Best Open-Source Python Libraries to Learn in 2020

Python is a pool of open-source libraries, but which are worth your time? Which of them will be most used in 2020? Let’s find out!

Python is a popular programming language that many developers use because of ease of learning and the capability to perform various tasks related to machine learning, data science, data visualization and analysis, and web development. The libraries in Python provide it with flexibility. Python is one of the most powerful programming languages, and its demand is increasing day by day. Thus there is a massive surge in jobs for a python expert. Python applications built with AI and ML integrations are the future. Python automates redundancy delivering more value.


A Python library is a collection of functions that allow users to perform the desired activity without writing code. They make coding simpler by reducing code writing and removes the need to write complex codes. The library is a collection of various modules, and different Python libraries have different purposes. Python is fantastic for data science and web development. If Python interests you, you can enroll for the python training course.


Learning of the Blog


  • Pandas
  • NumPy
  • Scikit-Learn
  • Keras
  • TensorFlow
  • Conclusion


Python libraries are useful in data manipulation and visualization, deployment, databases, and data modeling. In this article, we look at the top five open-source Python Libraries.




Pandas is a Machine Learning library that provides various analysis tools and high-level data structures. It uses very few commands to transform complex operations using in-built functions and methods like data combining, grouping, filtering, and many more. Pandas have made data modification easier by supporting operations like re-indexing, sorting, bug fixation, API changes, etc. When used in conjunction with other libraries such as SciPy, NumPy, and Matplotlib, Pandas supports various functions that help sort data to apply the best methods of getting results. Flexibility is its best feature. As a data analyst or scientist, this tool is worth learning as it makes working with data fast, easy, and intuitive. The library has two primary data structures- one-dimensional series and two-dimensional DataFrame. Both of them are often used in statistical computing, finance, engineering, and social science. Pandas offer importing data from various file formats, overcome missing data, and clean, analyze, and transform data. Columns can be inserted and deleted; data can be easily converted and grouped by. Slicing, subsetting, indexing, merging, reshaping, joining, and pivoting of data sets are possible. Outstanding speed indicators follow all these.




NumPy or Numeric Python is one of the best Python and most popular machine learning libraries that’s fundamental for scientific computing. It is an open-source and performs efficient numerical computing. NumPy provides high-performance multidimensional arrays and matrices along with tools to operate on them. It contains functions that can carry out Fourier transform, linear algebra, and random numbers. It is easy to use and interactive library. It has a lot of open- source contributions making coding very simple. It is good to use as it is used for expressing sounds, showing images, and more. Array interface is the most important and best NumPy feature. Libraries, including TensorFlow, use it internally for performing operations on Tensors. It is intuitive and interactive. Learning NumPy is essential for full-stack developers as its interface can be utilized for expressing binary raw streams as an N-dimensional array of real numbers.



Scikit-learn is an open-source library, which is a blessing for machine learning newbies and pros. From Support Vector Machines and KNN Maps to KNN classifiers and regression algorithms, it supports all. It is an excellent choice for small projects which uses statistical modeling, classification, predictive data analysis, and clustering. It works best with complex data in association with SciPy and NumPy and is most useful for text and image features’ extraction. It applies different methods to check the speed and accuracy of various data models, thus helping in cross-validation and improving training methods like nearest neighbors and logistic regression. It contains multiple algorithms for data mining tasks like classification, reducing dimensionality, regression, clustering, and model selection. Apart from this, there is a large spread such as principal component analysis to unsupervised neural networks, factor analysis, and clustering.



Keras is one of the most popular open-source deep-learning python libraries which allows users to build prototypes and create neural networks. Normalization, optimization, and activation algorithms are included. This deep learning development model is an extensible and user-friendly tool for beginners and can seamlessly run on CPUs and GPUs. It is a mechanism for expressing various neural networks, graph visualizations, and data processing. It uses the internal functions of Tensorflow or Theano but is slow in working. It is flexible, modular, and helps in innovative research. It’s debugging and exploring, and many interactive features are used by companies like Square, Uber, Yelp, and Netflix.




Launched by Google in 2020, TensorFlow is the most popular open-source library for Python applications in the world of machine learning and artificial intelligence that enables developers to work with numerical computation. It is utilized in CPUs and GPUs with a single API and is based upon data flow graphs. It is a computational library used in all Google applications and works with algorithms using a vast amount of tensor operations. It has extensive community support, and everyone can use it. It consists of various forms that can be trained on the CPU, such as visualizing each part of the graph. TensorFlow is designed for data flow and machine learning projects. The trained models can be deployed on the Cloud or a device. It is a useful tool for researchers because of its simple and flexible architecture. TensorFlow improves the workflow and accuracy of models.




These five libraries are only a drop in the ocean. Python is so popular because of its specialist tools. Python is a valuable skill loved by employers. If you want to learn it, consider a Python Crash course or best python certification course to go from beginner to advanced Python programmer.