Multiplication_Practice_Tool/Calc_Trainer_Viz at master · JonasAndrich/Multiplication_Practice_Tool · GitHub

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#visualize data
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import matplotlib.dates as mdates
from datetime import datetime
from IPython.display import display
def visualize_result_multiplication():

    #Read CSV with math training statistics
    result = pd.read_csv("result_multiplication.csv")

    #Er hat aus dem CSV nicht auf dem Schirm, dass es sich um TimeObjects handeln soll. Das macht er nun
    result['Start_t'] =pd.to_datetime(result.Start_t)
    result['t_req'] =pd.to_timedelta(result.t_req)


    #Prepare formatetted date and time colums for plotting
    #https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.html
    result['hour'] = pd.DatetimeIndex(result['Start_t']).hour
    result['Date_short'] =pd.DatetimeIndex(result['Start_t']).date
    result['t_req_sec'] = result['t_req'].dt.total_seconds()

    #Calulate Performance Parameters
    result["Percent"] = (result["Correct_Ans"]/result["Exerc"]*100)
    result["Hardness"] = (result["Prim"]*result["Sec"])
    result["corr_per_sec"] = (result["Correct_Ans"]/result["t_req_sec"])


    display (result)

    #Hex Bin Plot shows how my percentage of correct answers
    #The df.plot() function returns a matplotlib.axes.AxesSubplot object. You can set the labels on that object.

    pig, axes = plt.subplots(nrows=1, ncols=2)
    ax1 = result.plot.hexbin(x='Hardness', y='Percent', gridsize=10, sharex=False, ax=axes[0])
    ax2 = result.plot.hexbin(x='Hardness', y='corr_per_sec', gridsize=10, sharex=False, ax=axes[1])
    ax2.set_ylabel('Correct answers per second')
    plt.subplots_adjust(top=0.95, bottom=0.15, left=0.05, right=0.95, hspace=0.1,
                    wspace=0.1)
    mng1 = plt.get_current_fig_manager()
    mng1.full_screen_toggle()


    fig = plt.figure()
    ax3 = fig.add_subplot(121)
    ax3.set_xlabel('Date')
    ax3.set_ylabel('Correct answers per second')
    plt.xticks(rotation=45)
    #plt.gcf().autofmt_xdate()
    myFmt = mdates.DateFormatter('%d.%m.')
    plt.gca().xaxis.set_major_formatter(myFmt)
    ax3.plot_date(result.Start_t, result["corr_per_sec"], xdate=True, ydate=False, color='skyblue')

    ax4 = fig.add_subplot(122)
    ax4.set_xlabel('Hour of the day')
    ax4.set_ylabel('Correct answers per second')
    ax4.plot_date(result["hour"], result["corr_per_sec"], xdate=False, ydate=False, color='skyblue')
    plt.subplots_adjust(top=.95, bottom=0.15, left=0.05, right=0.95, hspace=0.1,
                    wspace=0.1)

    mng = plt.get_current_fig_manager()
    mng.full_screen_toggle()
    plt.show()


    #selection = result[result.Hardness >= 100]
    #fine_selection = selection[selection.Exerc >= 10]
    #finest_selection = fine_selection[fine_selection. ]
    #fine_selection.plot(x='Exerc', y='Correct_Ans')


visualize_result_multiplication()