Learn_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas核心数据结构DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "NDB_No               int64\n",
      "Shrt_Desc           object\n",
      "Water_(g)          float64\n",
      "Energ_Kcal           int64\n",
      "Protein_(g)        float64\n",
      "Lipid_Tot_(g)      float64\n",
      "Ash_(g)            float64\n",
      "Carbohydrt_(g)     float64\n",
      "Fiber_TD_(g)       float64\n",
      "Sugar_Tot_(g)      float64\n",
      "Calcium_(mg)       float64\n",
      "Iron_(mg)          float64\n",
      "Magnesium_(mg)     float64\n",
      "Phosphorus_(mg)    float64\n",
      "Potassium_(mg)     float64\n",
      "Sodium_(mg)        float64\n",
      "Zinc_(mg)          float64\n",
      "Copper_(mg)        float64\n",
      "Manganese_(mg)     float64\n",
      "Selenium_(mcg)     float64\n",
      "Vit_C_(mg)         float64\n",
      "Thiamin_(mg)       float64\n",
      "Riboflavin_(mg)    float64\n",
      "Niacin_(mg)        float64\n",
      "Vit_B6_(mg)        float64\n",
      "Vit_B12_(mcg)      float64\n",
      "Vit_A_IU           float64\n",
      "Vit_A_RAE          float64\n",
      "Vit_E_(mg)         float64\n",
      "Vit_D_mcg          float64\n",
      "Vit_D_IU           float64\n",
      "Vit_K_(mcg)        float64\n",
      "FA_Sat_(g)         float64\n",
      "FA_Mono_(g)        float64\n",
      "FA_Poly_(g)        float64\n",
      "Cholestrl_(mg)     float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "food_info = pandas.read_csv('food_info.csv')\n",
    "print(type(food_info))        #数据类型\n",
    "print(food_info.dtypes)       #DataFrame中包含的数据类型\n",
    "#print(help(pandas.read_csv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>NDB_No</th>\n",
       "      <th>Shrt_Desc</th>\n",
       "      <th>Water_(g)</th>\n",
       "      <th>Energ_Kcal</th>\n",
       "      <th>Protein_(g)</th>\n",
       "      <th>Lipid_Tot_(g)</th>\n",
       "      <th>Ash_(g)</th>\n",
       "      <th>Carbohydrt_(g)</th>\n",
       "      <th>Fiber_TD_(g)</th>\n",
       "      <th>Sugar_Tot_(g)</th>\n",
       "      <th>...</th>\n",
       "      <th>Vit_A_IU</th>\n",
       "      <th>Vit_A_RAE</th>\n",
       "      <th>Vit_E_(mg)</th>\n",
       "      <th>Vit_D_mcg</th>\n",
       "      <th>Vit_D_IU</th>\n",
       "      <th>Vit_K_(mcg)</th>\n",
       "      <th>FA_Sat_(g)</th>\n",
       "      <th>FA_Mono_(g)</th>\n",
       "      <th>FA_Poly_(g)</th>\n",
       "      <th>Cholestrl_(mg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>BUTTER WITH SALT</td>\n",
       "      <td>15.87</td>\n",
       "      <td>717</td>\n",
       "      <td>0.85</td>\n",
       "      <td>81.11</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>2499.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.368</td>\n",
       "      <td>21.021</td>\n",
       "      <td>3.043</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>BUTTER WHIPPED WITH SALT</td>\n",
       "      <td>15.87</td>\n",
       "      <td>717</td>\n",
       "      <td>0.85</td>\n",
       "      <td>81.11</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>2499.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50.489</td>\n",
       "      <td>23.426</td>\n",
       "      <td>3.012</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>BUTTER OIL ANHYDROUS</td>\n",
       "      <td>0.24</td>\n",
       "      <td>876</td>\n",
       "      <td>0.28</td>\n",
       "      <td>99.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3069.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>61.924</td>\n",
       "      <td>28.732</td>\n",
       "      <td>3.694</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>CHEESE BLUE</td>\n",
       "      <td>42.41</td>\n",
       "      <td>353</td>\n",
       "      <td>21.40</td>\n",
       "      <td>28.74</td>\n",
       "      <td>5.11</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>721.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>18.669</td>\n",
       "      <td>7.778</td>\n",
       "      <td>0.800</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>CHEESE BRICK</td>\n",
       "      <td>41.11</td>\n",
       "      <td>371</td>\n",
       "      <td>23.24</td>\n",
       "      <td>29.68</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>18.764</td>\n",
       "      <td>8.598</td>\n",
       "      <td>0.784</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NDB_No                 Shrt_Desc  Water_(g)  Energ_Kcal  Protein_(g)  \\\n",
       "0    1001          BUTTER WITH SALT      15.87         717         0.85   \n",
       "1    1002  BUTTER WHIPPED WITH SALT      15.87         717         0.85   \n",
       "2    1003      BUTTER OIL ANHYDROUS       0.24         876         0.28   \n",
       "3    1004               CHEESE BLUE      42.41         353        21.40   \n",
       "4    1005              CHEESE BRICK      41.11         371        23.24   \n",
       "\n",
       "   Lipid_Tot_(g)  Ash_(g)  Carbohydrt_(g)  Fiber_TD_(g)  Sugar_Tot_(g)  \\\n",
       "0          81.11     2.11            0.06           0.0           0.06   \n",
       "1          81.11     2.11            0.06           0.0           0.06   \n",
       "2          99.48     0.00            0.00           0.0           0.00   \n",
       "3          28.74     5.11            2.34           0.0           0.50   \n",
       "4          29.68     3.18            2.79           0.0           0.51   \n",
       "\n",
       "        ...        Vit_A_IU  Vit_A_RAE  Vit_E_(mg)  Vit_D_mcg  Vit_D_IU  \\\n",
       "0       ...          2499.0      684.0        2.32        1.5      60.0   \n",
       "1       ...          2499.0      684.0        2.32        1.5      60.0   \n",
       "2       ...          3069.0      840.0        2.80        1.8      73.0   \n",
       "3       ...           721.0      198.0        0.25        0.5      21.0   \n",
       "4       ...          1080.0      292.0        0.26        0.5      22.0   \n",
       "\n",
       "   Vit_K_(mcg)  FA_Sat_(g)  FA_Mono_(g)  FA_Poly_(g)  Cholestrl_(mg)  \n",
       "0          7.0      51.368       21.021        3.043           215.0  \n",
       "1          7.0      50.489       23.426        3.012           219.0  \n",
       "2          8.6      61.924       28.732        3.694           256.0  \n",
       "3          2.4      18.669        7.778        0.800            75.0  \n",
       "4          2.5      18.764        8.598        0.784            94.0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_info.head()    #默认显示前5条"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>NDB_No</th>\n",
       "      <th>Shrt_Desc</th>\n",
       "      <th>Water_(g)</th>\n",
       "      <th>Energ_Kcal</th>\n",
       "      <th>Protein_(g)</th>\n",
       "      <th>Lipid_Tot_(g)</th>\n",
       "      <th>Ash_(g)</th>\n",
       "      <th>Carbohydrt_(g)</th>\n",
       "      <th>Fiber_TD_(g)</th>\n",
       "      <th>Sugar_Tot_(g)</th>\n",
       "      <th>...</th>\n",
       "      <th>Vit_A_IU</th>\n",
       "      <th>Vit_A_RAE</th>\n",
       "      <th>Vit_E_(mg)</th>\n",
       "      <th>Vit_D_mcg</th>\n",
       "      <th>Vit_D_IU</th>\n",
       "      <th>Vit_K_(mcg)</th>\n",
       "      <th>FA_Sat_(g)</th>\n",
       "      <th>FA_Mono_(g)</th>\n",
       "      <th>FA_Poly_(g)</th>\n",
       "      <th>Cholestrl_(mg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8615</th>\n",
       "      <td>90480</td>\n",
       "      <td>SYRUP CANE</td>\n",
       "      <td>26.0</td>\n",
       "      <td>269</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>73.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>90560</td>\n",
       "      <td>SNAIL RAW</td>\n",
       "      <td>79.2</td>\n",
       "      <td>90</td>\n",
       "      <td>16.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.252</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>93600</td>\n",
       "      <td>TURTLE GREEN RAW</td>\n",
       "      <td>78.5</td>\n",
       "      <td>89</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.170</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      NDB_No         Shrt_Desc  Water_(g)  Energ_Kcal  Protein_(g)  \\\n",
       "8615   90480        SYRUP CANE       26.0         269          0.0   \n",
       "8616   90560         SNAIL RAW       79.2          90         16.1   \n",
       "8617   93600  TURTLE GREEN RAW       78.5          89         19.8   \n",
       "\n",
       "      Lipid_Tot_(g)  Ash_(g)  Carbohydrt_(g)  Fiber_TD_(g)  Sugar_Tot_(g)  \\\n",
       "8615            0.0     0.86           73.14           0.0           73.2   \n",
       "8616            1.4     1.30            2.00           0.0            0.0   \n",
       "8617            0.5     1.20            0.00           0.0            0.0   \n",
       "\n",
       "           ...        Vit_A_IU  Vit_A_RAE  Vit_E_(mg)  Vit_D_mcg  Vit_D_IU  \\\n",
       "8615       ...             0.0        0.0         0.0        0.0       0.0   \n",
       "8616       ...           100.0       30.0         5.0        0.0       0.0   \n",
       "8617       ...           100.0       30.0         0.5        0.0       0.0   \n",
       "\n",
       "      Vit_K_(mcg)  FA_Sat_(g)  FA_Mono_(g)  FA_Poly_(g)  Cholestrl_(mg)  \n",
       "8615          0.0       0.000        0.000        0.000             0.0  \n",
       "8616          0.1       0.361        0.259        0.252            50.0  \n",
       "8617          0.1       0.127        0.088        0.170            50.0  \n",
       "\n",
       "[3 rows x 36 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_info.tail(3)    #显示末尾3行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)',\n",
       "       'Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)',\n",
       "       'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)',\n",
       "       'Phosphorus_(mg)', 'Potassium_(mg)', 'Sodium_(mg)', 'Zinc_(mg)',\n",
       "       'Copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)',\n",
       "       'Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)',\n",
       "       'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg',\n",
       "       'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_Poly_(g)',\n",
       "       'Cholestrl_(mg)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_info.columns     #所有列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8618, 36)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_info.shape     #返回维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NDB_No                         1001\n",
       "Shrt_Desc          BUTTER WITH SALT\n",
       "Water_(g)                     15.87\n",
       "Energ_Kcal                      717\n",
       "Protein_(g)                    0.85\n",
       "Lipid_Tot_(g)                 81.11\n",
