Convert categorical data in pandas dataframe

Question

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

col1        int64
col2        int64
col3        category
col4        category
col5        categ         


        

Answer 1

This works for me:

pandas.factorize( ['B', 'C', 'D', 'B'] )[0]

Output:

[0, 1, 2, 0]

Answer 2

You can do it less code like below :

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

f['col1'] =f['col1'].astype('category').cat.codes
f['col2'] =f['col2'].astype('category').cat.codes
f['col3'] =f['col3'].astype('category').cat.codes

f

Answer 3

What I do is, I replace values.

Like this-

df['col'].replace(to_replace=['category_1', 'category_2', 'category_3'], value=[1, 2, 3], inplace=True)

In this way, if the col column has categorical values, they get replaced by the numerical values.

Answer 4

If your concern was only that you making a extra column and deleting it later, just dun use a new column at the first place.

dataframe = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':list('abcab'),  'col3':list('ababb')})
dataframe.col3 = pd.Categorical.from_array(dataframe.col3).codes

You are done. Now as Categorical.from_array is deprecated, use Categorical directly

dataframe.col3 = pd.Categorical(dataframe.col3).codes

If you also need the mapping back from index to label, there is even better way for the same

dataframe.col3, mapping_index = pd.Series(dataframe.col3).factorize()

check below

print(dataframe)
print(mapping_index.get_loc("c"))