Matplotlib | How to plot graphs! Tutorial

2024-06-18

As you plot a graph for the first time, you are anxious about how to get started. You may be lost, unable to understand the structure and elements of a graph.

This article provides a detailed explanation of how to plot graphs using Matplotlib in Python from scratch.

The focus is on understanding the composition and elements of a graph, so even beginners can get started without hesitation.

After reading this book, you will definitely be able to acquire basic skills in drawing graphs utilizing Matplotlib and gradually take on the challenge of visualizing complex data.

Table of Contents

Before plotting a graph

Drawing Graphs in Python Requires a Library

In this article, we will use Matplotlib, the most common library for graphs.

The installation procedure differs depending on whether you are using the general Python environment or the Anaconda environment, and is explained in detail below.

General Python Environment

If built from the installer from the official Python website below, it is a general Python environment.

Welcome to Python.org

This is the purest Python environment, so install Matplotlib at the command prompt.

pip install matplotlib

When the installation starts, enter y at the yes or no selection. After installation, you can check either pip list or pip freeze.

pip list

# matplotlib                    3.5.2
# matplotlib-inline             0.1.6

If this does not work, retry the following steps to build the Python environment.

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Anaconda Environment

If conducted from the following site, the Anaconda environment will be used.

Download Anaconda Distribution | Anaconda

Even if you have forgotten it, search for the apps installed on your PC using ANACONDA. You should be able to find some apps.

If you are in an Anaconda environment, Matplotlib is installed by default; start Anaconda Prompt and check it in the conda list.

conda list

# matplotlib                3.5.2            py39haa95532_0
# matplotlib-base           3.5.2            py39hd77b12b_0
# matplotlib-inline         0.1.6            py39haa95532_0

If Matplotlib is not installed, use conda install.

conda install -c conda-forge matplotlib

If this does not work, retry the following steps to build the Anaconda environment.

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A Simple Graph

Create simple line graphs using Matplotlib.

The following tabs explain with codes and flowcharts.

# step1 Import a library
import matplotlib.pyplot as plt

# step2 Create a Figure containing Axes
fig, ax = plt.subplots()

# step3 Plot data on Axes
ax.plot([1, 2, 3, 4, 5], [1, 4, 2, 5, 7])

# step4 Call the Figure
plt.show()

STEP

Import a library

Import matplotlib

Used in the code asmatplotlib.pyplot=plt

Jupyter Notebook requires %matplotlib notebook .

STEP

Create a Figure containing Axes

Matplotlib creates a Figure box and graphs the data containing one or more Axes within it.

Axes are regions that can be pointed to in x-y coordinates (θ-r for polar plots, x-y-z for 3D plots, etc.) There are several ways to create a Figure with Axes, the simplest being pyplot.subplots.

STEP

Plot data on Axes

Axes.plot was used to draw data on the axes.

Two lists were used for the x and y input values.

x: [1, 2, 3, 4, 5]
y: [1, 4, 2, 5, 7]

STEP

Call the Figure

The figure will not be displayed until plt.show() is called.

Not required in Jupyter Notebook.

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Components of a Figure

The following is a summary of the components of a Matplotlib diagram.

Components of a Figure

The whole figure (Figure)

All child Axes, titles, figure legends, color bars, etc. are supported.

Although it is convenient to write the graph in combination with Axes, it is also possible to draw the graph with Figure alone.

A region for plotting data (Axes)

Artist in the Figure that contains the plot area of the data.

It usually contains two (three in the case of 3D) Axis objects (note the difference between Axes and Axis) and provides tick and tick labels in the Axes. Each Axes also has a title, x label, and y label.

Figure Axis Setting (Axis)

Objects related to the axes of the figure.

Sets the scale and limits, and generates ticks and tick labels. The position of the scale is determined by the Locator object and the string of the scale label is formatted by the Formatter.

All elements in the figure (Artist)

Basically, anything visible on a Figure is an Artist (including Figure, Axes and Axis objects).

When Figure is invoked, all Artists are drawn.

https://matplotlib.org/stable/users/explain/quick_start.html

Quick start guide — Matplotlib 3.9.2 documentation

Types of inputs

The input for a graph is basically a list or a tuple, since an array can be used for input.

The following objects are assumed as input to the graph.

numpy.array
numpy.ma.masked_array
numpy.asarray

Pandas data and array-like classes such as numpy.matrix may not work as intended. Please convert them to numpy.array objects before plotting.

Coding styles

There are basically two ways to plot a graph using Matplotlib.

Figure and Axes graph drawing styles

Figure＋Axes (OO-style)

Creates Figures and Axes and calls them.

