数据清洗之透视图与交叉表

红太狼 2023-05-22 22:15 2阅读 0赞

# 透视图与交叉表 #

*  在数据分析中，数据透视表是常见的工具之一，需要根据行或列对数据进行各个维度数据的汇总，在pandas中，提供了相关函数解决此类问题
 *  交叉表更多用于频数的分析
 *  pivot\_table(data, index, columns, values, aggfunc, fill\_value, margins, margins\_name=)
    
     *  data：数据
     *  index: 行分组键
     *  columns：列分组键
     *  values：分组的字段，只能为数值型变量
     *  aggfunc：聚合函数
     *  fill\_value: 缺失值填补
     *  margins：是否需要总计 （字段均值/总和）
     *  margins\_name: 总计名称
 *  pd.crosstab(index, columns, normalize)
    
     *  index: 行索引
     *  columns：列索引
     *  normalize：对数据进行标准化，index表示行，column表示列

import pandas as pd
    import numpy as np
    import os

os.getcwd()

'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据清洗之数据统计'

os.chdir('D:\\Jupyter\\notebook\\Python数据清洗实战\\数据')

df = pd.read_csv('online_order.csv', encoding='gbk', dtype={ 'cunstomer':str, 'order':str})

df.head(5)

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>customer</th> 
   <th>order</th> 
   <th>total_items</th> 
   <th>discount%</th> 
   <th>weekday</th> 
   <th>hour</th> 
   <th>Food%</th> 
   <th>Fresh%</th> 
   <th>Drinks%</th> 
   <th>Home%</th> 
   <th>Beauty%</th> 
   <th>Health%</th> 
   <th>Baby%</th> 
   <th>Pets%</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>0</td> 
   <td>0</td> 
   <td>45</td> 
   <td>23.03</td> 
   <td>4</td> 
   <td>13</td> 
   <td>9.46</td> 
   <td>87.06</td> 
   <td>3.48</td> 
   <td>0.00</td> 
   <td>0.00</td> 
   <td>0.00</td> 
   <td>0.0</td> 
   <td>0.0</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>0</td> 
   <td>1</td> 
   <td>38</td> 
   <td>1.22</td> 
   <td>5</td> 
   <td>13</td> 
   <td>15.87</td> 
   <td>75.80</td> 
   <td>6.22</td> 
   <td>2.12</td> 
   <td>0.00</td> 
   <td>0.00</td> 
   <td>0.0</td> 
   <td>0.0</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>0</td> 
   <td>2</td> 
   <td>51</td> 
   <td>18.08</td> 
   <td>4</td> 
   <td>13</td> 
   <td>16.88</td> 
   <td>56.75</td> 
   <td>3.37</td> 
   <td>16.48</td> 
   <td>6.53</td> 
   <td>0.00</td> 
   <td>0.0</td> 
   <td>0.0</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>1</td> 
   <td>3</td> 
   <td>57</td> 
   <td>16.51</td> 
   <td>1</td> 
   <td>12</td> 
   <td>28.81</td> 
   <td>35.99</td> 
   <td>11.78</td> 
   <td>4.62</td> 
   <td>2.87</td> 
   <td>15.92</td> 
   <td>0.0</td> 
   <td>0.0</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>1</td> 
   <td>4</td> 
   <td>53</td> 
   <td>18.31</td> 
   <td>2</td> 
   <td>11</td> 
   <td>24.13</td> 
   <td>60.38</td> 
   <td>7.78</td> 
   <td>7.72</td> 
   <td>0.00</td> 
   <td>0.00</td> 
   <td>0.0</td> 
   <td>0.0</td> 
  </tr> 
 </tbody> 
</table>

df.info()

