\n",
       "      <td>0.284759</td>\n",
       "      <td>0.345554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.155605</td>\n",
       "      <td>0.252712</td>\n",
       "      <td>0.114011</td>\n",
       "      <td>0.126172</td>\n",
       "      <td>0.024648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.245740</td>\n",
       "      <td>0.461876</td>\n",
       "      <td>0.191090</td>\n",
       "      <td>0.295224</td>\n",
       "      <td>0.094655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.353927</td>\n",
       "      <td>0.609304</td>\n",
       "      <td>0.401004</td>\n",
       "      <td>0.565072</td>\n",
       "      <td>0.389053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.559964</td>\n",
       "      <td>0.762463</td>\n",
       "      <td>0.556721</td>\n",
       "      <td>0.693000</td>\n",
       "      <td>0.668621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.985457</td>\n",
       "      <td>0.932679</td>\n",
       "      <td>0.990827</td>\n",
       "      <td>0.986743</td>\n",
       "      <td>0.949529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0          1          2          3          4\n",
       "count  10.000000  10.000000  10.000000  10.000000  10.000000\n",
       "mean    0.432296   0.601318   0.444123   0.543053   0.413944\n",
       "std     0.268632   0.216483   0.298076   0.284759   0.345554\n",
       "min     0.155605   0.252712   0.114011   0.126172   0.024648\n",
       "25%     0.245740   0.461876   0.191090   0.295224   0.094655\n",
       "50%     0.353927   0.609304   0.401004   0.565072   0.389053\n",
       "75%     0.559964   0.762463   0.556721   0.693000   0.668621\n",
       "max     0.985457   0.932679   0.990827   0.986743   0.949529"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # 对于数据类型为数值型的列，查询其描述性统计的内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 统计次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s.value_counts(dropna=False) # 查询每个独特数据值出现次数统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 5.0    3\n",
       " 3.0    2\n",
       " 7.0    1\n",
       " 6.0    1\n",
       "NaN     1\n",
       " 4.0    1\n",
       " 2.0    1\n",
       " 1.0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,2,3,3,4,np.nan,5,5,5,6,7])\n",
    "s.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3.0\n",
       "1     4.0\n",
       "2     5.0\n",
       "3     5.0\n",
       "4     6.0\n",
       "5     NaN\n",
       "6     7.0\n",
       "7     7.0\n",
       "8     7.0\n",
       "9     8.0\n",
       "10    9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = s.apply(lambda x: x+1)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.apply(pd.Series.value_counts) # 查询数据框 (Data Frame) 中每个列的独特数据值出现次数统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[col] # 以数组 Series 的形式返回选取的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.452717\n",
       "1    0.407755\n",
       "2    0.549391\n",
       "3    0.759433\n",
       "4    0.153871\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(5, 5), columns=list('ABCDE'))\n",
    "df['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[[col1, col2]] # 选择多列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.431885</td>\n",
       "      <td>0.304796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.028960</td>\n",
       "      <td>0.187738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.176520</td>\n",
       "      <td>0.102980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.370277</td>\n",
       "      <td>0.098031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.247122</td>\n",
       "      <td>0.345735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C         D\n",
       "0  0.431885  0.304796\n",
       "1  0.028960  0.187738\n",
       "2  0.176520  0.102980\n",
       "3  0.370277  0.098031\n",
       "4  0.247122  0.345735"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(5, 5), columns=list('ABCDE'))\n",
    "df[['C', 'D']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s.iloc[0] # 按位置选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.array(['I', 'Love', 'China']))\n",
    "s.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s.loc['index_one'] # 按索引选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.array(['I', 'Love', 'China']))\n",
    "s.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.DataFrame[n, :] #选取第n行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       I\n",
       "1    Love\n",
       "2    Data\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.array([['I', 'Love', 'China'], ['I', 'Love', 'Data']]))\n",
    "df.iloc[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.iloc[0, 0] # 选取第一个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.786709</td>\n",
       "      <td>0.405902</td>\n",
       "      <td>0.151383</td>\n",
       "      <td>0.384778</td>\n",
       "      <td>0.871664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.491006</td>\n",
       "      <td>0.774710</td>\n",
       "      <td>0.388011</td>\n",
       "      <td>0.758102</td>\n",
       "      <td>0.762115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085647</td>\n",
       "      <td>0.543243</td>\n",
       "      <td>0.582565</td>\n",
       "      <td>0.664243</td>\n",
       "      <td>0.379896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.806211</td>\n",
       "      <td>0.794284</td>\n",
       "      <td>0.968755</td>\n",
       "      <td>0.883923</td>\n",
       "      <td>0.354820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.463902</td>\n",
       "      <td>0.481756</td>\n",
       "      <td>0.131181</td>\n",
       "      <td>0.590878</td>\n",
       "      <td>0.801769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.786709  0.405902  0.151383  0.384778  0.871664\n",
       "1  0.491006  0.774710  0.388011  0.758102  0.762115\n",
