Matplotlib Visualizations for Data Analysis

Introduction

In this tutorial, you will learn how to use Matplotlib to create visualizations in Python. Matplotlib is a popular data visualization library in Python used to create a variety of charts and graphs. With Matplotlib, you can create line plots, scatter plots, bar plots, histograms, and many other types of visualizations.

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Import Matplotlib

Before creating any visualization, we need to import the Matplotlib library. We will also use the NumPy library to generate some sample data.

import matplotlib.pyplot as plt
import numpy as np

Create Sample Data

Let's create some sample data that we will use to create visualizations. We will generate four sets of data, each with 11 x,y data points.

x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]
y1 = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]
y2 = [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74]
y3 = [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]
x4 = [8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8]
y4 = [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]

Create a Figure with Subplots

Now we will create a figure with four subplots, one for each dataset. We will also set the x and y limits to be the same for all subplots.

fig, axs = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(6, 6),
                        gridspec_kw={'wspace': 0.08, 'hspace': 0.08})
axs[0, 0].set(xlim=(0, 20), ylim=(2, 14))
axs[0, 0].set(xticks=(0, 10, 20), yticks=(4, 8, 12))

Plot the Data

For each subplot, we will plot the x and y data points and add a linear regression line. We will also add a text box with some statistics about the data.

datasets = {
    'I': (x, y1),
    'II': (x, y2),
    'III': (x, y3),
    'IV': (x4, y4)
}

for ax, (label, (x, y)) in zip(axs.flat, datasets.items()):
    ax.text(0.1, 0.9, label, fontsize=20, transform=ax.transAxes, va='top')
    ax.tick_params(direction='in', top=True, right=True)
    ax.plot(x, y, 'o')

    ## linear regression
    p1, p0 = np.polyfit(x, y, deg=1)  ## slope, intercept
    ax.axline(xy1=(0, p0), slope=p1, color='r', lw=2)

    ## add text box for the statistics
    stats = (f'$\\mu$ = {np.mean(y):.2f}\n'
             f'$\\sigma$ = {np.std(y):.2f}\n'
             f'$r$ = {np.corrcoef(x, y)[0][1]:.2f}')
    bbox = dict(boxstyle='round', fc='blanchedalmond', ec='orange', alpha=0.5)
    ax.text(0.95, 0.07, stats, fontsize=9, bbox=bbox,
            transform=ax.transAxes, horizontalalignment='right')

plt.show()

Interpret the Results

The resulting visualization is a set of four subplots, each showing a different dataset. The x and y limits are the same for all subplots. Each subplot contains the x and y data points as well as a linear regression line. The text box in the lower right corner of each subplot shows some statistics about the data, including the mean, standard deviation, and correlation coefficient.

Summary

In this tutorial, you learned how to use Matplotlib to create visualizations in Python. You learned how to create a figure with subplots, plot data points, add a linear regression line, and add a text box with statistics about the data. With Matplotlib, you can create a wide variety of visualizations to explore and analyze your data.