Pandas 基于拆分另一列添加新列
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Pandas add new columns based on splitting another column
提问by dagg3r
I have a pandas dataframe like the following:
我有一个如下所示的Pandas数据框：
A B
US,65,AMAZON 2016
US,65,EBAY 2016
My goal is to get to look like this:
我的目标是看起来像这样：
A B country code com
US.65.AMAZON 2016 US 65 AMAZON
US.65.AMAZON 2016 US 65 EBAY
I know this question has been asked before hereand herebut noneof them works for me. I have tried:
我知道这里和这里之前有人问过这个问题，但没有一个对我有用。我试过了：
df['country','code','com'] = df.Field.str.split('.')
and
和
df2 = pd.DataFrame(df.Field.str.split('.').tolist(),columns = ['country','code','com','A','B'])
Am I missing something? Any help is much appreciated.
我错过了什么吗？任何帮助深表感谢。
回答by jezrael
You can use splitwith parameter expand=Trueand add one []to left side:
您可以使用splitwith 参数expand=True并[]在左侧添加一个：
df[['country','code','com']] = df.A.str.split(',', expand=True)
Then replace,to .:
然后到：replace,.
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
Another solution with DataFrameconstructor if there are no NaNvalues:
DataFrame如果没有NaN值，则使用构造函数的另一种解决方案：
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
Also you can use column names in constructor, but then concatis necessary:
您也可以在构造函数中使用列名，但这concat是必要的：
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
回答by user10451754
This will not give the output as expected it will only give the df['A'] first value which is 'U'
这不会像预期的那样给出输出，它只会给出 df['A'] 的第一个值，即 'U'
This is okay to create column based on provided data df1=pd.DataFrame([x.split(',') for x in df['A'].tolist()],columns= ['country','code','com'])
可以根据提供的数据创建列 df1=pd.DataFrame([x.split(',') for x in df['A'].tolist()],columns= ['country','code' ,'com'])
instead of for lambda also can be use
也可以使用代替 lambda
回答by Nithin Narla
For getting the new columns I would prefer doing it as following:
为了获得新列，我更愿意按以下方式进行：
df['Country'] = df['A'].apply(lambda x: x[0])
df['Code'] = df['A'].apply(lambda x: x[1])
df['Com'] = df['A'].apply(lambda x: x[2])
As for the replacement of ,with a .you can use the following:
至于替换，用一个. 您可以使用以下内容：
df['A'] = df['A'].str.replace(',','.')
