Data Visualizations are an essential component of Data Science. Data visualization techniques derive insights from a dataset for monitoring purposes or to aid decision-making.
By observing data in a visual form, users can better understand trends. It is, therefore, easier to use the data for decision-making.
For most people, a visual is much easier to understand than a text. Thus, visualization is the most effective communication tool for analyzing and interpreting large volumes of data. It helps to identify trends, correlations, patterns, and other distributions.
Python programming language is an essential tool in Data Science, and many libraries are dedicated to DataViz. These include Seaborn, Pandas, Plotly, and Matplotlib.
What is Matplotlib?
Matplotlib is the basic plotting library for the Python language. It is the most widely used Python visualization package for its many advantages.
This library is relatively easy to use and offers various graphical tools. Exceptionally fast for multiple operations, it can export visuals to all popular formats, including PDF, SVG, JPG, PNG, BMP, and GIF.
In particular, it allows you to create graphs, histograms, scatter plots, bar charts, or even boxplots. Matplotlib can also plot 3D graphs.
This package is vital in the Python ecosystem, serving as the foundation for several Python libraries. It is, for example, the case of Pandas and Seaborn, which allow access to Matplotlib methods with less code.
Initially, Matplotlib was created by John Hunter in 2002. The tool was developed in a neurobiology study to present electrocorticogram data from epileptic patients.
Over the years, this open-source toolkit for the Python language has established itself as the most widely used plotting library. In particular, it was used to visualize data from the landing of the Phoenix spacecraft in 2008.
What are the advantages of Matplotlib?
The popularity of Matplotlib is related to several factors. First of all, it is an easy-to-learn and master tool for beginners. People who have already handled Matlab or other graphic plotting tools will quickly find their marks.
It is also a free and open-source library. It can be used in various contexts, such as Python scripts, Python and iPython shells, or Jupyter Notebooks. It can also be used on cloud services like IBM Watson Studio and Google Collab or web application servers like Flask and Django with pycharm or anaconda.
Although it is a 2D plotting library, there are several extensions to produce complex visualizations, such as 3D graphs. Many formats are supported, including png, pdf, and PGF. Also, Matplotlib excels for work on data tables.
It can also be compared with the commercial MATLAB application, giving users complete control over attributes of axes, fonts, lines, colors, and styles. Combined with NumPy, Matplotlib can be considered an open-source version of Matlab.
Matplotlib is an excellent tool for creating high-quality data visualizations, compatible with many third-party packages and libraries. It is a reference in the field of DataViz.
How to install Matplotlib?
Matplotlib and its dependency packages are available as wheel packages (WHL) files on standard Python package repositories. Using the pip package manager, they can be installed very easily on Windows, Linux, or macOS. After installing Python on the system, run the command “pip3 install matplotlib”.
If the Python package is installed for only some users on the system, it is necessary to install Microsoft Visual C++ 2008 or Microsoft Visual C++ 2010.
The dependency package on Windows can be installed easily from any browser. On macOS, just run the “Xcode-select -install” command.
It is possible to use Matplotlib and its features in Python by importing it into any environment, such as a Jupyter notebook or a Google Collab session.
The pyplot API
Pyplot is a state-based interface for Matplotlib. It is a collection of functions that do matplotlib work, like MATLAB. Each pyplot function brings changes to the visual.
The “matplotlib.pyplot.figure()” function is used to create a figure, while the “matplotlib.pyplot.plot()” function is used to create a plot area for the figure.
You can also use this API to draw lines in the plot area and embellish the visual with labels or annotations. This pyplot API is imported into Python with the code “import matplotlib. pyplot as plt”.
The main types of charts
Matplotlib allows you to create various visualizations, such as graphs and charts. Here is an overview of the most used.
The bar chart presents the distribution of data between several groups. It helps to compare multiple numeric values, giving the data with lengths and heights proportional to their values.
A histogram is used to understand the distribution of a continuous numeric variable. It allows you to take a series of data and divide it into several bins. Frequency data points are then plotted in each bin. This visual helps count variables or the distribution between two entities of variables.
A pie chart is divided into parts, illustrating numerical proportions. Each slice of this pie represents the proportion of portions of a whole. It is used in particular to visualize the market shares of a company.
The line graph represents time series to understand a trend over time. It is mainly used for prediction or monitoring models, such as financial analysis or weather prediction.
The boxplot summarizes the data and helps to understand the distribution better. It is used in particular when general statistical information on the distribution of data is required. This visual can, in particular, make it possible to detect erroneous data.
A scatter plot represents the values of two different numeric variables. It helps to identify the relationship between the data and each variable or to detect anomalies. It is used in particular for regression in machine learning.
You are now unbeatable on Matplotlib: one of the most used tools for Data Visualization. Using this Python library will significantly help you with any data science project!