[英]Splitting pandas data frame based on column name
Is there a way to split a pandas data frame based on the column name? 有没有办法根据列名拆分pandas数据框? As an example consider the data frame has the following columns df = ['A_x', 'B_x', 'C_x', 'A_y', 'B_y', 'C_y']
and I want to create two data frames X = ['A_x', 'B_x', 'C_x']
and Y = ['A_y', 'B_y', 'C_y']
. 作为一个例子,考虑数据帧有以下列df = ['A_x', 'B_x', 'C_x', 'A_y', 'B_y', 'C_y']
,我想创建两个数据帧X = ['A_x', 'B_x', 'C_x']
和Y = ['A_y', 'B_y', 'C_y']
。
I know there is a possibility to do this: 我知道有可能这样做:
d = {'A': df.A_x, 'B': df.B_x, 'C': df.B_x}
X = pd.DataFrame (data=d)
but this would not be ideal as in my case I have 2200 columns in df
. 但这不是理想的,因为在我的情况下,我在df
有2200列。 Is there a more elegant solution? 有更优雅的解决方案吗?
You could use df.filter(regex=...)
: 你可以使用df.filter(regex=...)
:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(2, 10),
columns='Time A_x A_y A_z B_x B_y B_z C_x C_y C-Z'.split())
X = df.filter(regex='_x')
Y = df.filter(regex='_y')
yields 产量
In [15]: X
Out[15]:
A_x B_x C_x
0 -0.706589 1.031368 -0.950931
1 0.727826 0.879408 -0.049865
In [16]: Y
Out[16]:
A_y B_y C_y
0 -0.663647 0.635540 -0.532605
1 0.326718 0.189333 -0.803648
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