       "Ash_(g)                        2.11\n",
       "Carbohydrt_(g)                 0.06\n",
       "Fiber_TD_(g)                      0\n",
       "Sugar_Tot_(g)                  0.06\n",
       "Calcium_(mg)                     24\n",
       "Iron_(mg)                      0.02\n",
       "Magnesium_(mg)                    2\n",
       "Phosphorus_(mg)                  24\n",
       "Potassium_(mg)                   24\n",
       "Sodium_(mg)                     643\n",
       "Zinc_(mg)                      0.09\n",
       "Copper_(mg)                       0\n",
       "Manganese_(mg)                    0\n",
       "Selenium_(mcg)                    1\n",
       "Vit_C_(mg)                        0\n",
       "Thiamin_(mg)                  0.005\n",
       "Riboflavin_(mg)               0.034\n",
       "Niacin_(mg)                   0.042\n",
       "Vit_B6_(mg)                   0.003\n",
       "Vit_B12_(mcg)                  0.17\n",
       "Vit_A_IU                       2499\n",
       "Vit_A_RAE                       684\n",
       "Vit_E_(mg)                     2.32\n",
       "Vit_D_mcg                       1.5\n",
       "Vit_D_IU                         60\n",
       "Vit_K_(mcg)                       7\n",
       "FA_Sat_(g)                   51.368\n",
       "FA_Mono_(g)                  21.021\n",
       "FA_Poly_(g)                   3.043\n",
       "Cholestrl_(mg)                  215\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_info.loc[0]    #索引第一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>NDB_No</th>\n",
       "      <th>Shrt_Desc</th>\n",
       "      <th>Water_(g)</th>\n",
       "      <th>Energ_Kcal</th>\n",
       "      <th>Protein_(g)</th>\n",
       "      <th>Lipid_Tot_(g)</th>\n",
       "      <th>Ash_(g)</th>\n",
       "      <th>Carbohydrt_(g)</th>\n",
       "      <th>Fiber_TD_(g)</th>\n",
       "      <th>Sugar_Tot_(g)</th>\n",
       "      <th>...</th>\n",
       "      <th>Vit_A_IU</th>\n",
       "      <th>Vit_A_RAE</th>\n",
       "      <th>Vit_E_(mg)</th>\n",
       "      <th>Vit_D_mcg</th>\n",
       "      <th>Vit_D_IU</th>\n",
       "      <th>Vit_K_(mcg)</th>\n",
       "      <th>FA_Sat_(g)</th>\n",
       "      <th>FA_Mono_(g)</th>\n",
       "      <th>FA_Poly_(g)</th>\n",
       "      <th>Cholestrl_(mg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>BUTTER OIL ANHYDROUS</td>\n",
       "      <td>0.24</td>\n",
       "      <td>876</td>\n",
       "      <td>0.28</td>\n",
       "      <td>99.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3069.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>61.924</td>\n",
       "      <td>28.732</td>\n",
       "      <td>3.694</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>CHEESE BLUE</td>\n",
       "      <td>42.41</td>\n",
       "      <td>353</td>\n",
       "      <td>21.40</td>\n",
       "      <td>28.74</td>\n",
       "      <td>5.11</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>721.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>18.669</td>\n",
       "      <td>7.778</td>\n",
       "      <td>0.800</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>CHEESE BRICK</td>\n",
       "      <td>41.11</td>\n",
       "      <td>371</td>\n",
       "      <td>23.24</td>\n",
       "      <td>29.68</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>18.764</td>\n",
       "      <td>8.598</td>\n",
       "      <td>0.784</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1006</td>\n",
       "      <td>CHEESE BRIE</td>\n",
       "      <td>48.42</td>\n",
       "      <td>334</td>\n",
       "      <td>20.75</td>\n",
       "      <td>27.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>592.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>17.410</td>\n",
       "      <td>8.013</td>\n",
       "      <td>0.826</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1007</td>\n",
       "      <td>CHEESE CAMEMBERT</td>\n",
       "      <td>51.80</td>\n",
       "      <td>300</td>\n",
       "      <td>19.80</td>\n",
       "      <td>24.26</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>820.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.259</td>\n",
       "      <td>7.023</td>\n",
       "      <td>0.724</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1008</td>\n",
       "      <td>CHEESE CARAWAY</td>\n",
       "      <td>39.28</td>\n",
       "      <td>376</td>\n",
       "      <td>25.18</td>\n",
       "      <td>29.20</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.584</td>\n",
       "      <td>8.275</td>\n",
       "      <td>0.830</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1009</td>\n",
       "      <td>CHEESE CHEDDAR</td>\n",
       "      <td>37.10</td>\n",
       "      <td>406</td>\n",
       "      <td>24.04</td>\n",
       "      <td>33.82</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>...</td>\n",
       "      <td>994.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>19.368</td>\n",
       "      <td>8.428</td>\n",
       "      <td>1.433</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1010</td>\n",
       "      <td>CHEESE CHESHIRE</td>\n",
       "      <td>37.65</td>\n",
       "      <td>387</td>\n",
       "      <td>23.37</td>\n",
       "      <td>30.60</td>\n",
       "      <td>3.60</td>\n",
       "      <td>4.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>985.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.475</td>\n",
       "      <td>8.671</td>\n",
       "      <td>0.870</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1011</td>\n",
       "      <td>CHEESE COLBY</td>\n",
       "      <td>38.20</td>\n",
       "      <td>394</td>\n",
       "      <td>23.76</td>\n",
       "      <td>32.11</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>...</td>\n",
       "      <td>994.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>20.218</td>\n",
       "      <td>9.280</td>\n",
       "      <td>0.953</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    NDB_No             Shrt_Desc  Water_(g)  Energ_Kcal  Protein_(g)  \\\n",
       "2     1003  BUTTER OIL ANHYDROUS       0.24         876         0.28   \n",
       "3     1004           CHEESE BLUE      42.41         353        21.40   \n",
       "4     1005          CHEESE BRICK      41.11         371        23.24   \n",
       "5     1006           CHEESE BRIE      48.42         334        20.75   \n",
       "6     1007      CHEESE CAMEMBERT      51.80         300        19.80   \n",
       "7     1008        CHEESE CARAWAY      39.28         376        25.18   \n",
       "8     1009        CHEESE CHEDDAR      37.10         406        24.04   \n",
       "9     1010       CHEESE CHESHIRE      37.65         387        23.37   \n",
       "10    1011          CHEESE COLBY      38.20         394        23.76   \n",
       "\n",
       "    Lipid_Tot_(g)  Ash_(g)  Carbohydrt_(g)  Fiber_TD_(g)  Sugar_Tot_(g)  \\\n",
       "2           99.48     0.00            0.00           0.0           0.00   \n",
       "3           28.74     5.11            2.34           0.0           0.50   \n",
       "4           29.68     3.18            2.79           0.0           0.51   \n",
       "5           27.68     2.70            0.45           0.0           0.45   \n",
       "6           24.26     3.68            0.46           0.0           0.46   \n",
       "7           29.20     3.28            3.06           0.0            NaN   \n",
       "8           33.82     3.71            1.33           0.0           0.28   \n",
       "9           30.60     3.60            4.78           0.0            NaN   \n",
       "10          32.11     3.36            2.57           0.0           0.52   \n",
       "\n",
       "         ...        Vit_A_IU  Vit_A_RAE  Vit_E_(mg)  Vit_D_mcg  Vit_D_IU  \\\n",
       "2        ...          3069.0      840.0        2.80        1.8      73.0   \n",
       "3        ...           721.0      198.0        0.25        0.5      21.0   \n",
       "4        ...          1080.0      292.0        0.26        0.5      22.0   \n",
       "5        ...           592.0      174.0        0.24        0.5      20.0   \n",
       "6        ...           820.0      241.0        0.21        0.4      18.0   \n",
       "7        ...          1054.0      271.0         NaN        NaN       NaN   \n",
       "8        ...           994.0      263.0        0.78        0.6      24.0   \n",
       "9        ...           985.0      233.0         NaN        NaN       NaN   \n",
       "10       ...           994.0      264.0        0.28        0.6      24.0   \n",
       "\n",
       "    Vit_K_(mcg)  FA_Sat_(g)  FA_Mono_(g)  FA_Poly_(g)  Cholestrl_(mg)  \n",
       "2           8.6      61.924       28.732        3.694           256.0  \n",
       "3           2.4      18.669        7.778        0.800            75.0  \n",
       "4           2.5      18.764        8.598        0.784            94.0  \n",
       "5           2.3      17.410        8.013        0.826           100.0  \n",
       "6           2.0      15.259        7.023        0.724            72.0  \n",
       "7           NaN      18.584        8.275        0.830            93.0  \n",
       "8           2.9      19.368        8.428        1.433           102.0  \n",
       "9           NaN      19.475        8.671        0.870           103.0  \n",
       "10          2.7      20.218        9.280        0.953            95.0  \n",
       "\n",
       "[9 rows x 36 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_info.loc[2:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water_(g)</th>\n",
       "      <th>Energ_Kcal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.87</td>\n",
       "      <td>717</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.87</td>\n",
       "      <td>717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.24</td>\n",
       "      <td>876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.41</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.11</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48.42</td>\n",
       "      <td>334</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>51.80</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39.28</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37.10</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37.65</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>38.20</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79.79</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>79.64</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>81.01</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>81.24</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>82.48</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>54.44</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>41.56</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55.22</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>37.92</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13.44</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>41.46</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>33.19</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>48.42</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>41.01</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50.01</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>48.38</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>53.78</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>45.54</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>41.77</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>8588</th>\n",
