Python数据帧在多次合并时重复列

使现代化

使用 duplicated :

>>> df.loc[:, ~df.columns.duplicated()]
   Ref    A    Z
0    1  NaN  1.0
1    2  2.0  2.0
2    3  3.0  NaN
3    4  NaN  NaN

老答案

您可以使用:

# Your code
...
df = pd.concat(df, axis=1)

# Use pop and insert to cleanup your dataframe
df.insert(0, 'Ref', df.pop('Ref').iloc[:, 0])

输出:

>>> df
   Ref    A    Z
0    1  NaN  1.0
1    2  2.0  2.0
2    3  3.0  NaN
3    4  NaN  NaN

在获取数据帧列表时,将“Ref”col设置为索引怎么样。(并重置索引,以便将Ref作为列返回)

df = pd.DataFrame({'Ref':[1,2,3,4]}) 
df1 = pd.DataFrame({'A':[2,3],'Z':[1,2]})


df = [df.merge(df1[col_name],left_on='Ref',right_on=col_name,how='left').set_index('Ref') for col_name in df1.columns]
df = pd.concat(df,axis=1)

df = df.reset_index()

        Ref  A   Z
        1   NaN 1.0
        2   2.0 2.0 
        3   3.0 NaN 
        4   NaN NaN

这是一个还原过程。代替列表理解,使用for-loop,甚至 reduce :

from functools import reduce

reduce(lambda x, y : x.merge(df1[y],left_on='Ref',right_on=y,how='left'), df1.columns, df)

   Ref    A    Z
0    1  NaN  1.0
1    2  2.0  2.0
2    3  3.0  NaN
3    4  NaN  NaN

以上内容类似于:

for y in df1.columns:
    df = df.merge(df1[y],left_on='Ref',right_on=y,how='left')
df
 
   Ref    A    Z
0    1  NaN  1.0
1    2  2.0  2.0
2    3  3.0  NaN
3    4  NaN  NaN