Timeline for How do I create a new column where the values are selected based on an existing column?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Apr 2, 2020 at 7:21 | comment | added | PythonWizard | For me, df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']] is working. | |
| Feb 10, 2020 at 1:51 | comment | added | AMC |
@cheekybastard Or don't, since .iterrows() is notoriously sluggish and the DataFrame shouldn't be modified while iterating.
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| Sep 17, 2019 at 15:28 | comment | added | Paul Rougieux |
Note this nice solution will not work if you need to take replacement values from another series in the data frame, such as df['color_type'] = np.where(df['Set']=='Z', 'green', df['Type'])
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| Jan 14, 2019 at 1:38 | comment | added | cheekybastard | Add iterrows to the dataframe, then you can access multiple columns via row: ['red' if (row['Set'] == 'Z') & (row['Type'] == 'B') else 'green' for index, row in in df.iterrows()] | |
| Jan 1, 2019 at 6:42 | comment | added | Matti |
Can the list comprehension method be used if the condition needs information from multiple columns? I am looking for something like this (this does not work): df['color'] = ['red' if (x['Set'] == 'Z') & (x['Type'] == 'B') else 'green' for x in df]
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| Aug 16, 2017 at 16:49 | history | edited | stackoverflowuser2010 | CC BY-SA 3.0 |
added 20 characters in body
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| Apr 20, 2017 at 16:45 | comment | added | boot-scootin |
Note that, with much larger dataframes (think pd.DataFrame({'Type':list('ABBC')*100000, 'Set':list('ZZXY')*100000})-size), numpy.where outpaces map, but the list comprehension is king (about 50% faster than numpy.where).
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| Jul 2, 2015 at 0:27 | history | answered | cheekybastard | CC BY-SA 3.0 |