Mastering Python Matplotlib Histogram Visualization

Introduction

This lab is a step-by-step tutorial on how to use Python Matplotlib library to create a histogram. A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable.

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Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL python(("`Python`")) -.-> python/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlottingDataGroup(["`Plotting Data`"]) python(("`Python`")) -.-> python/DataStructuresGroup(["`Data Structures`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/PythonStandardLibraryGroup(["`Python Standard Library`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) python/BasicConceptsGroup -.-> python/comments("`Comments`") matplotlib/BasicConceptsGroup -.-> matplotlib/importing_matplotlib("`Importing Matplotlib`") matplotlib/BasicConceptsGroup -.-> matplotlib/figures_axes("`Understanding Figures and Axes`") matplotlib/PlottingDataGroup -.-> matplotlib/line_plots("`Line Plots`") matplotlib/PlottingDataGroup -.-> matplotlib/histograms("`Histograms`") python/BasicConceptsGroup -.-> python/booleans("`Booleans`") python/DataStructuresGroup -.-> python/tuples("`Tuples`") python/ModulesandPackagesGroup -.-> python/importing_modules("`Importing Modules`") python/ModulesandPackagesGroup -.-> python/standard_libraries("`Common Standard Libraries`") python/PythonStandardLibraryGroup -.-> python/math_random("`Math and Random`") python/DataScienceandMachineLearningGroup -.-> python/numerical_computing("`Numerical Computing`") python/DataScienceandMachineLearningGroup -.-> python/data_visualization("`Data Visualization`") subgraph Lab Skills python/comments -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} matplotlib/importing_matplotlib -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} matplotlib/figures_axes -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} matplotlib/line_plots -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} matplotlib/histograms -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} python/booleans -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} python/tuples -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} python/importing_modules -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} python/standard_libraries -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} python/math_random -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} python/numerical_computing -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} python/data_visualization -.-> lab-48770{{"`Python Matplotlib Histogram Tutorial`"}} end

Import necessary libraries

In this step, we will import two libraries: numpy and matplotlib. Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices. Matplotlib is a plotting library for the Python programming language.

import numpy as np
import matplotlib.pyplot as plt

Generate sample data

In this step, we will generate sample data using numpy. We will generate random data from a normal distribution with a mean of 100 and standard deviation of 15.

np.random.seed(19680801)
mu = 100  ## mean of distribution
sigma = 15  ## standard deviation of distribution
x = mu + sigma * np.random.randn(437)

Create a histogram

In this step, we will create a histogram using matplotlib. We will set the number of bins to 50 and enable the density parameter to normalize bin heights so that the integral of the histogram is 1.

num_bins = 50
fig, ax = plt.subplots()
n, bins, patches = ax.hist(x, num_bins, density=True)

Add a best fit line

In this step, we will add a best fit line to the histogram. We will calculate the y-values for the line and plot it on top of the histogram.

y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
     np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--')

Customize the histogram

In this step, we will customize the histogram by adding labels, title, and adjusting the layout.

ax.set_xlabel('Smarts')
ax.set_ylabel('Probability density')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
fig.tight_layout()
plt.show()

Summary

In this lab, we learned how to use Python Matplotlib library to create a histogram. We generated sample data from a normal distribution and created a histogram using matplotlib. We also added a best fit line and customized the histogram by adding labels and a title.

Python Matplotlib Histogram Tutorial