Two (or more) charts on the same chart with different scales of the y axis and the y axis in python

Question

I want 3 graphs on one axes object, for example:

#example x- and y-data
x_values1=[1,2,3,4,5]
y_values1=[1,2,3,4,5]

x_values2=[-1000,-800,-600,-400,-200]
y_values2=[10,20,39,40,50]

x_values3=[150,200,250,300,350]
y_values3=[10,20,30,40,50]


#make axes
fig=plt.figure()
ax=fig.add_subplot(111)

Now I want to add all three datasets to ax. But they should not share any x or y axis (since then, due to differences in scales, one of them will be smaller than the other. I need something like ax.twinx (), ax.twiny (), but both x and y axes must be independent.

I want to do this because I want to put two attached graphs (and a third, similar to the second) in one plot ("put them on top of each other"). Plot1 Plot2

Then I would put the x / y-labels (and / or ticks, limits) of the second chart on the right / top and the x / y-limits of another chart in the lower part / left. I don't need x / y-labels 3. graphics.

? //, .

ps: stackoverflow - , . , !

Answer 1

. , , - - ax=fig.add_subplot(111, label="1").

, . , , .

import matplotlib.pyplot as plt

x_values1=[1,2,3,4,5]
y_values1=[1,2,2,4,1]

x_values2=[-1000,-800,-600,-400,-200]
y_values2=[10,20,39,40,50]

x_values3=[150,200,250,300,350]
y_values3=[10,20,30,40,50]


fig=plt.figure()
ax=fig.add_subplot(111, label="1")
ax2=fig.add_subplot(111, label="2", frame_on=False)
ax3=fig.add_subplot(111, label="3", frame_on=False)

ax.plot(x_values1, y_values1, color="C0")
ax.set_xlabel("x label 1", color="C0")
ax.set_ylabel("y label 1", color="C0")
ax.tick_params(axis='x', colors="C0")
ax.tick_params(axis='y', colors="C0")

ax2.scatter(x_values2, y_values2, color="C1")
ax2.xaxis.tick_top()
ax2.yaxis.tick_right()
ax2.set_xlabel('x label 2', color="C1") 
ax2.set_ylabel('y label 2', color="C1")       
ax2.xaxis.set_label_position('top') 
ax2.yaxis.set_label_position('right') 
ax2.tick_params(axis='x', colors="C1")
ax2.tick_params(axis='y', colors="C1")

ax3.plot(x_values3, y_values3, color="C3")
ax3.set_xticks([])
ax3.set_yticks([])

plt.show()

Answer 2

, , "". :

def standardize(data):
    for a in range(2):
        span = max(data[0][a]) - min(data[0][a])
        min_ = min(data[0][a])
        for idx in range(len(data)):
            standardize = (max(data[idx][a]) - min(data[idx][a]))/span
            data[idx][a] = [i/standardize + min_ - min([i/standardize 
                            for i in data[idx][a]]) for i in data[idx][a]]
    return data

, :

import matplotlib.pyplot as plt
data = [[[1,2,3,4,5],[1,2,2,4,1]], [[-1000,-800,-600,-400,-200], [10,20,39,40,50]], [[150,200,250,300,350], [10,20,30,40,50]]]
limits = [(min(data[1][a]), max(data[1][a])) for a in range(2)]

norm_data = standardize(data)

fig, ax = plt.subplots()

for x, y in norm_data:
    ax.plot(x, y)

ax2, ax3 = ax.twinx(), ax.twiny()
ax2.set_ylim(limits[1])
ax3.set_xlim(limits[0])

plt.show()

, . , x y, .