Top 10 Python Libraries for Data Science and Machine Learning

Python is a widely used and extremely popular high-level programming language. What makes it so accessible is its syntax, which means having less coding for all purposes. One of the best things about Python is that you don’t necessarily need to write new code every time you need to perform a particular function, you have preexisting modules online. These collections of modules are called Python Libraries. Today, we are going to look into the top 10 Python libraries that are everyone’s favorite. 

Python is a relatively old programming language. Current trends in programming languages show Python’s relevance for machine learning, data science, and the IoT. Read top 4 reasons to learn Python.

Python Libraries

A Python library defines lines of code that can be reused in other programs. It is basically a collection of modules. Their usefulness lies in the fact that new codes are not required to be written every time the same process is required to run. 

Python libraries play an important role in areas of data science, machine learning, data manipulation applications, etc.  

The life of a programmer becomes easy with the availability of a large number of standard libraries in python. This is mainly because the programmer is not required to keep on writing the codes. For example, a programmer can use the MySQLdb library to connect a MySQL database to a server.

The python libraries are mostly written in the C programming language that handles operations like I/O and other core modules.  The standard library consists of more than 200 core modules and around 137,000 python libraries have been developed to date.

Top 10 Python Libraries

By Pavan Somwanshi


Developed by Google, TensorFlow is one of the most popular python libraries for data science. This library is primarily used in machine learning and deep learning algorithms.

Pipelining is a feature of TensorFlow that allows you to train multiple models and GPUs. This improves the efficiency of models on the large-scale system. This library has an amazing community along with a large team of software engineers who constantly work on improving the library. C and C++ are the languages used to create TensorFlow.

We make use of apps that use TensorFlow such as Google Voice Search or Google Photos. Finally, this is an open-source library meaning anyone with an internet connection can access it.

You can visit the TensorFlow website to get a more detailed look at the library.


Scikit- learn is an open-source machine learning library that works in association with NumPy and SciPy. This library has a lot of tools used for predictive modeling and analysis that help build machine learning models.

Scikit-learn offers almost all machine learning algorithms and supports multiple supervised and unsupervised learning algorithms. Cross-validation is an important feature of this library wherein various methods can be employed to check the accuracy of supervised models on unseen data.

We use Scikit-Learn to perform data mining tasks such as classification, regression, clustering, and model selection. The use of  Scikit-learn in Spotify is a widely known application.


Pandas is a BSD licensed open-source Python library mainly used for data analysis. Panda offers an efficient data frame object for data manipulation and allows working with time-series data.

Slicing of the data frame, conversion of data into different formats, changing index values in a data frame, and merging & joining of data frames are some of the few operations that can be carried out in Panda. Machine learning libraries also revolve around Pandas DataFrames as an input.

Analysis, manipulation, and cleaning of data are the major uses of this library. A highly notable feature of this library is that it can translate complex operations with data using only one or two commands.


Numpy or Numerical Python is one of the most popular Python libraries in the area of scientific computation that makes coding really easy. Primarily used for its support for N-dimensional arrays, NumPy is the most used open-source package offered by python.

The Array interface is used to express binary raw streams as an array of real numbers. Another reason Numpy is so popular is that it provides built-in tools for scientific and mathematical calculations.

Numpy is highly used in data analysis. TensorFlow also uses Numpy for internal computations on tensors. Overall it’s a very efficient tool.

You can learn more about Numpy here:


LightGBM is a gradient boosting framework mainly popular because it lets developers build algorithms using decision trees. A few features of this library are, quick training of models and higher efficiency, low memory usage, and capability to handle large-scale data & support of parallel, distributed, and GPU learning.


Keras is another popular library used to interact with problems related to Deep Learning and Neural networks. The user interaction in Keras is minimal, this characteristic makes it highly efficient. Few features of this library are that it provides vast prelabeled datasets which allows you to quickly create neural networks & it is based on and acts as an interface for the TensorFlow library.

One thing to note here is that Keras is relatively slow than other libraries because it makes use of backend infrastructure to create computational graphs, which it later uses to perform operations. In essence, it is quite flexible, portable, and runs smoothly on CPU as well as GPU.

A notable application of Keras is that with the help of pre-trained deep learning models you can make predictions without creating new models. Netflix & Uber are some popular names where Keras is being used along with scientific organizations such as NASA and CERN.


Theano is a Python Machine learning library primarily used for the computation of arrays and mathematical operations. Theano can also be used in distributed environments just like TensorFlow but is comparatively less efficient. The inefficiency is caused because Theano is not capable to fit into production environments.

Here, data-intensive computations can be performed much faster than on GPU, and owing to the generation of dynamic C code generation you can evaluate expressions much faster which improves efficiency. in comparison, Theano is more useful than NumPy.

Vuclip, ZetaOps, and Cyanapse are some companies that reportedly use Theano in their tech stacks.


Matplotblib is a powerful Python library with over 700 contributors on GitHub. The main use of Matplotlib is for data visualizations. The fact that it is open-source popularly makes it a good alternative to MATLAB. You are not limited by operating systems when using Matplotlib as it supports a lot of backends and output types. Another benefit of this library is that it consumes low memory which improves its efficiency.

Learn more about Matplotlib.


Scipy is an open-source python library used for the scientific and mathematical functions it offers. The Scipy eco-system on the other hand is a stack of multiple Python libraries curated to perform intensive computations. Mathematics, science, and engineering are the major domains that use this library.

As the product website describes “The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge.”

Multidimensional image operations, Optimization algorithms & Linear Algebra are a few of the popular applications known as Scipy.


PyTorch is the largest ML library whose primary feature is executing Tensor computation with a strong GPU acceleration and the second being that it allows the building of a deep neural network on a tape-based autograd system that provides speed and flexibility.

PyTorch is based on Torch (an open-source library in C). With this library, you can work on projects that involve Machine learning, Deep learning, and Neural networks. PyTorch also has APIs for handling neural network-related problems. All of this makes it a huge upgrade over NumPy.

The major use of this library is in applications such as computer vision and natural language processing. It has also been used in the development of Facebook (who introduced the library in 2017) primarily for its Deep Learning projects. 

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About Pavan Somwanshi:


Pavan Somwanshi is an India-based freelance content writer and a curious human being trying to learn things every day to make a better sense of the world. Currently, Pavan is studying BSc Statistics from Pune University.

Additionally, Pavan is a management and entrepreneurship aspirant, a tech geek, an occasional philosopher, and a multitasker who excels at juggling tasks on both a professional and personal level.

He follows the liberal and egalitarian schools of thought, respects ideas, and encourages rational debates along with actively engaging in critical thinking. He finds his fulfillment with work, studies, communicating ideas, and being around people he cares about. You can connect with Pavan on LinkedIn.

Sources: 1, 2, 3, 4, 5.

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