What are the most Popular Python Libraries for Data Science?

Python is a name that is known to everyone who is even remotely a part of the tech world. With more than 8.2 million active users across the globe, Python is certainly a well-trusted name. It is not for no reason that the number of people enrolling with Python training and certification is ever-increasing.

Data scientists and machine learning engineers are adopting the language at an incredible pace. This is one of the major reasons why students who wish to exposure go for Python. However, the list does not end here. For data scientists, Python is a blessing for more reasons than can be possibly stated.

Python is famous for supporting a healthy and rich ecosystem. It has humongous libraries that aid in data I/O, data munging, and data analysis. In order to get the right opportunities as a data scientist, you need to ace the most popular Python libraries. Along with this, working knowledge of Python tools is a must.

We are going to dive into the most popular Python libraries for data science. However, before we do that, we need to ensure you have a clarified concept of Python libraries. Thus, we bring to you a brief on what are Python libraries.

 

What are Python Libraries?

In the world of Python, library refers to a cumulation of code that is reusable. Thus, a Python library incorporates code that can be used by the user even when they are writing the code for a new application.

The reasons why Python has become a mass favourite cannot possibly be stated here. However, this availability of humongous libraries is certainly one of them. In addition to this, Python boasts a wide range of supportive community. This comes a benefit to not just newcomers but also experienced people.

Additionally, Python has found its use across various applications. Frontend, backend, and middleware are not the only ones. Artificial intelligence, data science, deep learning, and machine learning are also areas where Python is highly used.

Depending upon the purpose, the user can choose to pick from amongst the multitude of libraries. In the case of data science, there are quite a few that are commonly used. Let us now dive towards the most popular Python libraries for data science that you should know. After all, this is how you will be able to unlock brilliant job opportunities in your future.

 

What are the most Popular Python Libraries for Data Science?

As we have already mentioned, there are quite a lot of Python libraries out there. Here are some of the most popular ones that you should know about.

 

• Pandas

Pandas is a Python library that consists high-level data structures as well as tools. All of these aid in manipulation of data in a simple yet effective way.

In order to ensure the library is offering an effective method of analysing data, it requires a lot of things. These include the ability to restructure, retrieve, join, split, index, and other analyses on single as well as multi-dimensional data.

Pandas, as a library, is equipped with various features. Here are some capabilities you should know –

• Allows the restructuring of data sets
• It aids in creating various labels for a given data item
• It offers the series and DataFrame objects, which are high-performance table and array structures
• Labelling on tabular and series data is provided by Pandas to allow automatic data indexing and alignment
• It facilitates grouping. This means performing split-apply-combine on both tabular and series data

 

• SciPy

SciPy, which is the acronym for Scientific Python is an assortment of algorithms and mathematical functions. These are all based on Python’s extension NumPy. This Python library is mostly considered useful for prototyping and data processing systems. It also offers multiple high-class classes and commands for visualizing and manipulating data.

The list of pros that tag along by sing SciPy are numerous. Here are some you should know about –

• Offers Python sessions that are highly interactive and robust in nature
• Facilitates manipulating and visualizing data via high-level classes and commands
• For parallel programming, SciPy offers classes as well as web and database procedures

 

• SQLAlchemy

This database toolkit aids in accessing the data warehouse in a more efficient manner. SQLAlchemy showcases the most popularly implemented patterns out there for high-performance database access.

There are two major components of this Python library namely SQLAlchemy Core and SQLAlchemy ORM. With this library, developers can also control their databases, all the while automating redundant activities.

SQLAlchemy offers a vast variety of features. Here are some of the top ones –

• It is a highly-accurate and high-performance library. It has been thoroughly tested after being deployed in millions of environments
• The Mapper is an optional package. The Core, on the contrary, is a complete SQL abstraction toolkit
• All the components of SQLAlchemy can be made use of independently.

 

• NumPy

NumPy is the acronym for Numerical Python. This Python library is specifically used for scientific computations and numerical calculations. With the vast number of features provided by NumPy, Python programmers and enthusiasts get to work with top-notch matrices and arrays.

DataFrame objects and Pandas Series majorly rely on NumPy arrays for major mathematical calculations. These include performing vector operations and slicing elements.

Here are some of the key features of NumPy which will give you a better insight into it this library –

• NumPy supports I/O operations based on memory-oriented file mappings. There are also many tools that can be employed to read/write humongous datasets from the disk
• It offers integration with legacy languages
• Users get an efficient and robust multi-dimensional array. It can perform vector-oriented arithmetic operations. Along with this, it also holds powerful broadcasting capabilities
• This library offers all major standard functions needed for performing operations on humongous datasets efficiently and swiftly.
• Random Number Generation, Fourier transform capabilities, and Linear Algebra are a part of the package

 

• Theano

Theano is a wildly popular Python library. It also acts as an optimizing compiler for evaluating and manipulating mathematical expressions, especially the matrix-valued ones. This library has found its core use in building Deep Learning projects. Also, its working is faster on GPU (Graphics Processing Unit) rather than on a CPU.

This Python library has been built on top of NumPy. The list of features and pros offered by Theano is list. However, we are going to give you a few top ones –

• Theano has always been the preference for being super reliable and superfast. It offers stability and efficiency while rapidly calculating expressions for gigantic values of x
• By leveraging a GPU, this library can undertake operations that are data-intensive in nature
• It has multiple tools that aid in self-verification and testing. Both of these lead in catching any potential problems at an early stage

 

• Matplotlib

Matplotlib is an extremely popular Python library used for creating animated, interactive, and static visualizations. This library offers object-oriented API to help embed plots into applications by using general-purpose GUI toolkits such as wxPython, GTK, Tkinter, and Qt.

Here are some of the features of Matplotlib you should know about –

• Matplotlib can be embedded in multiple Jupyter Lab, Graphical User Interfaces, and IDEs
• This library helps in enabling a huge variety of visualizations such as histograms, pie charts, data handling, line plots, bar charts, scatter plots, subplots, tables, images, log plots, and stream plots
• With Matplotlib, visualizations and images can be exported to various different file formats

 

• Seaborn

Built on top of Matplotlib, Seaborn has a high-level interface. It draws attractive visualizations and provides informative statistical graphs. This library has various plots such as distribution plots, multi-plot grids, categorical plots, regression plots, and matrix plots.

Seaborn proves to be super fast at visualizing bivariate and univariate data. It also aids in visualizing data in an aesthetically pleasing fashion. This Python library is also used in various IDEs.

If you are looking for some of the pros of using Seaborn, here they are –

• Seaborn offers an informative and interactive representation. This allows the user to visualize the data in a quicker fashion
• This library is faster as a visualization tool

 

Why use a Python Library for Data Science?

One of the simplest reasons why Python libraries are used for data science is that you get your work half done. And no sane person would want to give up having their work done easy. Re-writing hundreds of lines of code every single time is not an easy task. And for that purpose alone, the need for Python libraries arises.

Here are a few of the reasons why data scientists prefer Python libraries –

• Less coding
• Platform independence
• Ease of learning
• Prebuilt libraries
• Massive community support

 

Conclusion

We hope that this blog about the most popular Python libraries for data science has been able to help you out. beginning your career can be pretty difficult but with the right aid, you can achieve anything. Enrolling with Grras Solutions for Python training will help you begin your success journey at the earliest.