One hot encoding categorical features - Sparse form only

Question

I have a dataframe that has int and categorical features. The categorical features are 2 types: numbers and strings.

I was able to One hot encode columns that were int and categorical that were numbers. I get an error when I try to One hot encode categorical columns that are strings.

ValueError: could not convert string to float: '13367cc6'

Since the dataframe is huge with high cardinality so I only want to convert it to a Sparse form. I would prefer a solution that uses from sklearn.preprocessing import OneHotEncoder since I am familiar with it.

I checked other questions too but none of them addresses what I am asking.

data = [[623, 'dog', 4], [123, 'cat', 2],[623, 'cat', 1], [111, 'lion', 6]]

The above dataframe contains 4 rows and 3 columns

Column names - ['animal_id', 'animal_name', 'number']

Assume that animal_id and animal_name are stored in pandas as category and number as int64 dtype.

Answer 1

Assuming you have the following DF:

In [124]: df
Out[124]:
   animal_id animal_name  number
0        623         dog       4
1        123         cat       2
2        623         cat       1
3        111        lion       6

In [125]: df.dtypes
Out[125]:
animal_id         int64
animal_name    category
number            int64
dtype: object

first save animal_name column (if you need it in future):

In [126]: animal_name = df['animal_name']

convert animal_name column to categorical (memory saving) numeric column:

In [127]: df['animal_name'] = df['animal_name'].cat.codes.astype('category')

In [128]: df
Out[128]:
   animal_id animal_name  number
0        623           1       4
1        123           0       2
2        623           0       1
3        111           2       6

In [129]: df.dtypes
Out[129]:
animal_id         int64
animal_name    category
number            int64
dtype: object

Now OneHotEncoder should work:

In [130]: enc = OneHotEncoder()

In [131]: enc.fit(df)
Out[131]:
OneHotEncoder(categorical_features='all', dtype=<class 'numpy.float64'>,
       handle_unknown='error', n_values='auto', sparse=True)

In [132]: X = enc.fit(df)

In [134]: X.n_values_
Out[134]: array([624,   3,   7])

In [135]: enc.feature_indices_
Out[135]: array([  0, 624, 627, 634], dtype=int32)