Too much stuff happening in a single plot? No problem—use multiple subplots!

*This in-depth tutorial shows you everything you need to know to get started with Matplotlib’s subplots() function.*

If you want, just hit “play” and watch the explainer video. I’ll then guide you through the tutorial:

Let’s start with the short answer on how to use it—you’ll learn all the details later!

**The plt.subplots() function creates a Figure and a Numpy array of Subplot/Axes objects which you store in fig and axes respectively.**

**Specify the number of rows and columns you want with the nrows and ncols arguments.**

fig, axes = plt.subplots(nrows=3, ncols=1)

**This creates a Figure and Subplots in a 3×1 grid. The Numpy array axes has shape (nrows, ncols) the same shape as the grid, in this case (3,) (it’s a 1D array since one of nrows or ncols is 1). Access each Subplot using Numpy slice notation and call the plot() method to plot a line graph.**

Once all `Subplots`

have been plotted, call `plt.tight_layout()`

to ensure no parts of the plots overlap. Finally, call `plt.show()`

to display your plot.

# Import necessary modules and (optionally) set Seaborn style import matplotlib.pyplot as plt import seaborn as sns; sns.set() import numpy as np # Generate data to plot linear = [x for x in range(5)] square = [x**2 for x in range(5)] cube = [x**3 for x in range(5)] # Generate Figure object and Axes object with shape 3x1 fig, axes = plt.subplots(nrows=3, ncols=1) # Access first Subplot and plot linear numbers axes[0].plot(linear) # Access second Subplot and plot square numbers axes[1].plot(square) # Access third Subplot and plot cube numbers axes[2].plot(cube) plt.tight_layout() plt.show()

## Matplotlib Figures and Axes

Up until now, you have probably made all your plots with the functions in `matplotlib.pyplot`

i.e. all the functions that start with `plt.`

.

These work nicely when you draw one plot at a time. But to draw multiple plots on one `Figure`

, you need to learn the underlying classes in matplotlib.

Let’s look at an image that explains the main classes from the AnatomyOfMatplotlib tutorial:

To quote AnatomyOfMatplotlib:

The

`Figure`

is the top-level container in this hierarchy. It is the overall window/page that everything is drawn on. You can have multiple independent figures and`Figure`

s can contain multiple`Axes`

.Most plotting ocurs on an

`Axes`

. The axes is effectively the area that we plot data on and any ticks/labels/etc associated with it. Usually we’ll set up an Axes with a call to`subplots`

(which places Axes on a regular grid), so in most cases,`Axes`

and`Subplot`

are synonymous.Each

`Axes`

has an`XAxis`

and a`YAxis`

. These contain the ticks, tick locations, labels, etc. In this tutorial, we’ll mostly control ticks, tick labels, and data limits through other mechanisms, so we won’t touch the individual`Axis`

part of things all that much. However, it is worth mentioning here to explain where the term`Axes`

comes from.

The typical variable names for each object are:

`Figure`

–`fig`

or`f`

,`Axes`

(plural) –`axes`

or`axs`

,`Axes`

(singular) –`ax`

or`a`

The word `Axes`

refers to the area you plot on and is synonymous with `Subplot`

. However, you can have multiple `Axes`

(`Subplots`

) on a `Figure`

. In speech and writing use the same word for the singular and plural form. In your code, you should make a distinction between each – you plot on a singular `Axes`

but will store all the `Axes`

in a Numpy array.

An `Axis`

refers to the `XAxis`

or `YAxis`

– the part that gets ticks and labels.

The `pyplot`

module implicitly works on one `Figure`

and one `Axes`

at a time. When we work with `Subplots`

, we work with multiple `Axes`

on one `Figure`

. So, it makes sense to plot with respect to the `Axes`

and it is much easier to keep track of everything.

The main differences between using `Axes`

methods and `pyplot`

are:

- Always create a
`Figure`

and`Axes`

objects on the first line - To plot, write
`ax.plot()`

instead of`plt.plot()`

.

Once you get the hang of this, you won’t want to go back to using `pyplot`

. It’s much easier to create interesting and engaging plots this way. In fact, this is why most StackOverflow answers are written with this syntax.

All of the functions in `pyplot`

have a corresponding method that you can call on `Axes`

objects, so you don’t have to learn any new functions.

