Python has risen to become today’s most widely used programming language, and it is the top choice for tackling data science tasks. Python is used by data scientists every single day, and it is a great choice for amateurs and experts alike thanks to its easy-to-learn nature. Some of the other features that make Python so popular for data science is that it’s open-source, object-oriented, and a high-performance language.
But the biggest selling point of Python for data science is its wide variety of libraries that can help programmers solve a range of problems.
Let’s take a look at the 10 best Python libraries for data science:
Topping our list of 10 best Python libraries for data science is TensorFlow, developed by the Google Brain Team. TensorFlow is an excellent choice for both beginners and professionals, and it offers a wide range of flexible tools, libraries, and community resources.
The library is aimed at high-performance numerical computations, and it has around 35,000 comments and a community of more than 1,500 contributors. Its applications are used across scientific fields, and its framework lays the foundation for defining and running computation that involve tensors, which are partially defined computational objects that eventually produce a value.
TensorFlow is especially useful for tasks like speech and image recognition, text-based applications, time-series analysis, and video detection.
Here are some of the main features of TensorFlow for data science:
- Reduces error by 50 to 60 percent in neural machine learning
- Excellent library management
- Flexible architecture and framework
- Runs on a variety of computational platforms
Another top Python library for data science is SciPy, which is a free and open-source Python library used for high-level computations. Like TensorFlow, SciPy has a large and active community numbering in the hundreds of contributors. SciPy is especially useful for scientific and technical computations, and it provides various user-friendly and efficient routines for scientific calculations.
SciPy is based on Numpy, and it includes all of the functions while turning them into user-friendly, scientific tools. SciPy is excellent at performing scientific and technical computing on large datasets, and it’s often applied for multidimensional image operations, optimization algorithms, and linear algebra.
Here are some of the main features of SciPy for data science:
- High-level commands for data manipulation and visualization
- Built-in functions for solving differential equations
- Multidimensional image processing
- Large data set computation
Another one of the most widely used Python libraries for data science is Pandas, which provides data manipulation and analysis tools that can be used to analyze data. The library contains its own powerful data structures for manipulating numerical tables and time series analysis.
Two of the top features of the Pandas library are its Series and DataFrames, which are fast and efficient ways to manage and explore data. These represent data efficiently and manipulate it in different ways.
Some of the main applications of Pandas include general data wrangling and data cleaning, statistics, finance, date range generation, linear regression, and much more.
Here are some of the main features of Pandas for data science:
- Create your own function and run it across a series of data
- High-level abstraction
- High-level structures and manipulation tools
- Merging/joining of datasets
Numpy is a Python library that can be seamlessly utilized for large multi-dimensional array and matrix processing. It uses a large set of high-level mathematical functions that make it especially useful for efficient fundamental scientific computations.
NumPy is a general-purpose array-processing package providing high-performance arrays and tools, and it addresses slowness by providing the multidimensional arrays and functions and operators that operate efficiently on them.
The Python library is often applied for data analysis, the creation of powerful N-dimensional arrays, and forming the base of other libraries like SciPy and scikit-learn.
Here are some of the main features of NumPy for data science:
- Fast, precompiled functions for numerical routines
- Supports object-oriented approach
- Array-oriented for more efficient computing
- Data cleaning and manipulation
Matplotlib is a plotting library for Python that has a community of over 700 contributors. It produces graphs and plots that can be used for data visualization, as well as an object-oriented API for embedding the plots into applications.
One of the most popular choices for data science, Matplotlib has a variety of applications. It can be used for the correlation analysis of variables, to visualize confidence intervals of models and the distribution of data to gain insights, and for outlier detection using a scatter plot.
Here are some of the main features of Matplotlib for data science:
- Can be a MATLAB replacement
- Free and open source
- Supports dozens of backends and output types
- Low memory consumption
Scikit-learn is another great Python library for data science. The machine learning library provides a variety of useful machine learning algorithms, and it is designed to be interpolated into SciPy and NumPy.
Scikit-learn includes gradient boosting, DBSCAN, random forests within the classification, regression, clustering methods, and support vector machines.
The Python library is often used for applications like clustering, classification, model selection, regression, and dimensionality reduction.
Here are some of the main features of Scikit-learn for data science:
- Data classification and modeling
- Pre-processing of data
- Model selection
- End-to-end machine learning algorithms
Keras is a highly popular Python library often used for deep learning and neural network modules, similar to TensorFlow. The library supports both the TensorFlow and Theano backends, which makes it a great choice for those who don’t want to get too involved with TensorFlow.
The open-source library provides you with all of the tools needed to construct models, analyze datasets, and visualize graphs, and it includes prelabeled datasets that can be directly imported and loaded. The Keras library is modular, extensible, and flexible, making it a user-friendly option for beginners. On top of that, it also offers one of the widest ranges for data types.
Keras is often sought out for the deep learning models that are available with pretrained weights, and these can be used to make predictions or to extract its features without creating or training your own model.
Here are some of the main features of Keras for data science:
- Developing neural layers
- Data pooling
- Activation and cost functions
- Deep learning and machine learning models
Scrapy is one of the best known Python libraries for data science. The fast and open-source web crawling Python frameworks are often used to extract data from the web page with the help of XPath-based selectors.
The library has a wide range of applications, including being used to build crawling programs that retrieve structured data from the web. It is also used to gather data from APIs, and it enables users to write universal codes that can be reused for building and scaling large crawlers.
Here are some of the main features of Scrapy for data science:
- Lightweight and open source
- Robust web scraping library
- Extracts data form online pages with XPath selectors
- Built-in support
Nearing the end of our list is PyTorch, which is yet another top Python library for data science. The Python-based scientific computing package relies on the power of graphics processing units, and it is often chosen as a deep learning research platform with maximum flexibility and speed.
Created by Facebook’s AI research team in 2016, PyTorch’s best features include its high speed of execution, which it can achieve even when handling heavy graphs. It is highly flexible, capable of operating on simplified processors or CPUs and GPUs.
Here are some of the main features of PyTorch for data science:
- Control over datasets
- Highly flexible and fast
- Development of deep learning models
- Statistical distribution and operations
Closing out our list of 10 best Python libraries for data science is BeautifulSoup, which is most often used for web crawling and data scraping. With BeautifulSoup, users can collect data that’s available on a website without a proper CSV or API. At the same time, the Python library helps scrape the data and arrange it into the required format.
BeautifulSoup also has an established community for support and comprehensive documentation that allows for easy learning.
Here are some of the main features of BeautifulSoup for data science:
- Community support
- Web crawling and data scraping
- Easy to use
- Collect data without proper CSV or API