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80 lines
2.7 KiB
80 lines
2.7 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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class FatigueSum:
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def __init__(self):
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self.file = "cleaned/time_series_normalized_wellness.csv"
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self.end = "cleaned/fatigue_total_sum.csv"
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def calculate(self):
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df = pd.read_csv(self.file)
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# get some of the fatigue for a particular date
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diction = dict()
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dates = df["TimeSinceAugFirst"].unique()
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dates = set(dates)
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dates = list(dates)
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# for each date, get unique data and get calculation
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for date in dates:
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pdf = df[df["TimeSinceAugFirst"] == date]
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num_players = len(pdf["playerID"].unique())
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fatigue_sum = pdf["normFatigue"].sum()
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result = fatigue_sum / num_players
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diction[date] = result
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# Converting
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dates = diction.keys()
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values = diction.values()
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final_df = pd.DataFrame()
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final_df["TimeSinceAugFirst"] = dates
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final_df["fatigueSum"] = values
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final_df.to_csv(self.end)
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