Let’s get to it.

## Matplotlib Subplots Example

The `plt.subplots()`

function creates a `Figure`

and a Numpy array of `Subplots`

/`Axes`

objects which we store in `fig`

and `axes`

respectively.

Specify the number of rows and columns you want with the `nrows`

and `ncols`

arguments.

fig, axes = plt.subplots(nrows=3, ncols=1)

This creates a `Figure`

and `Subplots`

in a 3×1 grid. The Numpy array `axes`

is the same shape as the grid, in this case `(3,)`

. Access each `Subplot`

using Numpy slice notation and call the `plot()`

method to plot a line graph.

Once all `Subplots`

have been plotted, call `plt.tight_layout()`

to ensure no parts of the plots overlap. Finally, call `plt.show()`

to display your plot.

fig, axes = plt.subplots(nrows=2, ncols=2) plt.tight_layout() plt.show()

The most important arguments for `plt.subplots()`

are similar to the matplotlib subplot function but can be specified with keywords. Plus, there are more powerful ones which we will discuss later.

To create a `Figure`

with one `Axes`

object, call it without any arguments

fig, ax = plt.subplots()

Note: this is implicitly called whenever you use the `pyplot`

module. All ‘normal’ plots contain one `Figure`

and one `Axes`

.

In advanced blog posts and StackOverflow answers, you will see a line similar to this at the top of the code. It is much more Pythonic to create your plots with respect to a `Figure`

and `Axes`

.

To create a `Grid`

of subplots, specify `nrows`

and `ncols`

– the number of rows and columns respectively

fig, axes = plt.subplots(nrows=2, ncols=2)

The variable `axes`

is a numpy array with shape `(nrows, ncols)`

. Note that it is in the plural form to indicate it contains more than one `Axes`

object. Another common name is `axs`

. Choose whichever you prefer. If you call `plt.subplots()`

without an argument name the variable `ax`

as there is only one `Axes`

object returned.

I will select each `Axes`

object with slicing notation and plot using the appropriate methods. Since I am using Numpy slicing, the index of the first `Axes`

is 0, not 1.

# Create Figure and 2x2 gris of Axes objects fig, axes = plt.subplots(nrows=2, ncols=2) # Generate data to plot. data = np.array([1, 2, 3, 4, 5]) # Access Axes object with Numpy slicing then plot different distributions axes[0, 0].plot(data) axes[0, 1].plot(data**2) axes[1, 0].plot(data**3) axes[1, 1].plot(np.log(data)) plt.tight_layout() plt.show()

First I import the necessary modules, then create the `Figure`

and `Axes`

objects using `plt.subplots()`

. The `Axes`

object is a Numpy array with shape `(2, 2)`

and I access each subplot via Numpy slicing before doing a line plot of the data. Then, I call `plt.tight_layout()`

to ensure the axis labels don’t overlap with the plots themselves. Finally, I call `plt.show()`

as you do at the end of all matplotlib plots.

## Matplotlib Subplots Title

To add an overall title to the `Figure`

, use `plt.suptitle()`

.

To add a title to each `Axes`

, you have two methods to choose from:

`ax.set_title('bar')`

`ax.set(title='bar')`

In general, you can set anything you want on an `Axes`

using either of these methods. I recommend using `ax.set()`

because you can pass *any* setter function to it as a keyword argument. This is faster to type, takes up fewer lines of code and is easier to read.

Let’s set the title, xlabel and ylabel for two `Subplots`

using both methods for comparison

# Unpack the Axes object in one line instead of using slice notation fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2) # First plot - 3 lines ax1.set_title('many') ax1.set_xlabel('lines') ax1.set_ylabel('of code') # Second plot - 1 line ax2.set(title='one', xlabel='line', ylabel='of code') # Overall title plt.suptitle('My Lovely Plot') plt.tight_layout() plt.show()

Clearly using `ax.set()`

is the better choice.

Note that I unpacked the `Axes`

object into individual variables on the first line. You can do this instead of Numpy slicing if you prefer. It is easy to do with 1D arrays. Once you create grids with multiple rows and columns, it’s easier to read if you don’t unpack them.

To share the x axis for subplots in matplotlib, set `sharex=True`

in your `plt.subplots()`

call.

