Python is a programming language used in software development relevant to machine learning, deep learning, and artificial intelligence. Likewise, Python libraries are used by data scientists in order to perform various tasks and solve machine learning problems ingeniously.
Not to forget, all these Python modules and packages are easy-to-learn and bug/debug for Python developers. Additionally, it is an open-source, object-oriented, and high-performance language that makes coding for programmers totally a bliss.
Let’s learn more about the Python data science libraries you should use.
10 Best Python Libraries for data science
Check these incredible libraries that we suggest you should use for data science projects.
This is one of the most popular data science Python libraries, catering to multiple computations efficiently. This tool library performs high-level numerical computations with a community of 1500 contributors and 35,000 comments.
Notably, TensorFlow is used to define and run computations involving tensors (that are partially defined computational objects to produce a value).
It stands for Numerical Python, and is a perfect package for numerical computation. Regarding community support, it has 18,000 comments on GitHub and more than 700 contributors.
Not just this, a power-packed N-dimensional array object resides in it. NumPy addresses the slowness problem by providing operations and functions that work efficiently on multidimensional arrays.
Note: NumPy is one of the general-purpose Python libraries for data science, known as an array-processing package that mainly offers high-performance multidimensional objects.
Science Python (SciPy) is one more addition to the free data science Python Libraries built for both technical computing and scientific computing. Interestingly, it is a NumPy array object and falls under the same section.
SciPy enables data scientists to perform various computing tasks like a pro, such as handling data optimization, data interpolation, data integration, and data modification using Fourier transforms, special functions, linear algebra, etc.
When we talk about Python packages, Scikit-learn (a machine learning package) is considered the best tool.
It offers services for data mining and analysis. Plus, it is crucial for data classification, regression, processing, model selection, evaluation, predictive modeling, and clustering.
Top Python libraries for data science include Keras used for deep learning and neural network modules. It is a high-level neural network API that is operable on TensorFlow, Theano, and CNTK. Being a modular, user-friendly, and extensible toolkit Keras helps in creating deep learning models expediently.
With just a few code lines, it allows you to create, train, and compile neural networks. As you must know it is quite similar to TensorFlow, providing support to activation functions, optimizers, loss functions, and neural network layers.
With the most beautiful and powerful visualizations, Matplotlib is one of the data science Python libraries that gains a giant community of 700 contributors and over 26k comments on GitHub. Moreover, as it offers object-oriented API, it makes embedding plots into applications literally a bliss.
Interestingly, it is utilized during the data analysis process in order to generate graphs and charts. With these plotted charts, it is feasible to communicate data to a non-technical user.
Not to miss that with Matplotlib, you can get Exploratory Data Analysis done ingeniously to identify outliers, anomalies, and trends in the data.
All Python modules, packages, and libraries play a vital role in the field of data science by improving data set processing and manipulation capabilities. Being the popular Python data science library, it has the support of more than 1200 contributors with more than 17000 comments on GitHub.
Undoubtedly, Pandas is fast and facilitates data scientists with effective data structures. Also, it has a large number of functions that makes data visualization, manipulation, and analysis a flexible task.
An open-source library that falls under the most used Python libraries for data science and machine learning, PyTorch, based on the Torch library, is used in tasks like NLP (natural language processing) and computer vision.
As you must know it is created by Facebook’s artificial intelligence research team and thus, used in both academia and businesses.
Furthermore, PyTorch offers a dynamic computational graphical chart that allows data science experts to debug or perform computations instantly. It is a good tool for analyzing deep learning models.
While talking about the data science Python libraries, we can’t skip the name Beautiful Soup which is famous for extracting data from XML and HTML files for web crawling and scraping purposes.
Notably, website’s source code generates a parse tree that helps in extracting data or information in the most comprehensible and hierarchical way.
Plotly is among those Python modules, libraries, and packages which allow data scientists to create low-code (with less programming) applications for scaling, building, or deploying data applications in Python.
More to that, it aids in creating interactive graphical plots and while doing that, it saves enormous time for the expert indulged in the task.
The list of Python libraries doesn’t end here. But, hopefully, you have gotten a clear overview of them here. By using these Python libraries for data science, you can make data analysis and mining a super easy task.