如何在函数中修改pandas DataFrame，以便调用者可以看到更改? [英] How to modify a pandas DataFrame in a function so that changes are seen by the caller?

问题描述

我发现自己对各种[pandas][1] DataFrame执行重复的任务，所以我做了一个函数来进行处理.如何在函数process_df(df)中修改df，以便调用者看到所有更改(不分配返回值)?

I find myself doing repetitive tasks to various [pandas][1] DataFrames, so I made a function to do the processing. How do I modify df in the function process_df(df) so that the caller sees all changes (without assigning a return value)?

代码的简化版本:

def process_df(df):
    df.columns = map(str.lower, df.columns)

df = pd.DataFrame({'A': [1], 'B': [2]})
process_df(df)
print df

   A  B 
0  1  2

编辑新代码:

def process_df(df):
    df = df.loc[:, 'A']

df = pd.DataFrame({'A': [1], 'B': [2]})
process_df(df)
print df

   A  B 
0  1  2

推荐答案

使用ix，loc，iloc等为DataFrame编制索引将返回基础数据的视图(这是读取操作) ).为了修改框架的内容，您将需要使用就地转换.例如，

Indexing a DataFrame using ix, loc, iloc, etc. returns a view of the underlying data (it is a read operation). In order to modify the contents of the frame you will need to use in-place transforms. For example,

def process_df(df):
    # drop all columns except for A
    df.drop(df.columns[df.columns != 'A'], axis=1, inplace=True)

df = DataFrame({'A':[1,2,3], 'B':[1,2,3]})
process_df(df)

要更改列的顺序，您可以执行以下操作:

To change the order of columns, you can do something like this:

def process_df(df):
    # swap A and B
    df.columns = ['B', 'A']
    df[['B', 'A']] = df[['A', 'B']]

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