Top 5 Python Libraries For Data Science

Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming. Python has been built with extraordinary Python libraries that are used by programmers every day in solving problems.

Top 5 Python Libraries for Data Science

The above video reviews the top 5 Python libraries for data science:

  • TensorFlow 
  • NumPy 
  • SciPy 
  • Pandas 
  • Matplotlib

In addition to comparing these libraries, the top 5 Python libraries for data science video also walks you through a sample process in each one.

TensorFlow

TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. It’s used across various scientific fields. TensorFlow is basically a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.

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

TensorFlow is particularly useful for the following applications:

  • Speech and image recognition
  • Text-based applications
  • Time-series analysis
  • Video detection

The Python Libraries video takes you through an example of TensorFlow in action, reading handwritten digits by building a simple TensorFlow model.

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NumPy

NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.

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

From the video, you even learn how to create a simple array and change its shape using the arrange and reshape functions of NumPy.

SciPy

SciPy (Scientific Python) is another free and open-source Python library extensively used in data science for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It’s extensively used for scientific and technical computations, because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.

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

A simple demonstration of the functions of SciPy follows in the video of Python libraries for Data Science.

Pandas

Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provides fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very easily and intuitively.

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 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.

You can find the tutorial of how to create a dataframe using pandas in our Python libraries video.

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Matplotlib

Matplotlib has powerful yet beautiful visualizations. It’s a plotting library for Python with around 26,000 comments on GitHub and a very vibrant community of about 700 contributors. Because of the graphs and plots that it produces, it’s extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications.

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

The Python Libraries for Data Science video demonstrates a very simple plot to get a basic idea of the possibilities with Matplotlib.

Along with these libraries, data scientists are also leveraging the power of some other useful libraries:

  • Similar to TensorFlow, Keras is another popular library that is used extensively for deep learning and neural network modules. Keras supports both the TensorFlow and Theano backends, so it is a good option if you don’t want to dive into the details of TensorFlow.
  • Scikit-learn is a machine learning library that provides almost all the machine learning algorithms you might need. Scikit-learn is designed to be interpolated into NumPy and SciPy.
  • Seabourn is another library for data visualization. It’s an enhancement of matplotlib, as it introduces additional plot types.

To Conclude

In addition to the top five Python libraries and the three other useful Python libraries discussed here, there are many other helpful Python libraries for data science that deserve to be looked at. Share your favorites in the comments section below, as well as any interesting things about the libraries that we mentioned. Also if you are interested in learning data science with python, head onto Simpliearn’s Data Science With Python Training Course, which is one of the best data science certification training courses that you can find.

About the Author

Shivam AroraShivam Arora

Shivam Arora is a Senior Product Manager at Simplilearn. Passionate about driving product growth, Shivam has managed key AI and IOT based products across different business functions. He has 6+ years of product experience with a Masters in Marketing and Business Analytics.

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