pandas-base-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
      "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
      "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
      "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
      "3  7795-CFOCW    Male              0      No         No      45           No   \n",
      "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
      "5  9305-CDSKC  Female              0      No         No       8          Yes   \n",
      "6  1452-KIOVK    Male              0      No        Yes      22          Yes   \n",
      "7  6713-OKOMC  Female              0      No         No      10           No   \n",
      "8  7892-POOKP  Female              0     Yes         No      28          Yes   \n",
      "9  6388-TABGU    Male              0      No        Yes      62          Yes   \n",
      "\n",
      "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
      "0  No phone service             DSL             No  ...               No   \n",
      "1                No             DSL            Yes  ...              Yes   \n",
      "2                No             DSL            Yes  ...               No   \n",
      "3  No phone service             DSL            Yes  ...              Yes   \n",
      "4                No     Fiber optic             No  ...               No   \n",
      "5               Yes     Fiber optic             No  ...              Yes   \n",
      "6               Yes     Fiber optic             No  ...               No   \n",
      "7  No phone service             DSL            Yes  ...               No   \n",
      "8               Yes     Fiber optic             No  ...              Yes   \n",
      "9                No             DSL            Yes  ...               No   \n",
      "\n",
      "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
      "0          No          No              No  Month-to-month              Yes   \n",
      "1          No          No              No        One year               No   \n",
      "2          No          No              No  Month-to-month              Yes   \n",
      "3         Yes          No              No        One year               No   \n",
      "4          No          No              No  Month-to-month              Yes   \n",
      "5          No         Yes             Yes  Month-to-month              Yes   \n",
      "6          No         Yes              No  Month-to-month              Yes   \n",
      "7          No          No              No  Month-to-month               No   \n",
      "8         Yes         Yes             Yes  Month-to-month              Yes   \n",
      "9          No          No              No        One year               No   \n",
      "\n",
      "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
      "0           Electronic check          29.85         29.85    No  \n",
      "1               Mailed check          56.95        1889.5    No  \n",
      "2               Mailed check          53.85        108.15   Yes  \n",
      "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
      "4           Electronic check          70.70        151.65   Yes  \n",
      "5           Electronic check          99.65         820.5   Yes  \n",
      "6    Credit card (automatic)          89.10        1949.4    No  \n",
      "7               Mailed check          29.75         301.9    No  \n",
      "8           Electronic check         104.80       3046.05   Yes  \n",
      "9  Bank transfer (automatic)          56.15       3487.95    No  \n",
      "\n",
      "[10 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "# P31 导入文件\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('/Users/emmett/Downloads/tel_data.csv')\n",
    "print(df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的结果可以看出，这个文件一共有21列内容，有的是数据，有的是字符。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0          False   False          False    False       False   False   \n",
       "1          False   False          False    False       False   False   \n",
       "2          False   False          False    False       False   False   \n",
       "3          False   False          False    False       False   False   \n",
       "4          False   False          False    False       False   False   \n",
       "...          ...     ...            ...      ...         ...     ...   \n",
       "7038       False   False          False    False       False   False   \n",
       "7039       False   False          False    False       False   False   \n",
       "7040       False   False          False    False       False   False   \n",
       "7041       False   False          False    False       False   False   \n",
       "7042       False   False          False    False       False   False   \n",
       "\n",
       "      PhoneService  MultipleLines  InternetService  OnlineSecurity  ...  \\\n",
       "0            False          False            False           False  ...   \n",
       "1            False          False            False           False  ...   \n",
       "2            False          False            False           False  ...   \n",
       "3            False          False            False           False  ...   \n",
       "4            False          False            False           False  ...   \n",
       "...            ...            ...              ...             ...  ...   \n",
       "7038         False          False            False           False  ...   \n",
       "7039         False          False            False           False  ...   \n",
       "7040         False          False            False           False  ...   \n",
       "7041         False          False            False           False  ...   \n",
       "7042         False          False            False           False  ...   \n",
       "\n",
