Explore This 5 Useful Libraries in Python

One of the most appealing features of Python is the plethora of open-source modules available. A Python library is a repeatable code snippet that you may use in your applications and projects. In contrast to languages such as C++ or C, Python libraries are not tied to any specific environment. A ‘library’ is a vague term that refers to a collection of basic components.

Python libraries are a collection of helpful capabilities that allow you to write code without having to build from zero. Currently, there are approximately 137,000 Python libraries. And all these Python libraries are used to create Machine Learning, Data Science, data visualization, image and data manipulation, and other applications. So, let me walk you through the most prominent Python libraries. Python Training will assist you in gaining hands-on knowledge on how to write Python programs and use Python libraries.

The following are the top 5 libraries of Python you must know. 


If you’re currently residing on a Python Machine Learning project, you’ve probably aware of TensorFlow, a widely-used open-source framework. Google and the Brain Team collaborated on the creation of this library. 

TensorFlow is an open software library for strong numerical computing and is the foremost advanced deep learning system. It’s a well-known math library that’s also used in Machine Learning techniques.

Features of TensorFlow

These are some features of TensorFlow that are worth knowing.

  • TensorFlow apps may be run on a variety of devices, including Android, Cloud, and iOS. That makes it easy to use.  
  • It includes statistical graphing algorithms that are simple to implement and debug. 
  • TensorFlow converts raw information into forecasting models, this gives the system more flexibility for quickly developing insights.
  • TensorFlow reduces code complexity and implementation time by providing a specified degree of abstraction.
  • TensorFlow provides users with the flexibility and autonomy you need to create complicated networks with tools like the Keras Function API and Model Subclassing API.

Where Do We Use TensorFlow?

Classification, perception, comprehending, discovering, forecasting, and creation are some of the most common use cases where TensorFlow is used.


Next comes Pandas in the row of the top Python libraries.  Pandas is a quick, robust, versatile, and simple open-source data analysis and manipulation tool. It is developed on top of Numpy, a library that supports multi-dimensional arrays.

Pandas is a widely-used open-source Python library for Data Science, data analysis, and Machine Learning applications. The Python Tutorial will let you know everything you need to know about Python Programming Language.

Features of Pandas

Here are the features of Pandas that you need to know.

  • A data manipulation DataFrame object with inbuilt indexing that is quick and efficient.
  • Data sets may be reshaped and pivoted in a variety of ways in Pandas.
  • For size mutability, columns can be added and removed from data structures.
  • Tools for reading and writing data across different formats as well as in database systems
  • Merging and combining data sets with high performance.

Uses of Pandas

Pandas are mostly used to analyze data. Pandas support data input from a variety of file formats, including JSON, SQL, and Microsoft office excel. Pandas support a wide range of data manipulation tasks, including integrating, reorganizing, and choosing, as well as data cleaning and wrangling.


Numpy is a Python module for scientific computing that is widely used. NumPy is a Python package that allows you to interact with arrays. NumPy arrays make it easier to do complex mathematical and other calculations on enormous amounts of data. It also contains functions when working with matrices, Fourier transforms, and linear algebra.

Features of Numpy 

  • NumPy is open, fast, and flexible.
  • NumPy’s high-level syntax makes it usable and efficient for programmers.
  • NumPy can run on a variety of hardware and computer systems.
  • NumPy has a wide range of scientific calculations, as well as random number generators, linear algebra algorithms, and Fourier transformations.
  • NumPy is based on well-optimized C code. Take use of Python’s flexibility while benefiting from the speed of generated code.

When Numpy is Used

Numpy is a Python module for numerical computation that is widely used. It includes a multidimensional array structure, and also variants such as filters and matrix, that may be used for a variety of mathematical functions.


Scikit-learn is a popular Machine Learning package for the Python programming language. Scikit-learn is a set of Machine Learning techniques that includes mathematics, statistics, and particular techniques that serve as the foundation for a variety of Machine Learning algorithms.

Features of Scikit-Learn 

  • Tools for predictive data analysis that are easy and straightforward
  • Everyone may use it, and it can be reused in a variety of situations.
  • NumPy, SciPy, and Matplotlib were used to create this.
  • BSD license, open software, practically useful.

Use Cases of Scikit-Learn 

Scikit-learn is perhaps Python’s most helpful Machine Learning algorithm. Classification, modeling, clustering, and image segmentation are just a few of the applications where Scikit-learn is used in Machine Learning and statistical modeling.


Keras is a Python-based deep learning API that runs on top of TensorFlow, a Machine Learning platform. It was created with the goal of allowing for quick experimentation. It’s crucial to be able to get from idea to outcome as quickly as feasible when conducting research.

Keras Features

  • Keras is a Python-based platform, making it simple to debug and investigate. Keras is an elevated interface with a backend based on Theano or Tensorflow.
  • Keras enables nearly all neural networks, including densely integrated, convolutional, filtering, recurrence, embed, and so forth. These concepts can also be merged to create more sophisticated models.
  • Keras’ modular design makes it extremely expressive, versatile, and well-suited to cutting-edge research.
  • It operates on both the CPU and GPU without a hitch.

Keras is used for

Keras is a deep learning framework for creating and testing models. It encapsulates Theano and TensorFlow, two advanced parallel computing frameworks. So it is mainly used to design and develop neural network models with just only several lines of code.

So, I hereby conclude this blog. Here, we have witnessed some of the best Python libraries, which are one of the reasons for its success. We looked into the definition of what exactly they are. Additionally, We also learned about their features and where these libraries can be used.

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