# Generate data data = [0, 1, 2, 3, 4, 5] # 3x1 grid that shares the x axis fig, axes = plt.subplots(nrows=3, ncols=1, sharex=True) # 3 different plots axes[0].plot(data) axes[1].plot(np.sqrt(data)) axes[2].plot(np.exp(data)) plt.tight_layout() plt.show()

Here I created 3 line plots that show the linear, square root and exponential of the numbers 0-5.

As I used the same numbers, it makes sense to share the x-axis.

Here I wrote the same code but set `sharex=False`

(the default behavior). Now there are unnecessary axis labels on the top 2 plots.

You can also share the y axis for plots by setting `sharey=True`

in your `plt.subplots()`

call.

## Matplotlib Subplots Legend

To add a legend to each `Axes`

, you must

- Label it using the
`label`

keyword - Call
`ax.legend()`

on the`Axes`

you want the legend to appear

Let’s look at the same plot as above but add a legend to each `Axes`

.

# Generate data, 3x1 plot with shared XAxis data = [0, 1, 2, 3, 4, 5] fig, axes = plt.subplots(nrows=3, ncols=1, sharex=True) # Plot the distributions and label each Axes axes[0].plot(data, label='Linear') axes[1].plot(np.sqrt(data), label='Square Root') axes[2].plot(np.exp(data), label='Exponential') # Add a legend to each Axes with default values for ax in axes: ax.legend() plt.tight_layout() plt.show()

The legend now tells you which function has been applied to the data. I used a for loop to call `ax.legend()`

on each of the `Axes`

. I could have done it manually instead by writing:

axes[0].legend() axes[1].legend() axes[2].legend()

Instead of having 3 legends, let’s just add one legend to the `Figure`

that describes each line. Note that you need to change the color of each line, otherwise the legend will show three blue lines.

The matplotlib legend function takes 2 arguments

ax.legend(handles, labels)

`handles`

– the lines/plots you want to add to the legend (list)`labels`

– the labels you want to give each line (list)

Get the `handles`

by storing the output of you `ax.plot()`

calls in a list. You need to create the list of `labels`

yourself. Then call `legend()`

on the `Axes`

you want to add the legend to.

# Generate data and 3x1 grid with a shared x axis data = [0, 1, 2, 3, 4, 5] fig, axes = plt.subplots(nrows=3, ncols=1, sharex=True) # Store the output of our plot calls to use as handles # Plot returns a list of length 1, so unpack it using a comma linear, = axes[0].plot(data, 'b') sqrt, = axes[1].plot(np.sqrt(data), 'r') exp, = axes[2].plot(np.exp(data), 'g') # Create handles and labels for the legend handles = [linear, sqrt, exp] labels = ['Linear', 'Square Root', 'Exponential'] # Draw legend on first Axes axes[0].legend(handles, labels) plt.tight_layout() plt.show()

First I generated the data and a 3×1 grid. Then I made three `ax.plot()`

calls and applied different functions to the data.

Note that `ax.plot()`

returns a `list`

of `matplotlib.line.Line2D`

objects. You have to pass these `Line2D`

objects to `ax.legend()`

and so need to unpack them first.

Standard unpacking syntax in Python is:

a, b = [1, 2] # a = 1, b = 2

However, each `ax.plot()`

call returns a list of length 1. To unpack these lists, write

x, = [5] # x = 5

If you just wrote `x = [5]`

then `x`

would be a list and not the object inside the list.

After the `plot()`

calls, I created 2 lists of `handles`

and `labels`

which I passed to `axes[0].legend()`

to draw it on the first plot.

In the above plot, I changed the`legend`

call to `axes[1].legend(handles, labels)`

to plot it on the second (middle) `Axes`

.

## Matplotlib Subplots Size

You have total control over the size of subplots in matplotlib.

You can either change the size of the entire `Figure`

or the size of the `Subplots`

themselves.

First, let’s look at changing the `Figure`

.

### Matplotlib Figure Size

If you are happy with the size of your subplots but you want the final image to be larger/smaller, change the `Figure`

.

If you’ve read my article on the matplotlib subplot function, you know to use the `plt.figure()`

function to to change the `Figure`

. Fortunately, any arguments passed to `plt.subplots()`

are also passed to `plt.figure()`

. So, you don’t have to add any extra lines of code, just keyword arguments.

