Pandas 基于另一列值的条件填充

Question

我正在处理 bigmart 数据集，我想根据另一列的值替换一列的缺失值，实际上：

     Outlet_Size  sales_bin
0         Medium  3000-4000
1         Medium     0-1000
2         Medium  2000-3000
3            NaN     0-1000
4           High     0-1000
...          ...        ...
8518        High  2000-3000
8519         NaN     0-1000
8520       Small  1000-2000
8521      Medium  1000-2000
8522       Small     0-1000

So if train[“Outlet_Size”] value is a NaN and train[“sales_bin”] is  “0-1000”
train[“Outlet_Size”] value shoud become “Small”
else == Medium

但我真的不知道怎么写，而且我发现的所有信息都让我感到困惑

有可能吗？怎么样？

非常感谢

Answer 1

使用Series.isna 创建布尔掩码，然后使用np.where + Series.eq 根据sales_bin 等于0-1000 的条件从Small 和Medium 中选择选项：

m = df['Outlet_Size'].isna()
df.loc[m, 'Outlet_Size'] = np.where(df.loc[m, 'sales_bin'].eq('0-1000'), 'Small', 'Medium')

结果：

print(df)
     Outlet_Size  sales_bin
0         Medium  3000-4000
1         Medium     0-1000
2         Medium  2000-3000
3          Small     0-1000
4           High     0-1000
8518        High  2000-3000
8519       Small     0-1000
8520       Small  1000-2000
8521      Medium  1000-2000
8522       Small     0-1000

Answer 2

您可以使用pandas.Series.map 代替numpy.where。

pandas.Series.map 对于这些简单的情况似乎更方便，这使得使用字典（比如{'0-1000': 'Small', '2000-3000': 'High'}）更容易和明确地进行多重插补。

numpy.where 旨在处理更多逻辑（例如：如果 a

步骤：

1. 使用pandas.Series.isna() 生成掩码以标记缺少“Outlet_Size”的pandas.DataFrame 子集；
2. 使用映射定义字典，例如从“0-1000”到“小”；
3. 使用 pandas.Series.map 将定义的字典替换为定义的 pandas.DataFrame 子集中的“Outlet_Size”值作为 args 参数。
4. 使用pandas.Series.fillna() 捕获未映射的缺失“Outlet_Size”并将其归为默认值。

例子：

import pandas as pd
import numpy as np
fake_dataframe = pd.DataFrame({
    'Outlet_Size' : ['Medium', 'Medium', 'Medium', np.nan, 'High', 'High', np.nan, 'Small', 'Medium', 'Small', np.nan, np.nan],
    'sales_bin': ['3000-4000', '0-1000', '2000-3000', '0-1000', '0-1000', '2000-3000', '0-1000', '1000-2000', '1000-2000', '0-1000', '2000-3000', '1000-2000']
})
missing_mask = fake_dataframe['Outlet_Size'].isna()
mapping_dict = dict({'0-1000': 'Small'})
fake_dataframe.loc[missing_mask, 'Outlet_Size'] = fake_dataframe.loc[missing_mask, 'sales_bin'].map(mapping_dict)
fake_dataframe['Outlet_Size'] = fake_dataframe['Outlet_Size'].fillna('Medium')
print(fake_dataframe)
   Outlet_Size  sales_bin
0       Medium  3000-4000
1       Medium     0-1000
2       Medium  2000-3000
3        Small     0-1000
4         High     0-1000
5         High  2000-3000
6        Small     0-1000
7        Small  1000-2000
8       Medium  1000-2000
9        Small     0-1000
10      Medium  2000-3000
11      Medium  1000-2000

多重插补示例：

import pandas as pd
import numpy as np
fake_dataframe = pd.DataFrame({
    'Outlet_Size' : ['Medium', 'Medium', 'Medium', np.nan, 'High', 'High', np.nan, 'Small', 'Medium', 'Small', np.nan, np.nan],
    'sales_bin': ['3000-4000', '0-1000', '2000-3000', '0-1000', '0-1000', '2000-3000', '0-1000', '1000-2000', '1000-2000', '0-1000', '2000-3000', '1000-2000']
})
missing_mask = fake_dataframe['Outlet_Size'].isna()
mapping_dict = dict({'0-1000': 'Small', '2000-3000': 'High'})
fake_dataframe.loc[missing_mask, 'Outlet_Size'] = fake_dataframe.loc[missing_mask, 'sales_bin'].map(mapping_dict)
fake_dataframe['Outlet_Size'] = fake_dataframe['Outlet_Size'].fillna('Medium')
print(fake_dataframe)
    Outlet_Size sales_bin
0   Medium  3000-4000
1   Medium  0-1000
2   Medium  2000-3000
3   Small   0-1000
4   High    0-1000
5   High    2000-3000
6   Small   0-1000
7   Small   1000-2000
8   Medium  1000-2000
9   Small   0-1000
10  High    2000-3000
11  Medium  1000-2000

Answer 3

遵循 Shubham Sharma 的建议（使用 np.select）并使用该功能 “Item_Outlet_Sales”而不是“sales_bin”

所以：

        Outlet_Size  Item_Outlet_Sales
0         Medium          3735.1380
1         Medium           443.4228
2         Medium          2097.2700
3            NaN           732.3800
4           High           994.7052
...          ...                ...
8518        High          2778.3834
8519         NaN           549.2850
8520       Small          1193.1136
8521      Medium          1845.5976
8522       Small           765.6700 

    missing = train["Outlet_Size"].isna()
    condlist = [train.loc[missing, "Outlet_Size"] & train.loc[missing,'sales_bin'] <=1000, 
    train.loc[missing, "Outlet_Size"] & train.loc[missing,'sales_bin'] > 1000] 
    choicelist = ["Small", "Medium"] #PS, If I got it well it is possible to add as # many contiontions as wanted, as long condlist and choicelist has the same lenght 
    train.loc[missing, 'Outlet_Size'] = np.select(condlist, choicelist)
    train["Outlet_Size"].value_counts(dropna=False)

Small     4798
Medium    2793
High       932

非常感谢您的建议和这个精彩的论坛的存在:)