我有一個(gè)MultiIndex pandas DataFrame,我想在其中的一個(gè)列中應(yīng)用一個(gè)函數(shù),并將結(jié)果分配給同一列.

In [1]:

import numpy as np

import pandas as pd

cols = ['One', 'Two', 'Three', 'Four', 'Five']

df = pd.DataFrame(np.array(list('ABCDEFGHIJKLMNO'), dtype='object').reshape(3,5), index = list('ABC'), columns=cols)

df.to_hdf('/tmp/test.h5', 'df')

df = pd.read_hdf('/tmp/test.h5', 'df')

df

Out[1]:

One Two Three Four Five

A A B C D E

B F G H I J

C K L M N O

3 rows × 5 columns

In [2]:

df.columns = pd.MultiIndex.from_arrays([list('UUULL'), ['One', 'Two', 'Three', 'Four', 'Five']])

df['L']['Five'] = df['L']['Five'].apply(lambda x: x.lower())

df

-c:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.

Try using .loc[row_index,col_indexer] = value instead

Out[2]:

U L

One Two Three Four Five

A A B C D E

B F G H I J

C K L M N O

3 rows × 5 columns

In [3]:

df.columns = ['One', 'Two', 'Three', 'Four', 'Five']

df

Out[3]:

One Two Three Four Five

A A B C D E

B F G H I J

C K L M N O

3 rows × 5 columns

In [4]:

df['Five'] = df['Five'].apply(lambda x: x.upper())

df

Out[4]:

One Two Three Four Five

A A B C D E

B F G H I J

C K L M N O

3 rows × 5 columns

正如您所看到的,該函數(shù)未應(yīng)用于列,我猜是因?yàn)槲沂盏酱司?#xff1a;

-c:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.

Try using .loc[row_index,col_indexer] = value instead

奇怪的是,這個(gè)錯(cuò)誤有時(shí)只會(huì)發(fā)生,我無(wú)法理解它何時(shí)發(fā)生,何時(shí)不發(fā)生.

我設(shè)法應(yīng)用函數(shù)切片數(shù)據(jù)框與.loc作為建議的警告：

In [5]:

df.columns = pd.MultiIndex.from_arrays([list('UUULL'), ['One', 'Two', 'Three', 'Four', 'Five']])

df.loc[:,('L','Five')] = df.loc[:,('L','Five')].apply(lambda x: x.lower())

df

Out[5]:

U L

One Two Three Four Five

A A B C D e

B F G H I j

C K L M N o

3 rows × 5 columns

但是我想理解為什么這種行為在進(jìn)行類(lèi)似dict的切片時(shí)會(huì)發(fā)生(例如df [‘L’] [‘Five’])而不是在使用.loc切片時(shí).

注意：DataFrame來(lái)自一個(gè)沒(méi)有多索引的HDF文件,這可能是奇怪行為的原因？

編輯：我正在使用Pandas v.0.13.1和NumPy v.1.8.0

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