       "      <td>2.00</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8589</th>\n",
       "      <td>76.70</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8590</th>\n",
       "      <td>83.10</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8591</th>\n",
       "      <td>1.30</td>\n",
       "      <td>465</td>\n",
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       "    <tr>\n",
       "      <th>8592</th>\n",
       "      <td>5.00</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8593</th>\n",
       "      <td>2.80</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8594</th>\n",
       "      <td>81.60</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8595</th>\n",
       "      <td>59.60</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8596</th>\n",
       "      <td>14.50</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8597</th>\n",
       "      <td>49.90</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8598</th>\n",
       "      <td>21.70</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8599</th>\n",
       "      <td>0.00</td>\n",
       "      <td>884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8600</th>\n",
       "      <td>23.90</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8601</th>\n",
       "      <td>55.50</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8602</th>\n",
       "      <td>9.00</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8603</th>\n",
       "      <td>4.20</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8604</th>\n",
       "      <td>84.40</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8605</th>\n",
       "      <td>53.00</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8606</th>\n",
       "      <td>54.66</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8607</th>\n",
       "      <td>28.24</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8608</th>\n",
       "      <td>6.80</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8609</th>\n",
       "      <td>10.40</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8610</th>\n",
       "      <td>6.84</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8611</th>\n",
       "      <td>8.20</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8612</th>\n",
       "      <td>81.90</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>43.00</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8614</th>\n",
       "      <td>70.25</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8615</th>\n",
       "      <td>26.00</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>79.20</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>78.50</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8618 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Water_(g)  Energ_Kcal\n",
       "0         15.87         717\n",
       "1         15.87         717\n",
       "2          0.24         876\n",
       "3         42.41         353\n",
       "4         41.11         371\n",
       "5         48.42         334\n",
       "6         51.80         300\n",
       "7         39.28         376\n",
       "8         37.10         406\n",
       "9         37.65         387\n",
       "10        38.20         394\n",
       "11        79.79          98\n",
       "12        79.64          97\n",
       "13        81.01          72\n",
       "14        81.24          81\n",
       "15        82.48          72\n",
       "16        54.44         342\n",
       "17        41.56         357\n",
       "18        55.22         264\n",
       "19        37.92         389\n",
       "20        13.44         466\n",
       "21        41.46         356\n",
       "22        33.19         413\n",
       "23        48.42         327\n",
       "24        41.01         373\n",
       "25        50.01         300\n",
       "26        48.38         318\n",
       "27        53.78         254\n",
       "28        45.54         301\n",
       "29        41.77         368\n",
       "...         ...         ...\n",
       "8588       2.00         389\n",
       "8589      76.70          91\n",
       "8590      83.10          68\n",
       "8591       1.30         465\n",
       "8592       5.00         401\n",
       "8593       2.80         429\n",
       "8594      81.60          73\n",
       "8595      59.60         179\n",
       "8596      14.50         377\n",
       "8597      49.90         280\n",
       "8598      21.70         688\n",
       "8599       0.00         884\n",
       "8600      23.90         279\n",
       "8601      55.50         257\n",
       "8602       9.00         319\n",
       "8603       4.20         356\n",
       "8604      84.40          62\n",
       "8605      53.00         179\n",
       "8606      54.66         181\n",
       "8607      28.24         287\n",
       "8608       6.80         365\n",
       "8609      10.40         351\n",
       "8610       6.84         350\n",
       "8611       8.20         370\n",
       "8612      81.90          73\n",
       "8613      43.00         305\n",
       "8614      70.25         111\n",
       "8615      26.00         269\n",
       "8616      79.20          90\n",
       "8617      78.50          89\n",
       "\n",
       "[8618 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Water_(g)','Energ_Kcal']\n",
    "food_info[columns]    #按列名索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Water_(g)</th>\n",
       "      <th>Protein_(g)</th>\n",
       "      <th>Lipid_Tot_(g)</th>\n",
       "      <th>Ash_(g)</th>\n",
       "      <th>Carbohydrt_(g)</th>\n",
       "      <th>Fiber_TD_(g)</th>\n",
       "      <th>Sugar_Tot_(g)</th>\n",
       "      <th>FA_Sat_(g)</th>\n",
       "      <th>FA_Mono_(g)</th>\n",
       "      <th>FA_Poly_(g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.87</td>\n",
       "      <td>0.85</td>\n",
       "      <td>81.11</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>51.368</td>\n",
       "      <td>21.021</td>\n",
       "      <td>3.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.87</td>\n",
       "      <td>0.85</td>\n",
       "      <td>81.11</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>50.489</td>\n",
       "      <td>23.426</td>\n",
       "      <td>3.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.28</td>\n",
       "      <td>99.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>61.924</td>\n",
       "      <td>28.732</td>\n",
       "      <td>3.694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Water_(g)  Protein_(g)  Lipid_Tot_(g)  Ash_(g)  Carbohydrt_(g)  \\\n",
       "0      15.87         0.85          81.11     2.11            0.06   \n",
       "1      15.87         0.85          81.11     2.11            0.06   \n",
       "2       0.24         0.28          99.48     0.00            0.00   \n",
       "\n",
       "   Fiber_TD_(g)  Sugar_Tot_(g)  FA_Sat_(g)  FA_Mono_(g)  FA_Poly_(g)  \n",
       "0           0.0           0.06      51.368       21.021        3.043  \n",
       "1           0.0           0.06      50.489       23.426        3.012  \n",
       "2           0.0           0.00      61.924       28.732        3.694  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = food_info.columns.tolist()\n",
    "gram_columns = []    #寻找以g为单位的列\n",
    "\n",
    "for c in col_names:\n",
    "    if c.endswith('(g)'):\n",
    "        gram_columns.append(c)\n",
    "gram_df = food_info[gram_columns]\n",
    "gram_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8618, 37)\n"
     ]
    }
   ],
   "source": [
    "div_1000 = food_info['Iron_(mg)'] / 1000\n",
    "food_info['Iron_(g)'] = div_1000     #在DataFrame中添加列\n",
    "print(food_info.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_energy = food_info['Water_(g)'] * food_info['Energ_Kcal']    #对应位置相乘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_calories = food_info['Energ_Kcal'].max()    #最大值\n",
    "normalized_calories = food_info['Energ_Kcal'] / max_calories    #归一化操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理：排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "276     38758.0\n",
      "5814    27360.0\n",
      "6192    26050.0\n",
      "1242    26000.0\n",
      "1245    24000.0\n",
      "1243    24000.0\n",
      "1244    23875.0\n",
      "292     17000.0\n",
      "1254    11588.0\n",
      "5811    10600.0\n",
      "8575     9690.0\n",
      "291      8068.0\n",
      "1249     8031.0\n",
      "5812     7893.0\n",
      "1292     7851.0\n",
      "293      7203.0\n",
      "4472     7027.0\n",
      "4836     6820.0\n",
      "1261     6580.0\n",
      "3747     6008.0\n",
      "1266     5730.0\n",
      "4835     5586.0\n",
      "4834     5493.0\n",
      "1263     5356.0\n",
      "1553     5203.0\n",
      "1552     5053.0\n",
      "1251     4957.0\n",
      "1257     4843.0\n",
      "294      4616.0\n",
      "8613     4450.0\n",
      "         ...   \n",
      "8153        NaN\n",
      "8155        NaN\n",
      "8156        NaN\n",
      "8157        NaN\n",
      "8158        NaN\n",
      "8159        NaN\n",
      "8160        NaN\n",
      "8161        NaN\n",
      "8163        NaN\n",
      "8164        NaN\n",
      "8165        NaN\n",
      "8167        NaN\n",
      "8169        NaN\n",
      "8170        NaN\n",
      "8172        NaN\n",
      "8173        NaN\n",
      "8174        NaN\n",
      "8175        NaN\n",
      "8176        NaN\n",
      "8177        NaN\n",
      "8178        NaN\n",
      "8179        NaN\n",
      "8180        NaN\n",
      "8181        NaN\n",
      "8183        NaN\n",
      "8184        NaN\n",
      "8185        NaN\n",
      "8195        NaN\n",
      "8251        NaN\n",
      "8267        NaN\n",
      "Name: Sodium_(mg), Length: 8618, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "food_info.sort_values('Sodium_(mg)',inplace=True)\n",
    "#print(food_info['Sodium_(mg)'])\n",
    "food_info.sort_values('Sodium_(mg)',inplace=True, ascending=False)  #从大到小排序\n",
    "print(food_info['Sodium_(mg)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_survival = pandas.read_csv('titanic_train.csv')\n",
    "titanic_survival.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = titanic_survival['Age']\n",