       "2  0.085647  0.543243  0.582565  0.664243  0.379896\n",
       "3  0.806211  0.794284  0.968755  0.883923  0.354820\n",
       "4  0.463902  0.481756  0.131181  0.590878  0.801769"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(5, 5))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78670886755075187"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据的清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = ['a', 'b'] # 对列名重新命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  NaN  foo\n",
       "1  NaN  4.0  foo\n",
       "2  2.0  NaN  foo\n",
       "3  3.0  5.0  foo\n",
       "4  6.0  9.0  foo\n",
       "5  NaN  NaN  foo"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':np.array([1,np.nan,2,3,6,np.nan]),\n",
    "                   'B':np.array([np.nan,4,np.nan,5,9,np.nan]),\n",
    "                   'C':'foo'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b    c\n",
       "0  1.0  NaN  foo\n",
       "1  NaN  4.0  foo\n",
       "2  2.0  NaN  foo\n",
       "3  3.0  5.0  foo\n",
       "4  6.0  9.0  foo\n",
       "5  NaN  NaN  foo"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['a', 'b', 'c']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.isnull() # 检查数据中出现空值的情况, 返回一个布尔型的列\n",
    "pd.notnull() #相对应isnull 返回不是空值的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B      C\n",
       "0  False   True  False\n",
       "1   True  False  False\n",
       "2  False   True  False\n",
       "3  False  False  False\n",
       "4  False  False  False\n",
       "5   True   True  False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':np.array([1,np.nan,2,3,6,np.nan]),\n",
    "                   'B':np.array([np.nan,4,np.nan,5,9,np.nan]),\n",
    "                   'C':'foo'})\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2\n",
       "B    3\n",
       "C    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # 对每一列的空值进行统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.dropna(axis = 0, thresh=n) # 删除包含缺失值的行  axis = 1时删除列  # thresh = n移除空值超过(包括等于)n的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     A    B    C\n",
       "0  1.0  NaN  foo\n",
       "1  NaN  4.0  foo\n",
       "2  2.0  NaN  foo\n",
       "3  3.0  5.0  foo\n",
       "4  6.0  9.0  foo\n",
       "5  NaN  NaN  foo"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     A    B    C\n",
       "3  3.0  5.0  foo\n",
       "4  6.0  9.0  foo"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "df.dropna(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>foo</td>\n",
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      "text/plain": [
       "     C\n",
       "0  foo\n",
       "1  foo\n",
       "2  foo\n",
       "3  foo\n",
       "4  foo\n",
       "5  foo"
      ]
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     "execution_count": 32,
     "metadata": {},
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   "source": [
    "df.dropna(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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      "text/plain": [
       "     A    B    C\n",
       "0  1.0  NaN  foo\n",
       "1  NaN  4.0  foo\n",
       "2  2.0  NaN  foo\n",
       "3  3.0  5.0  foo\n",
       "4  6.0  9.0  foo"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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   ],
   "source": [
    "df.dropna(axis = 0, thresh = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.fillna(df.mean()) # 用平均值来填充空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.000000\n",
       "1    3.000000\n",
       "2    5.000000\n",
       "3    5.666667\n",
       "4    7.000000\n",
       "5    9.000000\n",
       "6    9.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,7,9,9])\n",
    "s.fillna(s.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s.astype(type) # 转换列的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    3\n",
       "2    5\n",
       "3    5\n",
       "4    7\n",
       "5    9\n",
       "6    9\n",
       "dtype: int32"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,7,9,9])\n",
    "s.fillna(s.mean()).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s.replace(1, 'one') # 将Series中的1替换为one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    one\n",
       "1      3\n",
       "2      5\n",
       "3    NaN\n",
       "4      7\n",
       "5      9\n",
       "6      9\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,7,9,9])\n",
    "s.replace(1,'one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s.replace([1,3],['one','three']) # 将数组(Series)中所有的1替换为'one', 所有的3替换为'three'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      one\n",
       "1    three\n",
       "2        5\n",
       "3      NaN\n",
       "4        7\n",
       "5        9\n",
       "6        9\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,7,9,9])\n",
    "s.replace([1,3],['one','three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.rename(columns=lambda x: x + 2) # 将全体列重命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
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       "      <td>0.271009</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "          0         1         2         3\n",
       "0  0.669102  0.548996  0.512802  0.449220\n",
       "1  0.108840  0.974720  0.665050  0.271009\n",
       "2  0.146804  0.060744  0.637770  0.383380\n",
       "3  0.108163  0.893999  0.216907  0.730504"
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     "execution_count": 47,
     "metadata": {},
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   "source": [
    "df = pd.DataFrame(np.random.rand(4,4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.669102</td>\n",
       "      <td>0.548996</td>\n",
       "      <td>0.512802</td>\n",
       "      <td>0.449220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.108840</td>\n",
       "      <td>0.974720</td>\n",
