Pandas对不支持内联的多个数据帧执行相同的转换

Question

我有一个情况，我想通过一行shift多个数据帧。首先想到的是for循环，但它实际上并不存储数据帧。其他SO帖子建议使用inline=True选项，它适用于某些转换，但不是shift作为内联不是一个选项。显然，我可以通过编写几行df = df.shift(1)来手动完成它，但本着学习最python的方法的精神……下面是一个MRE，显示了一个标准的for循环和一个基于函数的方法:

import pandas as pd
import numpy as np
def shifter(df):
df = df.shift(1)
return df
data1 = {'a':np.random.randint(0, 5, 20), 
'b':np.random.randint(0, 5, 20), 
'c':np.random.randint(0, 5, 20)}
data2 = {'foo':np.random.randint(0, 5, 20), 
'bar':np.random.randint(0, 5, 20), 
'again':np.random.randint(0, 5, 20)}
df1 = pd.DataFrame(data=data1)
df2 = pd.DataFrame(data=data2)
print(df1)      
for x in [df1, df2]:
x = x.shift(1)
print(df1)
for x in [df1, df2]:
x = shifter(x)

print(df1)

Answer 1

您需要重新分配内容，而不是创建一个新变量:

def shifter(df):
return  df.shift(1)
for x in [df1, df2]:
x[:] = shifter(x)

或者，在函数内:

def shifter(df):
df[:] = df.shift(1)
for x in [df1, df2]:
shifter(x)

Answer 2

另一个方法是:

import pandas as pd
import numpy as np
def shifter(df):
a = df.shift(1)
return a
data1 = {'a':np.random.randint(0, 5, 20), 
'b':np.random.randint(0, 5, 20), 
'c':np.random.randint(0, 5, 20)}
data2 = {'foo':np.random.randint(0, 5, 20), 
'bar':np.random.randint(0, 5, 20), 
'again':np.random.randint(0, 5, 20)}
df1 = pd.DataFrame(data=data1)
df2 = pd.DataFrame(data=data2)

Y = []
for x in [df1, df2]:
x = shifter(x)
Y.append(x)

Ydf = pd.concat(Y, axis = 1)    

print(Ydf)

返回

a    b    c  foo  bar  again
0   NaN  NaN  NaN  NaN  NaN    NaN
1   2.0  3.0  3.0  3.0  2.0    1.0
2   3.0  1.0  1.0  4.0  3.0    0.0
3   0.0  1.0  4.0  2.0  2.0    4.0
4   0.0  3.0  0.0  3.0  2.0    1.0
5   4.0  4.0  4.0  2.0  2.0    2.0
6   0.0  1.0  3.0  4.0  2.0    3.0
7   1.0  0.0  3.0  4.0  3.0    3.0
8   4.0  2.0  3.0  0.0  0.0    2.0
9   1.0  1.0  4.0  4.0  3.0    2.0
10  4.0  3.0  4.0  0.0  1.0    4.0
11  0.0  3.0  1.0  1.0  2.0    4.0
12  2.0  0.0  4.0  4.0  3.0    0.0
13  3.0  3.0  4.0  4.0  1.0    2.0
14  4.0  1.0  4.0  0.0  1.0    0.0
15  3.0  4.0  0.0  3.0  0.0    3.0
16  1.0  4.0  2.0  0.0  3.0    1.0
17  3.0  2.0  0.0  0.0  2.0    0.0
18  1.0  1.0  1.0  3.0  3.0    4.0
19  1.0  3.0  2.0  1.0  1.0    0.0