    "#print(age.loc[0:10])\n",
    "\n",
    "#缺失值处理\n",
    "age_is_null = pandas.isnull(age)   #缺失值序号\n",
    "#print(age_is_null)\n",
    "age_null_true = age[age_is_null]   #拿到所有缺失值\n",
    "#print(age_null_true)\n",
    "len(age_null_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不处理缺失值直接求均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    }
   ],
   "source": [
    "mean_age = sum(titanic_survival['Age']) / len(titanic_survival['Age'])\n",
    "print(mean_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除缺失值求均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n"
     ]
    }
   ],
   "source": [
    "good_ages = titanic_survival['Age'][age_is_null == False]\n",
    "corrent_mean_age = sum(good_ages) / len(good_ages)\n",
    "print(corrent_mean_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n"
     ]
    }
   ],
   "source": [
    "corrent_mean_age = titanic_survival['Age'].mean()\n",
    "print(corrent_mean_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 按舱室等级求均价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 84.15468749999992, 2: 20.66218315217391, 3: 13.675550101832997}\n"
     ]
    }
   ],
   "source": [
    "passenger_classes = [1,2,3]\n",
    "fares_by_class = {}\n",
    "for this_class in passenger_classes:\n",
    "    pclass_rows = titanic_survival[titanic_survival['Pclass']==this_class]\n",
    "    pclass_fares = pclass_rows['Fare']\n",
    "    fare_for_class = pclass_fares.mean()\n",
    "    fares_by_class[this_class] = fare_for_class\n",
    "print(fares_by_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pivot_table()函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Survived\n",
      "Pclass          \n",
      "1       0.629630\n",
      "2       0.472826\n",
      "3       0.242363\n"
     ]
    }
   ],
   "source": [
    "#求各等级舱室平均获救人数\n",
    "passenger_survival = titanic_survival.pivot_table(index='Pclass',values='Survived',aggfunc=numpy.mean)\n",
    "print(passenger_survival)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Age\n",
      "Pclass           \n",
      "1       38.233441\n",
      "2       29.877630\n",
      "3       25.140620\n"
     ]
    }
   ],
   "source": [
    "#求各等级舱室平均年龄\n",
    "passenger_age = titanic_survival.pivot_table(index='Pclass',values='Age')  #默认求均值\n",
    "print(passenger_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Fare  Survived\n",
      "Embarked                      \n",
      "C         10072.2962        93\n",
      "Q          1022.2543        30\n",
      "S         17439.3988       217\n"
     ]
    }
   ],
   "source": [
    "#对比登船地点Embarked，总船票价格与总获救人数之间的关系\n",
    "port_stats = titanic_survival.pivot_table(index='Embarked',values=['Fare','Survived'],aggfunc=numpy.sum)\n",
    "print(port_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_na_columns = titanic_survival.dropna(axis=1)   #删除有缺失值的列\n",
    "new_titanic_survival = titanic_survival.dropna(axis=0,subset=['Age','Sex'])#删除Age和Sex两列有缺失值的行\n",
    "drop_na_columns.head()\n",
    "new_titanic_survival.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.0\n"
     ]
    }
   ],
   "source": [
    "row_index_83_age = titanic_survival.loc[83,\"Age\"]   #第83行Age列\n",
    "print(row_index_83_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 去除索引排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>631</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>673</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mitchell, Mr. Henry Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24580</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Crosby, Capt. Edward Gifford</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>WE/P 5735</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>B22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Wheadon, Mr. Edward H</td>\n",
       "      <td>male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B30</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>281</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Duane, Mr. Frank</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>336439</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                   Svensson, Mr. Johan   \n",
       "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
       "96            97         0       1             Goldschmidt, Mr. George B   \n",
       "116          117         0       3                  Connors, Mr. Patrick   \n",
       "672          673         0       2           Mitchell, Mr. Henry Michael   \n",
       "745          746         0       1          Crosby, Capt. Edward Gifford   \n",
       "33            34         0       2                 Wheadon, Mr. Edward H   \n",
       "54            55         0       1        Ostby, Mr. Engelhart Cornelius   \n",
       "280          281         0       3                      Duane, Mr. Frank   \n",
       "\n",
       "      Sex   Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "630  male  80.0      0      0       27042  30.0000   A23        S  \n",
       "851  male  74.0      0      0      347060   7.7750   NaN        S  \n",
       "493  male  71.0      0      0    PC 17609  49.5042   NaN        C  \n",
       "96   male  71.0      0      0    PC 17754  34.6542    A5        C  \n",
       "116  male  70.5      0      0      370369   7.7500   NaN        Q  \n",
       "672  male  70.0      0      0  C.A. 24580  10.5000   NaN        S  \n",
       "745  male  70.0      1      1   WE/P 5735  71.0000   B22        S  \n",
       "33   male  66.0      0      0  C.A. 24579  10.5000   NaN        S  \n",
       "54   male  65.0      0      1      113509  61.9792   B30        C  \n",
       "280  male  65.0      0      0      336439   7.7500   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_titanic_survival = titanic_survival.sort_values('Age',ascending=False)\n",
    "new_titanic_survival[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>631</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>673</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mitchell, Mr. Henry Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24580</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Crosby, Capt. Edward Gifford</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>WE/P 5735</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>B22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Wheadon, Mr. Edward H</td>\n",
       "      <td>male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B30</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>281</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Duane, Mr. Frank</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>336439</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass                                  Name   Sex  \\\n",
       "0          631         1       1  Barkworth, Mr. Algernon Henry Wilson  male   \n",
       "1          852         0       3                   Svensson, Mr. Johan  male   \n",
       "2          494         0       1               Artagaveytia, Mr. Ramon  male   \n",
       "3           97         0       1             Goldschmidt, Mr. George B  male   \n",
       "4          117         0       3                  Connors, Mr. Patrick  male   \n",
       "5          673         0       2           Mitchell, Mr. Henry Michael  male   \n",
       "6          746         0       1          Crosby, Capt. Edward Gifford  male   \n",
       "7           34         0       2                 Wheadon, Mr. Edward H  male   \n",
       "8           55         0       1        Ostby, Mr. Engelhart Cornelius  male   \n",
       "9          281         0       3                      Duane, Mr. Frank  male   \n",
       "\n",
       "    Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "0  80.0      0      0       27042  30.0000   A23        S  \n",
       "1  74.0      0      0      347060   7.7750   NaN        S  \n",
       "2  71.0      0      0    PC 17609  49.5042   NaN        C  \n",
       "3  71.0      0      0    PC 17754  34.6542    A5        C  \n",
       "4  70.5      0      0      370369   7.7500   NaN        Q  \n",
       "5  70.0      0      0  C.A. 24580  10.5000   NaN        S  \n",
       "6  70.0      1      1   WE/P 5735  71.0000   B22        S  \n",
       "7  66.0      0      0  C.A. 24579  10.5000   NaN        S  \n",
       "8  65.0      0      1      113509  61.9792   B30        C  \n",
       "9  65.0      0      0      336439   7.7500   NaN        Q  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_reindexed = new_titanic_survival.reset_index(drop=True)\n",
    "titanic_reindexed[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply自定义函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId                  100\n",
      "Survived                       0\n",
      "Pclass                         2\n",
      "Name           Kantor, Mr. Sinai\n",
      "Sex                         male\n",
      "Age                           34\n",
      "SibSp                          1\n",
      "Parch                          0\n",
      "Ticket                    244367\n",
      "Fare                          26\n",
      "Cabin                        NaN\n",
      "Embarked                       S\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#返回第100行\n",
    "def hundredth_row(column):\n",
    "    hundredth_item = column.loc[99]\n",
    "    return hundredth_item\n",
    "\n",
    "hundredth_row = titanic_survival.apply(hundredth_row)\n",
    "print(hundredth_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#计数缺失值\n",
    "def not_nullcount(column):\n",
    "    column_null = pandas.isnull(column)\n",
    "    null = column[column_null]\n",
    "    return len(null)\n",
    "\n",
    "column_null_count = titanic_survival.apply(not_nullcount)\n",
    "print(column_null_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 连续值离散化 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       Third Class\n",
      "1       First Class\n",
      "2       Third Class\n",
      "3       First Class\n",
      "4       Third Class\n",
      "5       Third Class\n",
      "6       First Class\n",
      "7       Third Class\n",
      "8       Third Class\n",
      "9      Second Class\n",
      "10      Third Class\n",
      "11      First Class\n",
      "12      Third Class\n",
      "13      Third Class\n",
      "14      Third Class\n",
      "15     Second Class\n",
      "16      Third Class\n",
      "17     Second Class\n",
      "18      Third Class\n",
      "19      Third Class\n",
      "20     Second Class\n",
      "21     Second Class\n",
      "22      Third Class\n",
      "23      First Class\n",
      "24      Third Class\n",
      "25      Third Class\n",
      "26      Third Class\n",
      "27      First Class\n",
      "28      Third Class\n",
      "29      Third Class\n",
      "           ...     \n",
