plot

#loop through columns/assignments
    for i in range(2, np.size(grades, axis = 1)):
        #mean grade of each column/assignment
        y2.append(np.mean(grades[:,i]))
        #assignment number
        x2.append(i-1)

        #loop through rows/students
        for j in range(np.size(grades,axis = 0)):
            #computing small random number to display how many students got the grade
            random_val=np.random.uniform(-0.1,0.1,1)
            #assignment plus small random value
            x=i-1+random_val
            #grade plus small random value
            y=grades[j,i]+random_val
            #plot grades as black dots/stars
            plt.plot( x, y,"k*")

    #making plot with average trendline
    plt.plot(x2,y2,'r*', label = "Average grade")
    z = np.polyfit(x2, y2, 1) #plot
    p = np.poly1d(z)  #
    plt.plot(x2,p(x2),"r--",label = "Average grade trendline")

    #Plot
    #plt.plot(x,y,"b*", label='hgseljkgh') #plot
    plt.title("Grades per assignment")#title of the graph
    plt.legend(bbox_to_anchor=(1., 1.02, 0., .102), loc = "lower center")
    plt.xlabel("Assignments") #the x-axis label
    plt.ylabel("Grades") #the y-axis label
    plt.xlim() #the limits of the x-axis
    #y-axis limits is set from -4 to 13 to have a clearer view of the grades -3 and 12
    plt.ylim([-4, 13]) #the limits of the y-axis
    plt.grid() #show grid on graph

    #show all plots
    plt.show()