python - Setting nan to rows in pandas dataframe based on column value -


using:

import numpy np import pandas pd import matplotlib.pyplot plt  = pd.read_csv('file.csv', na_values=['-9999.0'], decimal=',') a.index = pd.to_datetime(a[['year', 'month', 'day', 'hour', 'minute']]) pd.options.mode.chained_assignment = none

the dataframe like:

index                  b       c      d 2016-07-20 18:00:00 9   4.0     nan    2 2016-07-20 19:00:00 9   2.64    0.0    3 2016-07-20 20:00:00 12  2.59    0.0    1 2016-07-20 21:00:00 9   4.0     nan    2

the main objective set np.nan entire row if value on column 9 , on d column 2 @ same time, exemple:

output expectation

index                  b       c      d 2016-07-20 18:00:00 nan nan     nan    nan 2016-07-20 19:00:00 9   2.64    0.0     3 2016-07-20 20:00:00 12  2.59    0.0     2 2016-07-20 21:00:00 nan nan     nan    nan

would thankful if help.

option 1
opposite of @jezrael's mask solution.

a.where(a.a.ne(9) | a.d.ne(2))                              b    c    d index                                     2016-07-20 18:00:00   nan   nan  nan  nan 2016-07-20 19:00:00   9.0  2.64  0.0  3.0 2016-07-20 20:00:00  12.0  2.59  0.0  1.0 2016-07-20 21:00:00   nan   nan  nan  nan

option 2
pd.dataframe.reindex

a[a.a.ne(9) | a.d.ne(2)].reindex(a.index)                              b    c    d index                                     2016-07-20 18:00:00   nan   nan  nan  nan 2016-07-20 19:00:00   9.0  2.64  0.0  3.0 2016-07-20 20:00:00  12.0  2.59  0.0  1.0 2016-07-20 21:00:00   nan   nan  nan  nan

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