Introduction to matplotlib

Note

This material is mostly adapted from the following resources:

• Earth and Environmental Data Science: Numpy and Matplotlib

• Data Science for Energy System Modelling: Introduction to numpy and matplotlib

Matplotlib is a comprehensive library for creating static and animated visualizations in Python.

• Website: https://matplotlib.org/

• GitHub: matplotlib/matplotlib

Note

Documentation for this package is available at https://matplotlib.org/stable/index.html.

Note

If you have not yet set up Python on your computer, you can execute this tutorial in your browser via Google Colab. Click on the rocket in the top right corner and launch “Colab”. If that doesn’t work download the .ipynb file and import it in Google Colab

Then, install numpy and matplotlib by executing the following command in a Jupyter cell at the top of the notebook. We will use numpy to create some of the data that we will visualize with matplotlib.

!pip install matplotlib numpy

Importing a Package

Besides numpy, we import the module pyplot from the matplotlib library and nicknames it as plt for brevity in the code.

import numpy as np
from matplotlib import pyplot as plt

Visualizing Arrays with Matplotlib

Let’s create an array to visualize it.

x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
y = np.linspace(-np.pi, np.pi, 50)
xx, yy = np.meshgrid(x, y)
xx.shape, yy.shape

((50, 100), (50, 100))

For plotting a 1D array as a line, we use the plot command.

plt.plot(x);

There are many ways to visualize 2D data. He we use pcolormesh.

plt.pcolormesh(xx);

f = np.sin(xx) * np.cos(0.5 * yy)

plt.pcolormesh(f)

<matplotlib.collections.QuadMesh at 0x7f7b5dc42c50>

g = f * x

plt.pcolormesh(g)

<matplotlib.collections.QuadMesh at 0x7f7b5ff98490>

# apply on just one axis
g_ymean = g.mean(axis=0)
g_xmean = g.mean(axis=1)

plt.plot(x, g_ymean)

[<matplotlib.lines.Line2D at 0x7f7b5ffdb450>]

plt.plot(g_xmean, y)

[<matplotlib.lines.Line2D at 0x7f7b5dd5cc50>]

Figures and Axes

The figure is the highest level of organization of matplotlib objects.

fig = plt.figure()

<Figure size 640x480 with 0 Axes>

fig = plt.figure(figsize=(13, 5))

<Figure size 1300x500 with 0 Axes>

fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])

Subplots

Subplot syntax is a more convenient way to specify the creation of multiple axes.

fig, ax = plt.subplots()

ax

<Axes: >

fig, axes = plt.subplots(ncols=2, figsize=(8, 4), subplot_kw={"facecolor": "blue"})

axes

array([<Axes: >, <Axes: >], dtype=object)

Drawing into Axes

All plots are drawn into axes.

# create some data to plot
import numpy as np

x = np.linspace(-np.pi, np.pi, 100)
y = np.cos(x)
z = np.sin(6 * x)

fig, ax = plt.subplots()
ax.plot(x, y)

[<matplotlib.lines.Line2D at 0x7f7b5db68d90>]

This does the same thing as

plt.plot(x, y)

[<matplotlib.lines.Line2D at 0x7f7b5d7c4c50>]

This starts to matter when we have multiple axes to manage.

fig, axes = plt.subplots(figsize=(8, 4), ncols=2)
ax0, ax1 = axes
ax0.plot(x, y)
ax1.plot(x, z)

[<matplotlib.lines.Line2D at 0x7f7b5c5a37d0>]

Labeling Plots

Labeling plots is very important! We want to know what data is shown and what the units are. matplotlib offers some functions to label graphics.

fig, ax = plt.subplots(figsize=(4, 4))

ax.plot(x, y)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("x vs. y")

# squeeze everything in
plt.tight_layout()

Customizing Plots

fig, ax = plt.subplots()
ax.plot(x, y, x, z)

[<matplotlib.lines.Line2D at 0x7f7b5dd554d0>,
 <matplotlib.lines.Line2D at 0x7f7b5c5dea10>]

It’s simple to switch axes

fig, ax = plt.subplots()
ax.plot(y, x, z, x)

[<matplotlib.lines.Line2D at 0x7f7b5dc1f610>,
 <matplotlib.lines.Line2D at 0x7f7b5c467b50>]

Line Styles

fig, ax = plt.subplots()
ax.plot(x, y, linestyle="--")
ax.plot(x, z, linestyle=":")

[<matplotlib.lines.Line2D at 0x7f7b5c4d8d90>]

Colors

As described in the colors documentation, there are some special codes for commonly used colors.

fig, ax = plt.subplots()
ax.plot(x, y, color="black")
ax.plot(x, z, color="red")

[<matplotlib.lines.Line2D at 0x7f7b5c382b50>]

Markers

There are lots of different markers availabile in matplotlib!

fig, ax = plt.subplots()
ax.plot(x[:20], y[:20], marker="o", markerfacecolor="red", markeredgecolor="black")
ax.plot(x[:20], z[:20], marker="^", markersize=10)

[<matplotlib.lines.Line2D at 0x7f7b5c3fed90>]

Axis Limits

fig, ax = plt.subplots()
ax.plot(x, y, x, z)
ax.set_xlim(-5, 5)
ax.set_ylim(-3, 3)

(-3.0, 3.0)

Scatter Plots

fig, ax = plt.subplots()

splot = ax.scatter(y, z, c=x, s=(100 * z**2 + 5), cmap="viridis")
fig.colorbar(splot)

<matplotlib.colorbar.Colorbar at 0x7f7b689e0490>

There are many different colormaps available in matplotlib: https://matplotlib.org/stable/tutorials/colors/colormaps.html

Bar Plots

labels = ["Bhadla", "Noor"]
values = [2225, 1177]

fig, ax = plt.subplots(figsize=(5, 5))
ax.bar(labels, values)

ax.set_ylabel("MW")

plt.tight_layout()