When we talk about data science, Python is becoming the language of choice. It is object-oriented and easy to code and has been ranking high at global data science surveys.
Python is used both by data scientists and programmers since it can automate operations, predict results, streamline processes, and provide insights.
There are numerous libraries for Python that you can use for things like data mining, mathematics, visualization, and data exploration. Read on to find out which are the best Python libraries currently available.
NumPy
One of the most powerful Python libraries when it comes to scientific computation. NumPy is an open-source Python library that is used a lot for Deep Learning and Machine Learning apps. It can be used to operate a large range of mathematical functions and has a multi-dimensional array and matrix data structures.
Keras
One of the finest machine learning Python libraries, Keras offers a straightforward method to express neural networks. With exclusive utilities for data set visualization, compiling models, and graph visualization, among others, Keras is expectedly popular. It can run on both CPU and GPU smoothly and since it is truly python-based, Keras is very simple to explore and debug.
PyTorch
An extensive library for machine learning you can use to produce interactive computational graphs, conduct GPU acceleration tensor computations, and calculate gradients. PyTorch enables efficient production and flexible, fast experimentation. It uses Python integrations together with a data science stack and comes with a very easy interface with APIs.
SciPy
SciPy is the go-to Python library when it comes to scientific computing. It is open-source and built on the NumPy library. SciPy is extensively used for science, math, and engineering and offers effective and very user-friendly numerical routines such as optimization routines and numerical integration.
Matplotlib
The most common Python library used for data visualization and exploration. Matplotlib is the foundation of every other library. It is open-source and supported by a large community. With countless options for customization and charts, you can use various themes, colors, pallets, or configure and customize your plots.
Plotly
An open-source visualization library, Plotly offers high-quality, immersive, and publication-ready charts. Built on top of HTML, CSS, and D3.js, this is one of the best tools for data visualization tools that are currently available. You can use Plotly to create interactive charts, build beautiful dashboards and slide decks.
Tensor Flow
TensorFlow is an open-source framework created by the Google Brain team and has drawn significant attention as a deep learning library. It is an end-to-end machine learning library that offers databases, research group tools, and resources to create deep learning and machine learning applications. TensorFlow is easy to run and fast to debug and offers a prediction of products, stocks, and more.
PyCaret
An open-source machine learning Python library you can use for functions such as the deployment of models and data preparation. PyCaret is a low-code library that is simple to understand and can save you a lot of time when conducting end-to-end machine learning tests. You can prototype quickly and efficiently and have a business-ready solution.
