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As per Categorical Data - Operations, by default groupby will show “unused” categories:
In [118]: cats = pd.Categorical(["a","b","b","b","c","c","c"], categories=["a","b","c","d"])
In [119]: df = pd.DataFrame({"cats":cats,"values":[1,2,2,2,3,4,5]})
In [120]: df.groupby("cats").mean()
Out[120]:
values
cats
a 1.0
b 2.0
c 4.0
d NaN
How to obtain the result with the “unused” categories dropped? e.g.
values
cats
a 1.0
b 2.0
c 4.0
747
To remove the specified categories from CategoricalIndex, use the remove_categories() method in Pandas.
This is done using the groupby() method given in pandas. It returns all the combinations of groupby columns. Along with group by we have to pass an aggregate function with it to ensure that on what basis we are going to group our variables. Some aggregate function are mean(), sum(), count() etc.
Groupby preserves the order of rows within each group.
Since version 0.23 you can specify observed=True in the groupby call to achieve the desired behavior.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html
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