What is Python Matplotlib: A Beginner’s Guide to Python Plotting

Introduction

A vast amount of data is being consumed and wasted in today's technologically advanced society. But, you can use various programming languages to manage the data sets that require statistical operations. Moreover, it is essential to visualize your data to draw the necessary statistical conclusions. Matplotlib is one such tool for Python users. It is a complete plotting library that is useful for Python and NumPy users. The main focus of this blog is Python plotting using a popular tool. It works on top of these data structures to generate visualizations. 

What Does Matplotlib Python Mean?

It is an open-source drawing library that handles various drawing types Thus, you can create plots, bar charts, and histograms using a few lines of code. Moreover, it is useful in shells, web application servers, and Python scripts.  Additionally, you can use it to make impressive and appealing charts or to do data exploration for a machine-learning project.

Common Matplotlib Use

It provides a complete set for building static, animated, and interactive visualizations. Thus, it makes difficult things possible and simple things simpler.
 

• Produce plots fit for publication.
• You can also use a broad range of third-party packages that use this library.
• One can also make interactive charts using pan, zoom, pan, and update options.
• You can also change the visual style and layout.
• Export to a variety of file types.
• Integrate with Graphical User Interfaces and JupyterLab.

What is Pyplot in Python?

Matplotlib pyplot is a collection of tasks that make it perform like MATLAB. Thus, each pyplot function modifies a figure in some way, such as by creating a figure, or a plotting region within a figure. It keeps track of details like the plotting area and current figure. Moreover, the plotting functions direct to the current axes. Various states are also preserved across function calls in pyplot matplotlib.

The Concept of Plotting

Plotting any data is easier using Python plotting libraries. Moreover, plotting graphs is a part of data visualization, and you can do this via the Python library. It uses many general-purpose GUI toolkits. For example wxPython, Tkinter, QT, etc. They offer object-oriented APIs for embedding plots into any application. 

The most popular free and open-source Python library for technical and scientific computing is Python SciPy. The most common matplotlib example that uses SciPy is the scientific calculations. These calculations use the plotting of graphs and diagrams. 

Common Types of Plots
 

1. Bar Graphs

Blocks with varying heights or lengths that are proportional to the values can represent data. A bar chart is also a kind of display. Moreover, both vertical and horizontal data plots are possible with bar charts. The heights and lengths of the bars in a bar graph represent the values they display. They are the most popular plots that you can prepare using the matplotlib library in Python.
 

2. Pie Plot

A pie plot is a circular graph that shows statistical data as parts, or slices. Furthermore, each of these parts corresponds to a different value in the data. This kind of plot is mainly used in business and the media.
 

3. Scatter Plots

A scatter plot is created by dot-plotting many variables along the x and y axes. Values for two different numeric variables are represented using scatter plots. This is an easy matplotlib plot example to represent two different numeric values. 
 

4. Line Plots

A line plot is useful to depict the frequency of data plotted along a line. Moreover, it is the simplest and most common plotting technique. Line plotting is a basic plotting approach because it was the first charting method in use. 
 

5. 3D Plot

The 3-dimensional plotting method involves plotting data along the x, y, and z axes. Thus, it improves the display of the data. Using 3D plotting you get a better view of the data representation along the three axes of the graph.

Advantages of Python Matplotlib

It offers several advantages, making it a valuable tool for data visualization and analysis:
 

1. Adaptable and allows several types of data representation

You may get the depiction of data in bar charts, graphs, dispersed plots, and other formats. Because of its adaptability, it can successfully meet the needs of your business.
 

2. Offers an easy way to access a lot of data

Developers may use it to produce accurate graphs using vast amounts of data. Moreover, few developers can make it simpler to spot patterns and trends in the data sets when analyzing these data displays. Thus, it makes data more understandable and accessible.
 

3. Simple to use

Both beginners and experts can use it to produce results that are of high quality. Subplots are another feature that makes it easier to create and contrast different data sets.
 

4. Ensures accessibility by offering high-quality photos

The plots and images must be of a high quality for easy access and displaying data. Additionally, you get high-quality images in various formats like PDF, PGF, and PNG.
 

5. Strong tool with a wide range of uses

There are many areas where it’s data-visualization abilities can be put to use. For example, Python scripts, shells, web application servers, and Jupyter notebooks. Hence, its operations are flexible.
 

6. Cost efficient due to open-source

A free license is not a must for an open-source platform. So, you avoid the additional expenses you often spend when creating data visualizations.
 

7. Provides a wide range of options

You have a wide variety of graph types, features, and configuration options available. Thus, you can customize it to meet any requirements of your business. Moreover, developers can modify its functionality to meet specific goals and projects.

Conclusion

Matplotlib is clearly the industry standard for data visualization. Many businesses use it for their needs in data visualization. This blog provides a clear example of what it is capable of. Its main applications in Python include dealing with large or large amounts of data. Additionally, it helps in visualizing the data in various plots for easier understanding. Several advantages have also been discussed in this post.