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scripts/encoding_func.py

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+from sklearn.preprocessing import OneHotEncoder
+import numpy as np 
+import pandas as pd
+
+
+def one_hot_encode_column(df, column_name):
+    """
+    One-hot encodes a specified column in the DataFrame.
+
+    Parameters:
+    df (pd.DataFrame): The DataFrame containing the column to encode.
+    column_name (str): The name of the column to one-hot encode.
+
+    Returns:
+    pd.DataFrame: The DataFrame with the one-hot encoded columns added.
+    """
+    # Replace NaN values with 'unknown' for consistency
+    df[[column_name]] = df[[column_name]].replace('', np.nan)
+    df[[column_name]]= df[[column_name]].fillna('unknown')
+
+    enc = OneHotEncoder(handle_unknown='ignore')
+    enc.fit(df[[column_name]])
+    encoded_array = enc.transform(df[[column_name]]).toarray()
+    encoded_df = pd.DataFrame(encoded_array, columns=enc.get_feature_names_out([column_name]))
+
+    return pd.concat([df, encoded_df], axis=1)
+
+
+# Example of usage
+filtered_df = one_hot_encode_column(filtered_df, 'pnns_groups_1')
+filtered_df.head()
+# filtered_df = filtered_df.drop(columns=['pnns_groups_1'])