Convert categorical data in pandas dataframe

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"))

First, to convert a Categorical column to its numerical codes, you can do this easier with: dataframe['c'].cat.codes.
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
Out[78]:
col1       int64
col2    category
col3    category
dtype: object

Then by using select_dtypes to select the columns, and then applying .cat.codes 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: x.cat.codes)

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

This works for me:

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

Output:

[0, 1, 2, 0]