Add a TARGET_VARIABLE constant to refer to Sales Price in a slightly cleaner way

1_data_cleaning.ipynb

@@ -30,7 +30,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2018-09-01 16:51:42 CEST\n",
+      "2018-09-02 18:50:50 CEST\n",
       "\n",
       "CPython 3.6.5\n",
       "IPython 6.5.0\n",
@@ -67,7 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The *utils.py* module defines helper dictionaries and lists that help with parsing the data types correctly, look up column descriptions, and refer to groups of data columns.\n",
+    "The *utils.py* module defines helper dictionaries, lists, and functions that help with parsing the data types correctly, look up column descriptions, and refer to groups of data columns.\n",
     "\n",
     "**Note:** the suffix \\_*COLUMNS* indicates a dictionary with all meta information on the provided data file and \\_*VARIABLES* a list with only the column names (i.e., the keys of the respective \\_*COLUMNS* dictionary)."
    ]
@@ -93,6 +93,7 @@
     "    NUMERIC_VARIABLES,  # groups continuous and discrete\n",
     "    ORDINAL_COLUMNS,\n",
     "    ORDINAL_VARIABLES,\n",
+    "    TARGET_VARIABLE,  # = Sale Price\n",
     "    correct_column_names,\n",
     "    print_column_list,\n",
     "    update_column_descriptions,\n",
@@ -199,7 +200,7 @@
     "# order as in the encoded description file.\n",
     "# Note that the target variable \"SalePrice\"\n",
     "# is not in the description file.\n",
-    "df = df[ALL_VARIABLES + [\"SalePrice\"]]"
+    "df = df[ALL_VARIABLES + TARGET_VARIABLE]"
    ]
   },
   {
@@ -266,7 +267,7 @@
    "outputs": [],
    "source": [
     "# Show that all \"continuous\" variables come as integers.\n",
-    "for column in NUMERIC_VARIABLES + [\"SalePrice\"]:\n",
+    "for column in NUMERIC_VARIABLES + TARGET_VARIABLE:\n",
     "    not_null = df[column].notnull()\n",
     "    mask = (\n",
     "        df.loc[not_null, column].astype(np.int64)\n",
@@ -2237,7 +2238,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "remaining_columns = sorted(set(ALL_VARIABLES) - set(missing_a_lot)) + [\"SalePrice\"]\n",
+    "remaining_columns = sorted(set(ALL_VARIABLES) - set(missing_a_lot)) + TARGET_VARIABLE\n",
     "mask = df[remaining_columns].isnull().any(axis=1)\n",
     "assert (100 * mask.sum() / df.shape[0]) < 1.1  # percent\n",
     "df = df[~mask]"
@@ -2287,7 +2288,7 @@
     "update_column_descriptions(df.columns)\n",
     "# Without any more missing data, cast all numeric\n",
     "# columns as floats or integers respectively.\n",
-    "for column in CONTINUOUS_VARIABLES + [\"SalePrice\"]:\n",
+    "for column in CONTINUOUS_VARIABLES + TARGET_VARIABLE:\n",
     "    df[column] = df[column].astype(np.float64)\n",
     "for column in DISCRETE_VARIABLES:\n",
     "    df[column] = df[column].astype(np.int64)"

utils.py

@@ -28,12 +28,14 @@ Implementation Note:
 
 import re
 
+import pandas as pd
 import requests
 import tabulate
 
 
 INDEX_COLUMNS = ["Order", "PID"]
 LABEL_TYPES = ["nominal", "ordinal"]
+TARGET_VARIABLE = ["SalePrice"]
 # Note that these dictionaries and lists are not actually constants but
 # filled in during import time which makes them "near"-constant.
 ALL_COLUMNS = {}
@@ -100,7 +102,7 @@ def _extract_meta_data(lines):
     # The two ID columns and the target variable "SalePrice"
     # are not put into the helper dicts / lists as they are
     # treated seperately in the modelling anyways.
-    non_feature_columns = INDEX_COLUMNS + ["SalePrice"]
+    non_feature_columns = INDEX_COLUMNS + TARGET_VARIABLE
 
     for line in lines:
         # Process the next variable in the list.