Added non-normalized with 0 NaN cleaned wellness csv

data_preparation/vectorization_ex.py

@@ -1,12 +1,51 @@
 import pandas as pd
 
-# read in CSV
-df = pd.read_csv('cleaned/dirty_wellness_na.csv')
 
-
-def vectorize_mult(column, dictionary, file=None):
+def vectorize_mult(df, column, dictionary):
+    """
+    Changes all the categorical values into its respective
+    number in the dictionary and then saves it in the DF
+    :param df: dataframe
+    :param column: column name
+    :param dictionary: alterations to make
+    """
     newCol = column + "Num"
     df[newCol] = df[column].map(dictionary)
-    if file is not None:
-        df.to_csv('cleaned/{}.csv'.format(file))
 
+
+class WellnessCSV:
+    def __init__(self):
+        self.file = "data/wellness.csv"
+        self.end = "cleaned/notnormalized_with_0NaN_wellness.csv"
+
+    def vectorize(self):
+        df = pd.read_csv(self.file)
+
+        # Vectorizing appropriate data
+        vectorize_mult(df, "Pain", {"No": 0, "Yes": 1})
+        vectorize_mult(df, "Illness", {"No": 0, "Slightly Off": 0.5, "Yes": 1})
+        vectorize_mult(df, "Menstruation", {"No": 0, "Yes": 1})
+        vectorize_mult(df, "Nutrition", {"Poor": 0, "Okay": 0.5, "Excellent": 1})
+        vectorize_mult(df, "NutritionAdjustment", {"No": 0, "Yes": 1})
+        vectorize_mult(df, "USGMeasurement", {"No": 0, "Yes": 1})
+
+        readiness = []
+        for i, value in df["TrainingReadiness"].iteritems():
+            value = value.split("%")[0]
+            value = int(value) * (1/100)
+            readiness.append(value)
+
+        df["TrainingReadinessNum"] = readiness
+
+        # Filling in NaNs for appropriate layers where they won't make a statistical difference
+        df["MenstruationNum"] = df["MenstruationNum"].fillna(0)
+        df["USGMeasurementNum"] = df["USGMeasurementNum"].fillna(0)
+        df["NutritionNum"] = df["MenstruationNum"].fillna(0)
+        df["NutritionAdjustmentNum"] = df["NutritionAdjustmentNum"].fillna(0)
+
+        # Saving the df to the "cleaned" CSV
+        df.to_csv(self.end)
+
+
+cls = WellnessCSV()
+cls.vectorize()