dtoolkit.accessor.series.bin#

dtoolkit.accessor.series.bin(s: Series, /, *args, **kwargs) → Series[source]#

Bin values into discrete intervals.

A sugary syntax wraps cut():

pd.cut(s, *args, **kwargs)

Parameters:

*args, **kwargs: See the documentation for pandas.cut() for complete details on the positional arguments and the keyword arguments.

Returns:

Series(Categorical)

See also

pandas.cut
dtoolkit.accessor.series.jenks_bin

Notes

This method could be called via s.bin or s.cut.

Examples

>>> import dtoolkit
>>> import pandas as pd

Create score samples:

>>> s = pd.Series([100, 10, 50, 20, 90, 60])

Bin score to rank level:

(0, 60] -> E

(60, 70] -> D

(70, 80] -> C

(80, 90] -> B

(90, 100] -> A

>>> s.bin([0, 60, 70, 80, 90, 100], labels=['E', 'D', 'C', 'B', 'A'], right=True)
0    A
1    E
2    E
3    E
4    B
5    E
dtype: category
Categories (5, object): ['E' < 'D' < 'C' < 'B' < 'A']