import pandas as pd
import matplotlib.pyplot as plt
import datetime

def load_dataframe():
    df = pd.read_excel("datasets/hardrock100_results_2022.xlsx", index_col=0)
    return df

def time_str_to_seconds(time_object):
    return time_object.second + time_object.minute * 60 + time_object.hour * 3600

def calc_mean(df):
    total_age = 0
    for _, row in df.iterrows():
        total_age += row["age"]
    mean_age = total_age / len(df.index)
    return mean_age

def main():
    df = load_dataframe()
    print(df.head(3))

    print(df.info()) # get column datatype

    print(df[df["finish"].isna()]) # filter finish column -> not available

    number_of_rows = len(df.index) # get number of rows
    print(f"Number of rows: {number_of_rows}")

    print(df.iloc[[5,8]]) # get place 5 & 8

    print(f" Mean age: {calc_mean(df)}") # get mean age
    print(df["age"].mean()) # alt get mean age

    sorted_age = sorted(df["age"]) # sort column age
    print(sorted_age)

    # get median age
    median_index = (number_of_rows -1) // 2
    if number_of_rows % 2:
        print(sorted_age[median_index])
    else:
        print((sorted_age[median_index] + sorted_age[median_index + 1])/2)
    
    print(df["age"].median()) # alt get median age
    
    print(df["age"].describe()) # 

    # get time in seconds
    time_str = "21:36:25"
    time_object = datetime.datetime.strptime(time_str, "%H:%M:%S")
    print(time_str_to_seconds(time_object))

    na_value = datetime.time(0,0)
    df["finish"] = df ["finish"].fillna(na_value)

    df["finish_seconds"] = df["finish"].apply(time_str_to_seconds)

    df.boxplot(column=["finish_seconds"])
    plt.title("Verteilung der Zielzeiten in Sekunden")
    plt.ylabel("Sekunden")
    plt.show()

if __name__ == "__main__":
    main()