replace row values with row values from another column if conditions met

Question

I would like to replace row values in pandas.

import pandas as pd
import numpy as np
data = {'type': ['place', 'home', 'place', 'walk', 'place', 'work', 'home', 'place'],'labels': ['NaN', 'NaN', 'shop', 'Nan', 'clinic', 'NaN', 'NaN', 'NaN']}
a = pd.DataFrame(data, columns = ['type', 'labels'])

Is there some possibilities to replace a['type'] with a['labels'] only the conditions if a['labels'] is not np.NaN and a['type'] == 'place' using pandas?

I would prefer to use df.loc[] if it is possible.

ALollz · Accepted Answer · 2018-05-13 17:59:49Z

2

First, the data you provided isn't actually np.NaN, it's just the string 'NaN' or 'Nan' which are handled differently. If that is your starting point you can do:

a['labels'] = a['labels'].str.lower().replace('nan', np.NaN)

Then define your mask and replace:

mask = (a['labels'].notnull()) & (a['type'] == 'place') 
df.loc[mask, 'type'] = df.loc[mask, 'labels']

     type  labels
0   place     NaN
1    home     NaN
2    shop    shop
3    walk     NaN
4  clinic  clinic
5    work     NaN
6    home     NaN
7   place     NaN

Alternatively, with the same mask defined above you can use numpy.where

a['type'] = np.where(mask, a['labels'], a['type'])

edited May 13, 2018 at 17:59

answered May 13, 2018 at 17:51

ALollz

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DonChilliConCarne Over a year ago

you are the best

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