Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
As for __rshift__, __lshift__, __irshift__ and __ilshift__ they don't work in version 2.2.2.
>>> import pandas as pd
>>> s = pd.Series([0, 1, 2])
>>> s <<= 2
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for <<=: 'Series' and 'int'
And numpy implements all methods:
>>> import numpy as np
>>> s = np.array([0, 1, 2])
>>> s <<= 3
>>> s
array([ 0, 8, 16])
Feature Description
Allow for __rshift__, __lshift__, __irshift__ and __ilshift__ calls in Series and DataFrames
>>> import pandas as pd
>>> s = pd.Series([0, 1, 2])
>>> s << 3
0 0
1 8
2 16
>>> s <<= 3
>>> s
0 0
1 8
2 16
Alternative Solutions
For now, the solution is to create a new Series operating over the underlying numpy array
It is curious that the last issue that talks about shift operations is from more than 10 years ago (https://github.com/pandas-dev/pandas/pull/2337). Indeed it's quite a niche feature and fits more computer scientists rather than data scientists. Anyway, I thought about this feature more for completeness and consistency with python operators rather than usefulness.
Feature Type
[X] Adding new functionality to pandas
[ ] Changing existing functionality in pandas
[ ] Removing existing functionality in pandas
Problem Description
Pandas has many in-place operators implemented:
As for
__rshift__
,__lshift__
,__irshift__
and__ilshift__
they don't work in version2.2.2
.And
numpy
implements all methods:Feature Description
Allow for
__rshift__
,__lshift__
,__irshift__
and__ilshift__
calls inSeries
andDataFrames
Alternative Solutions
For now, the solution is to create a new
Series
operating over the underlyingnumpy
arrayAdditional Context
It is curious that the last issue that talks about shift operations is from more than 10 years ago (https://github.com/pandas-dev/pandas/pull/2337). Indeed it's quite a niche feature and fits more computer scientists rather than data scientists. Anyway, I thought about this feature more for completeness and consistency with python operators rather than usefulness.