Warning when sending only some column names to profile report

[adamrossnelson]

Describe the bug
I was interested in hand-selecting a list of columns to report. Example:

from pathlib import Path

import pandas as pd
import pandas_profiling

# if __name__ == "__main__":
df = pd.read_csv(
    "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
)

df[['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch']].profile_report()

The code above returns the profile. But it also throws an warning.

/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:3781: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  return super(DataFrame, self).rename(**kwargs)

To Reproduce
The above code is reproducible using the Titanic dataset. Other code that also reproduces the error:

df[list(df.columns[0:8])].profile_report(title="Sliced Titanic Dataset")

Version information:

absl-py==0.3.0
alabaster==0.7.10
anaconda-client==1.6.14
anaconda-navigator==1.8.7
anaconda-project==0.8.2
appnope==0.1.0
appscript==1.0.1
asn1crypto==0.24.0
astor==0.7.1
astroid==1.6.3
astropy==3.0.2
atomicwrites==1.3.0
attrs==18.1.0
Babel==2.5.3
backcall==0.1.0
backports.shutil-get-terminal-size==1.0.0
beautifulsoup4==4.6.3
bitarray==0.8.1
bkcharts==0.2
blaze==0.11.3
bleach==2.1.3
bokeh==0.12.16
boto==2.48.0
boto3==1.9.78
botocore==1.12.78
Bottleneck==1.2.1
bz2file==0.98
certifi==2018.4.16
cffi==1.11.5
chardet==3.0.4
click==6.7
cloudpickle==0.5.3
clyent==1.2.2
colorama==0.3.9
conda==4.6.14
conda-build==3.10.5
conda-verify==2.0.0
confuse==1.0.0
contextlib2==0.5.5
contractions==0.0.16
cryptography==2.2.2
cycler==0.10.0
Cython==0.28.2
cytoolz==0.9.0.1
dask==0.17.5
datashape==0.5.4
decorator==4.3.0
Deprecated==1.2.4
distributed==1.21.8
docutils==0.14
entrypoints==0.2.3
et-xmlfile==1.0.1
fastcache==1.0.2
fbprophet==0.3.post2
filelock==3.0.4
flair==0.4.0
Flask==1.0.2
Flask-Cors==3.0.4
future==0.17.1
gast==0.2.0
gensim==3.4.0
gevent==1.3.0
glob2==0.6
gmpy2==2.0.8
graphviz==0.8.4
greenlet==0.4.13
grpcio==1.13.0
h5py==2.7.1
heapdict==1.0.0
html5lib==1.0.1
htmlmin==0.1.12
hyperopt==0.1.1
idna==2.6
imageio==2.3.0
imagesize==1.0.0
importlib-metadata==0.18
inflect==1.0.1
ipykernel==4.8.2
ipython==6.5.0
ipython-genutils==0.2.0
ipywidgets==7.2.1
isort==4.3.4
itsdangerous==0.24
jdcal==1.4
jedi==0.12.0
Jinja2==2.10
jmespath==0.9.3
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.3
jupyter-console==5.2.0
jupyter-core==4.4.0
jupyterlab==0.32.1
jupyterlab-launcher==0.10.5
Keras==2.2.2
Keras-Applications==1.0.4
Keras-Preprocessing==1.0.2
kiwisolver==1.0.1
lazy-object-proxy==1.3.1
llvmlite==0.29.0
locket==0.2.0
lxml==4.2.1
Markdown==2.6.11
MarkupSafe==1.0
matplotlib==3.0.0
mccabe==0.6.1
missingno==0.4.1
mistune==0.8.3
mkl-fft==1.0.0
mkl-random==1.0.1
more-itertools==4.1.0
mpld3==0.3
mpmath==1.0.0
