How to change entire row if NaN present if a single column has NaN

Question

I have a DataFrame like this

                    gauge       satellite
1979-06-23 18:00:00 6.700000    2.484378
1979-06-27 03:00:00 NaN         8.891460
1979-06-27 06:00:00 1.833333    4.053460
1979-06-27 09:00:00 NaN         2.876649
1979-07-31 18:00:00 6.066667    1.438324

I want to obtain a DataFrame Like this

                    gauge       satellite
1979-06-23 18:00:00 6.700000    2.484378
1979-06-27 03:00:00 NaN         NaN
1979-06-27 06:00:00 1.833333    4.053460
1979-06-27 09:00:00 NaN         NaN
1979-07-31 18:00:00 6.066667    1.438324

BENY · Accepted Answer · 2019-07-29 15:06:04Z

7

What I will do reindex

df.dropna().reindex(df.index)

answered Jul 29, 2019 at 15:06

BENY

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2 Comments

Scott Boston Over a year ago

Woooo... now, that is creative. +1

BENY Over a year ago

@modyrockett yw :-) happy coding

anky · Accepted Answer · 2019-07-29 15:07:06Z

3

mask:

df.mask(df.gauge.isna())

                        gauge  satellite
1979-06-23 18:00:00  6.700000   2.484378
1979-06-27 03:00:00       NaN        NaN
1979-06-27 06:00:00  1.833333   4.053460
1979-06-27 09:00:00       NaN        NaN
1979-07-31 18:00:00  6.066667   1.438324

answered Jul 29, 2019 at 15:07

anky

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Comments

Community · Accepted Answer · 2020-06-20 09:12:55Z

2

use np.where to add nan

import numpy as np
df['satellite'] = np.where(df['gauge'].isnull(),np.nan,df['satellite'])

Second solution

use .loc and isnull

df.loc[df['guage'].isnull(),'satellite'] = np.nan

edited Jun 20, 2020 at 9:12

CommunityBot

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answered Jul 29, 2019 at 15:02

tawab_shakeel

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Comments

Carsten · Accepted Answer · 2019-07-29 15:02:06Z

2

You can use np.where:

df['satellite'] = np.where(df['gauge'].isna(), np.NaN, df['satellite'])
df['gauge'] = np.where(df['satellite'].isna(), np.NaN, df['gauge'])

answered Jul 29, 2019 at 15:02

Carsten

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Comments

harpan · Accepted Answer · 2019-07-29 15:02:14Z

2

You need to find if a row has np.nan. .any(1) gives you masking for a row.

df.loc[df.isna().any(1)] = np.nan

Output:

                        gauge       satellite
1979-06-23  18:00:00    6.700000    2.484378
1979-06-27  03:00:00    NaN         NaN
1979-06-27  06:00:00    1.833333    4.053460
1979-06-27  09:00:00    NaN         NaN
1979-07-31  18:00:00    6.066667    1.438324

answered Jul 29, 2019 at 15:02

harpan

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