pandas - 根据现有的列名和列值在 python 数据框中创建列

Question

我在 pandas 中有一个数据框:

import pandas as pd
# assign data of lists.  
data = {'Gender': ['M', 'F', 'M', 'F','M', 'F','M', 'F','M', 'F','M', 'F'], 
        'Employment': ['R','U', 'E','R','U', 'E','R','U', 'E','R','U', 'E'],
        'Age': ['Y','M', 'O','Y','M', 'O','Y','M', 'O','Y','M', 'O']
       } 
    
# Create DataFrame  
df = pd.DataFrame(data)  
df

我想要的是为每个现有列的每个类别创建一个具有以下格式的新列:

Gender_M -> for when the gender equals M
Gender_F -> for when the gender equal F
Employment_R -> for when employment equals R
Employment_U -> for when employment equals U
and so on...

到目前为止，我已经创建了以下代码:

for i in range(len(df.columns)):
    curent_column=list(df.columns)[i]
    col_df_array = df[curent_column].unique()
    
    for j in range(col_df_array.size):
        new_col_name = str(list(df.columns)[i])+"_"+col_df_array[j]
    
        for index,row in df.iterrows():
            if(row[curent_column] == col_df_array[j]):
                df[new_col_name] = row[curent_column]       

问题是，尽管我已成功创建列名称，但我无法获取正确的列值。

例如，性别列应如下所示:

data2 = {'Gender': ['M', 'F', 'M', 'F','M', 'F','M', 'F','M', 'F','M', 'F'],
         'Gender_M': ['M', 'na', 'M', 'na','M', 'na','M', 'na','M', 'na','M', 'na'], 
         'Gender_F': ['na', 'F', 'na', 'F','na', 'F','na', 'F','na', 'F','na', 'F']
       } 

df2 = pd.DataFrame(data2)  

只是说，na 可以是任何内容，例如空格、点或 NAN。

Answer 1

您正在寻找pd.get_dummies .

>>> pd.get_dummies(df)
    Gender_F  Gender_M  Employment_E  Employment_R  Employment_U  Age_M  Age_O  Age_Y
0          0         1             0             1             0      0      0      1
1          1         0             0             0             1      1      0      0
2          0         1             1             0             0      0      1      0
3          1         0             0             1             0      0      0      1
4          0         1             0             0             1      1      0      0
5          1         0             1             0             0      0      1      0
6          0         1             0             1             0      0      0      1
7          1         0             0             0             1      1      0      0
8          0         1             1             0             0      0      1      0
9          1         0             0             1             0      0      0      1
10         0         1             0             0             1      1      0      0
11         1         0             1             0             0      0      1      0