Fix Python – Convert categorical data in pandas dataframe


Asked By – Gilaztdinov Rustam

I have a dataframe with this type of data (too many columns):

col1        int64
col2        int64
col3        category
col4        category
col5        category

Columns look like this:

Name: col3, dtype: category
Categories (8, object): [B, C, E, G, H, N, S, W]

I want to convert all the values in each column to integer like this:

[1, 2, 3, 4, 5, 6, 7, 8]

I solved this for one column by this:

dataframe['c'] = pandas.Categorical.from_array(dataframe.col3).codes

Now I have two columns in my dataframe – old col3 and new c and need to drop old columns.

That’s bad practice. It works but in my dataframe there are too many columns and I don’t want do it manually.

How can I do this more cleverly?

Now we will see solution for issue: Convert categorical data in pandas dataframe


First, to convert a Categorical column to its numerical codes, you can do this easier with: dataframe['c']
Further, it is possible to select automatically all columns with a certain dtype in a dataframe using select_dtypes. This way, you can apply above operation on multiple and automatically selected columns.

First making an example dataframe:

In [75]: df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':list('abcab'),  'col3':list('ababb')})

In [76]: df['col2'] = df['col2'].astype('category')

In [77]: df['col3'] = df['col3'].astype('category')

In [78]: df.dtypes
col1       int64
col2    category
col3    category
dtype: object

Then by using select_dtypes to select the columns, and then applying on each of these columns, you can get the following result:

In [80]: cat_columns = df.select_dtypes(['category']).columns

In [81]: cat_columns
Out[81]: Index([u'col2', u'col3'], dtype='object')

In [83]: df[cat_columns] = df[cat_columns].apply(lambda x:

In [84]: df
   col1  col2  col3
0     1     0     0
1     2     1     1
2     3     2     0
3     4     0     1
4     5     1     1

This question is answered By – joris

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