Dynamically add/create subplots in matplotlib

I want to create a plot consisting of several subplots with shared x/y axes.
It should look something like this from the documentation (though my subplots will be scatterblots): (code here)

But I want to create the subplots dynamically!

So the number of subplots depends on the output of a previous function. (It will probably be around 3 to 15 subplots per diagram, each from a distinct dataset, depending on the input of my script.)

Can anyone tell me how to accomplish that?

Answers:

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Method 1

Suppose you know total subplots and total columns you want to use:

import matplotlib.pyplot as plt

# Subplots are organized in a Rows x Cols Grid
# Tot and Cols are known

Tot = number_of_subplots
Cols = number_of_columns

# Compute Rows required

Rows = Tot // Cols 
Rows += Tot % Cols

# Create a Position index

Position = range(1,Tot + 1)

First instance of Rows accounts only for rows completely filled by subplots, then is added one more Row if 1 or 2 or … Cols – 1 subplots still need location.

Then create figure and add subplots with a for loop.

# Create main figure

fig = plt.figure(1)
for k in range(Tot):

  # add every single subplot to the figure with a for loop

  ax = fig.add_subplot(Rows,Cols,Position[k])
  ax.plot(x,y)      # Or whatever you want in the subplot

plt.show()

Please note that you need the range Position to move the subplots into the right place.

Method 2

Based on this post, what you want to do is something like this:

import matplotlib.pyplot as plt

# Start with one
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1,2,3])

# Now later you get a new subplot; change the geometry of the existing
n = len(fig.axes)
for i in range(n):
    fig.axes[i].change_geometry(n+1, 1, i+1)

# Add the new
ax = fig.add_subplot(n+1, 1, n+1)
ax.plot([4,5,6])

plt.show()

However, Paul H‘s answer points to the submodule called gridspec which might make the above easier. I am leaving that as an exercise for the reader ^_~.

Method 3

import matplotlib.pyplot as plt
from pylab import *
import numpy as np

x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)

subplots_adjust(hspace=0.000)
number_of_subplots=3

for i,v in enumerate(xrange(number_of_subplots)):
    v = v+1
    ax1 = subplot(number_of_subplots,1,v)
    ax1.plot(x,y)

plt.show()

This code works but you will need to correct the axes. I used to subplot to plot 3 graphs all in the same column. All you need to do is assign an integer to number_of_plots variable. If the X and Y values are different for each plot you will need to assign them for each plot.

subplot works as follows, if for example I had a subplot values of 3,1,1. This creates a 3×1 grid and places the plot in the 1st position. In the next interation if my subplot values were 3,1,2 it again creates a 3×1 grid but places the plot in the 2nd position and so forth.

Method 4

Instead of counting your own number of rows and columns, I found it easier to create the subplots using plt.subplots first, then iterate through the axes object to add plots.

import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(12, 8))
x_array = np.random.randn(6, 10)
y_array = np.random.randn(6, 10)

i = 0
for row in axes:
    for ax in row:
        x = x_array[i] 
        y = y_array[i]
        ax.scatter(x, y)
        ax.set_title("Plot " + str(i))
        i += 1
plt.tight_layout()
plt.show()

Here I use i to iterate through elements of x_array and y_array, but you can likewise easily iterate through functions, or columns of dataframes to dynamically generate graphs.

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0