Priya Reddy

Sep 21, 2021

5 min read

Top 10 Python Libraries For Data Science for 2021

1.TensorFlow

Features:

  • Better computational graph visualizations
  • Reduces error by 50 to 60 percent in neural machine learning
  • Parallel computing to execute complex models
  • Seamless library management backed by Google
  • Quicker updates and frequent new releases to provide you with the latest features
  • Speech and image recognition
  • Text-based applications
  • Time-series analysis
  • Video detection

2. SciPy

Features:

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

Applications:

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

3. NumPy

Features:

  • Provides fast, precompiled functions for numerical routines
  • Array-oriented computing for better efficiency
  • Supports an object-oriented approach
  • Compact and faster computations with vectorization

Applications:

  • Extensively used in data analysis
  • Creates powerful N-dimensional array
  • Forms the base of other libraries, such as SciPy and scikit-learn
  • Replacement of MATLAB when used with SciPy and matplotlib

4. Pandas

Features:

  • Eloquent syntax and rich functionalities that gives you the freedom to deal with missing data
  • Enables you to create your own function and run it across a series of data
  • High-level abstraction
  • Contains high-level data structures and manipulation tools

Applications:

  • General data wrangling and data cleaning
  • ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format
  • Used in a variety of academic and commercial areas, including statistics, finance and neuroscience
  • Time-series-specific functionality, such as date range generation, moving window, linear regression and date shifting.

5. Matplotlib

Features:

  • Usable as a MATLAB replacement, with the advantage of being free and open source
  • Supports dozens of backends and output types, which means you can use it regardless of which operating system you’re using or which output format you wish to use
  • Pandas itself can be used as wrappers around MATLAB API to drive MATLAB like a cleaner
  • Low memory consumption and better runtime behavior

Applications:

  • Correlation analysis of variables
  • Visualize 95 percent confidence intervals of the models
  • Outlier detection using a scatter plot etc.
  • Visualize the distribution of data to gain instant insights

6. Keras

Features:

  • Keras provides a vast prelabeled datasets which can be used to directly import and load.
  • It contains various implemented layers and parameters that can be used for construction, configuration, training, and evaluation of neural networks

Applications:

  • One of the most significant applications of Keras are the deep learning models that are available with their pretrained weights. You can use these models directly to make predictions or extract its features without creating or training your own new model.

7. Scikit-learn

Applications:

  • clustering
  • classification
  • regression
  • model selection
  • dimensionality reduction

8. PyTorch

Applications:

  • PyTorch is famous for providing two of the most high-level features
  • tensor computations with strong GPU acceleration support
  • building deep neural networks on a tape-based autograd system

9. Scrapy

Applications:

  • Scrapy helps in building crawling programs (spider bots) that can retrieve structured data from the web
  • Scrappy is also used to gather data from APIs and follows a ‘Don’t Repeat Yourself’ principle in the design of its interface, influencing users to write universal codes that can be reused for building and scaling large crawlers.

10. BeautifulSoup