Check if dataframe column is Categorical

Question or problem about Python programming:

I can’t seem to get a simple dtype check working with Pandas’ improved Categoricals in v0.15+. Basically I just want something like is_categorical(column) -> True/False.

import pandas as pd
import numpy as np
import random

df = pd.DataFrame({
    'x': np.linspace(0, 50, 6),
    'y': np.linspace(0, 20, 6),
    'cat_column': random.sample('abcdef', 6)
})
df['cat_column'] = pd.Categorical(df2['cat_column'])

We can see that the dtype for the categorical column is ‘category’:

df.cat_column.dtype
Out[20]: category

And normally we can do a dtype check by just comparing to the name
of the dtype:

df.x.dtype == 'float64'
Out[21]: True

But this doesn’t seem to work when trying to check if the x column
is categorical:

df.x.dtype == 'category'
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
 in ()
----> 1 df.x.dtype == 'category'

TypeError: data type "category" not understood

Is there any way to do these types of checks in pandas v0.15+?

How to solve the problem:

Solution 1:

Use the name property to do the comparison instead, it should always work because it’s just a string:

>>> import numpy as np
>>> arr = np.array([1, 2, 3, 4])
>>> arr.dtype.name
'int64'

>>> import pandas as pd
>>> cat = pd.Categorical(['a', 'b', 'c'])
>>> cat.dtype.name
'category'

So, to sum up, you can end up with a simple, straightforward function:

def is_categorical(array_like):
    return array_like.dtype.name == 'category'

Solution 2:

First, the string representation of the dtype is 'category' and not 'categorical', so this works:

In [41]: df.cat_column.dtype == 'category'
Out[41]: True

But indeed, as you noticed, this comparison gives a TypeError for other dtypes, so you would have to wrap it with a try .. except .. block.

---

Other ways to check using pandas internals:

In [42]: isinstance(df.cat_column.dtype, pd.api.types.CategoricalDtype)
Out[42]: True

In [43]: pd.api.types.is_categorical_dtype(df.cat_column)
Out[43]: True

For non-categorical columns, those statements will return False instead of raising an error. For example:

In [44]: pd.api.types.is_categorical_dtype(df.x)
Out[44]: False

For much older version of pandas, replace pd.api.types in the above snippet with pd.core.common.

Solution 3:

In my pandas version (v1.0.3), a shorter version of joris’ answer is available.

df = pd.DataFrame({'noncat': [1, 2, 3], 'categ': pd.Categorical(['A', 'B', 'C'])})

print(isinstance(df.noncat.dtype, pd.CategoricalDtype))  # False
print(isinstance(df.categ.dtype, pd.CategoricalDtype))   # True

print(pd.CategoricalDtype.is_dtype(df.noncat)) # False
print(pd.CategoricalDtype.is_dtype(df.categ))  # True

Solution 4:

Just putting this here because pandas.DataFrame.select_dtypes() is what I was actually looking for:

df['column'].name in df.select_dtypes(include='category').columns

Thanks to @Jeff.

Solution 5:

I ran into this thread looking for the exact same functionality, and also found out another option, right from the pandas documentation here.

It looks like the canonical way to check if a pandas dataframe column is a categorical Series should be the following:

hasattr(column_to_check, 'cat')

So, as per the example given in the initial question, this would be:

hasattr(df.x, 'cat') #True

Hope this helps!