Top Python Libraries for Data Scientists

 

Python has 8.2 million active users, with 69 per cent of data scientists and machine learning developers using it. With several modules for data analysis, I/O, and data munging, the Python language has a thriving and extensive ecosystem. The best method to ensure that you are prepared to work as a data scientist is to familiarise yourself with the numerous Python libraries and tools commonly used in the field.

What is a Library in Python?

A Python library is a piece of reusable codes you can add to your projects or programs.

In contrast to languages like C++ or C, Python libraries are independent of any particular context. A collection of essential modules is called a "library."

A library can be installed using a package manager like RubyGems or npm.

Python Standard Library 

The precise syntax, tokens, and semantics of Python are collected in the Python Standard Library. It includes the standard Python distribution. 

It manages functionality like I/O and other fundamental modules and is built in C. The combination of all these features makes Python the language it is.

However, you can also access a developing collection of several thousand components from the Python Package Index in addition to this library.

Most Widely Used Python Libraries in the Field of Data Science

Some of the best Python libraries for data science and machine learning will be discussed in this blog. These libraries have been broken down into three categories based on their intended uses: data processing and model deployment, data mining and scraping, and data visualisation.

Python libraries list in data processing includes:

• Requests

With the help of straightforward Python dictionaries, you can submit HTTP/1.1 requests, add headers, form data, multipart files, and arguments using the Python requests library.

The same method can be used to get the answer data.

 

• Pandas

On modest data sets, anyone can perform data analysis on paper and a pen. To evaluate and extract useful information from enormous datasets, we need specialised tools and methods. One of those data analysis libraries with high-level data structures and straightforward data manipulation tools is Pandas Python. 

The ability to index, retrieve, split, join, restructure, and perform other analyses on both multi- and single-dimensional data is necessary to provide an effortless yet efficient way to analyse data.

 

• NumPy

A Python package called NumPy is used to do mathematical and scientific computations. Python programmers and hobbyists can use NumPy's many features to deal with high-performance arrays and matrices. Compared to Python's looping structures, NumPy arrays offer vectorization of mathematical operations, which improves efficiency.

For all the mathematical calculations, such as element splitting and vector operations, Pandas Series and DataFrame objects heavily rely on NumPy arrays.

 

• Sci-Py

SciPy, a scientific Python code name, is a collection of mathematical algorithms and routines created with Python's NumPy extension. SciPy offers a number of high-level classes and instructions for handling and displaying data. SciPy is beneficial for data processing and system prototyping.

Aside from this, SciPy offers additional benefits for developing scientific applications as well as a variety of specialised, sophisticated applications supported by a strong and rapidly expanding Python community.

 

• OpenCV

OpenCV is a free machine learning and computer vision library that is BSD-licensed. To simplify the integration of computer vision into commercial goods, it provides a shared architecture for computer vision applications.

 

• Tensorflow

A free end-to-end open-source platform for machine learning called TensorFlow comes with a large selection of tools, libraries, and resources. On November 9, 2015, the Google Brain team initially made it available. Using high-level APIs like Keras and TensorFlow, it is seamless to create and train Machine Learning models. 

It also provides many levels of abstraction, enabling you to choose the appropriate strategy for your model. You may also use TensorFlow to deploy Machine Learning models across a variety of platforms, including your device, browser, and the cloud. 

If you want the full experience, go with TensorFlow Extended (TFX); if you want to use TensorFlow on mobile devices, go with TensorFlow Lite; and if you plan to train and deploy models in JavaScript contexts, go with TensorFlow.js.

The Python libraries for data mining are mentioned in the upcoming section:

 

• SQLAlchemy 

The Python database toolkit for efficient access to data warehouses is called SQLAlchemy. It includes the high-performance database access patterns that are most frequently used. 

The two primary parts of SQLAlchemy are SQLAlchemy Core and SQLAlchemy ORM. SQLAlchemy core offers a layer of abstraction by covering Python database APIs and features. Additionally, it provides users with SQL statements and schema.

 

• Scrapy

Scrapy is a legitimate Python module for you if you work with data scraping, where the data is obtained from the screen (display data). You can enhance web crawling and screen scraping using Scrapy. 

For automated testing and data mining, data scientists utilise Scrapy. Many IT specialists worldwide utilise Scrapy, an open-source program for scraping data from web pages. Because of Scrapy's excellent interactivity and cross-platform compatibility (it runs on Linux, Windows, BSD, and Mac OS X), many software professionals choose Python for data analysis and scraping applications.

Finally, we are about to discuss the libraries that serve the purpose of data visualization. These are as follows:

 

• Matplotlib

This open-source solution can manage several forms of data generated in epilepsy from numerous sources. A 2D graphical Python library is matplotlib. 

It does, however, also support 3D graphics, albeit to a very limited extent. The expansion of matplotlib has created fierce rivalry for industry heavyweights like MATLAB and Mathematica due to the exponentially rising demand for Python in recent years.

 

• Altair

Python-based Altair is a program for visualising statistical data. It is built on the declarative languages of Vega and Vega-Lite, which are employed to generate, store, and exchange interactive data visualisation designs. 

With little to no coding, Altair can produce visually appealing data visualisations of plots, including bar charts, pie charts, histograms, scatterplots, error charts, stemplots, and more. Python 3.6, NumPy, and Pandas are dependencies for Altair and are all set up automatically by the installation processes for Altair.

 

• Ggplot

Ggplot, a Python data visualisation toolkit with a 3k+ star rating on Github, is based on the ggplot2 implementation for the R programming language. 

Using a high-level API, Ggplot can produce various data visualisations, including bar charts, pie charts, histograms, scatterplots, error charts, and more. It lets you combine different data visualisation layers or components into a single visualization. 

Ggplot handles the rest after you specify which variables should be mapped to specific plot elements, allowing you to concentrate on analysis rather than representation design.

 

Conclusion

These were only a few of the many libraries in the Python ecosystem, which is like a vast ocean full of resources for data scientists. Check out Skill-Lync's repository for end-to-end completed data science projects using these Python libraries for data science.