Basic style, recommended for complex plots and large projects.

Only Figure (pyplot-style)

Create a Figure and Axes and call only the Figure using the pyplot function.

This pyplot style is very useful for quick interactive work.

Figure＋Axes (OO-style)

The explicit “Axes” interface creates other Artists using the methods of the Figure or Axes object to build the visualization step by step.

This is called “object-oriented”=OO-style.

Each object is explicitly referenced, making it very flexible and allowing for customization after the object is created and before it is displayed.

General style, recommended for complex plots and large projects.

# step1 Import libraries
import matplotlib.pyplot as plt
import numpy as np


# step2 Create data
x = np.arange(8)
apples = [5, 12, 8, 15, 20, 10, 18, 7]
oranges = [3, 14, 9, 17, 22, 11, 16, 6]
grapes = [8, 18, 12, 20, 25, 15, 23, 9]

# step3 Create graph frames
fig, ax = plt.subplots(figsize=(6, 4))

# step4 Plot graphs
ax.plot(x, apples, label='apples')
ax.plot(x, oranges, label='oranges')
ax.plot(x, grapes, label='grapes')

# step5 Set axis labels, a title and a legend
ax.set_xlabel('X label') # X label
ax.set_ylabel('Y label') # Y label
ax.set_title('Figure+Axes OO-style')  # Title
ax.legend()  # Legend

# step6 Call the Figure
plt.show()

STEP

Import libraries

Import matplotlib and numpy

Use matplotlib.pyplot=plt in your code

numpy is a library for numerical computations, often used in combination with Matplotlib

Jupyter Notebook requires%matplotlib notebook

STEP

Create data

Create an array data of 1 x and 3 y

x: an array with 8 elements from 0~7

x = np.arange(8) = [0, 1, 2, 3, 4, 5, 6, 7]

y: 3 lists of the same 8 elements as x

STEP

Create graph frames

To draw a graph in Matplotlib, you need to create a graph frame

Create the main fig and the ax that actually draws the graph in pyplot.subplots

pyplot.subplots (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : fig, ax
Create a figure and a set of subplots.

STEP

Plot a graph with Axes.plot

Plot data on an axis with Axes.plot

Axes.plot(x, y, label), and if x and y are arrays with the same number of elements, they can be drawn.

For details, please refer to the following article

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STEP

Set axis labels, a title, and a legend

Add each setting to the Axes object.

Axis label: ax.set_xlabel('str')

Title: ax.set_title('str')

Legend: ax.legend()

STEP

Call the Figure

The figure will not be displayed until plt.show() is called.

Not required in Jupyter Notebook

Only Figure (pyplot-style)

The implicit “pyplot” interface keeps track of the last created Figures and Axes and adds Artist to the object the user wants.

The code is simple and easy to understand, and graphs can be plotted quickly.

However, it is less flexible because it always tracks the last Figure, and if there are multiple Figures, it must be recalled and an Artist must be added.

# step1 Import libraries
import matplotlib.pyplot as plt
import numpy as np


# step2 Create data
x = np.arange(8)
apples = [5, 12, 8, 15, 20, 10, 18, 7]
oranges = [3, 14, 9, 17, 22, 11, 16, 6]
grapes = [8, 18, 12, 20, 25, 15, 23, 9]

# step3 Create a graph frame
plt.figure(figsize=(6, 4))

# step4 Plot graphs
plt.plot(x, apples, label='apples')
plt.plot(x, oranges, label='oranges')
plt.plot(x, grapes, label='grapes')

# step5 Set Axis labels, a title and a legend
plt.xlabel('X label') # X label
plt.ylabel('Y label') # Y label
plt.title('Only Figure pyplot-style')  # Title
plt.legend()  # legend

# step6 Call the Figure
plt.show()

STEP

Import libraries

Import matplotlib and numpy

Use matplotlib.pyplot=plt in your code

numpy is a library for numerical computations, often used in combination with Matplotlib

Jupyter Notebook requires%matplotlib notebook

STEP

Create data

Create an array data of 1 x and 3 y

x: an array with 8 elements from 0~7

x = np.arange(8) = [0, 1, 2, 3, 4, 5, 6, 7]

y: 3 lists of the same 8 elements as x

STEP

Create a graph frame

To draw a graph in Matplotlib, you need to create a graph frame

Create a figure with pyplot.figure

pyplot.figure (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : Figure
Create a new figure or activate an existing figure.

STEP

Plot a graph with Axes.plot

Plot data on an axis with Axes.plot

Axes.plot(x, y, label), and if x and y are arrays with the same number of elements, they can be drawn.

