pandas 基于拆分另一列添加新列 [英] Pandas add new columns based on splitting another column

问题描述

我有一个如下所示的pandas数据框:

I have a pandas dataframe like the following:

A              B
US,65,AMAZON   2016
US,65,EBAY     2016

我的目标是变得像这样:

My goal is to get to look like this:

A              B      country    code    com
US.65.AMAZON   2016   US         65      AMAZON
US.65.AMAZON   2016   US         65      EBAY

我知道在此处之前已问过此问题和此处，但其中的没有对我有用.我尝试过:

I know this question has been asked before here and here but none of them works for me. I have tried:

df['country','code','com'] = df.Field.str.split('.')

和

df2 = pd.DataFrame(df.Field.str.split('.').tolist(),columns = ['country','code','com','A','B'])

我错过了什么吗?任何帮助都将不胜感激.

Am I missing something? Any help is much appreciated.

推荐答案

您可以使用

You can use split with parameter expand=True and add one [] to left side:

df[['country','code','com']] = df.A.str.split(',', expand=True)

然后 replace ,至.:

df.A = df.A.str.replace(',','.')

print (df)
              A     B country code     com
0  US.65.AMAZON  2016      US   65  AMAZON
1    US.65.EBAY  2016      US   65    EBAY

如果没有NaN值，则使用DataFrame构造函数的另一种解决方案:

Another solution with DataFrame constructor if there are no NaN values:

df[['country','code','com']] = pd.DataFrame([ x.split(',') for x in df['A'].tolist() ])
df.A = df.A.str.replace(',','.')
print (df)
              A     B country code     com
0  US.65.AMAZON  2016      US   65  AMAZON
1    US.65.EBAY  2016      US   65    EBAY

您还可以在构造函数中使用列名，然后 concat 是必需的:

Also you can use column names in constructor, but then concat is necessary:

df1=pd.DataFrame([x.split(',') for x in df['A'].tolist()],columns= ['country','code','com'])
df.A = df.A.str.replace(',','.')
df = pd.concat([df, df1], axis=1)
print (df)
              A     B country code     com
0  US.65.AMAZON  2016      US   65  AMAZON
1    US.65.EBAY  2016      US   65    EBAY

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