10 Interesting Python Libraries for Data ScienceSaraschandraa M
Python and Data Science go hand in hand. They both are very instrumental when it comes to machine learning, artificial intelligence, deep learning, and other fields of data science.
Though programming is not the whole process of data science, it does play an important part.
For example, you may be having a great algorithm for your problem but if you can’t get it programmed well, then you are never going to achieve your goal. For that purpose, Python is an excellent choice for coding and data science-based programming.
Python has made a huge mark on data science.
Data scientists are increasingly adopting Python over other programming languages owing to its robust and highly popular libraries.
And in this article, we will discuss some of the most important and best Python libraries for data science.
Related: Commonly Asked Data Science Interview Questions and Answers
Python libraries for data science
Python is not just a simple, insightful, and enthralling language but it also finds crucial applications in the data science world.
Let’s take a look at some of the interesting Python libraries for data science:
Matplotlib is the most popular library for Python data visualization. It is a 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Using this tool, you can create simple figures and save them as images or generate high-quality plots for your reports.
It can be used in many other ways, to create presentations or interactive web applications. Matplotlib allows you to convert your code into inline HTML or image files using its GUI backend. You can use any of the supported output formats based on your programming convenience.
TensorFlow is an open-source software library for high-performance numerical computation. It lets you define and run computations involving tensors — multidimensional arrays of numbers that generalize conventional scalars as well as vectors and matrices.
Many scientists use TensorFlow to run computations related to machine learning, which is a branch of artificial intelligence based on algorithms. Because its syntax is similar to NumPy, which many scientists use in conjunction with scientific code, it’s easy for them to switch from one framework to another.
The library provides a range of useful features, including support for neural networks and deep learning, symbolic differentiation, and automatic differentiation. It also includes tensor visualization tools based on the Graphviz library.
Pandas is used for data analysis and data manipulation in the Python programming language. It is a Python library used for data munging, which means rearranging, cleaning, transforming, or building a dataset into a more presentable form for analysis.
Pandas is one of the most popular libraries that has rapidly gained popularity in recent years. It is a free library and can be used with any of the Python versions.
The primary function of Pandas is to provide high-performance, easy-to-use data structures, and data analysis tools to users. Pandas also allows you to read and write on various file formats such as CSV, Excel, HDF5, SQLite, and others.
Also read: Commonly asked Python Interview Questions and Answers
NumPy is an open-source library for scientific computing with Python programming language. It provides fast and efficient operations on multi-dimensional arrays of numerical data. NumPy can be used to solve real-world problems in various fields, such as machine learning, image processing, data analysis, numerical simulation, and many others.
The NumPy library contains rich functions that allow you to add mathematical operations over your data, apply statistical tests, and create new arrays in a fast and efficient way.
SciPy is a Python-based library of algorithms and convenience functions built on the NumPy extension of Python.
SciPy provides many user-friendly and efficient numerical routines such as linear algebra, optimization, integration, interpolation, and much more.
Scrapy is a fast high-level screen scraping and web crawling framework used to crawl websites and extract structured data from their pages.
Scrapy is simple and efficient, and it can be used for a wide range of purposes, from data mining to monitoring and automated testing. It can be easily adapted to your needs, and also extended with your own custom items and pipelines.
SciKit-Learn is a very popular library for machine learning and data mining. It provides some of the basic data structures to handle different types of datasets like DataFrame, DictVectorizer, and CategoricalVectorizer. The library also enables you to explore your dataset by utilizing its estimators.
SciKit-Learn features various classification, regression, and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
PyTorch is a Python-based machine learning library for neural networks, which are computing systems modeled after the human brain. Largely an open-source effort from Facebook, it provides several advantages over other neural network libraries. PyTorch is fast and comes with automatic gradient computation to improve efficiency, so it’s easier to use than comparable libraries. It also offers a clear path to deploy models in the cloud; it’s used in production at Facebook and Instagram.
PyTorch has the advantage of being used by many developers inside Facebook, many of whom have contributed back to the community under open source licenses.
More than 2,000 developers have contributed code to PyTorch since its launch in August 2016. It’s already used by startups like Luminoso, as well as large companies such as Cisco, IBM, and SAP.
Also read: Python Projects for Beginners
Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. It runs on top of either TensorFlow or Theano.
Keras was developed by researchers and engineers with the mission to help users be productive as quickly as possible and to enable fast experimentation. Keras has the following four main features:
- Minimalist core designed for extending
- Clean and declarative syntax
- Clear separation between frontend (API) and backend
- Convenient tools for model evaluation, etc.
Plotly is an interactive graphing library that lets users stream data in and then allows them to create beautiful graphs with minimal effort. It can be used within Jupyter notebooks or as a Python library.
Plotly aims to make it easier for users to turn their data into informative charts and images. This can be very useful in a data science context, where you have to process and visualize large amounts of data.
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As you start your journey with data science and Python, you need to choose the best libraries for better practice.
The libraries mentioned above are excellent in their functionality. They’re free to download, easy to use, and offer many features for your analytics.
Related: Steps to Learn Data Science as a Beginner
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