For details, please refer to the following article

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STEP

Set axis labels, a title, and a legend

Add each setting to the Axes object.

Axis label: ax.set_xlabel('str')

Title: ax.set_title('str')

Legend: ax.legend()

STEP

Call the Figure

The figure will not be displayed until plt.show() is called.

Not required in Jupyter Notebook

Styling of graphs

Styling can be added (1) when calling Axes.plot or (2) later using “setter” in Artist. The method (1) is recommended, and (2) needs to be replaced with a variable.

Three types of styling are performed: color, line thickness, and line type.

The code used OO-style.

# step3 Plot a graph

# 1 when calling Axes.plot
ax.plot(x, apples, label='apples', color='red', linewidth=3, linestyle='--')

# 2 later using "setter" in Artist
o, = ax.plot(x, oranges, label='oranges', color='#F44336')
o.set_linewidth(2)
o.set_linestyle('-.')

Multiple Graphs

Multiple graphs can be placed in one window using the matrix method.

The number of items is specified in parentheses in the order of rows and columns, as in plt.subplots(rows, columns).

Each call to Axes should be made as axs[0] or axs[1], as in the case of lists.

# step2 Create graph frames
fig, axs = plt.subplots(2, 1, figsize=(6, 4))

# step3 Plot graphs
axs[0].plot(x, apples, label='apples', color='r')
axs[1].plot(x, oranges, label='oranges', color='C1')

# step4 Set axis labels, a title and a legend
axs[0].set_xlabel('X label') # X label
axs[0].set_ylabel('Y label') # Y label
axs[0].legend()  # legend
fig.suptitle('Multiple Figures and Axes')

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Set axis scales and ticks

Each Axes has two Axis objects representing the X and Y axes. These control the scale, tick position, and tick style of the axis.

The upper and lower limits of the scale, the tick width of the scale, and the logarithmic scale are set.

The code is written in the same way as for axis labels and titles.

# step0 Import libraries
import matplotlib.pyplot as plt
import numpy as np

# step1 Create data
x = np.linspace(0, 10, 100)
y1 = np.exp(x) + 2 * np.sin(2 * x)
y2 = np.exp(x) + 2 * np.cos(2 * x)

# step2 Create graph frames
fig, axs = plt.subplots(1, 2, sharex=True)

# step3 Plot graphs
for ax in axs.flat:
    ax.plot(x, y1, label='Sample 1')
    ax.plot(x, y2, label='Sample 2')
    ax.legend()

# step4 Set axis labels and a title
axs[0].set_title('linear')
axs[1].set_title('log')

fig.suptitle('scale')
fig.supxlabel('X label')
fig.supylabel('Y label')

# step5 Set axis scales and ticks
axs[0].set_xlim(0, 10)
axs[0].set_ylim(0)
axs[0].set_yticks([0, 6000, 12000, 18000, 24000])
axs[1].set_yscale('log')

# step6 Call the figure
plt.show()

STEP

Create data

x is an array of 100 equal parts in the range 0~10
y is an array of sine and cosine waves, respectively

x and y should be arrays with the same number of elements.

numpy.linspace (start, stop, num) : ndarray
Return evenly spaced numbers over a specified interval.
Returns num evenly spaced samples, calculated over the interval [start, stop].

STEP

Create graph frames

To draw a graph in Matplotlib, you need to create a graph frame

Create the main fig and the ax that actually draws the graph in pyplot.subplots

pyplot.subplots (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : fig, ax
Create a figure and a set of subplots.

STEP

Plot line graphs with Axes.plot

A graph is displayed using the x and y data created in step 1.

The Axes object is made into a 1-dimensional array by axs.flat and a for statement is executed.

In this article, it is originally a one-dimensional array, but when (2, 2) is specified in step 2, the array becomes a two-dimensional array.

STEP

Set axis labels and a title

Add each setting to the Figure and each Axes object

Axis label: fig.supxlabel('X label')

Title: fig.suptitle('str'),axs[0].set_title('linear')

STEP

Set axis scales and ticks

Add settings using the same method as in STEP 4

Upper and lower limits : axs[0].set_xlim(0, 10)

Scale increment width : axs[0].set_yticks([0, 6000, 12000, 18000, 24000])

logarithmic scale : axs[1].set_yscale('log')

Matplotlib | How to plot graphs! Tutorial

Before plotting a graph

General Python Environment

Anaconda Environment

A Simple Graph

Components of a Figure

Types of inputs

Coding styles

Figure＋Axes (OO-style)

Only Figure (pyplot-style)

Styling of graphs

Multiple Graphs

Set axis scales and ticks

References

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