      "861    Second Class\n",
      "862     First Class\n",
      "863     Third Class\n",
      "864    Second Class\n",
      "865    Second Class\n",
      "866    Second Class\n",
      "867     First Class\n",
      "868     Third Class\n",
      "869     Third Class\n",
      "870     Third Class\n",
      "871     First Class\n",
      "872     First Class\n",
      "873     Third Class\n",
      "874    Second Class\n",
      "875     Third Class\n",
      "876     Third Class\n",
      "877     Third Class\n",
      "878     Third Class\n",
      "879     First Class\n",
      "880    Second Class\n",
      "881     Third Class\n",
      "882     Third Class\n",
      "883    Second Class\n",
      "884     Third Class\n",
      "885     Third Class\n",
      "886    Second Class\n",
      "887     First Class\n",
      "888     Third Class\n",
      "889     First Class\n",
      "890     Third Class\n",
      "Length: 891, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#数据替换\n",
    "def which_class(row):\n",
    "    pclass = row['Pclass']\n",
    "    if pandas.isnull(pclass):\n",
    "        return 'Unknown'\n",
    "    elif pclass == 1:\n",
    "        return \"First Class\"\n",
    "    elif pclass == 2:\n",
    "        return 'Second Class'\n",
    "    elif pclass == 3:\n",
    "        return 'Third Class'\n",
    "\n",
    "classes = titanic_survival.apply(which_class,axis=1)\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#判断是否未成年人\n",
    "def is_minor(row):\n",
    "    if row[\"Age\"] < 18:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "minors = titanic_survival.apply(is_minor,axis= 1)\n",
    "#print(minors)\n",
    "\n",
    "#三类年龄标签\n",
    "def generate_age_label(row):\n",
    "    age = row['Age']\n",
    "    if pandas.isnull(age):\n",
    "        return 'Unknown'\n",
    "    elif age < 18:\n",
    "        return 'minor'\n",
    "    else:\n",
    "        return 'adult'\n",
    "\n",
    "age_labels = titanic_survival.apply(generate_age_label,axis=1)\n",
    "#print(age_lebals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Survived\n",
      "age_labels          \n",
      "Unknown     0.293785\n",
      "adult       0.381032\n",
      "minor       0.539823\n"
     ]
    }
   ],
   "source": [
    "titanic_survival['age_labels'] = age_labels\n",
    "age_group_survival = titanic_survival.pivot_table(index='age_labels',values=\"Survived\")\n",
    "print(age_group_survival)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "fandango = pd.read_csv('fandango_score_comparison.csv')\n",
    "\n",
    "series_film = fandango['FILM']\n",
    "print(type(series_film))    #DataFrame中一列的类型是Series\n",
    "series_film[0:5]\n",
    "\n",
    "series_rt = fandango['RottenTomatoes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 新建Series结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Minions (2015)      54\n",
       "Leviathan (2014)    99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import Series\n",
    "\n",
    "film_names = series_film.values\n",
    "print(type(film_names))    #Series中values的类型是ndarray\n",
    "rt_scores = series_rt.values\n",
    "\n",
    "series_custom = Series(rt_scores,index=film_names)\n",
    "#print(series_custom)\n",
    "\n",
    "series_custom[['Minions (2015)','Leviathan (2014)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'71 (2015)                                         97\n",
      "5 Flights Up (2015)                                52\n",
      "A Little Chaos (2015)                              40\n",
      "A Most Violent Year (2014)                         90\n",
      "About Elly (2015)                                  97\n",
      "Aloha (2015)                                       19\n",
      "American Sniper (2015)                             72\n",
      "American Ultra (2015)                              46\n",
      "Amy (2015)                                         97\n",
      "Annie (2014)                                       27\n",
      "Ant-Man (2015)                                     80\n",
      "Avengers: Age of Ultron (2015)                     74\n",
      "Big Eyes (2014)                                    72\n",
      "Birdman (2014)                                     92\n",
      "Black Sea (2015)                                   82\n",
      "Black or White (2015)                              39\n",
      "Blackhat (2015)                                    34\n",
      "Cake (2015)                                        49\n",
      "Chappie (2015)                                     30\n",
      "Child 44 (2015)                                    26\n",
      "Cinderella (2015)                                  85\n",
      "Clouds of Sils Maria (2015)                        89\n",
      "Danny Collins (2015)                               77\n",
      "Dark Places (2015)                                 26\n",
      "Do You Believe? (2015)                             18\n",
      "Dope (2015)                                        87\n",
      "Entourage (2015)                                   32\n",
      "Escobar: Paradise Lost (2015)                      52\n",
      "Ex Machina (2015)                                  92\n",
      "Fantastic Four (2015)                               9\n",
      "                                                   ..\n",
      "The Loft (2015)                                    11\n",
      "The Longest Ride (2015)                            31\n",
      "The Man From U.N.C.L.E. (2015)                     68\n",
      "The Overnight (2015)                               82\n",
      "The Salt of the Earth (2015)                       96\n",
      "The Second Best Exotic Marigold Hotel (2015)       62\n",
      "The SpongeBob Movie: Sponge Out of Water (2015)    78\n",
      "The Stanford Prison Experiment (2015)              84\n",
      "The Vatican Tapes (2015)                           13\n",
      "The Water Diviner (2015)                           63\n",
      "The Wedding Ringer (2015)                          27\n",
      "The Wolfpack (2015)                                84\n",
      "The Woman In Black 2 Angel of Death (2015)         22\n",
      "The Wrecking Crew (2015)                           93\n",
      "Timbuktu (2015)                                    99\n",
      "Tomorrowland (2015)                                50\n",
      "Top Five (2014)                                    86\n",
      "Trainwreck (2015)                                  85\n",
      "True Story (2015)                                  45\n",
      "Two Days, One Night (2014)                         97\n",
      "Unbroken (2014)                                    51\n",
      "Unfinished Business (2015)                         11\n",
      "Unfriended (2015)                                  60\n",
      "Vacation (2015)                                    27\n",
      "Welcome to Me (2015)                               71\n",
      "What We Do in the Shadows (2015)                   96\n",
      "When Marnie Was There (2015)                       89\n",
      "While We're Young (2015)                           83\n",
      "Wild Tales (2014)                                  96\n",
      "Woman in Gold (2015)                               52\n",
      "Length: 146, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "original_index = series_custom.index.tolist()\n",
    "#print(original_index)\n",
    "sorted_index = sorted(original_index)\n",
    "#print(sorted_index)\n",
    "sorted_by_index = series_custom.reindex(sorted_index)\n",
    "print(sorted_by_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paul Blart: Mall Cop 2 (2015)     5\n",
       "Hitman: Agent 47 (2015)           7\n",
       "Hot Pursuit (2015)                8\n",
       "Fantastic Four (2015)             9\n",
       "Taken 3 (2015)                    9\n",
       "The Boy Next Door (2015)         10\n",
       "The Loft (2015)                  11\n",
       "Unfinished Business (2015)       11\n",
       "Mortdecai (2015)                 12\n",
       "Seventh Son (2015)               12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc2 = series_custom.sort_index()    #按照键排序\n",
    "sc3 = series_custom.sort_values()   #按照值排序\n",
    "sc3[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对两个Series结构进行相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avengers: Age of Ultron (2015)                    148\n",
      "Cinderella (2015)                                 170\n",
      "Ant-Man (2015)                                    160\n",
      "Do You Believe? (2015)                             36\n",
      "Hot Tub Time Machine 2 (2015)                      28\n",
      "The Water Diviner (2015)                          126\n",
      "Irrational Man (2015)                              84\n",
      "Top Five (2014)                                   172\n",
      "Shaun the Sheep Movie (2015)                      198\n",
      "Love & Mercy (2015)                               178\n",
      "Far From The Madding Crowd (2015)                 168\n",
      "Black Sea (2015)                                  164\n",
      "Leviathan (2014)                                  198\n",
      "Unbroken (2014)                                   102\n",
      "The Imitation Game (2014)                         180\n",
      "Taken 3 (2015)                                     18\n",
      "Ted 2 (2015)                                       92\n",
      "Southpaw (2015)                                   118\n",
      "Night at the Museum: Secret of the Tomb (2014)    100\n",
      "Pixels (2015)                                      34\n",
      "McFarland, USA (2015)                             158\n",
      "Insidious: Chapter 3 (2015)                       118\n",
      "The Man From U.N.C.L.E. (2015)                    136\n",
      "Run All Night (2015)                              120\n",
      "Trainwreck (2015)                                 170\n",
      "Selma (2014)                                      198\n",
      "Ex Machina (2015)                                 184\n",
      "Still Alice (2015)                                176\n",
      "Wild Tales (2014)                                 192\n",
      "The End of the Tour (2015)                        184\n",
      "                                                 ... \n",
      "Clouds of Sils Maria (2015)                       178\n",
      "Testament of Youth (2015)                         162\n",
      "Infinitely Polar Bear (2015)                      160\n",
      "Phoenix (2015)                                    198\n",
      "The Wolfpack (2015)                               168\n",
      "The Stanford Prison Experiment (2015)             168\n",
      "Tangerine (2015)                                  190\n",
      "Magic Mike XXL (2015)                             124\n",
      "Home (2015)                                        90\n",
      "The Wedding Ringer (2015)                          54\n",
      "Woman in Gold (2015)                              104\n",
      "The Last Five Years (2015)                        120\n",
      "Mission: Impossible â€“ Rogue Nation (2015)       184\n",
      "Amy (2015)                                        194\n",
      "Jurassic World (2015)                             142\n",
      "Minions (2015)                                    108\n",
      "Max (2015)                                         70\n",
      "Paul Blart: Mall Cop 2 (2015)                      10\n",
      "The Longest Ride (2015)                            62\n",
      "The Lazarus Effect (2015)                          28\n",
      "The Woman In Black 2 Angel of Death (2015)         44\n",
      "Danny Collins (2015)                              154\n",
      "Spare Parts (2015)                                104\n",
      "Serena (2015)                                      36\n",
      "Inside Out (2015)                                 196\n",
      "Mr. Holmes (2015)                                 174\n",
      "'71 (2015)                                        194\n",
      "Two Days, One Night (2014)                        194\n",
      "Gett: The Trial of Viviane Amsalem (2015)         200\n",
      "Kumiko, The Treasure Hunter (2015)                174\n",
      "Length: 146, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.add(series_custom,series_custom))\n",
    "#np.sin(series_custom)\n",
    "#np.max(series_custom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avengers: Age of Ultron (2015)                                            74\n",
      "The Water Diviner (2015)                                                  63\n",
      "Unbroken (2014)                                                           51\n",
      "Southpaw (2015)                                                           59\n",
      "Insidious: Chapter 3 (2015)                                               59\n",
      "The Man From U.N.C.L.E. (2015)                                            68\n",
      "Run All Night (2015)                                                      60\n",
      "5 Flights Up (2015)                                                       52\n",
      "Welcome to Me (2015)                                                      71\n",
      "Saint Laurent (2015)                                                      51\n",
      "Maps to the Stars (2015)                                                  60\n",
      "Pitch Perfect 2 (2015)                                                    67\n",
      "The Age of Adaline (2015)                                                 54\n",
      "The DUFF (2015)                                                           71\n",
      "Ricki and the Flash (2015)                                                64\n",
      "Unfriended (2015)                                                         60\n",
      "American Sniper (2015)                                                    72\n",
      "The Hobbit: The Battle of the Five Armies (2014)                          61\n",
      "Paper Towns (2015)                                                        55\n",
      "Big Eyes (2014)                                                           72\n",
      "Maggie (2015)                                                             54\n",
      "Focus (2015)                                                              57\n",
      "The Second Best Exotic Marigold Hotel (2015)                              62\n",
      "The 100-Year-Old Man Who Climbed Out the Window and Disappeared (2015)    67\n",
      "Escobar: Paradise Lost (2015)                                             52\n",
      "Into the Woods (2014)                                                     71\n",
      "Inherent Vice (2014)                                                      73\n",
      "Magic Mike XXL (2015)                                                     62\n",
      "Woman in Gold (2015)                                                      52\n",
      "The Last Five Years (2015)                                                60\n",
      "Jurassic World (2015)                                                     71\n",
      "Minions (2015)                                                            54\n",
      "Spare Parts (2015)                                                        52\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "series_greater_than_50 = series_custom[series_custom > 50]\n",
    "\n",
    "criteria_one = series_custom > 50\n",
    "criteria_two = series_custom < 75\n",
    "both_criteria = series_custom[criteria_one & criteria_two]\n",
    "print(both_criteria)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FILM\n",
      "Avengers: Age of Ultron (2015)                    80.0\n",
      "Cinderella (2015)                                 82.5\n",
      "Ant-Man (2015)                                    85.0\n",
      "Do You Believe? (2015)                            51.0\n",
      "Hot Tub Time Machine 2 (2015)                     21.0\n",
      "The Water Diviner (2015)                          62.5\n",
      "Irrational Man (2015)                             47.5\n",
      "Top Five (2014)                                   75.0\n",
      "Shaun the Sheep Movie (2015)                      90.5\n",
      "Love & Mercy (2015)                               88.0\n",
      "Far From The Madding Crowd (2015)                 80.5\n",
      "Black Sea (2015)                                  71.0\n",
      "Leviathan (2014)                                  89.0\n",
      "Unbroken (2014)                                   60.5\n",
      "The Imitation Game (2014)                         91.0\n",
      "Taken 3 (2015)                                    27.5\n",
      "Ted 2 (2015)                                      52.0\n",
      "Southpaw (2015)                                   69.5\n",
      "Night at the Museum: Secret of the Tomb (2014)    54.0\n",
      "Pixels (2015)                                     35.5\n",
      "McFarland, USA (2015)                             84.0\n",
      "Insidious: Chapter 3 (2015)                       57.5\n",
      "The Man From U.N.C.L.E. (2015)                    74.0\n",
      "Run All Night (2015)                              59.5\n",
      "Trainwreck (2015)                                 79.5\n",
      "Selma (2014)                                      92.5\n",
      "Ex Machina (2015)                                 89.0\n",
      "Still Alice (2015)                                86.5\n",
      "Wild Tales (2014)                                 94.0\n",
      "The End of the Tour (2015)                        90.5\n",
      "                                                  ... \n",
      "Clouds of Sils Maria (2015)                       78.0\n",
      "Testament of Youth (2015)                         80.0\n",
      "Infinitely Polar Bear (2015)                      78.0\n",
      "Phoenix (2015)                                    90.0\n",
      "The Wolfpack (2015)                               78.5\n",
      "The Stanford Prison Experiment (2015)             85.5\n",
      "Tangerine (2015)                                  90.5\n",
      "Magic Mike XXL (2015)                             63.0\n",
      "Home (2015)                                       55.0\n",
      "The Wedding Ringer (2015)                         46.5\n",
      "Woman in Gold (2015)                              66.5\n",
      "The Last Five Years (2015)                        60.0\n",
      "Mission: Impossible â€“ Rogue Nation (2015)       91.0\n",
      "Amy (2015)                                        94.0\n",
      "Jurassic World (2015)                             76.0\n",
      "Minions (2015)                                    53.0\n",
      "Max (2015)                                        54.0\n",
      "Paul Blart: Mall Cop 2 (2015)                     20.5\n",
      "The Longest Ride (2015)                           52.0\n",
      "The Lazarus Effect (2015)                         18.5\n",
      "The Woman In Black 2 Angel of Death (2015)        23.5\n",
      "Danny Collins (2015)                              76.0\n",
      "Spare Parts (2015)                                67.5\n",
      "Serena (2015)                                     21.5\n",
      "Inside Out (2015)                                 94.0\n",
      "Mr. Holmes (2015)                                 82.5\n",
      "'71 (2015)                                        89.5\n",
      "Two Days, One Night (2014)                        87.5\n",
      "Gett: The Trial of Viviane Amsalem (2015)         90.5\n",
      "Kumiko, The Treasure Hunter (2015)                75.0\n",
      "Length: 146, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "rt_critics = Series(fandango['RottenTomatoes'].values,index=fandango['FILM'])\n",
    "rt_users = Series(fandango['RottenTomatoes_User'].values,index=fandango['FILM'])\n",
    "rt_mean = (rt_critics + rt_users) / 2\n",
    "print(rt_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 给DataFrame指定索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Avengers: Age of Ultron (2015)', 'Cinderella (2015)', 'Ant-Man (2015)',\n",
      "       'Do You Believe? (2015)', 'Hot Tub Time Machine 2 (2015)',\n",
      "       'The Water Diviner (2015)', 'Irrational Man (2015)', 'Top Five (2014)',\n",
      "       'Shaun the Sheep Movie (2015)', 'Love & Mercy (2015)',\n",
      "       ...\n",
      "       'The Woman In Black 2 Angel of Death (2015)', 'Danny Collins (2015)',\n",
      "       'Spare Parts (2015)', 'Serena (2015)', 'Inside Out (2015)',\n",
      "       'Mr. Holmes (2015)', ''71 (2015)', 'Two Days, One Night (2014)',\n",
      "       'Gett: The Trial of Viviane Amsalem (2015)',\n",
      "       'Kumiko, The Treasure Hunter (2015)'],\n",
      "      dtype='object', name='FILM', length=146)\n"
     ]
    }
   ],
   "source": [
    "fandango_films = fandango.set_index('FILM',drop=False)\n",
    "print(fandango_films.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 以字符索引进行切片操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avengers: Age of Ultron (2015)</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>74</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>271107</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinderella (2015)</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>249</td>\n",
       "      <td>65709</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ant-Man (2015)</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>627</td>\n",
       "      <td>103660</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Do You Believe? (2015)</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>31</td>\n",
