10 Essential Libraries in Python Every Developer Should Know

In today’s time, Python is a popular programming language that finds its application in web applications development, data science and machine learning. It offers many benefits like simple syntax, structure, easy to learn structure and powerful libraries.

Here are the popular reasons for popularity of Python

  • There are huge collection of libraries that adds fast working
  • Python syntax are easy to learn and also of high level as compare to Java, C and C++
  • It’s a beginners level programming language due to its easiness and simplicity
  • It has portability feature which makes popular in the market

What are Python Libraries?

A libraries in Python is a group of interconnected modules which include a code bundles which can be reused in project.

It simplifies the developers working because there is no need to write the same code for different programs. They can be either built in or third party. The built-in libraries come with Python interpreters and offer basic functionality like string operation and math. The Python community develops third party libraries to offer more advanced functionality.

Top 10 Python libraries

Top Libraries of Python that Every Developer Must Know:


NumPy is a python standard library for scientific computing that provides support for large and multi-dimensional arrays. The most widely used libraries for numerical computation in Python. NumPy is easy to perform complex mathematical operations like linear algebra and random number generation.

Key Features of NumPy

  • NumPy is an interactive library and very easy to use
  • Making the coding real easy and also grasping the concept is simple
  • It is widely used, so lot of open source contribution is available
  • It makes complex mathematical implementation simple.


Pandas is an essential Python library for the analysis and data manipulation. It also offers an efficient data structure called DataFrame. Pandas library in python is mainly useful in data science projects for its powerful tools for analyzing data and manipulating.

Key Features of Pandas:

Fast and efficient DataFrame object with customized indexing.
It supports operations like Re-indexing, Sorting, Iteration and Aggregations.
It makes sure that the entire process of manipulating data will be easy.


It is a free Python library which adds an image processing ability. It allows saving various image file formats in Python along with manipulation. It is also an extensive file format support and offers an effective internal representation. This library enables fast access to data stored in few basic pixel formats.

Key Features of Pillow:

  • Good for batch processing applications.
  • Read various range of image file formats.
  • Supports arbitrary affine transforms, image resizing and point operations.
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PyTorch is a machine learning library. Developers can perform tensor computations wan ith acceleration of GPU using this library. It calculates gradients automatically. It also offers rich APIs for solving the neural network related application issues.

Key Features of PyTorch

  • Coding is simple in PyTorch and processing is also quick
  • PyTorch is a flexible framework. It supports a wide range of use cases, from simple neural networks to complex deep learning models.
  • Python is purely supported by PyTorch
    During code execution, Graphs can be set up dynamically and also computed dynamically.


Gradient boosting is a prominent machine learning package that helps developers develop new algorithms. There exists a dedicated library which may be used to implement the Gradient Boosting method. All these libraries are useful in solving the same problem.

Key Features of LightGMB

  • A very quick computation ensures the high production efficiency.
  • Due to its intuitive, it is user-friendly.
    Many other deep-learning libraries take longer to train as compared to this one.
  • There will be no errors while using NaN values and other canonical values.


Like other mathematical operation libraries in Python, it allows users to define and evaluate mathematical expressions. It also uses massive multi-dimensional arrays. The standard C-based codes become slow when dealing with large amounts of data. It is more useful than NumPy because unstable expressions can be detected.

Key Features of Theano

  • It is useful to perform data-intensive operations significantly faster than the CPU. 
  • C code generation is dynamic
  • Theano can help to detect and diagnose various types of problems. 
  • Transparent use of GPU: It performs data-intensive computations faster than on CPU. 


SciPy is a free and open source Python library useful for high performance, data processing and scientific computing. There are many user-friendly routines for quick computation. Alongside with NumPy, it is also useful for mathematical computations.

Key Features of SciPy

  • It offers integration techniques such as numerical integration, ordinary differential equation solvers, and partial differential equation solvers.
  • SciPy provides a wide range of optimization techniques including linear programming, quadratic programming, non-linear optimization, and least-squares minimization.
  • It provides tools for interpolating data in one, two, or higher dimensions. Also, provides tools for signal processing including Fourier transforms, wavelet transforms, signal filtering, and window functions.
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Keras is a Python based open source neural network library that enables users to experiment with deep neural networks. It is an Application Programming Interface designed only for humans. It is not for the machines. It has a higher adoption rate in the industry. It is usually recommended to install the TensorFlow backend before installing Keras.

Key Features of Keras

  • It runs without the hitch on both GPU (Graphics Processing Unit) and CPU (Central Processing Unit) Keras’ modular design makes it very expressive and well-suited to cutting-edge research. 
  • Keras is a Python-based framework which makes it simple to debug and also explore different models.


The outcome of machine learning model predictions is never accurate. However, Eli5 python helps to overcome such issues. It is a combination of visualization and debugging the entire machine-learning model. It finds its main application in mathematical concepts involving a lot of computation in a short time. It also plays an important role where dependencies with other Python packages exist. It is also useful in developing newer methodologies in various fields.

Key Features of Eli5

  • Eli5 supports lightning, scikit-learn and wother libraries XGBoost.
  • It helps users to identify the most crucial feature which the model used to make a prediction.
  • Eli5 is easy to integrate with popular machine learning frameworks like XGBoost and Scikit-learn.
  • Eli5 also offers extensive documentation that includes examples and tutorials for better understanding for the user.


It is another open-source library, developed by Google. It is a part of almost every Google App for machine learning. The working of TensorFlow is like a computational library for writing new algorithms which involve a large number of tensor operations.

Key Features of TensorFlow

  • It is flexible in its operability which means it has modularity and other parts that you want to make standalone.
  • TensorFlow is easily trainable on GPU and CPU as well for distributed computing.
  • As Google introduced this library, a large team of software offers a large community in this library.
  • There is pipelining in the sense which makes the models very efficient on large-scale systems.

Last words:

So these are the crucial Python libraries which every developer should be aware about. Whenever you hire a Python developer make sure he is well versed with these libraries. Python libraries make the working approach simple and save developers time. Python is widely being used in applications like DropBox and YouTube. So, python app development is crucial in todays time.