Let’s change the size of the `Figure`

.

# Create 2x1 grid - 3 inches wide, 6 inches long fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(3, 6)) plt.show()

I created a 2×1 plot and set the `Figure`

size with the `figsize`

argument. It accepts a tuple of 2 numbers – the `(width, height)`

of the image in inches.

So, I created a plot 3 inches wide and 6 inches long – `figsize=(3, 6)`

.

# 2x1 grid - twice as long as it is wide fig, axes = plt.subplots(nrows=2, ncols=1, figsize=plt.figaspect(2)) plt.show()

You can set a more general `Figure`

size with the matplotlib figaspect function. It lets you set the aspect ratio (height/width) of the `Figure`

.

Above, I created a `Figure`

twice as long as it is wide by setting `figsize=plt.figaspect(2)`

.

Note: Remember the aspect ratio (height/width) formula by recalling that `height`

comes first in the alphabet before `width`

.

## Matplotlib Subplots Different Sizes

If you have used `plt.subplot()`

before (I’ve written a whole tutorial on this too), you’ll know that the grids you create are limited. Each `Subplot`

must be part of a regular grid i.e. of the form `1/x`

for some integer `x`

. If you create a 2×1 grid, you have 2 rows and each row takes up 1/2 of the space. If you create a 3×2 grid, you have 6 subplots and each takes up 1/6 of the space.

Using `plt.subplots()`

you can create a 2×1 plot with 2 rows that take up any fraction of space you want.

Let’s make a 2×1 plot where the top row takes up 1/3 of the space and the bottom takes up 2/3.

You do this by specifying the `gridspec_kw`

argument and passing a dictionary of values. The main arguments we are interested in are `width_ratios`

and `height_ratios`

. They accept lists that specify the width ratios of columns and height ratios of the rows. In this example the top row is `1/3`

of the `Figure`

and the bottom is `2/3`

. Thus the height ratio is `1:2`

or `[1, 2]`

as a list.

# 2 x1 grid where top is 1/3 the size and bottom is 2/3 the size fig, axes = plt.subplots(nrows=2, ncols=1, gridspec_kw={'height_ratios': [1, 2]}) plt.tight_layout() plt.show()

The only difference between this and a regular 2×1 `plt.subplots()`

call is the `gridspec_kw`

argument. It accepts a dictionary of values. These are passed to the matplotlib GridSpec constructor (the underlying class that creates the grid).

Let’s create a 2×2 plot with the same `[1, 2]`

height ratios but let’s make the left hand column take up 3/4 of the space.

# Heights: Top row is 1/3, bottom is 2/3 --> [1, 2] # Widths : Left column is 3/4, right is 1/4 --> [3, 1] ratios = {'height_ratios': [1, 2], 'width_ratios': [3, 1]} fig, axes = plt.subplots(nrows=2, ncols=2, gridspec_kw=ratios) plt.tight_layout() plt.show()

Everything is the same as the previous plot but now we have a 2×2 grid and have specified `width_ratios`

. Since the left column takes up `3/4`

of the space and the right takes up `1/4`

the ratios are `[3, 1]`

.

## Matplotlib Subplots Size

In the previous examples, there were white lines that cross over each other to separate the `Subplots`

into a clear grid. But sometimes you will not have that to guide you. To create a more complex plot, you have to manually add `Subplots`

to the grid.

You could do this using the `plt.subplot()`

function. But since we are focusing on `Figure`

and `Axes`

notation in this article, I’ll show you how to do it another way.

You need to use the `fig.add_subplot()`

method and it has the same notation as `plt.subplot()`

. Since it is a `Figure`

method, you first need to create one with the `plt.figure()`

function.

fig = plt.figure()

<Figure size 432x288 with 0 Axes>

The hardest part of creating a `Figure`

with different sized `Subplots`

in matplotlib is figuring out what fraction of space each `Subplot`

takes up.

So, it’s a good idea to know what you are aiming for before you start. You could sketch it on paper or draw shapes in PowerPoint. Once you’ve done this, everything else is much easier.

I’m going to create this shape.

I’ve labeled the fraction each `Subplot`

takes up as we need this for our `fig.add_subplot()`

calls.

I’ll create the biggest `Subplot`

first and the others in descending order.

