Matplotlib Pyplot API

Data visualization plays a key role in data science and analysis. It enables us to grasp datasets by representing them. Matplotlib, a known Python library offers a range of tools, for generating informative and visually appealing plots and charts. One outstanding feature of Matplotlib is its user-versatile interface called Pyplot API, which simplifies the process of creating plots. In this article, we will learn about Matplotlib Pyplot API in Python.

Prerequisite

• Python Matplotlib
• Pyplot
• Figure and Axes: In Matplotlib a figure serves as the container, for all components of a plot while axes represent the plotting area within a figure. It is possible to have subplots within a figure.
• Types of Plots: Matplotlib Pyplot offers support for plot types, such as line plots, scatter plots, bar plots, histograms and more. Each type of plot is suitable for kinds of data and analysis purposes.

Getting Started with Matplotlib Pyplot Api

Step 1: Import Matplotlib

In this step, we are importing matplotlib that we will use in further steps.

import matplotlib.pyplot as plt

Step 2: Create Data

To visualize your desired data start by preparing it. This can include organizing a list of values, arrays or importing data from a source, like a CSV file.

Step 3: Create a Figure and Axes

Now, create a figure and axes; for your plot using the plt.subplots() function. This will provide you with the framework to create your representation.

fig, ax = plt.subplots()

Step 4: Plot Your Data

Create a representation of your data by choosing the plot type, such, as a line plot or scatter plot. Utilize the respective Pyplot function to generate your desired plot.

ax.plot(x_data, y_data, label='Data')

Step 5: Enhance Your Plot

In this step,.enhance your plot; labels, titles, legends and additional annotations to create an visually appealing visualization.

ax.set_xlabel('X-axis Label')
ax.set_ylabel('Y-axis Label')
ax.set_title('Title')
ax.legend()

Step 6: View or Store The Plot

Use plt.show() to display it in a window or utilize plt.savefig(‘plot.png’) to save it as an image.

plt.show()

Different Types of Plot

Functions	Description
Barh Plot	It makes a horizontal bar plot.
Scatter Plot	It makes a scatter plot of x vs y.
Bar Plot	It will create a bar plot.
Histograms	Plot a histogram
Pie Charts	It plot a pie chart.
Boxplot	It makes a box plot.
Heatmaps	It makes a heatmap.
Stem Plot	It create a stem plot.

Different Types of Figure Functions

Functions	Description
Figure	It create a new figure
Savefig	It is used to save the current figure
Show	It is used to display the figure
Close	It is used to close the window of the figure
Figtext	It is used to add text to the figure

Different Types of Image Functions

Functions	Description
Imread	Used to read an image from a file into an array.
Imsave	Used to save an array in an image file.
Imshow	Used to display the image on the axes.

Different Varieties of Axis Functions

Functions	Description
Axes	Used to add axes.
Text	Used to add text to the axes.
Title	Used to set a title of the current axes.
Ylabel	Used to set the y-axis label
Xlabel	Used to set the x-axis label
XScale	Used to set the scaling of the x-axis
YScale	Used to set the scaling of the y-axis
XLim	Used to get or set the x-limits
YLim	Used to get or set the y-limits
XTicks	Used to get or set the x-limits of the current tick locations and labels

Code Implementation Example

In object-oriented interface, a Figure can be divided into two different objects:

• Figure object
• Axes object

A Figure object can contain one or more axes objects. One axes represents one plot inside figure. Figure class and Axes class are discussed in detail in later sections.

Example: In this example we’ll explore how to create a line graph that displays the connection, between time and temperature. We’ll assume that we have two sets of data; one for time and another, for temperature.

Python3

import matplotlib.pyplot as plt

# Sample data
time = [0, 1, 2, 3, 4, 5]
temperature = [25, 27, 29, 31, 28, 26]

# Create a figure and axes
fig, ax = plt.subplots()

# Create a line plot
ax.plot(time, temperature, marker='o', linestyle='-',
        color='b', label='Temperature')

# Add labels and title
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Temperature (°C)')
ax.set_title('Temperature Variation Over Time')

# Add a legend
ax.legend()

# Display the plot
plt.show()

Output:

Matplotlib Line Plot