CODEWITHSUNDEEP
pythonpython-3.xpandasscikit-learnone-hot-encoding
Python Developer
Python Developer

Reputation: 633

One-hot-encoding multiple columns in sklearn and naming columns

I have the following code to one-hot-encode 2 columns I have.

# encode city labels using one-hot encoding scheme
city_ohe = OneHotEncoder(categories='auto')
city_feature_arr = city_ohe.fit_transform(df[['city']]).toarray()
city_feature_labels = city_ohe.categories_
city_features = pd.DataFrame(city_feature_arr, columns=city_feature_labels)

phone_ohe = OneHotEncoder(categories='auto')
phone_feature_arr = phone_ohe.fit_transform(df[['phone']]).toarray()
phone_feature_labels = phone_ohe.categories_
phone_features = pd.DataFrame(phone_feature_arr, columns=phone_feature_labels)

What I'm wondering is how I do this in 4 lines while getting properly named columns in the output. That is, I can create a properly one-hot-encoded array by include both columns names in fit_transform but when I try and name the resulting dataframe's columns, it tells me that there is a mismatch between the shape of the indices:

ValueError: Shape of passed values is (6, 50000), indices imply (3, 50000)

For background, both phone and city have 3 values. 

    city    phone
0   CityA   iPhone
1   CityB Android
2   CityB iPhone
3   CityA   iPhone
4   CityC   Android

Upvotes: 4

Views: 29359

Answers (4)

some_newbie
some_newbie

Reputation: 21

this solution gives column names same as in pd.get_dummies(), what is useful IMO

labels = ['Sex', 'Embarked', 'Pclass']

categorical_data = data[labels]

ohe = OneHotEncoder(categories='auto')

feature_arr = ohe
   .fit_transform(categorical_data)
   .toarray()

ohe_labels = ohe.get_feature_names(labels)

features = pd.DataFrame(
               feature_arr,
               columns=ohe_labels)

Upvotes: 2

naimur978
naimur978

Reputation: 154

cat_features = [
    "gender", "cholesterol", "gluc", "smoke", "alco"
]

data = pd.get_dummies(data, columns = cat_features)

Upvotes: 0

MaximeKan
MaximeKan

Reputation: 4221

You you are almost there... Like you said you can add all the columns you want to encode in fit_transform directly.

ohe = OneHotEncoder(categories='auto')
feature_arr = ohe.fit_transform(df[['phone','city']]).toarray()
feature_labels = ohe.categories_

And then you just need to do the following:

feature_labels = np.array(feature_labels).ravel()

Which enables you to name your columns like you wanted:

features = pd.DataFrame(feature_arr, columns=feature_labels)

Upvotes: 13

panktijk
panktijk

Reputation: 1614

Why don't you take a look at pd.get_dummies? Here's how you can encode:

df['city'] = df['city'].astype('category')
df['phone'] = df['phone'].astype('category')
df = pd.get_dummies(df)

Upvotes: 1

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