How to create basic plots (in Python, using Matplotlib)

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Task

Plotting is a huge topic with many options and variations, but the most foundational types of plots are a line plot and a scatterplot. How can we create those?

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• How to create bivariate plots to compare groups
• How to plot interaction effects of treatments

Solution

We will create some fake data using Python lists, for simplicity. But everything we show below works also if your data is in columns of a DataFrame, such as df['age'].

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patient_id     = [   0,   1,   2,   3,   4,   5,   6,   7,   8,   9 ]
patient_height = [  60,  64,  64,  65,  66,  66,  70,  72,  72,  76 ]
patient_weight = [ 141, 182, 169, 204, 138, 198, 180, 175, 244, 196 ]

The conventional way to import matplotlib in Python is as follows.

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import matplotlib.pyplot as plt

Make a line plot by giving the $x$ and $y$​ data values in separate lists (or in this case pandas Series). This line plot is very jagged just because the data was random.

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plt.plot( patient_id, patient_height )         # create plot
plt.show()                                     # display plot

You can make a scatterplot as follows.

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plt.scatter( patient_height, patient_weight )  # create plot
plt.show()                                     # display plot

If your data is already in a pandas DataFrame, there are shortcuts:

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# Plot all columns:
df.plot()
plt.show()

# Plot all columns in separate subplots:
df.plot( subplots = True )
plt.show()

# Plot one column:
df['column'].plot()
plt.show()

# Plot specific columns:
df.plot( x='col name', y='other col name' )
plt.show()

Content last modified on 24 July 2023.

See a problem? Tell us or edit the source.

Contributed by Nathan Carter (ncarter@bentley.edu)