Replace dataframe column negative values with nan, in method chain

Question

I want to replace by np.nan all the negative numbers that are in column 'b'

Here's the sample frame:

pd.DataFrame({'a': [1, 2] , 'b': [-3, 4], 'c': [5, -6]})

See this question for in-place and non-method solutions.

akuiper · Accepted Answer · 2018-01-18 02:02:14Z

6

If assign counts as a method on df, you can recalculate the column b and assign it to df to replace the old column:

df = pd.DataFrame({'a': [1, 2] , 'b': [-3, 4], 'c': [5, -6]})

df.assign(b = df.b.where(df.b.ge(0)))
#   a    b  c
#0  1  NaN  5
#1  2  4.0 -6

For better chaining behavior, you can use lambda function with assign:

df.assign(b = lambda x: x.b.where(x.b.ge(0)))

answered Jan 18, 2018 at 2:02

akuiper

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Comments

Jan · Accepted Answer · 2018-12-29 16:00:36Z

3

You can use the loc function.To replace the all the negative values and leverage numpy nan to replace them. sample code look like.

import numpy as np
df=pd.DataFrame({'a': [1, 2] , 'b': [-3, 4], 'c': [5, -6]})
df.loc[~(df['b'] > 0), 'b']=np.nan

Jan

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answered Jan 18, 2018 at 2:22

Avind

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