How to use .le() and .ge() when filtering pandas data frame columns?

Question

Here is an example pandas DataFrame:

import pandas as pd
import numpy as np

data = {\"first_column\": [\"item1\", \"item2\", \"item3\", \"item4\", \"item5\"         


        

Answer 1

You can use between() instead for your Series of interest.

df['both'] = df.third_column.between(10, 1000).astype(np.uint8)

Yielding

>>> df

  first_column second_column  third_column  both
0        item1          cat1             5     0
1        item2          cat1             1     0
2        item3          cat1             8     0
3        item4          cat2             3     0
4        item5          cat2           731     1
5        item6          cat2           189     1
6        item7          cat2             9     0

Answer 2

Use & to compound the conditions:

In [28]:
df['both'] = df['third_column'].ge(10) & df['third_column'].le(1000)
df

Out[28]:
  first_column second_column  third_column   both
0        item1          cat1             5  False
1        item2          cat1             1  False
2        item3          cat1             8  False
3        item4          cat2             3  False
4        item5          cat2           731   True
5        item6          cat2           189   True
6        item7          cat2             9  False

Answer 3

In [11]: df['both'] = df.eval("10 <= third_column <= 1000").astype(np.uint8)

In [12]: df
Out[12]:
  first_column second_column  third_column  both
0        item1          cat1             5     0
1        item2          cat1             1     0
2        item3          cat1             8     0
3        item4          cat2             3     0
4        item5          cat2           731     1
5        item6          cat2           189     1
6        item7          cat2             9     0

UPDATE:

In [13]: df.eval("second_column in ['cat2'] and 10 <= third_column <= 1000").astype(np.uint8)
Out[13]:
0    0
1    0
2    0
3    0
4    1
5    1
6    0
dtype: uint8