import pandas as pd
import numpy as np

df = pd.read_csv("daten.csv",
delimiter=";",
na_values=["."],
index_col="ID",
encoding="utf-8")

meanRet = df.max()
# print(type(meanRet))
# print(meanRet)

onlyNumsDf = df[["Alter", "Größe"]]
# print(onlyNumsDf.mean())

df["Alter2"] = df["Alter"] * 2
# print(df)

onlyAlter = df["Alter"]
alterMean = onlyAlter.max()
# print(type(alterMean))
# print(alterMean)

charMap = {
    0: "",
    1: "A",
    2: "M",
    3: "O",
    4: "G",
    5: "U",
    6: "S",
}

def intToChar(col):
    retString = ""
    for num in col:
        char = charMap.get(num, " ")
        retString += char
    
    return retString


def timesTwo(num):
    return num*2

charNums = df[["Alter", "Chars"]]
print(charNums)
print("")
def map_numbers_to_string(column):
    return "".join(charMap[num] for num in column)


print(charNums.apply(intToChar, axis=0))
print("")
print(charNums.apply(timesTwo, axis=0))
# print(charNums.apply(intToChar))
print("")
alterSeries = df[["Alter"]]
print(alterSeries.apply(np.sum, axis=0))

print(df)
raucherGeschlechtDf = df.loc[10:70 ,["Raucher","Geschlecht"]]
geschlechtGrößeDf = df.loc[50:90, ["Geschlecht", "Größe"]]
alterBlutgruppeDf = df.loc[50:90 ,["Alter", "Blutgruppe"]]
print("\nRaucher Geschlecht: ")
print(raucherGeschlechtDf)
print("\nGeschlecht Größe: ")
print(geschlechtGrößeDf)

# print("\nMerge: ")
# res = raucherGeschlechtDf.merge(geschlechtGrößeDf, on="Geschlecht")

print("\nGroup: ")
res = raucherGeschlechtDf.groupby(geschlechtGrößeDf, by="Geschlecht")

print(res)