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Top 10 Python Libraries You Should Know in 2022


 01. Pandas

70% to 80% of the day-to-day work of a data analyst involves understanding and cleaning data, aka data exploration and data mining.

Mainly used for data analysis, Pandas is one of the most used Python libraries. It gives you some of the most useful tools for exploring, cleaning, and analyzing your data. With Pandas, you can load, prepare, manipulate, and analyze all kinds of structured data.

 02. NumPy

NumPy is mainly used to support N-dimensional arrays. These multidimensional arrays are 50 times more robust than Python lists, making NumPy a favorite of many data scientists.

NumPy is used by other libraries like TensorFlow for the internal computation of tensors. NumPy provides fast precompiled functions for numerical routines that can be difficult to solve manually. For better efficiency, NumPy uses array-oriented computation, making it easy to handle multiple classes.

 03.Scikit-learn

Scikit-learn is arguably the most important machine learning library in Python. After cleaning and processing the data with Pandas or NumPy, it can be used to build machine learning models through Scikit-learn, since Scikit-learn includes a large number of tools for predictive modeling and analysis.

There are many advantages to using Scikit-learn. For example, you can use Scikit-learn to build several types of machine learning models, including supervised and unsupervised models, cross-validated model accuracy, and perform feature importance analysis.

 04. Gradio

Gradio lets you build and deploy web applications for machine learning models in just three lines of code. It serves the same purpose as Streamlight or Flask, but deploying models is much faster and easier.

The advantages of Gradio lie in the following points:

  • Allows further model validation. Specifically, different inputs in the model can be tested interactively
  • Easy to demonstrate
  • Easy to implement and distribute, anyone can access the web application via a public link.

 05. TensorFlow

TensorFlow is one of the most popular Python libraries for implementing neural networks. It uses multidimensional arrays, also known as tensors, that can perform multiple operations on specific inputs.

Because it is highly parallel in nature, it is possible to train multiple neural networks and GPUs for efficient and scalable models. This feature of TensorFlow is also known as pipelining.

 06. Hard

Keras is mainly used to create deep learning models, especially neural networks. It's built on top of TensorFlow and Theano, and it's easy to build neural networks with it. But since Keras uses backend infrastructure to generate computational graphs, it is relatively slow compared to other libraries.

 07. SciPy

SciPy is mainly used for its scientific functions and mathematical functions derived from NumPy. The library provides functions for statistics, optimization, and signal processing. In order to solve differential equations and provide optimization, it includes functions for numerically computing integrals.

The advantages of SciPy are:

  • Multidimensional image processing
  • Ability to solve Fourier transforms and differential equations
  • Very robust and efficient linear algebra calculations due to its optimization algorithms 

08. State models

Statsmodels is a library that excels at doing core statistics. This versatile library mixes functionality from many Python libraries, such as getting graphical features and functions from Matplotlib; data manipulation; using Pandas, for R-like formulas; using Pasty, and building on NumPy and SciPy.

Specifically, it is useful for creating statistical models such as OLS and for performing statistical tests.

 09. Plotly

Plotly is definitely a must-have tool for building visualizations, it's very powerful, easy to use, and able to interact with visualizations.

Also used with Plotly is Dash, a tool for building dynamic dashboards using Plotly visualizations. Dash is a web-based Python interface that addresses the need for JavaScript in this type of analytics web application and lets you draw on and off-line.

 10. Seaborn

Built on Matplotlib, Seaborn is a library capable of creating different visualizations.

One of Seaborn's most important features is the creation of magnified data visuals. This allows initially non-obvious correlation performance to stand out, allowing data workers to understand the model more correctly.

Seaborn also has customizable themes and interfaces and provides data visualization with a sense of design for better data reporting.

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