       "      DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                False        False        False            False     False   \n",
       "1                False        False        False            False     False   \n",
       "2                False        False        False            False     False   \n",
       "3                False        False        False            False     False   \n",
       "4                False        False        False            False     False   \n",
       "...                ...          ...          ...              ...       ...   \n",
       "7038             False        False        False            False     False   \n",
       "7039             False        False        False            False     False   \n",
       "7040             False        False        False            False     False   \n",
       "7041             False        False        False            False     False   \n",
       "7042             False        False        False            False     False   \n",
       "\n",
       "      PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
       "0                False          False           False         False  False  \n",
       "1                False          False           False         False  False  \n",
       "2                False          False           False         False  False  \n",
       "3                False          False           False         False  False  \n",
       "4                False          False           False         False  False  \n",
       "...                ...            ...             ...           ...    ...  \n",
       "7038             False          False           False         False  False  \n",
       "7039             False          False           False         False  False  \n",
       "7040             False          False           False         False  False  \n",
       "7041             False          False           False         False  False  \n",
       "7042             False          False           False         False  False  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P32 检查文件中是否有缺失值\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，所有没有缺失的单元格被标上了False。如果该单元格缺失，会被填上True。\n",
    "但是这里，我们没有办法看到所有单元格的情况，我们按列进行统计看看。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按行求和，所有的False会被转化为0，所有True会被转化为1，如果一整列都是0，说明该列没有缺失。\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# P33 对数据类型进行修正\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          11\n",
       "20.2      11\n",
       "19.75      9\n",
       "20.05      8\n",
       "19.9       8\n",
       "          ..\n",
       "6849.4     1\n",
       "692.35     1\n",
       "130.15     1\n",
       "3211.9     1\n",
       "6844.5     1\n",
       "Name: TotalCharges, Length: 6531, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在以上的结果中我们发现TotalCharges这一列的数据类型是object，然而这一列应该是数字。肯定是其中混入了一些不是数字的元素，我们对这一列进行统计频次，看看这一列都是啥东西。\n",
    "df[\"TotalCharges\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1397.475    11\n",
       "20.200      11\n",
       "19.750       9\n",
       "20.050       8\n",
       "19.900       8\n",
       "            ..\n",
       "6849.400     1\n",
       "692.350      1\n",
       "130.150      1\n",
       "3211.900     1\n",
       "6844.500     1\n",
       "Name: TotalCharges, Length: 6531, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 发现：有个含空格的单元格出现了11次。现在把这些空单元格替换为这组数据的中位数。\n",
    "\n",
    "# 中位数\n",
    "median = df[\"TotalCharges\"][df[\"TotalCharges\"] != \" \"].median()\n",
    "\n",
    "# 将median填到空格里\n",
    "df.loc[df[\"TotalCharges\"] == \" \", \"TotalCharges\"] = median\n",
    "\n",
    "# 再把整列的数据类型修改为float\n",
    "df[\"TotalCharges\"] = df[\"TotalCharges\"].astype(float)\n",
    "\n",
    "# 检查\n",
    "df[\"TotalCharges\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
       "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
       "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P34 将数字类型外的其他列都改成分类变量（category）\n",
    "# 先看下都有哪些列\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   customerID        7043 non-null   category\n",
      " 1   gender            7043 non-null   category\n",
      " 2   SeniorCitizen     7043 non-null   category\n",
      " 3   Partner           7043 non-null   category\n",
      " 4   Dependents        7043 non-null   category\n",
      " 5   tenure            7043 non-null   float64 \n",
      " 6   PhoneService      7043 non-null   category\n",
      " 7   MultipleLines     7043 non-null   category\n",
      " 8   InternetService   7043 non-null   category\n",
      " 9   OnlineSecurity    7043 non-null   category\n",
      " 10  OnlineBackup      7043 non-null   category\n",
      " 11  DeviceProtection  7043 non-null   category\n",
      " 12  TechSupport       7043 non-null   category\n",
      " 13  StreamingTV       7043 non-null   category\n",
      " 14  StreamingMovies   7043 non-null   category\n",
      " 15  Contract          7043 non-null   category\n",
      " 16  PaperlessBilling  7043 non-null   category\n",
      " 17  PaymentMethod     7043 non-null   category\n",
      " 18  MonthlyCharges    7043 non-null   float64 \n",
      " 19  TotalCharges      7043 non-null   float64 \n",
      " 20  Churn             7043 non-null   category\n",
      "dtypes: category(18), float64(3)\n",
      "memory usage: 611.1 KB\n"
     ]
    }
   ],
   "source": [
    "# 只有tenure, MonthlyCharges, TotalCharges是数字类型（float），其他的都改成分类变量（categorical）\n",
    "\n",
    "# 先把这三列的列名挑出来\n",
    "\n",
    "number_columns = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]\n",
    "for column in number_columns:\n",
    "    df[column] = df[column].astype(float)\n",
    "\n",
    "# 修改其他列 set的减法：所有列名的set减去number_columns的set\n",
    "for column in set(df.columns) - set(number_columns):\n",
