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51 lines
1.7 KiB
51 lines
1.7 KiB
import pandas as pd
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def vectorize_mult(df, column, dictionary):
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"""
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Changes all the categorical values into its respective
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number in the dictionary and then saves it in the DF
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:param df: dataframe
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:param column: column name
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:param dictionary: alterations to make
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"""
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newCol = column + "Num"
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df[newCol] = df[column].map(dictionary)
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class WellnessCSV:
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def __init__(self):
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self.file = "data/wellness.csv"
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self.end = "cleaned/notnormalized_with_0NaN_wellness.csv"
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def vectorize(self):
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df = pd.read_csv(self.file)
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# Vectorizing appropriate data
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vectorize_mult(df, "Pain", {"No": 0, "Yes": 1})
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vectorize_mult(df, "Illness", {"No": 0, "Slightly Off": 0.5, "Yes": 1})
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vectorize_mult(df, "Menstruation", {"No": 0, "Yes": 1})
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vectorize_mult(df, "Nutrition", {"Poor": 0, "Okay": 0.5, "Excellent": 1})
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vectorize_mult(df, "NutritionAdjustment", {"No": 0, "Yes": 1})
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vectorize_mult(df, "USGMeasurement", {"No": 0, "Yes": 1})
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readiness = []
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for i, value in df["TrainingReadiness"].iteritems():
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value = value.split("%")[0]
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value = int(value) * (1/100)
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readiness.append(value)
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df["TrainingReadinessNum"] = readiness
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# Filling in NaNs for appropriate layers where they won't make a statistical difference
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df["MenstruationNum"] = df["MenstruationNum"].fillna(0)
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df["USGMeasurementNum"] = df["USGMeasurementNum"].fillna(0)
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df["NutritionNum"] = df["MenstruationNum"].fillna(0)
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df["NutritionAdjustmentNum"] = df["NutritionAdjustmentNum"].fillna(0)
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# Saving the df to the "cleaned" CSV
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df.to_csv(self.end)
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cls = WellnessCSV()
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cls.vectorize()
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