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Tag: Open Source Python Library

Matplotlib vs Seaborn – Who owns the Python visualization throne?

Matplotlib vs Seaborn – Matplotlib is often the first choice when it comes to creating mathematical plots with Python. But is it always the best choice? With Seaborn there is a potent competitor.

Matplotlib was developed by John D. Hunter back in 2003 and has become indispensable. Due to the increasing importance of the Python programming language in almost all scientific areas, the importance of fully compatible visualization methods is also growing.


Due to its open source concept, Matplotlib can be used absolutely free of charge and is a basic component of many popular Python distribution platforms, such as Anaconda.


The library offers a MATLAB-like interface and can be used in combination with NumPy, Pandas and Scipy, just like MATLAB.

SciPy is a collection of mathematical algorithms and convenience functions and is mainly used by scientists, analysts and engineers for scientific computing, visualization and related activities.
NumPy allows easy handling of vectors, matrices, or large multidimensional arrays in general.
NumPy’s operators and functions are optimized for multidimensional array operations and evaluate particularly efficiently.

Pandas is also an open source Python library that can be used to perform data analysis and manipulation efficiently. Its strength lies in the processing and evaluation of tabular data and time series.

These components, which are absolutely compatible with each other, offer in their entirety an absolutely free, but fully comprehensive alternative to the commercial analysis software MATLAB.

This figure shows some Python libraries, which together form an open source MATLAB alternative.
Matplotlib vs Seaborn – Together the Python libraries form a MATLAB replacement

Python Matplotlib – What are the features?

The library offers a wide range of visualization functions. Some of them are listed in the figure below.

Matplotlib vs Seaborn - This figure shows Matplotlib features sorted by their use cases.
Matplotlib vs Seaborn – Matplotlib Features

Matplotlib is designed to effectively visualize the results of mathematical calculations. Visualization is an efficient and important data analysis tool.
The library is able to generate all the usual diagrams and figures by default. It is even possible to create animations that can be used to better understand the flow of certain algorithms.

Event Handling

Matplotlib offers an important feature with event handling. Behind the name is a UI-neutral event model. This allows the library to connect to events without knowing which UI Matplotlib will eventually plug into.


This allows me to develop a very flexible and portable code.
However, the events can then be used to transfer things like the data coordinate.

PyLab vs Pyplot

PyLab is a collection of functions that is installed together with Matplotlib and make the library work like MATLAB.
The module brings a set of NumPy functions and classes into the namespace. This makes them accessible without having to import them.
However, this often led to conflicts between individual Matplotlib functions.
For this reason, the use of PyLab is now no longer recommended.
Pyplot is a module in Matplotlib and provides the state-machine interface to the underlying plotting library.


The conflicts are prevented because an import is done with Pyplot and a separate NumPy import.

Python Matplotlib – Third party packages

If the standard library features are not enough, you can extend Matplotlib with additional external packages. In the following figure some of the possible extensions are listed and grouped by application.

Python Matplotlib - This figure shows Matplotlib Third Party Packages sorted by their use cases.
Matplotlib vs Seaborn – Matplotlib Third Party Packages

These external packages must be installed individually and extend the functionality of the plotting library, or build on existing features.
They sometimes offer more complex graphics or higher performance data analysis methods. Most of these packages are open source and are constantly updated by very active communities.

Matplotlib also has weaknesses

Matplotlib is not perfect despite the wide feature set. For example, only poor default options for the size and colors of plots are offered. Matplotlib is often considered to be a low-level technology compared to today’s requirements. Thus, very specialized code is needed to generate appealing plots.

What is Seaborn?

Seaborn is a Python visualization library, but based on Matplotlib. This library provides a high-level interface for visualization of statistical data and not only has its own graphics library, but internally uses Matplotlib’s functionalities and data structures.
It thus offers a variety of additional features besides the śtandard Matplotlib functions.

This scheme shows the main features of Seaborn
Matplotlib vs Seaborn – Main features of Seaborn

Among other things, Seaborn provides built-in themes for designing matplotlib graphs and a dataset-oriented API for determining the relationship between variables. It can visualize both univariate and bivariate data and plot statistical time series. Estimation and plotting of linear regression models run automatically and Seaborn, unlike Matplotlib, offers optimization when processing NumPy and Pandas data structures.

So what should you choose?

Especially when it comes to deep statistics, Seaborn clearly has the edge. Matplotlib, however, is often the leaner solution due to its simplicity. So both have their strengths and weaknesses. Which tool you ultimately choose depends on the situation. You can’t do much wrong. With one solution, however, you have more contextual options. But now that you know the differences between the two, this decision will be easier for you.

scikit-learn – Machine learning, Data Mining and Data Analysis in Python for free

In almost no scientific discipline you can get around the programming language Python nowadays.
With it, powerful algorithms can be applied to large amounts of data in a performant way.
Open source libraries and frameworks enable the simple implementation of mathematical methods and data transports.

What is scikit-learn?

One of the most popular Python libraries is scikit-learn. It can be used to implement both supervised and unsupervised machine learning algorithms. scikit-learn primarily offers ready-made solutions for data mining, preprocessing and data analysis.
The library is based on the SciPy Toolkit (SciKit) and makes extensive use of NumPy for high performance linear algebra and array operations. If you don’t know what NumPy is, check out our article on the popular Python library.
The library was first released in 2007 and since then it is constantly extended and optimized by a very active community.
The library was written primarily in Python and is based on Cython only for some high-level operations.
This makes the library easy to integrate into Python applications.

scikit-learn Features

Easily implement many machine learning algorithms with scikit-learn. Both supervised and unsupervised machine learning are supported. If you don’t know what the difference is between the two machine learning categories, check out this article from us on the topic.
The figure below lists all the algorithms provided by the library.

The figure  lists all the upervised and unsupervised machine learning algorithms provided by scikit-learn..
machine learning algorithms provided by scikit-learn..

scikit-learn thus offers rich capabilities to recognize patterns and data relationships in a dataset. Thus, high dimensions can be reduced to visualize the relationships without sacrificing much information.
Features can be extracted and data clustering algorithms can be easily created.

Dependencies

scikit-learn is powerful and versatile. However, the library does not exist completely solitary. Besides the obvious dependency on Python, the library requires the import of other libraries for special operations.

NumPy allows easy handling of vectors, matrices or generally large multidimensional arrays. SciPy complements these functions with useful features like minimization, regression or the Fourier transform. With joblib Python functions can be built as lightweighted pipeline jobs and with threadpoolctl methods can be coordinated as threads to save resources.