im building one hot encoding function from and pandas dataframe and cant figure out how to get the data back into the dataframe. I get :

"IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices

How do I reintegrate this back into pandas data frame?

def one_hot_encoder (features, df_to_encode):
    """encoder to encoder  

    Parameters:
    features (list): features to normalise
    df_to_encode (pandas dataframe): dataframe to encode

    Returns:
    dataframe: dataframe to encode 
    """
   from sklearn.preprocessing import OneHotEncoder    
   for column in features: 
        # one hot encoder 
        enc = OneHotEncoder(sparse=False)
        column_norm = column   "_encoded"
        df = enc.fit_transform(df_to_encode[[column]])

    return df

columns_to_one_hot_encode = ["type"]
df = one_hot_encoder(columns_to_one_hot_encode,df)

The data im using is from https://www.kaggle.com/ealaxi/paysim1

CodePudding user response：

You don't need sklearn, you can simply use pandas.get_dummies

import pandas as pd

def one_hot_encoder (features, df_to_encode):
    """encoder to encoder  

    Parameters:
    features (list): features to normalise
    df_to_encode (pandas dataframe): dataframe to encode

    Returns:
    dataframe: dataframe to encode 
    """
    return pd.get_dummies(df_to_encode, columns=features)

columns_to_one_hot_encode = ["type"]
df = one_hot_encoder(columns_to_one_hot_encode, df)

CodePudding user response：

You can use the get_feature_names that is built-in SciKit's OneHotEncoder and then subsequently drop the old column. In this way, you can still use OneHotEncoder instead of pd.get_dummies

import pandas as pd

def one_hot_encoder (features, df_to_encode):
    """encoder to encoder  

    Parameters:
    features (list): features to normalise
    df_to_encode (pandas dataframe): dataframe to encode

    Returns:
    dataframe: dataframe to encode 
    """  
    from sklearn.preprocessing import OneHotEncoder
    for column in features: 
       enc = OneHotEncoder(sparse=False)
       df_enc = pd.DataFrame(enc.fit_transform(df_to_encode[[column]]))
       df_enc.columns = enc.get_feature_names([column])
       df_to_encode.drop(column, axis = 1, inplace = True)
       df_fin = pd.concat([df_to_encode, df_enc], axis = 1)

       
       return df_fin


columns_to_one_hot_encode = ["type"]
df = one_hot_encoder(columns_to_one_hot_encode,df)