       "      <td>0.665050</td>\n",
       "      <td>0.271009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.146804</td>\n",
       "      <td>0.060744</td>\n",
       "      <td>0.637770</td>\n",
       "      <td>0.383380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.108163</td>\n",
       "      <td>0.893999</td>\n",
       "      <td>0.216907</td>\n",
       "      <td>0.730504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2         3         4         5\n",
       "0  0.669102  0.548996  0.512802  0.449220\n",
       "1  0.108840  0.974720  0.665050  0.271009\n",
       "2  0.146804  0.060744  0.637770  0.383380\n",
       "3  0.108163  0.893999  0.216907  0.730504"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns=lambda x: x+ 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.rename(columns={'old_name': 'new_ name'}) # 将选择的列重命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>New A</th>\n",
       "      <th>New B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.680941</td>\n",
       "      <td>0.766561</td>\n",
       "      <td>0.486226</td>\n",
       "      <td>0.301537</td>\n",
       "      <td>0.289970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.917036</td>\n",
       "      <td>0.100054</td>\n",
       "      <td>0.464342</td>\n",
       "      <td>0.181454</td>\n",
       "      <td>0.933591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.253549</td>\n",
       "      <td>0.766181</td>\n",
       "      <td>0.085607</td>\n",
       "      <td>0.969627</td>\n",
       "      <td>0.630674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377840</td>\n",
       "      <td>0.909920</td>\n",
       "      <td>0.214338</td>\n",
       "      <td>0.011844</td>\n",
       "      <td>0.392257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.608564</td>\n",
       "      <td>0.587614</td>\n",
       "      <td>0.039867</td>\n",
       "      <td>0.630492</td>\n",
       "      <td>0.402101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.361074</td>\n",
       "      <td>0.937618</td>\n",
       "      <td>0.787055</td>\n",
       "      <td>0.054157</td>\n",
       "      <td>0.300325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.605472</td>\n",
       "      <td>0.608429</td>\n",
       "      <td>0.052152</td>\n",
       "      <td>0.669343</td>\n",
       "      <td>0.745648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.660738</td>\n",
       "      <td>0.158713</td>\n",
       "      <td>0.352756</td>\n",
       "      <td>0.028325</td>\n",
       "      <td>0.195899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.855695</td>\n",
       "      <td>0.578177</td>\n",
       "      <td>0.447043</td>\n",
       "      <td>0.093923</td>\n",
       "      <td>0.316234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.337392</td>\n",
       "      <td>0.645260</td>\n",
       "      <td>0.140221</td>\n",
       "      <td>0.616652</td>\n",
       "      <td>0.727144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      New A     New B         C         D         E\n",
       "0  0.680941  0.766561  0.486226  0.301537  0.289970\n",
       "1  0.917036  0.100054  0.464342  0.181454  0.933591\n",
       "2  0.253549  0.766181  0.085607  0.969627  0.630674\n",
       "3  0.377840  0.909920  0.214338  0.011844  0.392257\n",
       "4  0.608564  0.587614  0.039867  0.630492  0.402101\n",
       "5  0.361074  0.937618  0.787055  0.054157  0.300325\n",
       "6  0.605472  0.608429  0.052152  0.669343  0.745648\n",
       "7  0.660738  0.158713  0.352756  0.028325  0.195899\n",
       "8  0.855695  0.578177  0.447043  0.093923  0.316234\n",
       "9  0.337392  0.645260  0.140221  0.616652  0.727144"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.rename(columns={'A':'New A', 'B':'New B'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.set_index('column_one') # 改变索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.824403</th>\n",
       "      <td>0.013092</td>\n",
       "      <td>0.055626</td>\n",
       "      <td>0.895268</td>\n",
       "      <td>0.837350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.310559</th>\n",
       "      <td>0.689064</td>\n",
       "      <td>0.541375</td>\n",
       "      <td>0.275461</td>\n",
       "      <td>0.808554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.533495</th>\n",
       "      <td>0.072835</td>\n",
       "      <td>0.563758</td>\n",
       "      <td>0.695029</td>\n",
       "      <td>0.524957</td>\n",
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       "    <tr>\n",
       "      <th>0.541211</th>\n",
       "      <td>0.820817</td>\n",
       "      <td>0.591130</td>\n",
       "      <td>0.268978</td>\n",
       "      <td>0.546996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.286587</th>\n",
       "      <td>0.936692</td>\n",
       "      <td>0.343227</td>\n",
       "      <td>0.383610</td>\n",
       "      <td>0.811302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.878391</th>\n",
       "      <td>0.938883</td>\n",
       "      <td>0.636148</td>\n",
       "      <td>0.776493</td>\n",
       "      <td>0.025840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.156482</th>\n",
       "      <td>0.918591</td>\n",
       "      <td>0.030869</td>\n",
       "      <td>0.235020</td>\n",
       "      <td>0.096212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.857049</th>\n",
       "      <td>0.613991</td>\n",
       "      <td>0.810541</td>\n",
       "      <td>0.917927</td>\n",
       "      <td>0.921329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.713271</th>\n",
       "      <td>0.949683</td>\n",
       "      <td>0.811386</td>\n",
       "      <td>0.920452</td>\n",
       "      <td>0.213173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.686945</th>\n",
       "      <td>0.522276</td>\n",