The right hand side is half of the plot. It is one of two plots on a `Figure`

with 1 row and 2 columns. To select it with `fig.add_subplot()`

, you need to set `index=2`

.

Remember that indexing starts from 1 for the functions `plt.subplot()`

and `fig.add_subplot()`

.

In the image, the blue numbers are the index values each `Subplot`

has.

ax1 = fig.add_subplot(122)

As you are working with `Axes`

objects, you need to store the result of `fig.add_subplot()`

so that you can plot on it afterwards.

Now, select the bottom left `Subplot`

in a a 2×2 grid i.e. `index=3`

ax2 = fig.add_subplot(223)

Lastly, select the top two `Subplots`

on the left hand side of a 4×2 grid i.e. `index=1`

and `index=3`

.

ax3 = fig.add_subplot(423) ax4 = fig.add_subplot(421)

When you put this altogether you get

# Initialise Figure fig = plt.figure() # Add 4 Axes objects of the size we want ax1 = fig.add_subplot(122) ax2 = fig.add_subplot(223) ax3 = fig.add_subplot(423) ax4 = fig.add_subplot(421) plt.tight_layout(pad=0.1) plt.show()

Perfect! Breaking the `Subplots`

down into their individual parts and knowing the shape you want, makes everything easier.

Now, let’s do something you can’t do with `plt.subplot()`

. Let’s have 2 plots on the left hand side with the bottom plot twice the height as the top plot.

Like with the above plot, the right hand side is half of a plot with 1 row and 2 columns. It is `index=2`

.

So, the first two lines are the same as the previous plot

fig = plt.figure() ax1 = fig.add_subplot(122)

The top left takes up `1/3`

of the space of the left-hand half of the plot. Thus, it takes up `1/3 x 1/2 = 1/6`

of the total plot. So, it is `index=1`

of a 3×2 grid.

ax2 = fig.add_subplot(321)

The final subplot takes up 2/3 of the remaining space i.e. `index=3`

and `index=5`

of a 3×2 grid. But you can’t add both of these indexes as that would add two `Subplots`

to the `Figure`

. You need a way to add one `Subplot`

that *spans* two rows.

You need the matplotlib subplot2grid function – `plt.subplot2grid()`

. It returns an `Axes`

object and adds it to the current `Figure`

.

Here are the most important arguments:

ax = plt.subplot2grid(shape, loc, rowspan, colspan)

`shape`

– tuple of 2 integers – the*shape*of the overall grid e.g. (3, 2) has 3 rows and 2 columns.`loc`

– tuple of 2 integers – the*location*to place the`Subplot`

in the grid. It uses 0-based indexing so (0, 0) is first row, first column and (1, 2) is second row, third column.`rowspan`

– integer, default 1- number of rows for the`Subplot`

to span to the right`colspan`

– integer, default 1 – number of columns for the`Subplot`

to span down

From those definitions, you need to select the middle left `Subplot`

and set `rowspan=2`

so that it spans down 2 rows.

Thus, the arguments you need for `subplot2grid`

are:

`shape=(3, 2)`

– 3×2 grid`loc=(1, 0)`

– second row, first colunn (0-based indexing)`rowspan=2`

– span down 2 rows

This gives

ax3 = plt.subplot2grid(shape=(3, 2), loc=(1, 0), rowspan=2)

Sidenote: why matplotlib chose 0-based indexing for `loc`

when *everything else* uses 1-based indexing is a mystery to me. One way to remember it is that `loc`

is similar to `locating`

. This is like slicing Numpy arrays which use 0-indexing. Also, if you use `GridSpec`

, you will often use Numpy slicing to choose the number of rows and columns that `Axes`

span.

Putting this together, you get

fig = plt.figure() ax1 = fig.add_subplot(122) ax2 = fig.add_subplot(321) ax3 = plt.subplot2grid(shape=(3, 2), loc=(1, 0), rowspan=2) plt.tight_layout() plt.show()

## Matplotlib Subplots_Adjust

If you aren’t happy with the spacing between plots that `plt.tight_layout()`

provides, manually adjust the spacing with the matplotlib subplots_adjust function.