<class 'pandas.core.frame.DataFrame'>
    RangeIndex: 30000 entries, 0 to 29999
    Data columns (total 14 columns):
    customer       30000 non-null int64
    order          30000 non-null object
    total_items    30000 non-null int64
    discount%      30000 non-null float64
    weekday        30000 non-null int64
    hour           30000 non-null int64
    Food%          30000 non-null float64
    Fresh%         30000 non-null float64
    Drinks%        30000 non-null float64
    Home%          30000 non-null float64
    Beauty%        30000 non-null float64
    Health%        30000 non-null float64
    Baby%          30000 non-null float64
    Pets%          30000 non-null float64
    dtypes: float64(9), int64(4), object(1)
    memory usage: 3.2+ MB

df.columns

Index(['customer', 'order', 'total_items', 'discount%', 'weekday', 'hour',
           'Food%', 'Fresh%', 'Drinks%', 'Home%', 'Beauty%', 'Health%', 'Baby%',
           'Pets%'],
          dtype='object')

pd.pivot_table(data=df, index='weekday', values='total_items', aggfunc=[np.mean, np.sum], 
                  margins=True, margins_name='总计商品情况')

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>mean</th> 
   <th>sum</th> 
  </tr> 
  <tr> 
   <th></th> 
   <th>total_items</th> 
   <th>total_items</th> 
  </tr> 
  <tr> 
   <th>weekday</th> 
   <th></th> 
   <th></th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>1</th> 
   <td>30.662177</td> 
   <td>191240</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>31.868612</td> 
   <td>158387</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>31.869796</td> 
   <td>150043</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>32.251899</td> 
   <td>131620</td> 
  </tr> 
  <tr> 
   <th>5</th> 
   <td>31.406619</td> 
   <td>79710</td> 
  </tr> 
  <tr> 
   <th>6</th> 
   <td>32.154814</td> 
   <td>74149</td> 
  </tr> 
  <tr> 
   <th>7</th> 
   <td>32.373837</td> 
   <td>167049</td> 
  </tr> 
  <tr> 
   <th>总计</th> 
   <td>31.739933</td> 
   <td>952198</td> 
  </tr> 
 </tbody> 
</table>

pd.pivot_table(data=df, index='weekday', columns='customer', values='total_items', aggfunc=[np.mean, np.sum], 
                  margins=True, margins_name='总计商品情况', fill_value=0)

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th colspan="10">mean</th> 
   <th>...</th> 
   <th colspan="10">sum</th> 
  </tr> 
  <tr> 
   <th>customer</th> 
   <th>0</th> 
   <th>1</th> 
   <th>2</th> 
   <th>3</th> 
   <th>4</th> 
   <th>5</th> 
   <th>6</th> 
   <th>7</th> 
   <th>8</th> 
   <th>9</th> 
   <th>...</th> 
   <th>10230</th> 
   <th>10231</th> 
   <th>10232</th> 
   <th>10233</th> 
   <th>10234</th> 
   <th>10235</th> 
   <th>10236</th> 
   <th>10237</th> 
   <th>10238</th> 
   <th>总计商品情况</th> 
  </tr> 
  <tr> 
   <th>weekday</th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>1</th> 
   <td>0</td> 
   <td>32.538462</td> 
   <td>0</td> 
   <td>28.571429</td> 
   <td>15.818182</td> 
   <td>0</td> 
   <td>40.000000</td> 
   <td>40</td> 
   <td>26.333333</td> 
   <td>9.000000</td> 
   <td>...</td> 
   <td>13</td> 
   <td>15</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>191240</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>0</td> 
   <td>42.333333</td> 
   <td>0</td> 
   <td>9.500000</td> 
   <td>13.400000</td> 
   <td>21</td> 
   <td>40.500000</td> 
   <td>0</td> 
   <td>54.500000</td> 
   <td>18.500000</td> 
   <td>...</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>158387</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>22.333333</td> 
   <td>0</td> 
   <td>38.333333</td> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>39</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>150043</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>48</td> 
   <td>12.333333</td> 
   <td>0</td> 
   <td>41.500000</td> 
   <td>12.666667</td> 
   <td>0</td> 
   <td>44.000000</td> 
   <td>0</td> 
   <td>28.500000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>31</td> 
   <td>8</td> 
   <td>2</td> 
   <td>5</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
   <td>54</td> 
   <td>2</td> 
   <td>131620</td> 
  </tr> 
  <tr> 
   <th>5</th> 
   <td>38</td> 
   <td>36.000000</td> 
   <td>0</td> 
   <td>28.000000</td> 
   <td>3.000000</td> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>0</td> 
   <td>37.000000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>0</td> 
   <td>15</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>4</td> 
   <td>0</td> 
   <td>10</td> 
   <td>0</td> 
   <td>79710</td> 
  </tr> 
  <tr> 
   <th>6</th> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>26</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>26.333333</td> 
   <td>...</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>74149</td> 
  </tr> 
  <tr> 
   <th>7</th> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>0</td> 
   <td>45.000000</td> 
   <td>24.500000</td> 
   <td>0</td> 
   <td>46.000000</td> 
   <td>0</td> 
   <td>0.000000</td> 
   <td>20.333333</td> 
   <td>...</td> 
   <td>0</td> 
   <td>22</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
   <td>167049</td> 
  </tr> 
  <tr> 
   <th>总计商品情况</th> 
   <td>44</td> 
   <td>31.150000</td> 
   <td>26</td> 
   <td>27.782609</td> 
   <td>17.103448</td> 
   <td>21</td> 
   <td>40.769231</td> 
   <td>40</td> 
   <td>35.250000</td> 
   <td>17.750000</td> 
   <td>...</td> 
   <td>83</td> 
   <td>60</td> 
   <td>2</td> 
   <td>5</td> 
   <td>1</td> 
   <td>4</td> 
   <td>1</td> 
   <td>64</td> 
   <td>2</td> 
   <td>952198</td> 
  </tr> 
 </tbody> 
</table>