       "      <td>0.881299</td>\n",
       "      <td>0.936260</td>\n",
       "      <td>0.030993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A         C         D         E\n",
       "B                                               \n",
       "0.824403  0.013092  0.055626  0.895268  0.837350\n",
       "0.310559  0.689064  0.541375  0.275461  0.808554\n",
       "0.533495  0.072835  0.563758  0.695029  0.524957\n",
       "0.541211  0.820817  0.591130  0.268978  0.546996\n",
       "0.286587  0.936692  0.343227  0.383610  0.811302\n",
       "0.878391  0.938883  0.636148  0.776493  0.025840\n",
       "0.156482  0.918591  0.030869  0.235020  0.096212\n",
       "0.857049  0.613991  0.810541  0.917927  0.921329\n",
       "0.713271  0.949683  0.811386  0.920452  0.213173\n",
       "0.686945  0.522276  0.881299  0.936260  0.030993"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.set_index('B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.rename(index = lambda x: x+ 1) # 改变全体索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.382337</td>\n",
       "      <td>0.185501</td>\n",
       "      <td>0.457958</td>\n",
       "      <td>0.009713</td>\n",
       "      <td>0.628963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.024175</td>\n",
       "      <td>0.223274</td>\n",
       "      <td>0.698171</td>\n",
       "      <td>0.071715</td>\n",
       "      <td>0.063272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.913995</td>\n",
       "      <td>0.713092</td>\n",
       "      <td>0.269621</td>\n",
       "      <td>0.575365</td>\n",
       "      <td>0.805266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.612708</td>\n",
       "      <td>0.220953</td>\n",
       "      <td>0.090858</td>\n",
       "      <td>0.425472</td>\n",
       "      <td>0.018996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.045363</td>\n",
       "      <td>0.153343</td>\n",
       "      <td>0.730828</td>\n",
       "      <td>0.323554</td>\n",
       "      <td>0.364821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.462096</td>\n",
       "      <td>0.614072</td>\n",
       "      <td>0.993130</td>\n",
       "      <td>0.988894</td>\n",
       "      <td>0.788648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.887381</td>\n",
       "      <td>0.802119</td>\n",
       "      <td>0.191248</td>\n",
       "      <td>0.980064</td>\n",
       "      <td>0.628450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.138270</td>\n",
       "      <td>0.922870</td>\n",
       "      <td>0.250827</td>\n",
       "      <td>0.297472</td>\n",
       "      <td>0.289915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.258687</td>\n",
       "      <td>0.807993</td>\n",
       "      <td>0.930009</td>\n",
       "      <td>0.811335</td>\n",
       "      <td>0.609763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.588020</td>\n",
       "      <td>0.392127</td>\n",
       "      <td>0.590799</td>\n",
       "      <td>0.923180</td>\n",
       "      <td>0.722801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4\n",
       "1   0.382337  0.185501  0.457958  0.009713  0.628963\n",
       "2   0.024175  0.223274  0.698171  0.071715  0.063272\n",
       "3   0.913995  0.713092  0.269621  0.575365  0.805266\n",
       "4   0.612708  0.220953  0.090858  0.425472  0.018996\n",
       "5   0.045363  0.153343  0.730828  0.323554  0.364821\n",
       "6   0.462096  0.614072  0.993130  0.988894  0.788648\n",
       "7   0.887381  0.802119  0.191248  0.980064  0.628450\n",
       "8   0.138270  0.922870  0.250827  0.297472  0.289915\n",
       "9   0.258687  0.807993  0.930009  0.811335  0.609763\n",
       "10  0.588020  0.392127  0.590799  0.923180  0.722801"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5))\n",
    "df.rename(index = lambda x: x+ 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据的过滤(filter),排序(sort)和分组(groupby)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[df[col] > 0.5] # 选取数据df中对应行的数值大于0.5的全部列  支持逻辑运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051900</td>\n",
       "      <td>0.548808</td>\n",
       "      <td>0.744936</td>\n",
       "      <td>0.848002</td>\n",
       "      <td>0.299505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.979053</td>\n",
       "      <td>0.216078</td>\n",
       "      <td>0.394286</td>\n",
       "      <td>0.520654</td>\n",
       "      <td>0.584194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.185679</td>\n",
       "      <td>0.453151</td>\n",
       "      <td>0.839947</td>\n",
       "      <td>0.730177</td>\n",
       "      <td>0.392377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.161267</td>\n",
       "      <td>0.981833</td>\n",
       "      <td>0.890858</td>\n",
       "      <td>0.613972</td>\n",
       "      <td>0.467528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.091140</td>\n",
       "      <td>0.369805</td>\n",
       "      <td>0.600035</td>\n",
       "      <td>0.372857</td>\n",
       "      <td>0.897063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.612195</td>\n",
       "      <td>0.981150</td>\n",
       "      <td>0.578304</td>\n",
       "      <td>0.220064</td>\n",
       "      <td>0.488182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.898736</td>\n",
       "      <td>0.626289</td>\n",
       "      <td>0.788306</td>\n",
       "      <td>0.747086</td>\n",
       "      <td>0.386097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.568531</td>\n",
       "      <td>0.362593</td>\n",
       "      <td>0.644950</td>\n",
       "      <td>0.510410</td>\n",
       "      <td>0.092556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.872898</td>\n",
       "      <td>0.771917</td>\n",
       "      <td>0.853365</td>\n",
       "      <td>0.227531</td>\n",
       "      <td>0.045184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.898296</td>\n",