       "      <td>3136</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hot Tub Time Machine 2 (2015)</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>88</td>\n",
       "      <td>19560</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Water Diviner (2015)</th>\n",
       "      <td>The Water Diviner (2015)</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "      <td>50</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>34</td>\n",
       "      <td>39373</td>\n",
       "      <td>397</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          FILM  \\\n",
       "FILM                                                             \n",
       "Avengers: Age of Ultron (2015)  Avengers: Age of Ultron (2015)   \n",
       "Cinderella (2015)                            Cinderella (2015)   \n",
       "Ant-Man (2015)                                  Ant-Man (2015)   \n",
       "Do You Believe? (2015)                  Do You Believe? (2015)   \n",
       "Hot Tub Time Machine 2 (2015)    Hot Tub Time Machine 2 (2015)   \n",
       "The Water Diviner (2015)              The Water Diviner (2015)   \n",
       "\n",
       "                                RottenTomatoes  RottenTomatoes_User  \\\n",
       "FILM                                                                  \n",
       "Avengers: Age of Ultron (2015)              74                   86   \n",
       "Cinderella (2015)                           85                   80   \n",
       "Ant-Man (2015)                              80                   90   \n",
       "Do You Believe? (2015)                      18                   84   \n",
       "Hot Tub Time Machine 2 (2015)               14                   28   \n",
       "The Water Diviner (2015)                    63                   62   \n",
       "\n",
       "                                Metacritic  Metacritic_User  IMDB  \\\n",
       "FILM                                                                \n",
       "Avengers: Age of Ultron (2015)          66              7.1   7.8   \n",
       "Cinderella (2015)                       67              7.5   7.1   \n",
       "Ant-Man (2015)                          64              8.1   7.8   \n",
       "Do You Believe? (2015)                  22              4.7   5.4   \n",
       "Hot Tub Time Machine 2 (2015)           29              3.4   5.1   \n",
       "The Water Diviner (2015)                50              6.8   7.2   \n",
       "\n",
       "                                Fandango_Stars  Fandango_Ratingvalue  RT_norm  \\\n",
       "FILM                                                                            \n",
       "Avengers: Age of Ultron (2015)             5.0                   4.5     3.70   \n",
       "Cinderella (2015)                          5.0                   4.5     4.25   \n",
       "Ant-Man (2015)                             5.0                   4.5     4.00   \n",
       "Do You Believe? (2015)                     5.0                   4.5     0.90   \n",
       "Hot Tub Time Machine 2 (2015)              3.5                   3.0     0.70   \n",
       "The Water Diviner (2015)                   4.5                   4.0     3.15   \n",
       "\n",
       "                                RT_user_norm         ...           IMDB_norm  \\\n",
       "FILM                                                 ...                       \n",
       "Avengers: Age of Ultron (2015)           4.3         ...                3.90   \n",
       "Cinderella (2015)                        4.0         ...                3.55   \n",
       "Ant-Man (2015)                           4.5         ...                3.90   \n",
       "Do You Believe? (2015)                   4.2         ...                2.70   \n",
       "Hot Tub Time Machine 2 (2015)            1.4         ...                2.55   \n",
       "The Water Diviner (2015)                 3.1         ...                3.60   \n",
       "\n",
       "                                RT_norm_round  RT_user_norm_round  \\\n",
       "FILM                                                                \n",
       "Avengers: Age of Ultron (2015)            3.5                 4.5   \n",
       "Cinderella (2015)                         4.5                 4.0   \n",
       "Ant-Man (2015)                            4.0                 4.5   \n",
       "Do You Believe? (2015)                    1.0                 4.0   \n",
       "Hot Tub Time Machine 2 (2015)             0.5                 1.5   \n",
       "The Water Diviner (2015)                  3.0                 3.0   \n",
       "\n",
       "                                Metacritic_norm_round  \\\n",
       "FILM                                                    \n",
       "Avengers: Age of Ultron (2015)                    3.5   \n",
       "Cinderella (2015)                                 3.5   \n",
       "Ant-Man (2015)                                    3.0   \n",
       "Do You Believe? (2015)                            1.0   \n",
       "Hot Tub Time Machine 2 (2015)                     1.5   \n",
       "The Water Diviner (2015)                          2.5   \n",
       "\n",
       "                                Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                         3.5              4.0   \n",
       "Cinderella (2015)                                      4.0              3.5   \n",
       "Ant-Man (2015)                                         4.0              4.0   \n",
       "Do You Believe? (2015)                                 2.5              2.5   \n",
       "Hot Tub Time Machine 2 (2015)                          1.5              2.5   \n",
       "The Water Diviner (2015)                               3.5              3.5   \n",
       "\n",
       "                                Metacritic_user_vote_count  \\\n",
       "FILM                                                         \n",
       "Avengers: Age of Ultron (2015)                        1330   \n",
       "Cinderella (2015)                                      249   \n",
       "Ant-Man (2015)                                         627   \n",
       "Do You Believe? (2015)                                  31   \n",
       "Hot Tub Time Machine 2 (2015)                           88   \n",
       "The Water Diviner (2015)                                34   \n",
       "\n",
       "                                IMDB_user_vote_count  Fandango_votes  \\\n",
       "FILM                                                                   \n",
       "Avengers: Age of Ultron (2015)                271107           14846   \n",
       "Cinderella (2015)                              65709           12640   \n",
       "Ant-Man (2015)                                103660           12055   \n",
       "Do You Believe? (2015)                          3136            1793   \n",
       "Hot Tub Time Machine 2 (2015)                  19560            1021   \n",
       "The Water Diviner (2015)                       39373             397   \n",
       "\n",
       "                                Fandango_Difference  \n",
       "FILM                                                 \n",
       "Avengers: Age of Ultron (2015)                  0.5  \n",
       "Cinderella (2015)                               0.5  \n",
       "Ant-Man (2015)                                  0.5  \n",
       "Do You Believe? (2015)                          0.5  \n",
       "Hot Tub Time Machine 2 (2015)                   0.5  \n",
       "The Water Diviner (2015)                        0.5  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_films['Avengers: Age of Ultron (2015)':'The Water Diviner (2015)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avengers: Age of Ultron (2015)</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>74</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>271107</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinderella (2015)</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>249</td>\n",
       "      <td>65709</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ant-Man (2015)</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>627</td>\n",
       "      <td>103660</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Do You Believe? (2015)</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>31</td>\n",
       "      <td>3136</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hot Tub Time Machine 2 (2015)</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>88</td>\n",
       "      <td>19560</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Water Diviner (2015)</th>\n",
       "      <td>The Water Diviner (2015)</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "      <td>50</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>34</td>\n",
       "      <td>39373</td>\n",
       "      <td>397</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          FILM  \\\n",
       "FILM                                                             \n",
       "Avengers: Age of Ultron (2015)  Avengers: Age of Ultron (2015)   \n",
       "Cinderella (2015)                            Cinderella (2015)   \n",
       "Ant-Man (2015)                                  Ant-Man (2015)   \n",
       "Do You Believe? (2015)                  Do You Believe? (2015)   \n",
       "Hot Tub Time Machine 2 (2015)    Hot Tub Time Machine 2 (2015)   \n",
       "The Water Diviner (2015)              The Water Diviner (2015)   \n",
       "\n",
       "                                RottenTomatoes  RottenTomatoes_User  \\\n",
       "FILM                                                                  \n",
       "Avengers: Age of Ultron (2015)              74                   86   \n",
       "Cinderella (2015)                           85                   80   \n",
       "Ant-Man (2015)                              80                   90   \n",
       "Do You Believe? (2015)                      18                   84   \n",
       "Hot Tub Time Machine 2 (2015)               14                   28   \n",
       "The Water Diviner (2015)                    63                   62   \n",
       "\n",
       "                                Metacritic  Metacritic_User  IMDB  \\\n",
       "FILM                                                                \n",
       "Avengers: Age of Ultron (2015)          66              7.1   7.8   \n",
       "Cinderella (2015)                       67              7.5   7.1   \n",
       "Ant-Man (2015)                          64              8.1   7.8   \n",
       "Do You Believe? (2015)                  22              4.7   5.4   \n",
       "Hot Tub Time Machine 2 (2015)           29              3.4   5.1   \n",
       "The Water Diviner (2015)                50              6.8   7.2   \n",
       "\n",
       "                                Fandango_Stars  Fandango_Ratingvalue  RT_norm  \\\n",
