数据清洗之 分组对象与apply函数

分组对象与apply函数

  • 函数apply即可用于分组对象，也可以作用于dataframe数据
  • Groupby.apply(func)
  • 需要注意axis=0和axis=1的区别
    • np.sum, axis=0 相当于计算每列的总和
    • np.sum, axis=1 相当于计算每行的总和
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={'customer':str, 'order':str})

df.head(5)

<div>

<style scoped>

.dataframe tbody tr th:only-of-type {

    vertical-align: middle;

}

.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

</style>

<table border=”1″ class=”dataframe”>

<thead>

<tr style="text-align: right;">

  <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>

</div>

grouped = df.groupby('weekday')

# 只可传入一个统计参数
# agg可传入多个
# grouped.apply([np.mean, np.sum])

grouped.apply(np.mean)[['total_items', 'discount%', 'weekday']]

<div>

<style scoped>

.dataframe tbody tr th:only-of-type {

    vertical-align: middle;

}

.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

</style>

<table border=”1″ class=”dataframe”>

<thead>

<tr style="text-align: right;">

  <th></th>

  <th>total_items</th>

  <th>discount%</th>

  <th>weekday</th>

</tr>

<tr>

  <th>weekday</th>

  <th></th>

  <th></th>

  <th></th>

</tr>

</thead>

<tbody>

<tr>

  <th>1</th>

  <td>30.662177</td>

  <td>8.580705</td>

  <td>1.0</td>

</tr>

<tr>

  <th>2</th>

  <td>31.868612</td>

  <td>8.638014</td>

  <td>2.0</td>

</tr>

<tr>

  <th>3</th>

  <td>31.869796</td>

  <td>7.794507</td>

  <td>3.0</td>

</tr>

<tr>

  <th>4</th>

  <td>32.251899</td>

  <td>8.068155</td>

  <td>4.0</td>

</tr>

<tr>

  <th>5</th>

  <td>31.406619</td>

  <td>9.159031</td>

  <td>5.0</td>

</tr>

<tr>

  <th>6</th>

  <td>32.154814</td>

  <td>8.414258</td>

  <td>6.0</td>

</tr>

<tr>

  <th>7</th>

  <td>32.373837</td>

  <td>8.710171</td>

  <td>7.0</td>

</tr>

</tbody>

</table>

</div>

df.columns

Index(['customer', 'order', 'total_items', 'discount%', 'weekday', 'hour',

       'Food%', 'Fresh%', 'Drinks%', 'Home%', 'Beauty%', 'Health%', 'Baby%',

       'Pets%'],

      dtype='object')

var = ['Food%', 'Fresh%', 'Drinks%', 'Home%', 'Beauty%', 'Health%', 'Baby%',
       'Pets%']

df[var].head(5)

<div>

<style scoped>

.dataframe tbody tr th:only-of-type {

    vertical-align: middle;

}

.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

</style>

<table border=”1″ class=”dataframe”>

<thead>

<tr style="text-align: right;">

  <th></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>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>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>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>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>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>

</div>

# 计算每个变量的总和
df[var].apply(np.sum, axis=0)

Food%      706812.19

Fresh%     606818.38

Drinks%    700477.06

Home%      406187.25

Beauty%    176788.48

Health%     33988.76

Baby%      332884.34

Pets%       31292.61

dtype: float64

# 对每一行求和
df[var].apply(np.sum, axis=1).head(5)

0    100.00

1    100.01

2    100.01

3     99.99

4    100.01

dtype: float64

# Food% - Fresh%
df[var].apply(lambda x: x[0] - x[1], axis=1).head(5)

0   -77.60

1   -59.93

2   -39.87

3    -7.18

4   -36.25

dtype: float64

正文完
发表至： Python 技术教程
2022-06-19