pandas dataframe 新增单列和多列

喜欢ヅ旅行 2023-02-28 05:37 12阅读 0赞

## dataframe 新增单列 ##

### assign方法 ###

dataframe assign方法，返回一个新对象（副本），不影响旧dataframe对象

import pandas as pd
    
        df = pd.DataFrame({ 
            'col_1': [0, 1, 2, 3],
            'col_2': [4, 5, 6, 7]
        })
        sLength = len(df['col_1'])
        df2 = df.assign(col_3=pd.Series([8, 9, 10, 11]).values)
        print(df)
        print(df2)

结果展示：

col_1  col_2
    0      0      4
    1      1      5
    2      2      6
    3      3      7
       col_1  col_2  col_3
    0      0      4      8
    1      1      5      9
    2      2      6     10
    3      3      7     11

### 简单的方法和insert方法 ###

简单的方法df\[‘col\_3’\] = pd.Series(\[8, 9, 10, 11\])  
insert方法 df.insert(loc=len(df.columns), column=“col\_4”, value=\[8, 9, 10, 11\])  
这种方式会对旧的dataframe新增列

import pandas as pd
    
        df = pd.DataFrame({ 
            'col_1': [0, 1, 2, 3],
            'col_2': [4, 5, 6, 7]
        })
    
        df['col_3'] = pd.Series([8, 9, 10, 11])
        print(df)
        df.insert(loc=len(df.columns), column="col_4", value=[8, 9, 10, 11])
        print(df)

## dataframe 新增多列 ##

### list unpacking ###

import pandas as pd
        import numpy as np
    
        df = pd.DataFrame({ 
            'col_1': [0, 1, 2, 3],
            'col_2': [4, 5, 6, 7]
        })
    
        df['column_new_1'], df['column_new_2'], df['column_new_3'] = [np.nan, 'dogs', 3]
        print(df)

结果展示：

col_1  col_2  column_new_1 column_new_2  column_new_3
    0      0      4           NaN         dogs             3
    1      1      5           NaN         dogs             3
    2      2      6           NaN         dogs             3
    3      3      7           NaN         dogs             3

### DataFrame也可以一行匹配 ###

df[['column_new_1', 'column_new_2', 'column_new_3']] = pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index)

### concat方法 ###

df = pd.concat(
            [
                df,
                pd.DataFrame(
                    [[np.nan, 'dogs', 3]],
                    index=df.index,
                    columns=['column_new_1', 'column_new_2', 'column_new_3']
                )
            ], axis=1
        )

### join方法 ###

df = df.join(pd.DataFrame(
        [[np.nan, 'dogs', 3]], 
        index=df.index, 
        columns=['column_new_1', 'column_new_2', 'column_new_3']
    ))

### join + 字典 ###

df = df.join(pd.DataFrame(
        { 
            'column_new_1': np.nan,
            'column_new_2': 'dogs',
            'column_new_3': 3
        }, index=df.index
    ))

### assign方法 ###

df = df.assign(column_new_1=np.nan, column_new_2='dogs', column_new_3=3)

### 土方法 ###

df['column_new_1'] = np.nan
        df['column_new_2'] = 'dogs'
        df['column_new_3'] = 3