pandas split list into columns with regex

Question

I have a string list:

content
01/09/15, 10:07 - message1
01/09/15, 10:32 - message2
01/09/15, 10:44 - message3

I want a data frame, like:

     date                message
01/09/15, 10:07          message1
01/09/15, 10:32          message2
01/09/15, 10:44          message3

Considering the fact that all my strings in the list starts in that format, I can just split by -, but I rather look for a smarter way to do so.

history = pd.DataFrame([line.split(" - ", 1) for line in content], columns=['date', 'message'])

(I'll convert the date to date time afterwards)

Any help would be appreciated.

Zero · Accepted Answer · 2017-10-25 09:40:21Z

24

You can use str.extract - where named groups can become column names

In [5827]: df['content'].str.extract('(?P<date>[\s\S]+) - (?P<message>[\s\S]+)', 
                                     expand=True)
Out[5827]:
              date   message
0  01/09/15, 10:07  message1
1  01/09/15, 10:32  message2
2  01/09/15, 10:44  message3

Details

In [5828]: df
Out[5828]:
                      content
0  01/09/15, 10:07 - message1
1  01/09/15, 10:32 - message2
2  01/09/15, 10:44 - message3

answered Oct 25, 2017 at 9:40

Zero

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1 Comment

LNI Over a year ago

This is brilliant, thanks! By the way, if the columns are being extracted so they can be tacked on to the existing dataframe, we can just follow this up with df = pd.concat([df, df_e], axis=1) where df_e is the extracted dataframe.

jezrael · Accepted Answer · 2017-10-25 09:24:57Z

8

Use str.split by \s+-\s+ - \s+ is one or more whitespaces:

df[['date','message']] = df['content'].str.split('\s+-\s+', expand=True)
print (df)
                      content             date   message
0  01/09/15, 10:07 - message1  01/09/15, 10:07  message1
1  01/09/15, 10:32 - message2  01/09/15, 10:32  message2
2  01/09/15, 10:44 - message3  01/09/15, 10:44  message3

If need remove content column add DataFrame.pop:

df[['date','message']] = df.pop('content').str.split('\s+-\s+', expand=True)

print (df)
              date   message
0  01/09/15, 10:07  message1
1  01/09/15, 10:32  message2
2  01/09/15, 10:44  message3

answered Oct 25, 2017 at 9:24

jezrael

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