       "      <td>0.683850</td>\n",
       "      <td>0.138142</td>\n",
       "      <td>0.956854</td>\n",
       "      <td>0.335476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "0  0.051900  0.548808  0.744936  0.848002  0.299505\n",
       "1  0.979053  0.216078  0.394286  0.520654  0.584194\n",
       "2  0.185679  0.453151  0.839947  0.730177  0.392377\n",
       "3  0.161267  0.981833  0.890858  0.613972  0.467528\n",
       "4  0.091140  0.369805  0.600035  0.372857  0.897063\n",
       "5  0.612195  0.981150  0.578304  0.220064  0.488182\n",
       "6  0.898736  0.626289  0.788306  0.747086  0.386097\n",
       "7  0.568531  0.362593  0.644950  0.510410  0.092556\n",
       "8  0.872898  0.771917  0.853365  0.227531  0.045184\n",
       "9  0.898296  0.683850  0.138142  0.956854  0.335476"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>1</th>\n",
       "      <td>0.979053</td>\n",
       "      <td>0.216078</td>\n",
       "      <td>0.394286</td>\n",
       "      <td>0.520654</td>\n",
       "      <td>0.584194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.612195</td>\n",
       "      <td>0.981150</td>\n",
       "      <td>0.578304</td>\n",
       "      <td>0.220064</td>\n",
       "      <td>0.488182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.898736</td>\n",
       "      <td>0.626289</td>\n",
       "      <td>0.788306</td>\n",
       "      <td>0.747086</td>\n",
       "      <td>0.386097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.568531</td>\n",
       "      <td>0.362593</td>\n",
       "      <td>0.644950</td>\n",
       "      <td>0.510410</td>\n",
       "      <td>0.092556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.872898</td>\n",
       "      <td>0.771917</td>\n",
       "      <td>0.853365</td>\n",
       "      <td>0.227531</td>\n",
       "      <td>0.045184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.898296</td>\n",
       "      <td>0.683850</td>\n",
       "      <td>0.138142</td>\n",
       "      <td>0.956854</td>\n",
       "      <td>0.335476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "1  0.979053  0.216078  0.394286  0.520654  0.584194\n",
       "5  0.612195  0.981150  0.578304  0.220064  0.488182\n",
       "6  0.898736  0.626289  0.788306  0.747086  0.386097\n",
       "7  0.568531  0.362593  0.644950  0.510410  0.092556\n",
       "8  0.872898  0.771917  0.853365  0.227531  0.045184\n",
       "9  0.898296  0.683850  0.138142  0.956854  0.335476"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['A'] > 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1</th>\n",
       "      <td>0.979053</td>\n",
       "      <td>0.216078</td>\n",
       "      <td>0.394286</td>\n",
       "      <td>0.520654</td>\n",
       "      <td>0.584194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.898736</td>\n",
       "      <td>0.626289</td>\n",
       "      <td>0.788306</td>\n",
       "      <td>0.747086</td>\n",
       "      <td>0.386097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.568531</td>\n",
       "      <td>0.362593</td>\n",
       "      <td>0.644950</td>\n",
       "      <td>0.510410</td>\n",
       "      <td>0.092556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.898296</td>\n",
       "      <td>0.683850</td>\n",
       "      <td>0.138142</td>\n",
       "      <td>0.956854</td>\n",
       "      <td>0.335476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "1  0.979053  0.216078  0.394286  0.520654  0.584194\n",
       "6  0.898736  0.626289  0.788306  0.747086  0.386097\n",
       "7  0.568531  0.362593  0.644950  0.510410  0.092556\n",
       "9  0.898296  0.683850  0.138142  0.956854  0.335476"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['A'] > 0.5) & (df['B'] < 0.7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.sort_values(col, ascending=True) #按照列进行排序  # ascending: True 升序 False 降序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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>3</th>\n",
       "      <td>0.015834</td>\n",
       "      <td>0.758417</td>\n",
       "      <td>0.123415</td>\n",
       "      <td>0.802403</td>\n",
       "      <td>0.782450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.068046</td>\n",
       "      <td>0.373240</td>\n",
       "      <td>0.414358</td>\n",
       "      <td>0.105285</td>\n",
       "      <td>0.759001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.134238</td>\n",
       "      <td>0.104416</td>\n",
       "      <td>0.551595</td>\n",
       "      <td>0.472277</td>\n",
       "      <td>0.015997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.236628</td>\n",
       "      <td>0.391852</td>\n",
       "      <td>0.390275</td>\n",
       "      <td>0.904988</td>\n",
       "      <td>0.650108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.469382</td>\n",
       "      <td>0.426359</td>\n",
       "      <td>0.137109</td>\n",
       "      <td>0.253183</td>\n",
       "      <td>0.894667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.508937</td>\n",
       "      <td>0.443894</td>\n",
       "      <td>0.147076</td>\n",
       "      <td>0.149885</td>\n",
       "      <td>0.434802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.572640</td>\n",
       "      <td>0.369032</td>\n",
       "      <td>0.412343</td>\n",
       "      <td>0.402019</td>\n",
       "      <td>0.445365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.663964</td>\n",
       "      <td>0.533604</td>\n",
       "      <td>0.217605</td>\n",
       "      <td>0.602667</td>\n",
       "      <td>0.637232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.765109</td>\n",
       "      <td>0.646277</td>\n",
       "      <td>0.885381</td>\n",
       "      <td>0.743307</td>\n",
       "      <td>0.649711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.962494</td>\n",
