import pandas as pd
import 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")