8 rows × 20480 columns

# 不同折扣下的样本(订单)数量
    pd.crosstab(index=df['weekday'], columns=df['discount%'])

7 rows × 3579 columns

pd.crosstab(index=df['weekday'], columns=df['discount%'], margins=True, normalize='index')

<table> 
 <thead> 
  <tr> 
   <th>discount%</th> 
   <th>-65.15</th> 
   <th>-63.64</th> 
   <th>-47.26</th> 
   <th>-39.84</th> 
   <th>-26.15</th> 
   <th>-23.98</th> 
   <th>-23.89</th> 
   <th>-20.59</th> 
   <th>-19.79</th> 
   <th>-18.06</th> 
   <th>...</th> 
   <th>95.49</th> 
   <th>95.79</th> 
   <th>96.65</th> 
   <th>97.12</th> 
   <th>98.02</th> 
   <th>98.36</th> 
   <th>98.39</th> 
   <th>98.54</th> 
   <th>98.6</th> 
   <th>100.0</th> 
  </tr> 
  <tr> 
   <th>weekday</th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
   <th></th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>1</th> 
   <td>0.000160</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000160</td> 
   <td>...</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000160</td> 
   <td>0.000000</td> 
   <td>0.000160</td> 
   <td>0.000000</td> 
   <td>0.011384</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000201</td> 
   <td>0.000402</td> 
   <td>0.000201</td> 
   <td>0.000201</td> 
   <td>...</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000201</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.015694</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000212</td> 
   <td>0.000212</td> 
   <td>0.000000</td> 
   <td>0.000212</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.012532</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.014457</td> 
  </tr> 
  <tr> 
   <th>5</th> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000394</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>0.000394</td> 
   <td>0.000394</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000394</td> 
   <td>0.020489</td> 
  </tr> 
  <tr> 
   <th>6</th> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000434</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000434</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.010841</td> 
  </tr> 
  <tr> 
   <th>7</th> 
   <td>0.000000</td> 
   <td>0.000194</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>...</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000194</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.000194</td> 
   <td>0.000000</td> 
   <td>0.000000</td> 
   <td>0.010078</td> 
  </tr> 
  <tr> 
   <th>All</th> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000100</td> 
   <td>0.000033</td> 
   <td>0.000067</td> 
   <td>...</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.000033</td> 
   <td>0.013200</td> 
  </tr> 
 </tbody> 
</table>

8 rows × 3579 columns