How to select rows based categories in Pandas dataframe

Question

this is really trivial but can't believe I have wandered around for an hour and still can find the answer, so here you are:

    df = pd.DataFrame({"cats":["a","b"], "vals":[1,2]})
    df.cats = df.cats.astype("category")
    df

My problem is how to select the row that its "cats" columns's category is "a". I know that df.loc[df.cats == "a"] will work but it's based on equality on element. Is there a way to select based on levels of category?

Answer 1

This works:

df.cats[df.cats=='a']

UPDATE

The question was updated. New solution:

df[df.cats.cat.categories == ['a']]

Answer 2

You can query the categorical list using df.cats.cat.categories which prints output as

Index(['a', 'b'], dtype='object')

For this case, to select a row with category of 'a' which is df.cats.cat.categories['0'] , you just use:

df[df.cats == df.cats.cat.categories[0]]

Answer 3

df[df.cats.cat.categories == df.cats.cat.categories[0]]

Answer 4

For those who are trying to filter rows based on a numerical categorical column:

df[df['col'] == pd.Interval(46, 53, closed='right')]

This would keep the rows where the col column has category (46, 53] .

This kind of categorical column is common when you discretize numerical columns using pd.qcut() method.

Answer 5

Using the isin function to create a boolean index is an approach that will extend to multiple categories, similar to R's %in% operator.

# will return desired subset
df[df.cats.isin(['a'])]

# can be extended to multiple categories
df[df.cats.isin(['a', 'b'])]