Check if all dataframe row values are in specified range

Question

How to check for each row in dataframe if all its values are in specified range?

import pandas as pd

new = pd.DataFrame({'a': [1,2,3], 'b': [-5,-8,-3], 'c': [20,0,0]})

For instance range <-5, 5>:

>>    a  b   c
>> 0  1 -5  20  # abs(20) > 5, hence no
>> 1  2 -8   0  # abs(-8) > 5, hence no
>> 2  3 -3   0  # abs(-3) <= 5, hence yes

Solution with iteration

print(['no' if any(abs(i) > 5 for i in a) else 'yes' for _, a in new.iterrows()])

>> ['no', 'no', 'yes']

Asked By: aeiou

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Source

Answer 1

Doing:

out = (df.gt(-5) & df.lt(5)).all(axis=1)
# Or if you just want to supply a single value:
# df.abs().lt(5).all(axis=1)
print(out)

Output:

0    False
1    False
2     True
dtype: bool

You could add this as a new column, and change things to no/yes if desired (which imo is a terrible idea):

df['valid'] = np.where(df.abs().lt(5).all(1), 'yes', 'no')
print(df)

# Output:

   a  b   c valid
0  1 -5  20    no
1  2 -8   0    no
2  3 -3   0   yes

Answered By: BeRT2me

Answer 2

For operations with DataFrames of numbers you should use numpy.

import pandas as pd
import numpy as np


df = pd.DataFrame({'a': [1, 2, 3], 'b': [-5, -8, -3], 'c': [20, 0, 0]})


df_ndarray = df.values

bin_mask = np.where((df_ndarray > 5) | (df_ndarray < -5), 1, 0)

res = np.equal(bin_mask.sum(axis=0), np.arange(len(df.columns)))

Answered By: nikblg

Check if all dataframe row values are in specified range

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