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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I have confirmed this bug exists on the master branch of pandas.
Reproducible Example
import pandas as pd
df = (pd.DataFrame({'gender' : ['male', 'female', 'male', 'female', 'male'],
'country': ['BR', 'BR', 'US', 'BR', 'US']})
.astype('category')
)
df.value_counts() # drops all empty categories
# gender country
# female BR 2
# male US 2
# BR 1
# dtype: int64
# also compare
(df
.query("country != 'BR'")
.value_counts()
) # drops non-observed categories
# gender country
# male US 2
# dtype: int64
(df
.query("country != 'BR'")
['gender']
.value_counts()
) # shows 0 for non-observed catgories
# male 2
# female 0
# Name: gender, dtype: int64Issue Description
DataFrame.value_counts() drops any non observed categories while Series.value_counts() shows 0 for non-observed categories.
There was some discussion about the zeroes for Series.value_counts() in this issue #8559, based on that I checked behaviour in R which also keeps the zeros:
gender <- factor(c('male', 'female', 'male', 'female', 'male'))
country <- factor( c( 'BR', 'BR', 'US', 'BR', 'US') )
df <- data.frame(gender=gender, country=country)
table(df)
#> country
#> gender BR US
#> female 2 0
#> male 1 2Expected Behavior
The following work-around gives the expected behaviour:
df.groupby("gender").country.value_counts()
# gender
# female BR 2
# US 0
# male US 2
# BR 1
# Name: country, dtype: int64This fails however for more than two categorical variables.
Installed Versions
INSTALLED VERSIONS
commit : 73c6825
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Sep 16 20:58:47 PDT 2021; root:xnu-6153.141.40.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None