Filtering rows of a dataframe based on values in columns

Question

I want to filter the rows of a dataframe that contains values less than ,say 10.

import numpy as np
import pandas as pd
from pprint import pprint
df = pd.DataFrame(np.random.randint(0,100,size=(10, 4)), columns=list('ABCD'))
df = df[df <10]

gives,

A   B    C    D
0  5.0 NaN  NaN  NaN
1  NaN NaN  NaN  NaN
2  0.0 NaN  6.0  NaN
3  NaN NaN  NaN  NaN
4  NaN NaN  NaN  NaN
5  6.0 NaN  NaN  NaN
6  NaN NaN  NaN  NaN
7  NaN NaN  NaN  7.0
8  NaN NaN  NaN  NaN
9  NaN NaN  NaN  NaN

Expected:

0   5  57  87  95
2   0  80   6  82
5   6  33  74  75
7  71  44  60   7

Any suggestions on how to obtain expected result?

jezrael · Accepted Answer · 2019-09-27 05:30:58Z

Use:

np.random.seed(21)
df = pd.DataFrame(np.random.randint(0,100,size=(10, 4)), columns=list('ABCD'))

If want filter by any value of condition, is necessary add DataFrame.any for test at least one True of boolean DataFrame:

df1 = df[(df < 10).any(axis=1)]
print (df1)
    A   B   C   D
0  73  79  56   4
5   5  18  70  50
7   5  80  35  91
9   6  84  90  28

print (df < 10)
       A      B      C      D
0  False  False  False   True
1  False  False  False  False
2  False  False  False  False
3  False  False  False  False
4  False  False  False  False
5   True  False  False  False
6  False  False  False  False
7   True  False  False  False
8  False  False  False  False
9   True  False  False  False

print ((df < 10).any(axis=1))
0     True
1    False
2    False
3    False
4    False
5     True
6    False
7     True
8    False
9     True
dtype: bool

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