data_et = np.loadtxt('Torino_enjoy_Booking.txt')
c2 = 0
for i in range(0,len(data_et)):
    if data_et[i] > 1000 and data_et[i] < 1100:
        c2 = c2+1
print c2/len(data_et)
sort_data_et = np.sort(data_et)
y_et = np.arange(1,len(data_et)+1)/float(len(data_et))
plt.plot(sort_data_et,y_et)


data_c2 = np.loadtxt('Torino_Car2Go_Booking.txt')
c = 0
for i in range(0,len(data_c2)):
    if data_c2[i] > 1000 and data_c2[i] < 1100:
        c = c+1
count = c/len(data_c2)
sort_data_c2 = np.sort(data_c2)
y_c2 = np.arange(1,len(data_c2)+1)/float(len(data_c2))
plt.plot(sort_data_c2,y_c2)
plt.title("CDF for Torino PermantentBooking")
plt.xlabel("Booking duration [m]")
plt.ylabel("Peercentage of booking")

data_m = np.loadtxt('Madrid_Car2Go_Booking.txt')
sort_data_m = np.sort(data_m)
y_m = np.arange(1,len(data_m)+1)/float(len(data_m))
plt.plot(sort_data_m,y_m)