如何使用不同的列将选定的列附加到df的pandas数据框中 [英] How to append selected columns to pandas dataframe from df with different columns

问题描述

我希望能够将df1，df2和df3附加到一个df_All中，但是由于每个数据帧都有不同的列.我该如何在for循环中执行此操作(我在for循环中必须执行其他操作)?

I want to be able to append df1 df2, df3 into one df_All , but since each of the dataframe has different column. How could I do this in for loop ( I have others stuff that i have to do in the for loop ) ?

import pandas as pd
import numpy as np

df1 = pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
df2 = pd.DataFrame.from_items([('B', [5, 6, 7]), ('A', [8, 9, 10])])
df3 = pd.DataFrame.from_items([('C', [5, 6, 7]), ('D', [8, 9, 10]), ('A',[1,2,3]), ('B',[4,5,7])])
list = ['df1','df2','df3']
df_All = pd.DataFrame()
for i in list:
   # doing something else as well --- 
    df_All = df_All.append(i)

我希望我的df_All仅具有(A& B)，在上面的循环中有没有办法解决此问题?像只追加这两列?

I want my df_All to only have ( A & B ) only, is there a way to this in loop above ? something like append only this two columns ?

推荐答案

如果我了解您想要的内容，则只需从df3中选择列"A"和"B"，然后使用

If I understand what you want then you need to select just columns 'A' and 'B' from df3 and then use pd.concat :

In [35]:

df1 = pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
df2 = pd.DataFrame.from_items([('B', [5, 6, 7]), ('A', [8, 9, 10])])
df3 = pd.DataFrame.from_items([('C', [5, 6, 7]), ('D', [8, 9, 10]), ('A',[1,2,3]), ('B',[4,5,7])])
df_list = [df1,df2,df3[['A','B']]]
pd.concat(df_list, ignore_index=True)
Out[35]:
    A  B
0   1  4
1   2  5
2   3  6
3   8  5
4   9  6
5  10  7
6   1  4
7   2  5
8   3  7

请注意，在您的原始代码中，这是不好的做法:

Note that in your original code this is poor practice:

list = ['df1','df2','df3']

这掩盖了内置类型list，即使它实际上是一个有效的var名称(如df_list)，您也创建了一个字符串列表而不是dfs列表.

This shadows the built in type list plus even if it was actually a valid var name like df_list you've created a list of strings and not a list of dfs.

如果要确定公共列，则可以使用列上的np.intersection方法来确定:

If you want to determine the common columns then you can determine this using the np.intersection method on the columns:

In [39]:

common_cols = df1.columns.intersection(df2.columns).intersection(df3.columns)
common_cols
Out[39]:
Index(['A', 'B'], dtype='object')

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