Searching for the best Python libraries for data science in 2021? Well, you are in the right place..!! Before we start the list let’s discuss what is data science and why python is the best choice.
What Is Data Science Firstly?
Well, Data science is an extremely important field in current times! And Python is one of the best programming languages to extract value from this data because of its capacity for statistical analysis, data modelling, and easy readability.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning, and big data.
Why Python Libraries For Data Science?
Following are some of the main reasons why developers and data scientists prefer to use Python over other programming languages for their data science projects.
- Less coding
- Easy to Learn
- Platform Independent
- Huge Community Support
Well, there are many Python libraries for data science, that contain a host of tools, functions, and methods to manage and analyze data. Each of these libraries has a particular focus with some libraries managing image and textual data, data mining, neural networks, data visualization, and so on.
Now let’s begin..!!
TensorFlow is an end-to-end Python library for data science for performing high-end numerical computations. It is one of the most recommended Python Libraries for machine learning. It can handle deep neural networks for NLP (Natural Language Processing), recurrent neural networks, image recognition, word embedding, handwritten digit classification, and PDE (Partial Differential Equation). TensorFlow Python assures excellent architecture support to allow easy computation deployments across a wide range of platforms, including desktops, servers, and mobile devices.
One of the major benefits of TensorFlow is Abstraction for machine learning and AI projects. This feature allows the developers to focus on the comprehensive logic of the app instead of dealing with the mundane details of implementing algorithms. Besides, with this library, python developers can now effortlessly leverage AI and ML to create unique responsive applications, which respond to user inputs like facial or voice expression.
- Easily Trainable
- Responsive Construct
- It is optimized for speed, it makes use of techniques like XLA for quick linear algebra operations.
- Parallel Neural Network Training
- Git Stars: 153k
- Forks: 83.8k
NumPy or Numerical Python is linear algebra developed in Python. It is another most loved python library for data science. A large number of developers and experts prefer it to the other Python libraries.
Furthermore, it comes with functions for dealing with complex mathematical operations like Fourier transformation, linear algebra, random number, and features that work with matrices and n-arrays in Python. Also, this python package performs scientific computations. Thus, it is widely used in handling sound waves, images, and other binary functions.
- High-performance N-dimensional array object
- Multidimensional container for generic data
- Git Stars: 16k
- Forks: 5.2k
This is one of the best python libraries for data science. Scrapy is also known as spider bots, which is responsible for crawling programs and retrieving structured data from web applications. This open-source library is written in Python. As per the name it was designed for scraping. It is the complete framework with the potential to collect data through APIs and act like a crawler.
With help of this python library, one can write codes, reuse universal programs and create scalable crawlers for their application. Besides, it is created across the Spider class which contains the instructions for a crawler.
- It generates feed exports in formats such as JSON, CSV, and XML.
- Scrapy is based on crawler, allows extracting data from the web pages automatically.
- It has built-in support for selecting and extracting data from sources either by XPath or CSS expressions.
- Git Stars: 39.5k
- Forks: 9k
Plotly is one of the most famous Python libraries for data science. This package offers the designing of visualization models with varieties of APIs supported by multiple programming languages including Python. Furthermore, you can easily use interactive graphics and numerous robust accessible through its main website Plotly.
For using Plotly in your working model you need to set up available API keys properly. The accessible graphics are processed on the server-side and once successfully executed they will appear on your browser screen.
- File Export
- App Manager
- Jobs Queue
- Snapshot Engine
- Git Stars: 8.7k
- Forks: 1.7k
Seaborn is designed to visualize complex statistical models. It has the potential to deliver accurate graphs such as heat maps. Seaborn was created on the concept of Matplotlib and somehow it is highly dependent on that. Even the smallest data distributions can be easily visualized through this library which is why it is one of the favourite python libraries for data science, among data scientists and developers.
- Seaborn works well with NumPy and Pandas data structures
- Built-in themes for styling matplotlib graphics
- Plotting statistical time-series data
- Fitting in and visualizing linear regression models
- It comes with built-in themes for styling Matplotlib graphics
- Visualizing univariate and bivariate data
- Git Stars: 8k
- Forks: 1.4k
Beautiful Soup is a Python library for getting data out of HTML, XML, and other markup languages. It is a tool for web scraping that helps you clean up and parse the documents you have pulled down from the web.
Besides, this library helps you pull particular content from a webpage, remove the HTML markup, and save the information. For example, you’ve found some web pages that display data relevant to your research, such as date or address information, but that does not provide any way of downloading the data directly.
The Beautiful Soup documentation will give you a view of the variety of things that this library will help with, from isolating titles and links to extracting all of the text from the HTML tags to altering the HTML within the document you’re working with.
Well, in current times, business data has become as valuable as money. There is no doubt, that we are in the era of big data and generating a considerable amount of data every second. Also, big businesses heavily depend on this data for their growth in the market.
Thus, Using Data Science and other technologies, we extract informative detail from the data to solve complex real-world problems and to build predictive models. Data Science is not a tool or technique; it is a skill that you build and nourish by mastering some tools and libraries present in the market.
That’s why we have prepared this list of Python libraries for data science. We hope this list helps you pick the right one for you. Do tell us which one is your favourite in the comment section below.
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