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Set the values out of the defined interval limits to a given value (f.e. NaN) for a column in pandas data frame

Having a defined interval limits of valid values, all the pandas data frame column values out of it should be set to a given value, f.e. NaN. The values defining limits and data frame contents can be assumed to be of numerical type.

NaN

Having the following limits and data frame:

min = 2
max = 7
df = pd.DataFrame('a': [5, 1, 7, 22],'b': [12, 3 , 10, 9])

a b
0 5 12
1 1 3
2 7 10
3 22 9


Setting the limit on column a would result in:

a

a b
0 5 12
1 NaN 3
2 7 10
3 NaN 9


2 Answers
2

Using where with between

where

between

df.a=df.a.where(df.a.between(min,max),np.nan)
df
Out[146]:
a b
0 5.0 12
1 NaN 3
2 7.0 10
3 NaN 9


Or clip

clip

df.a.clip(min,max)
Out[147]:
0 5.0
1 NaN
2 7.0
3 NaN
Name: a, dtype: float64


clip

df.a.between(min,max)

you can use .loc with between also

.loc

between

import pandas as pd
import numpy as np

df = pd.DataFrame('a': [5, 1, 7, 22],'b': [12, 3 , 10, 9])

min = 2
max = 7

df.loc[~df.a.between(min,max), 'a'] = np.nan


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