    "    df[column] = pd.Categorical(df[column])\n",
    "\n",
    "# 再查看每一列的数据类型\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0002-ORFBO</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>3555</td>\n",
       "      <td>5901</td>\n",
       "      <td>3641</td>\n",
       "      <td>4933</td>\n",
       "      <td>6361</td>\n",
       "      <td>3390</td>\n",
       "      <td>3096</td>\n",
       "      <td>3498</td>\n",
       "      <td>3088</td>\n",
       "      <td>3095</td>\n",
       "      <td>3473</td>\n",
       "      <td>2810</td>\n",
       "      <td>2785</td>\n",
       "      <td>3875</td>\n",
       "      <td>4171</td>\n",
       "      <td>2365</td>\n",
       "      <td>5174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customerID gender  SeniorCitizen Partner Dependents PhoneService  \\\n",
       "count         7043   7043           7043    7043       7043         7043   \n",
       "unique        7043      2              2       2          2            2   \n",
       "top     0002-ORFBO   Male              0      No         No          Yes   \n",
       "freq             1   3555           5901    3641       4933         6361   \n",
       "\n",
       "       MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "count           7043            7043           7043         7043   \n",
       "unique             3               3              3            3   \n",
       "top               No     Fiber optic             No           No   \n",
       "freq            3390            3096           3498         3088   \n",
       "\n",
       "       DeviceProtection TechSupport StreamingTV StreamingMovies  \\\n",
       "count              7043        7043        7043            7043   \n",
       "unique                3           3           3               3   \n",
       "top                  No          No          No              No   \n",
       "freq               3095        3473        2810            2785   \n",
       "\n",
       "              Contract PaperlessBilling     PaymentMethod Churn  \n",
       "count             7043             7043              7043  7043  \n",
       "unique               3                2                 4     2  \n",
       "top     Month-to-month              Yes  Electronic check    No  \n",
       "freq              3875             4171              2365  5174  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P35 Describe描述所有分类变量的列\n",
    "df.describe(include=[\"category\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里和数据类型的describe结果是不一样的\n",
    "\n",
    "count表示了所有的个数，unique表示种类数，也就是去重之后的，比如gender里面，只有男女，所以unique是2。Top和freq是最多的分类和对应的频数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     5174\n",
       "Yes    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P36 看某一列的数据分布\n",
    "df[\"Churn\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn  PaymentMethod            \n",
       "No     Bank transfer (automatic)    65.049417\n",
       "       Credit card (automatic)      64.562209\n",
       "       Electronic check             74.232032\n",
       "       Mailed check                 41.403911\n",
       "Yes    Bank transfer (automatic)    77.875581\n",
       "       Credit card (automatic)      77.356034\n",
       "       Electronic check             78.700980\n",
       "       Mailed check                 54.557143\n",
       "Name: MonthlyCharges, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P37 多维度查看月费字段统计\n",
    "# Churn：用户是否流失\n",
    "# PaymentMethod：支付方式\n",
    "# MonthlyCharges：月费\n",
    "\n",
    "# 查看按照Churn、PaymentMethod两个指标分组后的月费均值\n",
    "df.groupby([\"Churn\", \"PaymentMethod\"])[\"MonthlyCharges\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从这里可以很明显的的发现，无论是哪种支付方式，用户流失了（Churn=Yes）的月费均值都大于未流失的。这就是隐藏在数据中的规律。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5174\n",
       "1    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P38 Churn字段的数据映射\n",
    "# 需要把Yes/No转化为1-0数据，这对于机器学习数据的处理是一个很有用的操作\n",
    "\n",
    "df[\"Churn\"] = df[\"Churn\"].map({\"Yes\":1,\"No\":0})\n",
    "df[\"Churn\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这和之前对于Churn的描述是一致的，只不过No被替换为了0，Yes被替换为了1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3g/cxwzy4z940n8qvfv8vrkc97m0000gn/T/ipykernel_84349/1043428941.py:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()\n"
     ]
    },
    {
     "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>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.247900</td>\n",
       "      <td>0.825464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.247900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.650864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.825464</td>\n",
       "      <td>0.650864</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tenure  MonthlyCharges  TotalCharges\n",
       "tenure          1.000000        0.247900      0.825464\n",
       "MonthlyCharges  0.247900        1.000000      0.650864\n",
       "TotalCharges    0.825464        0.650864      1.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P39 查看字段的相关性矩阵\n",
    "\n",
    "# 统计DataFrame的相关性矩阵\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以发现，相关性矩阵只会通过数值型的变量来计算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4824</th>\n",
       "      <td>3339-EAQNV</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>97.25</td>\n",
       "      <td>7133.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>5071-FBJFS</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>50.30</td>\n",
       "      <td>217.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6006</th>\n",
       "      <td>1360-RCYRT</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>23.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>30.35</td>\n",
       "      <td>678.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4841</th>\n",