msgpack-python==0.5.6
multipledispatch==0.5.0
mysql-connector-python==8.0.12
navigator-updater==0.2.1
nbconvert==5.3.1
nbformat==4.4.0
networkx==2.1
nltk==3.3
nose==1.3.7
notebook==5.5.0
numba==0.44.1
numexpr==2.6.5
numpy==1.14.3
numpydoc==0.8.0
odo==0.5.1
olefile==0.45.1
openpyxl==2.5.3
packaging==17.1
pandas==0.23.4
pandas-datareader==0.7.0
pandas-profiling==2.0.3
pandocfilters==1.4.2
parso==0.2.0
partd==0.3.8
path.py==11.0.1
pathlib2==2.3.2
patsy==0.5.0
pep8==1.7.1
pexpect==4.6.0
phik==0.9.8
pickleshare==0.7.4
Pillow==5.2.0
pkginfo==1.4.2
pluggy==0.12.0
ply==3.11
prompt-toolkit==1.0.15
protobuf==3.6.0
psutil==5.4.5
psycopg2==2.7.5
ptyprocess==0.5.2
py==1.5.3
pycodestyle==2.4.0
pycosat==0.6.3
pycparser==2.18
pycrypto==2.6.1
pycurl==7.43.0.1
pyflakes==1.6.0
Pygments==2.2.0
pylint==1.8.4
pymongo==3.7.2
pyodbc==4.0.23
pyOpenSSL==18.0.0
pyparsing==2.2.0
PyPDF2==1.26.0
PySocks==1.6.8
pystan==2.18.0.0
pytest==4.6.3
pytest-arraydiff==0.2
pytest-astropy==0.3.0
pytest-doctestplus==0.1.3
pytest-openfiles==0.3.0
pytest-pylint==0.14.0
pytest-remotedata==0.2.1
python-dateutil==2.7.3
pytorch-pretrained-bert==0.3.0
pytz==2018.4
PyWavelets==0.5.2
PyYAML==3.12
pyzmq==17.0.0
QtAwesome==0.4.4
qtconsole==4.3.1
QtPy==1.4.1
regex==2018.8.29
requests==2.19.1
rope==0.10.7
ruamel-yaml==0.15.35
s3transfer==0.1.13
scikit-image==0.13.1
scikit-learn==0.19.1
scipy==1.1.0
seaborn==0.8.1
segtok==1.5.7
selenium==3.14.0
Send2Trash==1.5.0
simplegeneric==0.8.1
singledispatch==3.4.0.3
six==1.11.0
sklearn==0.0
smart-open==1.7.1
snowballstemmer==1.2.1
sortedcollections==0.6.1
sortedcontainers==1.5.10
Sphinx==1.7.4
sphinxcontrib-websupport==1.0.1
spyder==3.2.8
SQLAlchemy==1.2.7
sqlitedict==1.6.0
stata-kernel==1.5.2
statsmodels==0.9.0
sympy==1.1.1
tables==3.4.3
tblib==1.3.2
tensorboard==1.9.0
tensorflow==1.9.0
termcolor==1.1.0
terminado==0.8.1
testpath==0.3.1
toolz==0.9.0
torch==1.0.0
tornado==5.0.2
tqdm==4.26.0
traitlets==4.3.2
typing==3.6.4
unicodecsv==0.14.1
urllib3==1.22
wcwidth==0.1.7
webencodings==0.5.1
Werkzeug==0.14.1
widgetsnbextension==3.2.1
wrapt==1.10.11
xlrd==1.1.0
XlsxWriter==1.0.4
xlwings==0.11.8
xlwt==1.2.0
zict==0.1.3
zipp==0.5.1

Additional context
For my purposes this code produced the desired output without throwing a warning:

df.iloc[:,0:8].profile_report(title="Sliced Titanic Dataset")

[sbrugman]

Thank you for reporting this. As I understand it, pandas does not recommend chaining with indexing.

Reference:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#why-does-assignment-fail-when-using-chained-indexing

[adamrossnelson]

I thought this would likely be a pandas issue - not a pandas-profiling issue.

Happy to have reported. Nice to have it documented for others that might have thought to try a similar strategy.

Thanks for a great package. This is great work.

Closing.