       "      <td>0.650830</td>\n",
       "      <td>0.754514</td>\n",
       "      <td>0.578115</td>\n",
       "      <td>0.659846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "3  0.015834  0.758417  0.123415  0.802403  0.782450\n",
       "1  0.068046  0.373240  0.414358  0.105285  0.759001\n",
       "7  0.134238  0.104416  0.551595  0.472277  0.015997\n",
       "4  0.236628  0.391852  0.390275  0.904988  0.650108\n",
       "8  0.469382  0.426359  0.137109  0.253183  0.894667\n",
       "2  0.508937  0.443894  0.147076  0.149885  0.434802\n",
       "0  0.572640  0.369032  0.412343  0.402019  0.445365\n",
       "9  0.663964  0.533604  0.217605  0.602667  0.637232\n",
       "6  0.765109  0.646277  0.885381  0.743307  0.649711\n",
       "5  0.962494  0.650830  0.754514  0.578115  0.659846"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.sort_values('A', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.sort_values([col1,col2],ascending=[True,False]) # 按照数据框的列col1升序，col2降序的方式对数据框df做排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1</th>\n",
       "      <td>0.083526</td>\n",
       "      <td>0.969267</td>\n",
       "      <td>0.012550</td>\n",
       "      <td>0.672851</td>\n",
       "      <td>0.866501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.107839</td>\n",
       "      <td>0.383900</td>\n",
       "      <td>0.982337</td>\n",
       "      <td>0.390914</td>\n",
       "      <td>0.308559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.148742</td>\n",
       "      <td>0.306888</td>\n",
       "      <td>0.853949</td>\n",
       "      <td>0.144650</td>\n",
       "      <td>0.414474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.190011</td>\n",
       "      <td>0.794060</td>\n",
       "      <td>0.514756</td>\n",
       "      <td>0.272207</td>\n",
       "      <td>0.086894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.423982</td>\n",
       "      <td>0.229873</td>\n",
       "      <td>0.992318</td>\n",
       "      <td>0.495706</td>\n",
       "      <td>0.971735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.532263</td>\n",
       "      <td>0.106900</td>\n",
       "      <td>0.528114</td>\n",
       "      <td>0.456583</td>\n",
       "      <td>0.362642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619678</td>\n",
       "      <td>0.800373</td>\n",
       "      <td>0.927766</td>\n",
       "      <td>0.742667</td>\n",
       "      <td>0.645809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.815312</td>\n",
       "      <td>0.920682</td>\n",
       "      <td>0.833351</td>\n",
       "      <td>0.266840</td>\n",
       "      <td>0.132698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.842293</td>\n",
       "      <td>0.049499</td>\n",
       "      <td>0.780198</td>\n",
       "      <td>0.343752</td>\n",
       "      <td>0.341800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.932379</td>\n",
       "      <td>0.398721</td>\n",
       "      <td>0.080358</td>\n",
       "      <td>0.200681</td>\n",
       "      <td>0.549237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "1  0.083526  0.969267  0.012550  0.672851  0.866501\n",
       "7  0.107839  0.383900  0.982337  0.390914  0.308559\n",
       "0  0.148742  0.306888  0.853949  0.144650  0.414474\n",
       "4  0.190011  0.794060  0.514756  0.272207  0.086894\n",
       "6  0.423982  0.229873  0.992318  0.495706  0.971735\n",
       "9  0.532263  0.106900  0.528114  0.456583  0.362642\n",
       "2  0.619678  0.800373  0.927766  0.742667  0.645809\n",
       "3  0.815312  0.920682  0.833351  0.266840  0.132698\n",
       "5  0.842293  0.049499  0.780198  0.343752  0.341800\n",
       "8  0.932379  0.398721  0.080358  0.200681  0.549237"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.sort_values(['A','E'],ascending=[True,False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.groupby(col) # 按照某列对数据框df做分组 # 常与count进行连用，统计出各词的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>small</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>large</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>large</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>small</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>small</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B       C  D\n",
       "0  foo    one   small  1\n",
       "1  foo    one  medium  2\n",
       "2  foo    two   large  2\n",
       "3  foo    two   large  3\n",
       "4  bar  three   small  3\n",
       "5  bar  three   small  5"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),\n",
    "      'B':np.array(['one','one','two','two','three','three']),\n",
    "     'C':np.array(['small','medium','large','large','small','small']),\n",
    "     'D':np.array([1,2,2,3,3,5])})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A  C  D\n",
       "B             \n",
       "one    2  2  2\n",
       "three  2  2  2\n",
       "two    2  2  2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('B').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.groupby([col1,col2]) # 按照列col1和col2对数据框df做分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <th>three</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           D\n",
       "A   B       \n",
       "bar three  8\n",
       "foo one    3\n",
       "    two    5"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),\n",
    "      'B':np.array(['one','one','two','two','three','three']),\n",
    "     'C':np.array(['small','medium','large','large','small','small']),\n",
    "     'D':np.array([1,2,2,3,3,5])})\n",
    "df.groupby(['A', 'B']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.groupby(col1)[col2].mean() # 按照列col1对数据框df做分组处理后，返回对应的col2的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A\n",
       "bar    4\n",
       "foo    2\n",
       "Name: D, dtype: int32"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),\n",
