Reset the categories of categorical index in Pandas - pandas

Question

I have a dataframe with a column being categorical.
I remove all the rows having one the categories.
How can I make sure the resulting dataframe has only those categories that exist and does not keep the deleted categories in its index?

Answer 1

df = pd.DataFrame({'color':np.random.choice(['Blue','Green','Brown','Red'], 50)})
df.color = df.color.astype('category')
df.color.head()
Output:
0 Blue
1 Green
2 Blue
3 Green
4 Brown
Name: color, dtype: category
Categories (4, object): [Blue, Brown, Green, Red]
Remove Brown from dataframe and category.
df = df.query('color != "Brown"')
df.color = df.color.cat.remove_categories('Brown')
df.color.head()
Output:
0 Blue
1 Green
2 Blue
3 Green
7 Red
Name: color, dtype: category
Categories (3, object): [Blue, Green, Red]

Answer 2

How can I make sure the resulting dataframe has only those categories that exist and does not keep the deleted categories in its index?
There's (now?) a pandas function doing exactly that: remove_unused_categories
This function only has one parameter inplace, which is deprecated since pandas 1.2.0. Hence, the following example (based on Scott's answer) does not use inplace:
>>> df = pd.DataFrame({'color':np.random.choice(['Blue','Green','Brown','Red'], 50)})
... df.color = df.color.astype('category')
... df.color.head()
0 Green
1 Brown
2 Blue
3 Red
4 Brown
Name: color, dtype: category
Categories (4, object): [Blue, Brown, Green, Red]
>>> df = df[df.color != "Brown"]
... df.color = df.color.cat.remove_unused_categories()
... df.color.head()
0 Green
2 Blue
3 Red
5 Red
6 Green
Name: color, dtype: category
Categories (3, object): [Blue, Green, Red]