Top 10 libraries of Data Science

Top 10 libraries of Data Science

1. Pandas

Pandas is primarily used for data analysis, and it is one of the most commonly used Python libraries. It provides you with some of the most useful set of tools to explore, clean, and analyze your data. With Pandas, you can load, prepare, manipulate, and analyze all kinds of structured data. Machine learning libraries also revolve around Pandas DataFrames as an input.

2. NumPy

NumPy is an open-source numerical Python library. NumPy contains a multi-dimensional array and matrix data structures. It can be utilised to perform a number of mathematical operations on arrays such as trigonometric, statistical, and algebraic routines.

3. Scikit-learn

Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

4. TensorFlow

TensorFlow is an open-source library developed by Google primarily for deep learning applications. It also supports traditional machine learning. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind.

5. Keras

Keras is a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models. It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in just a few lines of code.

6. Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes.

7. Matplotlib

Matplotlib interactively from the python shell and have plotting windows pop up when they type commands. Some people run Jupyter notebooks and draw inline plots for quick data analysis.

8. Pytorch

PyTorch, which is a Python-based scientific computing package that uses the power of graphics processing units. PyTorch is one of the most commonly preferred deep learning research platforms built to provide maximum flexibility and speed.

9. NLTK

Natural Language Toolkit. NLTK is an essential library that supports tasks such as classification, stemming, tagging, parsing, semantic reasoning, and tokenization in Python. It's basically your main tool for natural language processing and machine learning.

10. OpenCv

OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture and analysis including features like face detection and object detection. In this tutorial, we explain how you can use OpenCV in your applications.