Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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(optional) I have confirmed this bug exists on the master branch of pandas.
Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.
Minimal Working Example
One of the documentation examples, slightly modified:
import pandas as pd
df = pd.DataFrame({
"lev1": [1, 1, 1, 2, 2, 2],
"lev2": [1, 1, 2, 1, 1, 2],
"lev3": [1, 2, 1, 2, 1, 2],
"lev4": [1, 2, 3, 4, 5, 6],
"values": [0, 1, 2, 3, 4, 5]})
a = ["lev1", "lev2"]
b=["lev3"]
df.pivot(index=a, columns=b)
print(a)Displays:
['lev1', 'lev2', 'lev3']
Problem description
I would expect that my arguments passed to .pivot remain unchanged. I understand that lists, being mutable, can be modified by a function when they are used as arguments. I also understand that I can pass a set here instead of a list to keep it from being modified. However, I think it's reasonable to expect functions to be side-effect free in this case, especially when the documentation asks for a list and does not mention side effects. (FWIW, this behavior does not occur if I set the values= parameter).
The issue cropped up when I wanted to .pivot multiple DataFrames using the same index. To do so, I created a partial function of .pivot with an anonymous list as the index= paramter. Here's an MWE using the dataframe from above:
from functools import partial
alt_pivot = lambda df, index, columns: df.pivot(index=index, columns=columns)
ppivot = partial(alt_pivot, index=["lev1", "lev2"])
print(ppivot)
ppivot(df, columns=b)
print(ppivot)produces the output:
functools.partial(<function <lambda> at 0x00000159C45EAEE8>, index=['lev1', 'lev2'])
functools.partial(<function <lambda> at 0x00000159C45EAEE8>, index=['lev1', 'lev2', 'lev3'])
Expected Output
In the MWE above,
print(a)produces
['lev1', 'lev2']
Output of pd.show_versions()
(Version in my project. Confirmed same behavior in pandas==1.1.4 and master branch.)
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None