It takes 6 optional, self explanatory arguments. Each is a float in the range [0.0, 1.0] and is a fraction of the font size:

`left`

,`right`

,`bottom`

and`top`

is the spacing on each side of the`Suplots`

`wspace`

– the**width**between`Subplots`

`hspace`

– the**height**between`Subplots`

Let’s compare `tight_layout`

with `subplots_adjust`

.

fig, axes = plt.subplots(nrows=2, ncols=2, sharex=<strong>True</strong>, sharey=<strong>True</strong>) plt.tight_layout() plt.show()

Here is a 2×2 grid with `plt.tight_layout()`

. I’ve set `sharex`

and `sharey`

to `True`

to remove unnecessary axis labels.

fig, axes = plt.subplots(nrows=2, ncols=2, sharex=<strong>True</strong>, sharey=<strong>True</strong>) plt.subplots_adjust(wspace=0.05, hspace=0.05) plt.show()

Now I’ve decreased the height and width between `Subplots`

to `0.05`

and there is hardly any space between them.

To avoid loads of similar examples, I recommend you play around with the arguments to get a feel for how this function works.

## Matplotlib Subplots Colorbar

Adding a colorbar to each `Axes`

is similar to adding a legend. You store the `ax.plot()`

call in a variable and pass it to `fig.colorbar()`

.

Colorbars are `Figure`

methods since they are placed on the `Figure`

itself and not the `Axes`

. Yet, they do take up space from the `Axes`

they are placed on.

Let’s look at an example.

# Generate two 10x10 arrays of random numbers in the range [0.0, 1.0] data1 = np.random.random((10, 10)) data2 = np.random.random((10, 10)) # Initialise Figure and Axes objects with 1 row and 2 columns # Constrained_layout=True is better than plt.tight_layout() # Make twice as wide as it is long with figaspect fig, axes = plt.subplots(nrows=1, ncols=2, constrained_layout=True, figsize=plt.figaspect(1/2)) pcm1 = axes[0].pcolormesh(data1, cmap='Blues') # Place first colorbar on first column - index 0 fig.colorbar(pcm1, ax=axes[0]) pcm2 = axes[1].pcolormesh(data2, cmap='Greens') # Place second colorbar on second column - index 1 fig.colorbar(pcm2, ax=axes[1]) plt.show()

First, I generated two 10×10 arrays of random numbers in the range [0.0, 1.0] using the `np.random.random()`

function. Then I initialized the 1×2 grid with `plt.subplots()`

.

The keyword argument `constrained_layout=True`

achieves a similar result to calling `plt.tight_layout()`

. However, `tight_layout`

only checks for tick labels, axis labels and titles. Thus, it ignores colorbars and legends and often produces bad looking plots. Fortunately, `constrained_layout`

takes colorbars and legends into account. Thus, it should be your go-to when automatically adjusting these types of plots.

Finally, I set `figsize=plt.figaspect(1/2)`

to ensure the plots aren’t too squashed together.

After that, I plotted the first heatmap, colored it blue and saved it in the variable `pcm1`

. I passed that to `fig.colorbar()`

and placed it on the first column – `axes[0]`

with the `ax`

keyword argument. It’s a similar story for the second heatmap.

The more `Axes`

you have, the fancier you can be with placing colorbars in matplotlib. Now, let’s look at a 2×2 example with 4 `Subplots`

but only 2 colorbars.

# Set seed to reproduce results np.random.seed(1) # Generate 4 samples of the same data set using a list comprehension # and assignment unpacking data1, data2, data3, data4 = [np.random.random((10, 10)) for _ in range(4)] # 2x2 grid with constrained layout fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True) # First column heatmaps with same colormap pcm1 = axes[0, 0].pcolormesh(data1, cmap='Blues') pcm2 = axes[1, 0].pcolormesh(data2, cmap='Blues') # First column colorbar - slicing selects all rows, first column fig.colorbar(pcm1, ax=axes[:, 0]) # Second column heatmaps with same colormap pcm3 = axes[0, 1].pcolormesh(data3+1, cmap='Greens') pcm4 = axes[1, 1].pcolormesh(data4+1, cmap='Greens') # Second column colorbar - slicing selects all rows, second column # Half the size of the first colorbar fig.colorbar(pcm3, ax=axes[:, 1], shrink=0.5) plt.show()

If you pass a list of `Axes`

to `ax`

, matplotlib places the colorbar along those `Axes`

. Moreover, you can specify where the colorbar is with the `location`

keyword argument. It accepts the strings `'bottom'`

, `'left'`

, `'right'`

, `'top'`

or `'center'`

.

