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One of the most widely used languages for data science activities is Python, which both software engineers and data scientists utilize. It can provide business intelligence insights, automate jobs, streamline processes, and predict outcomes.
Python is built with great data science packages/libraries that programmers utilize on a daily basis to solve challenges.
Listing out the best 10 and most widely used Python libraries for data science that you should know
Top 10 python libraries for data science
NumPy stands for Numerical Python and it is a core scientific computing library in Python. It provides efficient multi-dimensional array objects and various operations to work with these array objects.
By offering these multidimensional arrays and functions and operators that work effectively on these arrays, NumPy partially overcomes the slowness issue.
Learning NumPy is the first step on every Python data scientist’s path because it serves as the cornerstone on which nearly all of the toolkit’s capabilities are constructed.
The source code for NumPy is located in this GitHub repository
Pandas stand for panel data is a free and open-source Python library used for analyzing and manipulating data. Numerous features and techniques are offered to quicken this data science process. Given that Pandas is based on the NumPy package, it draws a lot of its fundamental ideas from it.
In particular, if you came to Python looking for something more potent than Excel and VBA, Pandas is a game-changer for data science and analytics. Working with relational or labeled data is simple and natural thanks to Pandas’ use of quick, adaptable, and expressive data structures
The source code for pandas is located in this GitHub repository
sklearn is a free machine learning package formerly known as Scikits.learn and also referred to as Scikit-learn. Support-vector machines, random forests, gradient boosting, k-means, and DBSCAN are just a few of the classification, regression, and clustering algorithms it offers. sklearn is also built to work with Python’s NumPy and SciPy scientific and numerical libraries.
Using a Python consistency interface, it offers a suite of effective tools for statistical modeling and machine learning, including classification, regression, clustering, and dimensionality reduction. The core components of this package are NumPy, SciPy, and Matplotlib.
In the last six years, data scientists and researchers working in machine learning have gravitated to the scikit-learn Python package. And In addition to a uniform interface for using models and training it also contains a collection of tools for evaluating, chaining, and tweaking model hyperparameters.
The source code for sklearn is located at this GitHub repository
The big data numerical management tool NumPy includes the graphing package Matplotlib. Python programs that use Matplotlib can include charts using an object-oriented API.
It emulates MATLAB-like graphs and visualizations and is simple to use. This library’s plots, which include line charts, bar charts, histograms, and more, are constructed on top of NumPy arrays. Although it offers a great deal of flexibility, writing more code is a drawback.
The source code for matplotlib is located in this GitHub repository
Keras (κέρας) means horn in Greek. It is a high-level deep learning API developed by Google to implement neural networks.
“Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.”. – Keras.io
To define your neural network, Keras offers a number of APIs:
These models offer a simple, user-friendly way to define a neural network, which will then be built for you by TensorFlow.
TensorFlow is a computational framework for building machine learning models. It is the second generation system from Google Brain headed by Jeff Dean. Launched in early 2017, it has disrupted the ML world by bringing in numerous capabilities from scalability to building production-ready models.
Thanks to TensorFlow’s range of abstraction levels, you may select the appropriate level for your needs. The high-level Keras API, which simplifies getting started with TensorFlow and machine learning, may be used to build and train models.
TensorFlow is continuously improved with new versions, which may include solutions for potential security holes or better TensorFlow and GPU interaction.
More of Tensorflow
XGBoost is lightweight, adaptable, and effective. It provides parallel tree boosting, which aids teams in resolving a variety of data science issues. Using the same code across popular distributed platforms like Hadoop, SGE, and MPI is another benefit.
Due to its role in practically every victory in the previous several years in the Kaggle structured data competitions, XGBoost has grown significantly in popularity.
More on xgboost
SciPy stands for Scientific Python is another free and open-source Python library for data science that is extensively used for high-level computations.
Modules for linear algebra, integration, optimization, and statistics are all included in this helpful collection. Its arrays utilize this library since NumPy served as the foundation for its primary feature. Scipy provides effective numerical procedures in submodules, including integration, numerical optimization, and others. The thorough documentation makes using this module really simple.
The source code for scipy is located in this GitHub repository
Second last in the list of top python libraries for data science but not the least is PyTorch, which is a Python-based scientific computing package that uses the power of graphics processing units.
Tensor calculations may be performed with GPU acceleration with this tool. It’s also used for other things, including constructing dynamic computational networks and automatically calculating gradients. PyTorch is based on Torch, an open-source deep-learning library written in C with a Lua wrapper.
Learn the basics of Pytorch here
BeautifulSoup is named after the so-called ‘tag soup‘, which refers to “syntactically or structurally incorrect HTML written for a web page”.
Beautiful Soup is the package to use if you intend to collect data from HTML and XML files. Beautiful Soup offers several ways that allow you to traverse, search, and change the parse tree to quickly retrieve the data you want, potentially saving you days of effort.
It is one of the best APIs for web scraping
Practice tutorials for beautifulsoup