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  • python - Using Scikit-Learn OneHotEncoder with a Pandas DataFrame . . .
    Do not think there is a simpler way to do so at the moment to go from an original pandas DataFrame to a one-hot encoded DataFrame # Apply one-hot encoder to each column with categorical data OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False) OH_cols_train = pd DataFrame(OH_encoder fit_transform(X_train[low_cardinality_cols])) OH
  • How to give column names after one-hot encoding with sklearn?
    BUT THE PROBLEM IS, I need column names after one hot encoder For example, column A with categorical values before encoding A = [1,2,3,4, ] It should be like that after encoding, A-1, A-2, A-3 Anyone know how to assign column names to (old column names -value name or number) after one hot encoding Here is my one hot encoding and it's output;
  • python - OneHotEncoder - encoding only some of categorical variable . . .
    from sklearn preprocessing import LabelEncoder label_encoder = LabelEncoder() data['seniority'] = label_encoder fit_transform(data['seniority']) from sklearn preprocessing import OneHotEncoder one_hot_encoder = OneHotEncoder(categorical_features=[1]) data = one_hot_encoder fit_transform(data values) But then I am getting this error
  • How to one-hot-encode from a pandas column containing a list?
    I would like to break down a pandas column consisting of a list of elements into as many columns as there are unique elements i e one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence) For example, taking dataframe df
  • python - One hot encoding of string categorical features - Stack Overflow
    But the one hot encoder doesn't support strings as features; it only discretizes integers So then you would use a LabelEncoder, which would encode the strings into integers But then you have to apply the label encoder into each of the columns and store each one of these label encoders (as well as the columns they were applied on)
  • python - How to use OneHotEncoder for multiple columns and . . .
    features_to_encode = [---list of features to one hot encode--] enc = My_encoder() enc fit(X_train,features_to_encode) X_train = enc transform(X_train) X_test = enc transform(X_test) It returns dataframe with columns names So, covers both the disadvantages of OneHotEncoder and pd get_dummies()
  • python - save and load one hot encoding for ML - Stack Overflow
    The problem is that new data was not part of initial encoding so I need to hot encode it in same way as learning data on my PC I found out that I can save the encoder (sklearn preprocessing -> OneHotEncoder) But I cannot manage to get the data into the correct format
  • How to add your own categories into the OneHotEncoder
    When this parameter is set to ‘ignore’ and an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros In the inverse transform, an unknown category will be denoted as None





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