Matplotlib¶
Introduction¶
Matplotlib is a plotting library for Python. It is a very powerful library and is used by many other Python libraries, such as Pandas, Seaborn, and Plotly.
Matplotlib is a relatively low-level library, meaning that it provides lots of flexibility to create custom plots, while at the same time it requires more code to do so. Other libraries, such as Seaborn, are built on top of Matplotlib and provide a higher-level interface.
Note
This article is just an introduction to matplotlib, and is based on the Matplotlib documentation. For more details, please refer to the documentation.
Plotting with subplots¶
Typically, we import Matplotlib as follows and then create a subplots object:
subplots is a function that returns a Figure object and an Axes object. You can imagine the Figure object
as a canvas that contains all the plots that we want to create, while the Axes object is the actual plot (the blank
sheet you can plot and hold your data), and is synonymous with the term subplot (because each axes can have
several subplots).
Note
To specify the number of subplots that we want to create, we can pass the nrows and ncols arguments to the
subplots function. For example, to create a figure with 2 rows and 2 columns, we can do:
After we have called subplots, we can use the plot method of the Axes object to plot some data with a line
connecting the different data points.
The input to the plot method is typically two arrays, one for the x-axis and one for the y-axis. For example,
if we have time-series data, we can plot the time on the x-axis and the values on the y-axis:
ax.plot(climate_change['date'], climate_change['co2'])
ax.set(
title='Amount of CO2 (ppm) in each year',
xlabel='Year',
ylabel='Amount of CO2 (ppm)'
)
Note
In the last example, the input to the plot method were two columns of a Pandas DataFrame. However, the plot
method can also take two arrays as input. Basically, matplotlib .plot(...) requires the first two arguments to be
of array-like type (e.g. lists, NumPy arrays, etc.), and the rest of the arguments are optional.
If we are creating several subplots at the same time, we can use the ax argument to specify which subplot we want
to plot on:
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(x, y)
axs[0, 0].set_title('Axis [0, 0]')
axs[0, 1].plot(x, y, 'tab:orange')
axs[0, 1].set_title('Axis [0, 1]')
axs[1, 0].plot(x, -y, 'tab:green')
axs[1, 0].set_title('Axis [1, 0]')
axs[1, 1].plot(x, -y, 'tab:red')
axs[1, 1].set_title('Axis [1, 1]')
Among other parameters, .subplots() have two parameters to specify the grid size. nrows and ncols are used
to point out the number of rows and columns we need respectively.
.plot() parameters¶
The .plot() method has a lot of parameters that can be used to customize the plot. For example, we can change the
color, width and the line style of the plot:
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2 # 0 to 15 point radii
fig, axs = plt.subplots(1, 1)
ax.scatter(x, y, s=area, c=colors, alpha=0.5)
The color can be specified in different ways. For example, we can use the name of the color, as in the previous example, or we can use the hexadecimal code of the color:
Other types of plots¶
Scatter plots¶
To create a histogram of the distribution of the data for a single 1D array, we can use the hist method:
Histograms¶
To create a histogram of the distribution of the data for a single 1D array, we can use the hist method:
fig, axs = plt.subplots(1, 2)
n_bins = 20
axs[0].hist(dist1, bins=n_bins)
axs[1].hist(dist2, bins=n_bins)
Bar plots¶
To create a bar plot, we can use the bar method:
fig, ax = plt.subplots(1, 1)
# Defines X-axis labels and Y-axis values
fruits = ['apple', 'blueberry', 'cherry', 'orange']
counts = [40, 100, 30, 55]
bar_labels = ['red', 'blue', '_red', 'orange']
bar_colors = ['tab:red', 'tab:blue', 'tab:red', 'tab:orange']
ax.bar(fruits, counts, label=bar_labels, color=bar_colors)
More types of plots¶
You can find a list of all the different types of plots that can be created with Matplotlib in the Matplotlib gallery.
Show figure¶
Once the plot is ready, we can show the figure with the show method:
Save figure¶
Finally, we can also save the figure as a file (e.g., a png or an svg file) with the savefig method:
Minimal working example¶
import matplotlib.pyplot as plt
import numpy as np
# Create data for plotting. Any pair of 1D data can be used here,
# for example two columns of a Pandas DataFrame.
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)
# Create figure and axes
fig, ax = plt.subplots()
# Plot data
ax.plot(t, s)
# Customize plot
ax.set(xlabel='time (s)', ylabel='voltage (mV)',
title='About as simple as it gets, folks')
# add a grid and legend
ax.grid()
ax.legend()
# save as file and show window with plot
fig.savefig("test.png")
plt.show()
Other options to customize the plot¶
Legends¶
There are many other options that can be used to customize the plot. For example, we can add a legend to the plot
with the legend method:
The legend method will use the label argument of the plot method to create the legend. We can also specify
the location of the legend with the loc argument:
xlim and ylim¶
Another useful method is set_xlim and set_ylim, which sets the limits of the x-axis and y-axis. They are used
to only show a part of the plot, for example:
Customizing the font¶
We can customize the fonts of the plot defining a fontdict dictionary and passing it to the set_XXX methods:
fontdict={'fontsize': 18, 'fontweight': 'bold', 'color': 'blue', 'family': 'serif'}
ax.set_title('Amount of CO2 (ppm) in each year', **fontdict)
ax.set_xlabel('Year', **fontdict)
ax.set_ylabel('Amount of CO2 (ppm)', **fontdict)
Another way to customize the font is to use the rcParams dictionary. This will change the default font for all the plots. For example, to change the font size and family, we can do:
Customizing ticks¶
We can customize the ticks of the plot (i.e., what numbers are shown in the x-axis and y-axis)
with the set_xticks and set_yticks methods:
Grid¶
We can also add a grid to the plot with the grid method:
