Displaying data

Let’s Do Digital Team

Python data plotting library

• matplotlib (specifically pyplot)

What is matplotlib.pyplot?

• A library for plotting data in python.
• It is a powerful tool for creating graphs and charts.
• It is widely used in data science and machine learning.
• It is easy to use and has a wide range of features.

matplotlib.pyplot documentation

• There are many ways to look up how to do things with different python libraries.
• You can use a internet search engine, stackoverflow, or the official library documentation, as below:

https://matplotlib.org/3.5.3/api/_as_gen/matplotlib.pyplot.html

Let’s plot some 1D data

pyplot_1d.py

import matplotlib.pyplot as plot

array_1x7 = np.array([1, 2, 2, 4, 1, 1, 7])

plot.plot(array_1x7)



plot.show()

Let’s plot some 1D data

Let’s plot some 1D data

• We can however improve this plot by adding some labels and a title.

Let’s plot some 1D data

pyplot_1d.py

import matplotlib.pyplot as plot

array_1x7 = np.array([1, 2, 2, 4, 1, 1, 7])

plot.plot(array_1x7)
plot.title("1x7 Array Visualization")
plot.xlabel("Index")
plot.ylabel("Value")
plot.show()

Let’s plot some 1D data

Tweaking the plot

• As you can see, there is a lot of details on the 1D plot.
• The more dimensions you have, the more tweaking that you will need to do.
• Hence, we will not go into the minutiae of all of the settings you can change, but we will go through the main process of setting up plots.

Plotting 2D data

• Heatmaps are useful

pyplot_2d.py

array_2x7 = np.array([[0, 2, 3, 4, 5, 6, 7],
                      [8, 9, 10, 11, 12, 13, 13]])

plot.imshow(array_2x7)





plot.show()

Plotting 2D data

Plotting 2D data

• We too we can improve things with this plot.
• Let’s add a colour bar, a title, labels and format the y-axis ticks.

Plotting 2D data

• Heatmaps are useful

pyplot_2d.py

array_2x7 = np.array([[0, 2, 3, 4, 5, 6, 7],
                      [8, 9, 10, 11, 12, 13, 13]])

plot.imshow(array_2x7, aspect='auto')
plot.colorbar()
plot.title("2x7 Array Heatmap")
plot.gca().set_yticks(np.arange(array_2x7.shape[0]))
plot.xlabel("Columns")
plot.ylabel("Rows")
plot.show()

Plotting 2D data

Plotting 3D data

• Here is some 3-dimensional data to plot.

pyplot_3d.py

array_3D = np.array([[[0, 80, 2, 3, 4, 5, 6],
                      [7, 8, 9, 10, 111, 12, 13]],
                     [[1, 2, 3, 4, 5, 6, 7],
                      [8, 9, 10, 11, 122, 13, 14]],
                     [[15, 16, 17, 18, 19, 20, 21],
                      [22, 23, 24, 25, 26, 27, 28]]])

Plotting 3D data

• 3D scatter plots are useful.

pyplot_3d.py

fig = plot.figure()
ax = fig.add_subplot(111, projection='3d')

x, y, z = np.indices(array_3D.shape)
values = array_3D.flatten()

sc = ax.scatter(x.flatten(), y.flatten(), z.flatten(), c=values)












plot.show()

Plotting 3D data

Plotting 3D data

• 3D scatter plots are useful

pyplot_3d.py

fig = plot.figure()
ax = fig.add_subplot(111, projection='3d')

x, y, z = np.indices(array_3D.shape)
values = array_3D.flatten()

sc = ax.scatter(x.flatten(), y.flatten(), z.flatten(), c=values, s=100)

cbar_ax = fig.add_axes([0.9, 0.15, 0.05, 0.7]) 
cbar = fig.colorbar(sc, ax=ax, cax=cbar_ax, shrink=0.5, aspect=5)

ax.set_title('3D Array Visualization')
ax.set_xlabel('Rows')
ax.set_ylabel('Columns')
ax.set_zlabel('Depth')

ax.set_xticks(np.arange(array_3D.shape[0]))
ax.set_yticks(np.arange(array_3D.shape[1]))

plot.show()

Plotting 3D data

Now try it yourself!

• Go to the Lesson 3 folder.
• Open lesson_3.ipynb.
• Don’t forget to ask your tutor if you need help.
• See you in 20 minutes.

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