Pandas isin() function for continuous intervals

Series objects (including dataframe columns) have a between method:

>>> s = pd.Series(np.linspace(0, 20, 8))
>>> s
0     0.000000
1     2.857143
2     5.714286
3     8.571429
4    11.428571
5    14.285714
6    17.142857
7    20.000000
dtype: float64
>>> s.between(1, 14.5)
0    False
1     True
2     True
3     True
4     True
5     True
6    False
7    False
dtype: bool

This works:

df['numdum'] = (df.number >= 1) & (df.number <= 10)

You could also do the same thing with cut(). No real advantage if there are just two categories:

>>> df['numdum'] = pd.cut( df['number'], [-99,10,99], labels=[1,0] )

   number numdum
0       8      1
1       9      1
2      10      1
3      11      0
4      12      0
5      13      0
6      14      0

But it's nice if you have multiple categories:

>>> df['numdum'] = pd.cut( df['number'], [-99,8,10,99], labels=[1,2,3] )

   number numdum
0       8      1
1       9      2
2      10      2
3      11      3
4      12      3
5      13      3
6      14      3

Labels can be True and False if that is preferred, or you can not specify the label at all, in which case the labels will contain info on the cutoff points.