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DataFrame.xs("key", axis=1, drop_level=False) still dropping level #19056

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boulund opened this issue Jan 3, 2018 · 3 comments
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DataFrame.xs("key", axis=1, drop_level=False) still dropping level #19056

boulund opened this issue Jan 3, 2018 · 3 comments
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@boulund
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@boulund boulund commented Jan 3, 2018

Code Sample, a copy-pastable example if possible

df = pd.DataFrame([[1, 2, 3], [2, 4, 6]], columns=["type1_subtype1_subsubtype1", "type1_subtype1_subsubtype2", "type2_subtype1_subsubtype2"], index=["rowtype_rowsubtype1_rowlevel1", "row_rowlevel0_rowlevel1"])
df.columns = d.columns.str.split("_", expand=True).rename(["Sample_type", "Subtype", "SubSubtype"])
df.index = d.index.str.split("_", expand=True).rename(["Rowtype", "rowsubtype", "rowlevel"])
print("Incorrect behavior:")
print(df.xs("type1", axis=1, drop_level=False))
print("Expected output:")
print(df.transpose().xs("type1", drop_level=False).transpose())

Problem description

I checked if there was a previous issue about this in the issue tracker but my searches gave nothing similar.

It appears that drop_level=False doesn't work as intended when taking a cross section along axis=1. Current workaround is to transpose the dataframe, then do the cross section, and then transpose it back again (as in my example above).

Expected Output

Sample_type                         type1            
Subtype                          subtype1            
SubSubtype                    subsubtype1 subsubtype2
Rowtype rowsubtype  rowlevel                         
rowtype rowsubtype1 rowlevel1           1           2
row     rowlevel0   rowlevel1           2           4

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.11.1.el7.x86_64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: 0.3.1
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
None

@tothandor
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@tothandor tothandor commented Mar 1, 2018

I can confirm that for Pandas version 0.22 (Python 2.7 64-bit).

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@simonjayhawkins
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@simonjayhawkins simonjayhawkins commented Dec 21, 2018

#6507 maybe related.

drop_level=False also ignored on index when fully specified tuple..

import pandas as pd
df = pd.DataFrame([[1, 2, 3], [2, 4, 6]], columns=["type1_subtype1_subsubtype1", "type1_subtype1_subsubtype2", "type2_subtype1_subsubtype2"], index=["rowtype_rowsubtype1_rowlevel1", "row_rowlevel0_rowlevel1"])
df.columns = df.columns.str.split("_", expand=True).rename(["Sample_type", "Subtype", "SubSubtype"])
df.index = df.index.str.split("_", expand=True).rename(["Rowtype", "rowsubtype", "rowlevel"])
df.xs(('row','rowlevel0','rowlevel1'), drop_level=False)
Sample_type  Subtype   SubSubtype 
type1        subtype1  subsubtype1    2
                       subsubtype2    4
type2        subtype1  subsubtype2    6
Name: (row, rowlevel0, rowlevel1), dtype: int64

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@bicarlsen
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@bicarlsen bicarlsen commented Apr 28, 2019

I am having the same issue for Pandas version 0.23.0 (Python v 3.6.5 64-bit)

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@jbrockmendel jbrockmendel added this to MultiIndex in Indexing Feb 19, 2020
@jreback jreback added this to the 1.2 milestone Nov 13, 2020
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