[英]Dropping column during for loop - Pandas
I have two basic DataFrames, and I combine them into a list called dfCombo: 我有两个基本的DataFrame,并将它们组合到一个名为dfCombo的列表中:
import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'])
df2 = pd.DataFrame(np.arange(12,24).reshape(3,4), columns=['A', 'B', 'C', 'D'])
dfCombo = [df, df2]
They are both 3x4 DF's with 4 columns A, B, C, D. 它们都是具有4列A,B,C,D的3x4 DF。
I am able to use a for loop to add a column to both the DF with the following code: 我可以使用for循环通过以下代码将列添加到DF中:
for df3 in dfCombo:
df3['E'] = df3['A'] + df3['B']
With this both df and df2 will both have an new column E. However when I try to drop a column using this method with the below code, no columns are dropped: 与此同时,df和df2都将具有新的列E。但是,当我尝试使用以下代码使用此方法删除列时,不会删除任何列:
for df3 in dfCombo:
df3 = df3.drop('B', axis = 1)
or 要么
for df3 in dfCombo:
df3 = df3.drop(columns = ['B'])
If I use the same code on a single DF the column is dropped: 如果我在单个DF上使用相同的代码,则会删除该列:
df2 = df2.drop('B', axis = 1)
or 要么
df2 = df2.drop(columns = ['B'])
If you could help me understand what is going on I would be most appreciative. 如果您能帮助我了解正在发生的事情,我将非常感激。
You need to use inplace=True
: 您需要使用inplace=True
:
for df3 in dfCombo:
df3.drop('B', axis = 1, inplace=True)
Which returns: 哪个返回:
A C D E
0 0 2 3 1
1 4 6 7 9
2 8 10 11 17
A C D E
0 12 14 15 25
1 16 18 19 33
2 20 22 23 41
The default inplace=False
is intended for assigning back to the original dataframe, because it returns a new copy. 默认的inplace=False
用于分配回原始数据帧,因为它返回一个新副本。 However inplace=True
operates on the same copy and returns None
, therefore there is no need to assign back to the original dataframe. 但是inplace=True
在同一副本上操作并返回None
,因此无需分配回原始数据帧。
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