       "      <td>9058-MJLZC</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>94.60</td>\n",
       "      <td>2283.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6002</th>\n",
       "      <td>3727-RJMEO</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>82.85</td>\n",
       "      <td>460.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5155</th>\n",
       "      <td>8294-UIMBA</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>94.40</td>\n",
       "      <td>2638.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3793</th>\n",
       "      <td>6278-FEPBZ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.25</td>\n",
       "      <td>186.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>0219-QAERP</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>19.80</td>\n",
       "      <td>576.65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5258</th>\n",
       "      <td>8634-CILSZ</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>104.70</td>\n",
       "      <td>7220.35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2766</th>\n",
       "      <td>1229-RCALF</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>115.00</td>\n",
       "      <td>7396.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender SeniorCitizen Partner Dependents  tenure  \\\n",
       "4824  3339-EAQNV    Male             1     Yes         No    72.0   \n",
       "1539  5071-FBJFS  Female             0     Yes        Yes     4.0   \n",
       "6006  1360-RCYRT    Male             0     Yes        Yes    23.0   \n",
       "4841  9058-MJLZC  Female             0      No         No    24.0   \n",
       "6002  3727-RJMEO    Male             0     Yes         No     6.0   \n",
       "5155  8294-UIMBA  Female             0      No         No    30.0   \n",
       "3793  6278-FEPBZ  Female             0      No         No     9.0   \n",
       "2273  0219-QAERP    Male             0     Yes         No    30.0   \n",
       "5258  8634-CILSZ    Male             0      No         No    69.0   \n",
       "2766  1229-RCALF  Female             0     Yes         No    64.0   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService       OnlineSecurity  ...  \\\n",
       "4824          Yes               Yes     Fiber optic                   No  ...   \n",
       "1539          Yes                No             DSL                  Yes  ...   \n",
       "6006           No  No phone service             DSL                   No  ...   \n",
       "4841          Yes                No     Fiber optic                  Yes  ...   \n",
       "6002          Yes               Yes     Fiber optic                   No  ...   \n",
       "5155          Yes                No     Fiber optic                   No  ...   \n",
       "3793          Yes                No              No  No internet service  ...   \n",
       "2273          Yes                No              No  No internet service  ...   \n",
       "5258          Yes               Yes     Fiber optic                   No  ...   \n",
       "2766          Yes               Yes     Fiber optic                  Yes  ...   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "4824                   No                   No                  Yes   \n",
       "1539                   No                   No                   No   \n",
       "6006                   No                  Yes                   No   \n",
       "4841                   No                   No                  Yes   \n",
       "6002                   No                   No                  Yes   \n",
       "5155                   No                  Yes                  Yes   \n",
       "3793  No internet service  No internet service  No internet service   \n",
       "2273  No internet service  No internet service  No internet service   \n",
       "5258                   No                  Yes                  Yes   \n",
       "2766                  Yes                  Yes                  Yes   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling  \\\n",
       "4824                  Yes        One year              Yes   \n",
       "1539                   No  Month-to-month               No   \n",
       "6006                   No  Month-to-month              Yes   \n",
       "4841                  Yes  Month-to-month              Yes   \n",
       "6002                   No  Month-to-month              Yes   \n",
       "5155                  Yes        One year              Yes   \n",
       "3793  No internet service  Month-to-month               No   \n",
       "2273  No internet service        One year               No   \n",
       "5258                  Yes        One year              Yes   \n",
       "2766                  Yes        One year              Yes   \n",
       "\n",
       "                  PaymentMethod MonthlyCharges  TotalCharges  Churn  \n",
       "4824    Credit card (automatic)          97.25       7133.10      0  \n",
       "1539           Electronic check          50.30        217.10      0  \n",
       "6006  Bank transfer (automatic)          30.35        678.75      0  \n",
       "4841           Electronic check          94.60       2283.15      0  \n",
       "6002           Electronic check          82.85        460.25      1  \n",
       "5155  Bank transfer (automatic)          94.40       2638.10      0  \n",
       "3793               Mailed check          20.25        186.15      0  \n",
       "2273    Credit card (automatic)          19.80        576.65      0  \n",
       "5258  Bank transfer (automatic)         104.70       7220.35      1  \n",
       "2766           Electronic check         115.00       7396.15      0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P40 数据的采样\n",
    "\n",
    "# 随机挑选一定的行数\n",
    "df.sample(10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}