Matplotlib is a Python visualization package for 2D array charts. Matplotlib is a multi-platform data visualization package based on NumPy arrays and intended to operate with the SciPy stack as a whole. It was first introduced in 2002 by John Hunter.
One of the most significant advantages of visualization is that it provides us with visual access to massive volumes of data in simply understandable graphics. Matplotlib has a variety of plots such as lines, bars, scatter, histograms, and so on.
The Python programming language’s ability to manipulate and visualize data is one of its strongest features. Matplotlib is a Python package that generates graphs and allows you to make a figure from a dataset.
We can design very customized graphics using it, and we can adjust the visual features of our graphic to make it more appealing. As a result, it is one of Python’s most powerful data science tools.
Its goal is to create a cross-platform, open-source alternative to MATLAB. MATLAB (Matrix Laboratory) is a programming language that engineers and data scientists can use to analyze and design systems and products.
Matplotlib APIs (application programming interfaces) can also be used by developers to integrate graphics into GUI programs.
Matplotlib supports a wide range of backends and output formats, so you can rely on it to operate regardless of your operating system or desired output format. One of the library’s major assets is its cross-platform approach.
As a result of this feature, the library attracted a wide user base, resulting in an active community that is constantly working to develop the library and its powerful tools. This is why Matplotlib is so widely used in Python’s scientific community.
Users have created new packages that build on Matplotlib’s powerful internal components to use Matplotlib through cleaner and more modern frameworks such as Seaborn, ggpy, HoloViews, Altair, and Pandas, and users have developed new packages that build on its powerful internal components to use Matplotlib through cleaner and more modern frameworks such as Seaborn, ggpy, HoloViews, Altair, and Pandas.
Matplotlib has two interfaces.
The first is a state-based interface that is based on MATLAB. Much of the library’s syntax recalls the fact that it was originally created as a Python replacement for MATLAB users. The pyplot (plt) interface contains MATLAB-style features.
This interface keeps track of the figure and each current x and y axis, which is where all plt commands are applied.
While this style of interface is quick and easy to use for simple plots, it might cause issues. The second approach is to use an object-oriented interface to circumvent the constraints of this interface.
It can be used in more difficult situations or when you desire more control over your figure. The charting operations in the object-oriented interface are explicit Figure and Axes object methods, rather than relying on some idea of a “active” figure or axes.
Choosing which style to adopt is mostly a question of personal preference for smaller plots, but as plots become more intricate, the object-oriented approach may become necessary.
Common Matplotlib Commands
Matplotlib in Python offers several useful tools for creating many types of charts, including bar charts, scatter charts, pie charts, line charts, and so on.
Bar charts are one of the most frequent graph forms, and they are used to depict data with categorical variables. The bar() method in the Pyplot interface creates bar graphs with the following arguments: category variables, their values, and colour (if you want to specify one).
The barh() method can be used to create horizontal bar charts. To depict the variation in our data, we can pass an argument xerr (x-axis) with its value or yerr (y-axis in the case of the vertical bar graphs above).
Additionally, by calling the bar() method twice and giving the inputs that define the index and width of our bar charts, we can construct horizontally stacked bar charts.
A pie chart is another fundamental style of chart that may be created with the pie() method. We may also use arguments as parameters to personalize our pie chart, such as adding a shadow, highlighting a slice, or skewing it.
Finally, when working with data that is continuous in nature, such as height and weight, stock prices, customer waiting time, and so on, histograms are a typical style of graphing. Within a range, histogram data is plotted in accordance to its frequency.
The normal distribution, t distribution, and other distributions are based on these probability and statistics charts. Simply type the plt.hist command in Python matplotlib to use it.
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