Refactor the preprocessing module

src/preprocessing.py

@@ -1,20 +1,49 @@
 from pandas import DataFrame, read_csv
-from sklearn.preprocessing import normalize
+from sklearn.preprocessing import LabelEncoder, normalize
 from sklearn.model_selection import KFold
 
 
-def replace_values(df) -> DataFrame:
-    for column in df.columns:
-        df[column].fillna(value=df[column].mean(), inplace=True)
-    return df
+def construct_dataframes(train, test):
+    file_list = [train, test]
+    df_dict = {}
+    for file in file_list:
+        df_dict[file] = read_csv(filepath_or_buffer=file)
+    df_list = list(df_dict.values())
+    return df_list
 
 
-def remove_letters(df) -> DataFrame:
+def drop_null_values(df_list) -> DataFrame:
+    for df in df_list:
+        df.dropna(inplace=True)
+        df.drop(columns="Tipo_marchas", inplace=True)
+    return df_list
+
+
+def trim_column_names(df_list) -> DataFrame:
     columns = ["Consumo", "Motor_CC", "Potencia"]
-    processed_df = df.copy()
+    for df in df_list:
         for col in columns:
-        processed_df[col] = df[col].str.replace(pat="[^.0-9]", repl="").astype(float)
-    return processed_df
+            df[col] = df[col].str.replace(pat="[^.0-9]", repl="").astype(float)
+    return df_list
+
+
+def encode_fields(df_list):
+    files = [
+        "ao"
+        "asientos"
+        "ciudad"
+        "combustible"
+        "consumo"
+        "descuento"
+        "kilometros"
+        "mano"
+        "motor_cc"
+        "nombre"
+        "potencia"
+        "potencia"
+    ]
+    for data in files:
+        pass
 
 
 def split_k_sets(df):
@@ -22,8 +51,8 @@ def split_k_sets(df):
     return k_fold.split(df)
 
 
-def parse_data(source) -> DataFrame:
-    df = read_csv(filepath_or_buffer=source, na_values="?")
-    processed_df = df.dropna()
-    numeric_df = remove_letters(processed_df)
-    return numeric_df
+def parse_data(train, test):
+    df_list = construct_dataframes(train=train, test=test)
+    processed_df_list = drop_null_values(df_list)
+    numeric_df_list = trim_column_names(processed_df_list)
+    return numeric_df_list