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