    "      'B':np.array(['one','one','two','two','three','three']),\n",
    "     'C':np.array(['small','medium','large','large','small','small']),\n",
    "     'D':np.array([1,2,2,3,3,5])})\n",
    "df.groupby('A')['D'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.pivot_table(index=col1,values=[col2,col3],aggfunc=mean) # 做透视表，索引为col1,针对的数值列为col2和col3，分组函数为平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>large</th>\n",
       "      <th>medium</th>\n",
       "      <th>small</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <th>three</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              D             \n",
       "C         large medium small\n",
       "A   B                       \n",
       "bar three   NaN    NaN   8.0\n",
       "foo one     NaN    2.0   1.0\n",
       "    two     5.0    NaN   NaN"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),\n",
    "      'B':np.array(['one','one','two','two','three','three']),\n",
    "     'C':np.array(['small','medium','large','large','small','small']),\n",
    "     'D':np.array([1,2,2,3,3,5])})\n",
    "df.pivot_table(df, index=['A', 'B'], columns=['C'], aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     D\n",
       "A     \n",
       "bar  4\n",
       "foo  2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':np.array(['foo','foo','foo','foo','bar','bar']),\n",
    "      'B':np.array(['one','one','two','two','three','three']),\n",
    "     'C':np.array(['small','medium','large','large','small','small']),\n",
    "     'D':np.array([1,2,2,3,3,5])})\n",
    "df.groupby('A').agg(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.apply(np.mean, axis=0) # 对数据框df的每一列求平均值 axis: 0对列名（横着的）进行处理  1对索引（竖着的）进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.800902</td>\n",
       "      <td>0.933676</td>\n",
       "      <td>0.461338</td>\n",
       "      <td>0.353398</td>\n",
       "      <td>0.057885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.988580</td>\n",
       "      <td>0.681318</td>\n",
       "      <td>0.533361</td>\n",
       "      <td>0.486016</td>\n",
       "      <td>0.220004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.695034</td>\n",
       "      <td>0.643920</td>\n",
       "      <td>0.694040</td>\n",
       "      <td>0.280063</td>\n",
       "      <td>0.641867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.925290</td>\n",
       "      <td>0.084906</td>\n",
       "      <td>0.120247</td>\n",
       "      <td>0.880991</td>\n",
       "      <td>0.399596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.697742</td>\n",
       "      <td>0.372860</td>\n",
       "      <td>0.881456</td>\n",
       "      <td>0.565627</td>\n",
       "      <td>0.272549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.614245</td>\n",
       "      <td>0.658123</td>\n",
       "      <td>0.797487</td>\n",
       "      <td>0.609511</td>\n",
       "      <td>0.544633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.153517</td>\n",
       "      <td>0.354870</td>\n",
       "      <td>0.910838</td>\n",
       "      <td>0.416895</td>\n",
       "      <td>0.098821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.088223</td>\n",
       "      <td>0.501401</td>\n",
       "      <td>0.702754</td>\n",
       "      <td>0.334938</td>\n",
       "      <td>0.182708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.737348</td>\n",
       "      <td>0.569340</td>\n",
       "      <td>0.291342</td>\n",
       "      <td>0.847058</td>\n",
       "      <td>0.193331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.083915</td>\n",
       "      <td>0.396210</td>\n",
       "      <td>0.589415</td>\n",
       "      <td>0.806525</td>\n",
       "      <td>0.598841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "0  0.800902  0.933676  0.461338  0.353398  0.057885\n",
       "1  0.988580  0.681318  0.533361  0.486016  0.220004\n",
       "2  0.695034  0.643920  0.694040  0.280063  0.641867\n",
       "3  0.925290  0.084906  0.120247  0.880991  0.399596\n",
       "4  0.697742  0.372860  0.881456  0.565627  0.272549\n",
       "5  0.614245  0.658123  0.797487  0.609511  0.544633\n",
       "6  0.153517  0.354870  0.910838  0.416895  0.098821\n",
       "7  0.088223  0.501401  0.702754  0.334938  0.182708\n",
       "8  0.737348  0.569340  0.291342  0.847058  0.193331\n",
       "9  0.083915  0.396210  0.589415  0.806525  0.598841"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.578480\n",
       "B    0.519662\n",
       "C    0.598228\n",
       "D    0.558102\n",
       "E    0.321024\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.mean, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据的连接(join)与组合(combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1.append(df2) # 在数据框df2的末尾添加数据框df1，其中df1和df2的列数应该相等  列合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1\n",
       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3\n",
       "4  A4  B4  C4  D4\n",
       "5  A5  B5  C5  D5\n",
       "6  A6  B6  C6  D6\n",
       "7  A7  B7  C7  D7"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                    'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                    'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                    'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                   index=[0, 1, 2, 3])\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                    'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                    'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                    'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                   index=[4, 5, 6, 7])\n",
    "df1.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.concat([df1, df2], axis=1) # 在数据框df1的列最后添加数据框df2,其中df1和df2的行数应该相等  # 中括号可以换成圆括号  # axis: 0进行行合并  1进行列合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
