jrtechs
/
PerryXDeng-wheatyeeters
mirror of https://github.com/PerryXDeng/wheatyeeters.git

import pandas as pdimport numpy as np

def vectorize_mult(column, dictionary, postfix, df, file=None):  newCol = column + postfix  df[newCol] = df[column].map(dictionary)  if file is not None:    df.to_csv('cleaned/{}.csv'.format(file))

csv = pd.read_csv("cleaned/notnormalized_with_continuousNan_rpe.csv")# vectorize_mult("Training", {"No": 0, "Yes": 1}, "", csv)## mapping = {"Mobility/Recovery": 1, "Game": 0, "Skills": 0, "Conditioning": 0,#            "Strength": 0, "Combat": 0, "Speed": 0, np.nan: 0}# vectorize_mult("SessionType", mapping, "Mobility/Recovery", csv)# mapping["Mobility/Recovery"] = 0# mapping["Game"] = 1# vectorize_mult("SessionType", mapping, "Game", csv)# mapping["Game"] = 0# mapping["Skills"] = 1# vectorize_mult("SessionType", mapping, "Skills", csv)# mapping["Skills"] = 0# mapping["Conditioning"] = 1# vectorize_mult("SessionType", mapping, "Conditioning", csv)# mapping["Conditioning"] = 0# mapping["Strength"] = 1# vectorize_mult("SessionType", mapping, "Strength", csv)# mapping["Strength"] = 0# mapping["Combat"] = 1# vectorize_mult("SessionType", mapping, "Combat", csv)# mapping["Combat"] = 0# mapping["Speed"] = 1# vectorize_mult("SessionType", mapping, "Speed", csv)# mapping["Speed"] = 0# mapping[np.nan] = 1# vectorize_mult("SessionType", mapping, "Unknown", csv)# mapping[np.nan] = 0## mapping = {"Not at all": 1, "Absolutely": 0, "Somewhat": 0, np.nan: 0}# vectorize_mult("BestOutOfMyself", mapping, "NotAtAll", csv)# mapping["Not at all"] = 0# mapping["Absolutely"] = 1# vectorize_mult("BestOutOfMyself", mapping, "Absolutely", csv)# mapping["Absolutely"] = 0# mapping["Somewhat"] = 1# vectorize_mult('BestOutOfMyself', mapping, "Somewhat", csv)# mapping["Somewhat"] = 0# mapping[np.nan] = 1# vectorize_mult('BestOutOfMyself', mapping, "Unknown", csv)# mapping[np.nan] = 0## csv.to_csv("cleaned/notnormalized_with_continuousNan_rpe.csv")
for i in range(len(csv.dtypes)):  type = csv.dtypes[i]  if type != "object":    colname = csv.columns[i]    if csv[colname].hasnans:      print(colname)      csv[colname] = csv[colname].fillna(0)#print(csv.isnull().sum())csv.to_csv("cleaned/notnormalized_with_0Nan_rpe.csv")