The code is similar to the 1×2 plot I made above. First, I set the seed to 1 so that you can reproduce the results – you will soon plot this again with the colorbars in different places.

I used a list comprehension to generate 4 samples of the same dataset. Then I created a 2×2 grid with `plt.subplots()`

and set `constrained_layout=True`

to ensure nothing overlaps.

Then I made the plots for the first column – `axes[0, 0]`

and `axes[1, 0]`

– and saved their output. I passed one of them to `fig.colorbar()`

. It doesn’t matter which one of `pcm1`

or `pcm2`

I pass since they are just different samples of the same dataset. I set `ax=axes[:, 0]`

using Numpy slicing notation, that is all rows `:`

and the first column `0`

.

It’s a similar process for the second column but I added 1 to `data3`

and `data4`

to give a range of numbers in [1.0, 2.0] instead. Lastly, I set `shrink=0.5`

to make the colorbar half its default size.

Now, let’s plot the same data with the same colors on each row rather than on each column.

# Same as above np.random.seed(1) data1, data2, data3, data4 = [np.random.random((10, 10)) for _ in range(4)] fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True) # First row heatmaps with same colormap pcm1 = axes[0, 0].pcolormesh(data1, cmap='Blues') pcm2 = axes[0, 1].pcolormesh(data2, cmap='Blues') # First row colorbar - placed on first row, all columns fig.colorbar(pcm1, ax=axes[0, :], shrink=0.8) # Second row heatmaps with same colormap pcm3 = axes[1, 0].pcolormesh(data3+1, cmap='Greens') pcm4 = axes[1, 1].pcolormesh(data4+1, cmap='Greens') # Second row colorbar - placed on second row, all columns fig.colorbar(pcm3, ax=axes[1, :], shrink=0.8) plt.show()

This code is similar to the one above but the plots of the same color are on the same row rather than the same column. I also shrank the colorbars to 80% of their default size by setting `shrink=0.8`

.

Finally, let’s set the blue colorbar to be on the bottom of the heatmaps.

You can change the location of the colorbars with the `location`

keyword argument in `fig.colorbar()`

. The only difference between this plot and the one above is this line

fig.colorbar(pcm1, ax=axes[0, :], shrink=0.8, location='bottom')

If you increase the `figsize`

argument, this plot will look much better – at the moment it’s quite cramped.

I recommend you play around with matplotlib colorbar placement. You have total control over how many colorbars you put on the `Figure`

, their location and how many rows and columns they span. These are some basic ideas but check out the docs to see more examples of how you can place colorbars in matplotlib.

## Matplotlib Subplot Grid

I’ve spoken about `GridSpec`

a few times in this article. It is the underlying class that *specifies the geometry of the grid that a subplot can be placed in*.

You can create any shape you want using `plt.subplots()`

and `plt.subplot2grid()`

. But some of the more complex shapes are easier to create using `GridSpec`

. If you want to become a total pro with matplotlib, check out the docs and look out for my article discussing it in future.

## Summary

You can now create *any* shape you can imagine in matplotlib. Congratulations! This is a huge achievement. Don’t worry if you didn’t fully understand everything the first time around. I recommend you bookmark this article and revisit it from time to time.

You’ve learned the underlying classes in matplotlib: `Figure`

, `Axes`

, `XAxis`

and `YAxis`

and how to plot with respect to them. You can write shorter, more readable code by using these methods and `ax.set()`

to add titles, xlabels and many other things to each `Axes`

. You can create more professional looking plots by sharing the x-axis and y-axis and add legends anywhere you like.

You can create `Figures`

of any size that include `Subplots`

of any size – you’re no longer restricted to those that take up `1/x`

th of the plot. You know that to make the best plots, you should plan ahead and figure out the shape you are aiming for.

You know when to use `plt.tight_layout()`

(ticks, labels and titles) and `constrained_layout=True`

(legends and colorbars) and how to manually adjust spacing between plots with `plt.subplots_adjust()`

.

Finally, you can add colorbars to as many `Axes`

as you want and place them wherever you’d like.

You’ve done everything now. All that is left is to practice these plots so that you can quickly create amazing plots whenever you want.

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