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       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1\n",
       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3\n",
       "4  A4  B4  C4  D4\n",
       "5  A5  B5  C5  D5\n",
       "6  A6  B6  C6  D6\n",
       "7  A7  B7  C7  D7"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                    'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                    'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                    'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                   index=[0, 1, 2, 3])\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                    'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                    'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                    'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                   index=[4, 5, 6, 7])\n",
    "pd.concat([df1, df2], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D   E   F   G   H\n",
       "0  A0  B0  C0  D0  A4  B4  C4  D4\n",
       "1  A1  B1  C1  D1  A5  B5  C5  D5\n",
       "2  A2  B2  C2  D2  A6  B6  C6  D6\n",
       "3  A3  B3  C3  D3  A7  B7  C7  D7"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                    'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                    'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                    'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                   index=[0, 1, 2, 3])\n",
    "df2 = pd.DataFrame({'E': ['A4', 'A5', 'A6', 'A7'],\n",
    "                    'F': ['B4', 'B5', 'B6', 'B7'],\n",
    "                    'G': ['C4', 'C5', 'C6', 'C7'],\n",
    "                    'H': ['D4', 'D5', 'D6', 'D7']},\n",
    "                   index=[0, 1, 2, 3])\n",
    "pd.concat((df1, df2), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1.join(df2,on=col1,how='inner') # 对数据框df1和df2做内连接，其中连接的列为col1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>key</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>K1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>K1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key   C   D\n",
       "0  A0  B0  K0  C0  D0\n",
       "2  A2  B2  K0  C0  D0\n",
       "1  A1  B1  K1  C1  D1\n",
       "3  A3  B3  K1  C1  D1"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],           \n",
    "                     'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                     'key': ['K0', 'K1', 'K0', 'K1']})\n",
    "   \n",
    "\n",
    "df2 = pd.DataFrame({'C': ['C0', 'C1'],\n",
    "                      'D': ['D0', 'D1']},\n",
    "                     index=['K0', 'K1'])\n",
    "df1.join(df2, on='key', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据的统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.mean() # 得到数据框df中每一列的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.362158\n",
       "B    0.432248\n",
       "C    0.554478\n",
       "D    0.331155\n",
       "E    0.438283\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.corr() # 得到数据框df中每一列与其他列的相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.167715</td>\n",
       "      <td>0.198216</td>\n",
       "      <td>0.036939</td>\n",
       "      <td>0.113714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.167715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.449789</td>\n",
       "      <td>0.015883</td>\n",
       "      <td>-0.236658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.198216</td>\n",
       "      <td>0.449789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.296943</td>\n",
       "      <td>0.386206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.036939</td>\n",
       "      <td>0.015883</td>\n",
       "      <td>-0.296943</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.777327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.113714</td>\n",
       "      <td>-0.236658</td>\n",
       "      <td>0.386206</td>\n",
       "      <td>-0.777327</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "A  1.000000 -0.167715  0.198216  0.036939  0.113714\n",
       "B -0.167715  1.000000  0.449789  0.015883 -0.236658\n",
       "C  0.198216  0.449789  1.000000 -0.296943  0.386206\n",
       "D  0.036939  0.015883 -0.296943  1.000000 -0.777327\n",
       "E  0.113714 -0.236658  0.386206 -0.777327  1.000000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.count() # 得到数据框df中每一列的非空值个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     9\n",
       "B    10\n",
       "C    10\n",
       "D    10\n",
       "E    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "# df.loc[0][0] = np.nan\n",
    "df.iloc[0, 0] = np.nan\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.max() # 得到数据框df中每一列的最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.812708\n",
       "B    0.886171\n",
       "C    0.987035\n",
       "D    0.977146\n",
       "E    0.959625\n",
       "dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.min() # 得到数据框df中每一列的最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.128560\n",
       "B    0.135905\n",
       "C    0.167476\n",
       "D    0.137062\n",
       "E    0.050306\n",
       "dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.median() # 得到数据框df中每一列的中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.409373\n",
       "B    0.597418\n",
       "C    0.678203\n",
       "D    0.705762\n",
       "E    0.519713\n",
       "dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.std() # 得到数据框df中每一列的标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.272847\n",
       "B    0.254870\n",
       "C    0.258956\n",
       "D    0.301168\n",
       "E    0.295753\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10,5),columns=list('ABCDE'))\n",
    "df.std()"
   ]
  }
 ],
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