BUG: Clipping a data frame after selecting only numeric columns does not work

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Code Sample, a copy-pastable example

import pandas as pd
import numpy as np

df1 = pd.DataFrame({'A': [100] * 10, 'B': [100] * 10, 'C': ['abc'] * 10})
df1.loc[:, df1.select_dtypes(include=[np.number]).columns].clip(-90, 90, inplace=True)

Problem description

I am trying to clip the values of all numeric columns in the dataframe.

Initially, the data frame is

	A	B	C
0	100	100	abc
1	100	100	abc
2	100	100	abc
3	100	100	abc
4	100	100	abc
5	100	100	abc
6	100	100	abc
7	100	100	abc
8	100	100	abc
9	100	100	abc

The numeric columns are A and B, so df1.loc[:, df1.select_dtypes(include=[np.number]).columns] selects the required columns.

df1.loc[:, df1.select_dtypes(include=[np.number]).columns].clip(-90, 90, inplace=True) should clip values in A and B to 90.

Dataframe after clipping is:

	A	B	C
0	100	100	abc
1	100	100	abc
2	100	100	abc
3	100	100	abc
4	100	100	abc
5	100	100	abc
6	100	100	abc
7	100	100	abc
8	100	100	abc
9	100	100	abc

Clearly, the values are not getting clipped.

Expected Output

	A	B	C
0	90	90	abc
1	90	90	abc
2	90	90	abc
3	90	90	abc
4	90	90	abc
5	90	90	abc
6	90	90	abc
7	90	90	abc
8	90	90	abc
9	90	90	abc

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None

pandas : 1.0.3 numpy : 1.17.2 pytz : 2019.3 dateutil : 2.8.1 pip : 19.2.3 setuptools : 41.4.0 Cython : 0.29.13 pytest : 5.2.1 hypothesis : None sphinx : 2.4.4 blosc : None feather : None xlsxwriter : 1.2.1 lxml.etree : 4.4.1 html5lib : 1.0.1 pymysql : None psycopg2 : 2.8.4 (dt dec pq3 ext lo64) jinja2 : 2.10.3 IPython : 7.8.0 pandas_datareader: None bs4 : 4.8.0 bottleneck : 1.2.1 fastparquet : None gcsfs : None lxml.etree : 4.4.1 matplotlib : 3.1.1 numexpr : 2.7.0 odfpy : None openpyxl : 3.0.0 pandas_gbq : None pyarrow : None pytables : None pytest : 5.2.1 pyxlsb : None s3fs : None scipy : 1.3.1 sqlalchemy : 1.3.15 tables : 3.5.2 tabulate : 0.8.6 xarray : None xlrd : 1.2.0 xlwt : 1.3.0 xlsxwriter : 1.2.1 numba : 0.45.1

[jreback]

pls read the docs; https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html?highlight=settingwith#indexing-view-versus-copy

doing an inplace operation after indexing works in a copy of the data and this doesn’t do anything

this is generally but not always detectable (in this case it’s not)

[santhisenan]

@dsaxton, shouldn’t SettingWithCopyWarning be thrown then? I think it is confusing otherwise.