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TOP 10 python library to start learn Data Science in 2024!!

Below are the top 10 Python Library to Learn in in 2024 for a Beginner

  • TensorFlow
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Keras
  • SciKit-Learn
  • PyTorch
  • Scrapy
  • BeautifulSoup
  1. TensorFlow is a library for high-performance numerical computations with a vibrant community of around 1,500 contributors and around 35,000 comments. It is used across various scientific fields and is particularly useful for speech and image recognition, text-based applications, time-series analysis, and video detection. TensorFlow is a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value. Its features include:
  • Better computational graph visualizations
  • Reduction in error by 50–60% in neural machine learning
  • Parallel computing to execute complex models
  • Seamless library management, backed by Google
  • Quick updates and frequent new releases to provide the latest features

TensorFlow is known for its ability to handle large-scale computations and is particularly useful for machine learning applications.

2. SciPy is a Python library that is used for scientific and technical computations. It is built on top of NumPy, and provides a range of functions and algorithms that are useful for data manipulation, visualization, and analysis. SciPy has around 19,000 comments on GitHub, and an active community of about 600 contributors.

Some key features of SciPy include:

  • A collection of algorithms and functions that are built on the NumPy extension of Python
  • High-level commands for data manipulation and visualization
  • Multidimensional image processing with the SciPy ndimage submodule
  • Built-in functions for solving differential equations

SciPy has a number of applications, including:

  • Multidimensional image operations
  • Solving differential equations and the Fourier transform
  • Optimization algorithms
  • Linear algebra

SciPy is a powerful and widely-used library that is an important part of the Python data science ecosystem. It is used by scientists, engineers, and researchers in a variety of fields, including physics, chemistry, biology, and finance.

3. NumPy is a fundamental package for numerical computation in Python. It features:

  • Fast, precompiled functions for numerical routines
  • Array-oriented computing for improved efficiency
  • Support for an object-oriented approach
  • Compact and faster computations through vectorization

NumPy is widely used in data analysis and has many applications, including:

  • Creating powerful N-dimensional arrays
  • Serving as the base for other libraries such as SciPy and scikit-learn
  • Providing an alternative to MATLAB when used with SciPy and matplotlib

NumPy has an active community of contributors, with over 18,000 comments on GitHub and over 700 contributors.

you can check more information here ‘

Getting Started with NumPy: A Beginner’s Guide, Most commonly used functions and its uses!

4. Pandas is a powerful and widely used open-source library for data analysis and manipulation in Python. It is particularly useful for working with structured data and provides fast, flexible data structures like data frames for easy and intuitive data manipulation. Pandas has a large community of active contributors, with over 17,000 comments on GitHub and over 1,200 contributors.

Some key features of Pandas include:

  • Eloquent syntax and rich functionalities that allow you to easily handle missing data
  • The ability to create and apply custom functions across a series of data
  • High-level data structures and manipulation tools

Pandas is commonly used for tasks such as:

  • General data wrangling and cleaning
  • Extract, transform, load (ETL) jobs to transform and store data, as it has excellent support for loading CSV files into its data frame format
  • Time-series-specific functionality, such as date range generation, moving window statistics, linear regression, and date shifting

Pandas is widely used in a variety of fields, including statistics, finance, and neuroscience.

below you can check more in detail:


Start to use pandas! — Say GoodBye to Excel in 2023!

5. Matplotlib is a popular open-source library for data visualization in Python. It has a strong community of contributors, with over 26,000 comments on GitHub and around 700 contributors. Matplotlib is known for producing beautiful, high-quality plots and graphs, and is often used for data visualization in research and other fields.

Some key features of Matplotlib include:

  • The ability to be used as a free and open-source alternative to MATLAB
  • Support for a wide range of backends and output types, allowing it to be used on any operating system and to generate various output formats
  • Integration with other libraries such as Pandas, allowing for easy and intuitive data visualization
  • Low memory consumption and efficient runtime behavior

Matplotlib has many applications, including:

  • Correlation analysis of variables
  • Visualization of 95% confidence intervals for statistical models
  • Outlier detection using scatter plots
  • Visualization of the distribution of data for insights and analysis

6.Keras is an open-source library for building and training deep learning models in Python. It is designed to be easy to use and provides a high-level interface for building and training neural networks. Keras can be used with either TensorFlow or Theano as a backend, making it a good option for those who want to use deep learning without having to learn the details of TensorFlow.

Some key features of Keras include:

  • A large collection of prelabeled datasets that can be easily imported and used for training and evaluation
  • A range of implemented layers and parameters that can be easily configured for constructing, training, and evaluating neural networks

One of the main applications of Keras is in building and using deep learning models, which are widely used for tasks such as image and speech recognition, natural language processing, and more. Keras provides a range of pretrained models with their weights, which can be used directly for making predictions or extracting features without the need to create and train a new model.

7.Scikit-learn is a machine learning library for Python that provides a variety of algorithms for tasks such as:

  • Clustering
  • Classification
  • Regression
  • Model selection
  • Dimensionality reduction

Scikit-learn is designed to be integrated with NumPy and SciPy, and is widely used in data science for its simplicity and efficiency. It is a popular choice for many machine learning tasks and has a large and active community of users and contributors.

8.PyTorch is a scientific computing package for Python that uses GPUs to accelerate computations. It is a popular choice for deep learning research and is known for its flexibility and speed. PyTorch provides two main features:

  • Tensor computations with strong GPU acceleration support
  • The ability to build deep neural networks using a tape-based autograd system

PyTorch is widely used for tasks such as image and speech recognition, natural language processing, and more. It has a large and active community of users and contributors, and is constantly being improved and updated.

9.Scrapy is an open-source web crawling framework written in Python that is widely used for extracting data from websites. It allows users to build “spider bots” that can retrieve structured data from the web using selectors based on XPath. Scrapy follows a “Don’t Repeat Yourself” principle in its design, encouraging users to write universal code that can be reused for building and scaling large crawlers.

Some common applications of Scrapy include:

  • Building crawling programs (spider bots) that can retrieve structured data from the web
  • Gathering data from APIs
  • Scraping data from websites when no proper CSV or API is available

Scrapy is known for its speed and efficiency and has a large and active community of users and contributors.

10.BeautifulSoup is a popular Python library used for web crawling and data scraping. It can be used to collect data from websites that do not have a proper CSV or API available and helps users scrape and arrange the data into a desired format. Some common applications of BeautifulSoup include:

  • Web crawling and data scraping
  • Extracting data from HTML and XML documents
  • Parsing and navigating complex HTML and XML structures

BeautifulSoup is known for its simplicity and ease of use and has a large and active community of users and contributors.

Well i am just concluding few library to start with, As Data Science is a sea full of information and to choose where to start dive in is your choice. I suggest with start with most useful and frequently ones which will actually help you.

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