Python has a vast and wide range of libraries and this is one of the reasons for the popularity of the language.
Apart from the numerous standard libraries that come with Python, there are also third-party libraries that you can leverage to quickly create applications.
With these libraries, you don’t have to reinvent the wheel, as most of the functionality you will ever code has been precoded.
Whether you are creating a web application, building a machine learning model or visualizing data, there are tons of libraries available to make your work easier.
Here are some of the important libraries in Python
1. TensorFlow
This is an open-source library used for building machine-learning models. It was originally developed by Google for internal use in research and production. Over the years, TensorFlow has become one of the leading machine learning frameworks.
It has been used in different industries and academia as a machine-learning tool for solving real-world problems. All over the world, big and small companies use Tensorflow to improve their businesses.
Google uses TensorFlow for enhancing its search engine. PayPal uses Tensorflow for fraud detection. Airbnb uses Tensorflow to categorize images in home listings as a way to improve customer experience.
2. Matplotlib
Matplotlib is one of the most popular data visualization libraries in Python. With this library, you can easily create static, animated and interactive plots and figures of different types. You can generate graphs and plots like histograms, pie charts, bar charts and scatter plots and so on.
3. Numpy
Numpy stands for numerical python and is a popular Python library for mathematic and scientific computing in Python. It is used for working with multidimensional arrays and matrices.
Numpy comes with a lot of mathematical functions for efficient calculations and can be used to solve mathematical problems like statistics, linear algebra, random number generation and much more.
4. Pandas
Pandas is a powerful library for working with data frames and data manipulations. It is one of the most used libraries by data scientists and analysts in Python for reading and analyzing data. It is fast, easy to use and powerful.
Pandas are used for a broad range of data analysis including stock predictions, customer analytics, inferential analysis, healthcare recommendation and much more.
It can fetch data from a wide range of sources including spreadsheet packages like Microsoft Excel and other data sources including Comma Separated files (CSVs), JSON files and relational databases.
Pandas also work well with other libraries like matplotlib for creating charts and figures with your data.
5. Keras
Keras is one of the most popular frameworks for deep learning. It is used together with TensorFlow for creating deep-learning solutions. It is simple, flexible and powerful, making it possible to quickly experiment with ideas.
Keras is a broadly accepted machine learning library among developers, companies and researchers. Big companies and organizations like NASA, YouTube, Netflix, and Uber use it for deep learning.
6. Pygame
Pygame is a free and open-source library that allows you to create video games in Python. It contains numerous resources for game development including images, visuals and sounds.
Popular games including Flappy Bird, Frets on Fire, Snake and Chess games have been developed with pygame. Video games created with pygame can work on different platforms and operating systems.
7. Scikit-learn
Scikit-learn is a machine-learning library for building different kinds of statistical models. It is widely used in industry and academics.
Companies like JP Morgan, Bookings.com and Birchbox used it for machine learning. Netflix and Spotify use it in their recommendation engines. JP Morgan uses it for predictive analytics by combining historical data with machine learning. Bookings.com use it for recommending hotels and destinations to their customers.
8. Scrapy
It is an open-source framework for fetching data from the internet. Scrapy uses a non-blocking mechanism to allow you to scrape data across the internet undetected.
It is suitable for large-scale web scraping. Interestingly, it is easy to learn and integrate with applications.