       "FILM                                                                            \n",
       "Avengers: Age of Ultron (2015)             5.0                   4.5     3.70   \n",
       "Cinderella (2015)                          5.0                   4.5     4.25   \n",
       "Ant-Man (2015)                             5.0                   4.5     4.00   \n",
       "Do You Believe? (2015)                     5.0                   4.5     0.90   \n",
       "Hot Tub Time Machine 2 (2015)              3.5                   3.0     0.70   \n",
       "The Water Diviner (2015)                   4.5                   4.0     3.15   \n",
       "\n",
       "                                RT_user_norm         ...           IMDB_norm  \\\n",
       "FILM                                                 ...                       \n",
       "Avengers: Age of Ultron (2015)           4.3         ...                3.90   \n",
       "Cinderella (2015)                        4.0         ...                3.55   \n",
       "Ant-Man (2015)                           4.5         ...                3.90   \n",
       "Do You Believe? (2015)                   4.2         ...                2.70   \n",
       "Hot Tub Time Machine 2 (2015)            1.4         ...                2.55   \n",
       "The Water Diviner (2015)                 3.1         ...                3.60   \n",
       "\n",
       "                                RT_norm_round  RT_user_norm_round  \\\n",
       "FILM                                                                \n",
       "Avengers: Age of Ultron (2015)            3.5                 4.5   \n",
       "Cinderella (2015)                         4.5                 4.0   \n",
       "Ant-Man (2015)                            4.0                 4.5   \n",
       "Do You Believe? (2015)                    1.0                 4.0   \n",
       "Hot Tub Time Machine 2 (2015)             0.5                 1.5   \n",
       "The Water Diviner (2015)                  3.0                 3.0   \n",
       "\n",
       "                                Metacritic_norm_round  \\\n",
       "FILM                                                    \n",
       "Avengers: Age of Ultron (2015)                    3.5   \n",
       "Cinderella (2015)                                 3.5   \n",
       "Ant-Man (2015)                                    3.0   \n",
       "Do You Believe? (2015)                            1.0   \n",
       "Hot Tub Time Machine 2 (2015)                     1.5   \n",
       "The Water Diviner (2015)                          2.5   \n",
       "\n",
       "                                Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "FILM                                                                          \n",
       "Avengers: Age of Ultron (2015)                         3.5              4.0   \n",
       "Cinderella (2015)                                      4.0              3.5   \n",
       "Ant-Man (2015)                                         4.0              4.0   \n",
       "Do You Believe? (2015)                                 2.5              2.5   \n",
       "Hot Tub Time Machine 2 (2015)                          1.5              2.5   \n",
       "The Water Diviner (2015)                               3.5              3.5   \n",
       "\n",
       "                                Metacritic_user_vote_count  \\\n",
       "FILM                                                         \n",
       "Avengers: Age of Ultron (2015)                        1330   \n",
       "Cinderella (2015)                                      249   \n",
       "Ant-Man (2015)                                         627   \n",
       "Do You Believe? (2015)                                  31   \n",
       "Hot Tub Time Machine 2 (2015)                           88   \n",
       "The Water Diviner (2015)                                34   \n",
       "\n",
       "                                IMDB_user_vote_count  Fandango_votes  \\\n",
       "FILM                                                                   \n",
       "Avengers: Age of Ultron (2015)                271107           14846   \n",
       "Cinderella (2015)                              65709           12640   \n",
       "Ant-Man (2015)                                103660           12055   \n",
       "Do You Believe? (2015)                          3136            1793   \n",
       "Hot Tub Time Machine 2 (2015)                  19560            1021   \n",
       "The Water Diviner (2015)                       39373             397   \n",
       "\n",
       "                                Fandango_Difference  \n",
       "FILM                                                 \n",
       "Avengers: Age of Ultron (2015)                  0.5  \n",
       "Cinderella (2015)                               0.5  \n",
       "Ant-Man (2015)                                  0.5  \n",
       "Do You Believe? (2015)                          0.5  \n",
       "Hot Tub Time Machine 2 (2015)                   0.5  \n",
       "The Water Diviner (2015)                        0.5  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_films.loc['Avengers: Age of Ultron (2015)':'The Water Diviner (2015)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metacritic_User               1.505529\n",
      "IMDB                          0.955447\n",
      "Fandango_Stars                0.538532\n",
      "Fandango_Ratingvalue          0.501106\n",
      "RT_norm                       1.503265\n",
      "RT_user_norm                  0.997787\n",
      "Metacritic_norm               0.972522\n",
      "Metacritic_user_nom           0.752765\n",
      "IMDB_norm                     0.477723\n",
      "RT_norm_round                 1.509404\n",
      "RT_user_norm_round            1.003559\n",
      "Metacritic_norm_round         0.987561\n",
      "Metacritic_user_norm_round    0.785412\n",
      "IMDB_norm_round               0.501043\n",
      "Fandango_Difference           0.152141\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "types = fandango_films.dtypes\n",
    "#print(types)     #各列数据类型\n",
    "float_columns = types[types.values == 'float64'].index\n",
    "#print(float_columns)\n",
    "float_df = fandango_films[float_columns]\n",
    "#print(float_df)\n",
    "deviations = float_df.apply(lambda x:np.std(x))\n",
    "print(deviations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FILM\n",
       "Avengers: Age of Ultron (2015)                    0.375\n",
       "Cinderella (2015)                                 0.125\n",
       "Ant-Man (2015)                                    0.225\n",
       "Do You Believe? (2015)                            0.925\n",
       "Hot Tub Time Machine 2 (2015)                     0.150\n",
       "The Water Diviner (2015)                          0.150\n",
       "Irrational Man (2015)                             0.575\n",
       "Top Five (2014)                                   0.100\n",
       "Shaun the Sheep Movie (2015)                      0.150\n",
       "Love & Mercy (2015)                               0.050\n",
       "Far From The Madding Crowd (2015)                 0.050\n",
       "Black Sea (2015)                                  0.150\n",
       "Leviathan (2014)                                  0.175\n",
       "Unbroken (2014)                                   0.125\n",
       "The Imitation Game (2014)                         0.250\n",
       "Taken 3 (2015)                                    0.000\n",
       "Ted 2 (2015)                                      0.175\n",
       "Southpaw (2015)                                   0.050\n",
       "Night at the Museum: Secret of the Tomb (2014)    0.000\n",
       "Pixels (2015)                                     0.025\n",
       "McFarland, USA (2015)                             0.425\n",
       "Insidious: Chapter 3 (2015)                       0.325\n",
       "The Man From U.N.C.L.E. (2015)                    0.025\n",
       "Run All Night (2015)                              0.350\n",
       "Trainwreck (2015)                                 0.350\n",
       "Selma (2014)                                      0.375\n",
       "Ex Machina (2015)                                 0.175\n",
       "Still Alice (2015)                                0.175\n",
       "Wild Tales (2014)                                 0.100\n",
       "The End of the Tour (2015)                        0.350\n",
       "                                                  ...  \n",
       "Clouds of Sils Maria (2015)                       0.100\n",
       "Testament of Youth (2015)                         0.000\n",
       "Infinitely Polar Bear (2015)                      0.075\n",
       "Phoenix (2015)                                    0.025\n",
       "The Wolfpack (2015)                               0.075\n",
       "The Stanford Prison Experiment (2015)             0.050\n",
       "Tangerine (2015)                                  0.325\n",
       "Magic Mike XXL (2015)                             0.250\n",
       "Home (2015)                                       0.200\n",
       "The Wedding Ringer (2015)                         0.825\n",
       "Woman in Gold (2015)                              0.225\n",
       "The Last Five Years (2015)                        0.225\n",
       "Mission: Impossible â€“ Rogue Nation (2015)       0.250\n",
       "Amy (2015)                                        0.075\n",
       "Jurassic World (2015)                             0.275\n",
       "Minions (2015)                                    0.125\n",
       "Max (2015)                                        0.350\n",
       "Paul Blart: Mall Cop 2 (2015)                     0.300\n",
       "The Longest Ride (2015)                           0.625\n",
       "The Lazarus Effect (2015)                         0.650\n",
       "The Woman In Black 2 Angel of Death (2015)        0.475\n",
       "Danny Collins (2015)                              0.100\n",
       "Spare Parts (2015)                                0.300\n",
       "Serena (2015)                                     0.700\n",
       "Inside Out (2015)                                 0.025\n",
       "Mr. Holmes (2015)                                 0.025\n",
       "'71 (2015)                                        0.175\n",
       "Two Days, One Night (2014)                        0.250\n",
       "Gett: The Trial of Viviane Amsalem (2015)         0.200\n",
       "Kumiko, The Treasure Hunter (2015)                0.025\n",
       "Length: 146, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt_mt_user = float_df[['RT_user_norm','Metacritic_user_nom']]\n",
    "rt_mt_user.apply(lambda x:np.std(x),axis=1)   #按行求标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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