Get seperate plots and one accumulated in matplotlib.pyplot

As to expect I get 2 different plots from the code below. Besides those two I would also like to have one seperate plot that shows both lines in the same graph, with different colours and legend.

I do not want them as sublots, I want each of them as a single plot and one with all the lines in the same plot.

import matplotlib.pyplot as plt
import numpy as np

for i in range(2):

    x = np.arange(0, 4*np.pi, 0.1)
    y = np.sin(x)

    plt.plot(x, i*y+i)
    plt.title("Values")
    plt.xlabel("x")
    plt.ylabel("y")
    plt.show()

I had a look at this stack exchange question.

I could write x and y values of each graph to a list to save for later, but for large dataset that does not seem clever.

I'm not sure which IDE you're using, but your code technically doesn't create two figures. If you only call plt.plot, it will create a figure if none exist or add to the last figure if one does exist. You would need to call plt.figure if you want to create a new figure.

So, for your case, you want to create a main figure for the combined plots and then create a new figure each loop for the individual plots. The figures can be created using plt.subplots() (without any nrows or ncols arguments it creates a single plot, not a matrix of plots).

In the loop I chose to create fig_i and ax_i for the individual plots and fig and ax for the global plots. Since you want a legend, you should add a label for the combined plot.

import matplotlib.pyplot as plt
import numpy as np

plt.close("all")

# figure for the combined plot
fig, ax = plt.subplots()
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Combined Plot")

x = np.arange(0, 4*np.pi, 0.1)
y = np.sin(x)

for i in range(2):
    # creates a new figure each loop
    fig_i, ax_i = plt.subplots()
    ax_i.plot(x, i*y+i)
    ax_i.set_title("Values")
    ax_i.set_xlabel("x")
    ax_i.set_ylabel("y")
    fig_i.show()

    # adds the data to the combine figure with a label for the legend
    ax.plot(x, i*y+i, label=i)

ax.legend()
fig.show()

Individual 1:

Individual 2:

Combined: