Python language is helping developers in building PC, mobile, and other enterprise applications. Today, it is one of the most used programming languages. Moreover, Python never stops to surprise its users when it comes to solving data science challenges and tasks. There are several scientists who are already leveraging the power of this programming language every day. 

Python is indeed an easy-to-debug, easy-to-learn, object-oriented, widely used programming language. It has been built with extraordinary libraries which are used by programmers in solving problems. This is why several enterprises can be seen using Python applications.

However, different Python libraries are meant for different uses. For example, the Python Imaging Library (PIL) is intended for image manipulation. While we also have a TensorFlow library which is essential for training and developing deep learning models using python. 

Before moving ahead, let’s just have a glance at the top 10 Python libraries for DataScience. 

Pandas

This is an open-source Python package that offers easy-to-use and high-performance data structures as well as data analysis tools for the labeled data in Python programming language. Do you know what Pandas actually stand for? Well, it basically stands for Python Data Analysis Library. 

It is a perfect tool for data munging. This Python Library is designed for easy and quick data manipulation, aggregation, visualization, and reading. Pandas mainly take data in a TSV or CSV file or a SQL database. Then, it creates a data frame which is similar to a table in statistical software like SPSS or Excel.

NumPy

NumPy is intended for processing large matrices and multidimensional arrays. This Python library is meant for scientific computing. It comes with support for broadcasting functions as well as a powerful N-dimensional array object. In addition to this, NumPy also provides Fourier transforms, tools for integrating C/C++, random number capabilities, and Fortran code. This Python library is quite easy to use and is interactive. 

With NumPy, you can do basic array operations such as slice, multiply, flatten, reshape, and add. This Python library helps in providing fast and precompiled functions for numerical routines. It also supports an object-oriented approach as well as provides faster computations with vectorization. 

Matplotlib

This is one of the most essential Python libraries. With this library, you can create stories with the help of data visualized. In addition to this, Matplotlib also helps in plotting 2D figures. It offers an object-oriented API for installing plots into applications. 

However, one can use this for designing myriads of figures in multiple formats which is compatible across their respective platforms. One can use this in their IPython shells, application servers, and Python code. With the help of Matplotlib, you can create plots, scatter plots, histograms, bar charts, etc.

Nevertheless, if you want to build high-performing, robust, and secure web applications using the Matplotlib library, then all you need is to hire a Python developer who possesses great expertise. 

TensorFlow

It is an open-source library designed by Google in order to compute data low graphs with the help of machine learning algorithms. This Python for DataScience was designed to meet the high demand for training neural networks work. This library is not just limited to scientific compilations but it is widely being used in real-world Python application as well. 

This Python library has a better computational graph visualization. It also provides you with quick updates as well as frequent new releases, thereby offering you the latest features. TensorFlow is useful for speech and image recognition, video detection, time-series analysis, and text-based applications. 

Seaborn

This Python library was designed in order to visualize the complex statistical models. This library has the potential to deliver precise graphs like heat maps. This Python for DataScience is dependent on Matplotlib as it was created on the concept of it. 

Minor data distributions can be visualized with the help of this library. And this is the main reason why this library has become quite familiar among the developers as well as data scientists.

SciPy

SciPy is another famous Python library for developers, researchers, and data scientists. SciPy stack and library are both different. It offers optimizations, integrations, statistics, and linear algebra packages for computation. In order to deal with complex mathematical problems, it is based on the NumPy concept. 

SciPy offers numerical routines for integration and optimization. However, SciPy can be useful in the case if you’ve started your data science career. Moreover, this Python library also includes built-in functions for resolving differential equations.

Read more: Is Python for Financial App Development the Right fit?

SciKit-Learn

It is a simple tool for mining-related tasks as well as data analysis. This library is licensed under BSD and is open-source. The best part is that anyone can reuse and access it. It is developed over the SciPy, Matplotlib, and NumPy.

This Python library is used for regression, classification, and image recognition, customer segmentation, stock pricing, etc. it also enables model selection, dimensionality reduction, and pre-processing. 

Gensim

It is an open-source Python for data science that enables space vector computations and topic modeling with the implemented varieties of tools. Gensim can be used for finishing unsupervised topic modeling tasks and natural language processing.

This library is specially developed for managing large text collections by means of incremental online algorithms and data streaming. The most distinctive feature of this library is that it does not target only in-memory processing.

NLTK (Natural Language Toolkit)

This library is quite useful for accomplishing Natural language processing tasks. NLTK mainly comprises text processing libraries with which you can perform parsing, tokenization, stemming, classification, semantic reasoning of data, and other complex AI tasks. 

Challenging works like semantic analysis and summarization or automation has indeed become quite simple and it can be easily completed with NLTK. 

Plotly

Plotly is one of the most popular web-based frameworks for data scientists. This toolbox provides the designing of visualization models with myriads of APIs including Python. However, the Plotly graph has an array of graphs that you can plot. 

You can use the interactive graphics as well as several robust accessible through its main website. In order to use this library in your working model, all you need is to set up API keys properly.

However, to get customized, highly dynamic, and interactive web applications, you need to simply hire a web app developer. 

Conclusion

That sums up the list of the top 10 Python libraries for DataScience. With the rise of machine learning and data science, advancements are made to data science libraries. Additionally, newer Python machine learning is being created. 

However, if you’ve ever used any of these Python libraries for data science, then do share your favorites and interesting things about the libraries that we have mentioned in the comment section.