Add pandas to nix-shell instead of poetry

pyproject.toml

@@ -7,14 +7,11 @@ authors = ["coolneng <akasroua@gmail.com>"]
 [tool.poetry.dependencies]
 python = "^3.8"
 scikit-learn = "^0.24.0"
-pandas = "^1.2.0"
 imbalanced-learn = "^0.7.0"
+numpy = "^1.19.4"
 
 [tool.poetry.dev-dependencies]
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
-
-[tool.poetry.scripts]
-competition = "processing:main"

shell.nix

@@ -2,4 +2,4 @@
 
 with pkgs;
 
-mkShell { buildInputs = [ python38 poetry ]; }
+mkShell { buildInputs = [ python38 python38Packages.pandas poetry ]; }

src/preprocessing.py

@@ -21,39 +21,33 @@ def rename_columns(df_list) -> DataFrame:
 
 def drop_null_values(df_list):
     for df in df_list:
-        df.dropna(inplace=True)
         df.drop(columns="tipo_marchas", inplace=True)
-        df["descuento"].fillna(0)
+        df.drop(columns="descuento", inplace=True)
-    return df_list
+        df.dropna(inplace=True)
-
-
-def trim_column_names(df_list) -> DataFrame:
-    columns = ["consumo", "motor_CC", "potencia"]
-    for df in df_list:
-        for col in columns:
-            df[col] = df[col].str.replace(pat="[^.0-9]", repl="").astype(float)
     return df_list
 
 
 def encode_columns(df_list):
     label_encoder = LabelEncoder()
     files = [
-        "ao"
+        "ao",
-        "asientos"
+        "asientos",
-        "ciudad"
+        "ciudad",
-        "combustible"
+        "combustible",
-        "consumo"
+        "consumo",
-        "descuento"
+        "kilometros",
-        "kilometros"
+        "mano",
-        "mano"
+        "motor_cc",
-        "motor_cc"
+        "nombre",
-        "nombre"
+        "potencia",
-        "potencia"
     ]
     for data in files:
         for df in df_list:
             label = label_encoder.fit(read_csv("data/" + data + ".csv", squeeze=True))
-            df[data] = label.transform(df[data])
+            if data == "ao":
+                df["año"] = label.transform(df["año"])
+            else:
+                df[data] = label.transform(df[data])
     return df_list
 
 
@@ -72,8 +66,10 @@ def balance_training_data(df):
     smote_tomek = SMOTETomek(random_state=42)
     data, target = split_data_target(df=df, dataset="data")
     balanced_data, balanced_target = smote_tomek.fit_resample(data, target)
-    balanced_data_df = DataFrame(balanced_data, columns=data.columns)
+    balanced_data_df = DataFrame(
-    balanced_target_df = DataFrame(balanced_target, columns=target.columns)
+        balanced_data, columns=df.columns.difference(["precio_cat"])
+    )
+    balanced_target_df = DataFrame(balanced_target, columns=["precio_cat"])
     return balanced_data_df, balanced_target_df
 
 
@@ -86,8 +82,7 @@ def parse_data(train, test):
     df_list = construct_dataframes(train=train, test=test)
     renamed_df_list = rename_columns(df_list)
     processed_df_list = drop_null_values(renamed_df_list)
-    numeric_df_list = trim_column_names(processed_df_list)
+    encoded_df_list = encode_columns(processed_df_list)
-    encoded_df_list = encode_columns(numeric_df_list)
     train_data, train_target = balance_training_data(encoded_df_list[0])
     test_data, test_ids = split_data_target(encoded_df_list[1], dataset="test")
     return train_data, train_target, test_data, test_ids