datafest competition 2019
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import pandas as pd
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)
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()