Python has risen to the most widely used programming language today and is the best choice for tackling data science tasks. Python is used by data professionals every day and is a great choice for both amateurs and experts, thanks to its easy to learn nature. Some of the other features that make Python so popular in data science are that it is an open source, object-oriented, and high-performance language.
But Python’s biggest selling point for data science is its wide variety of libraries that can help programmers solve a number of problems.
Let’s take a look at the 10 best Python data science libraries:
At the top of our list of the 10 best Python data science libraries is TensorFlow, developed by the Google Brain team. TensorFlow is an excellent choice for both beginners and professionals and offers a wide range of flexible tools, libraries and community resources.
The library is focused on high-performance numerical computing and has about 35,000 comments and a community of more than 1,500 contributors. Its applications are used in various scientific fields and its framework lays the groundwork for defining and performing calculations that include tensors, which are partially defined computational objects that ultimately produce value.
TensorFlow is especially useful for tasks such as speech and image recognition, text applications, time series analysis and video detection.
Here are some of the key features of TensorFlow for data science:
- Reduces errors by 50 to 60 percent in neural machine learning
- Excellent library management
- Flexible architecture and frame
- Works on different computing platforms
Another top Python library for data science is SciPy, which is a free open source Python library used for high-level computing. Like TensorFlow, SciPy has a large and active community of hundreds of contributors. SciPy is particularly useful for scientific and technical calculations and provides a variety of user-friendly and efficient scientific calculation procedures.
SciPy is based on Numpy and includes all features, making them user-friendly scientific tools. SciPy excels in performing scientific and technical calculations on large data sets and is often used for multidimensional image operations, optimization algorithms, and linear algebra.
Here are some of the key features of SciPy for data science:
- High-level commands for data manipulation and visualization
- Built-in functions for solving differential equations
- Multidimensional image processing
- Calculate a large data set
Another one of Python’s most widely used data science libraries 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 best features of the Pandas library are its series and DataFrame, which are fast and efficient ways to manage and research data. They present the data efficiently and manipulate it in different ways.
Some of Pandas’ main applications include general data dispute and data cleanup, statistics, finance, time range generation, linear regression, and more.
Here are some of the key features of Pandas for data science:
- Create your own function and run it on a series of data
- High level abstraction
- High level structures and manipulation tools
- Merge / merge data sets
Numpy is a Python library that can be used seamlessly to process a large multidimensional array and matrix. It uses a wide range of high-level mathematical functions that make it particularly useful for effective fundamental scientific calculations.
NumPy is a general-purpose array processing package that provides high-performance arrays and tools and handles slowness by providing multidimensional arrays and functions and operators that work efficiently with them.
The Python library is often used to analyze data, create powerful N-dimensional arrays, and build on other libraries such as SciPy and scikit-learn.
Here are some of the key features of NumPy for data science:
- Fast, pre-compiled functions for numerical procedures
- Supports an object-oriented approach
- Array oriented for more efficient calculations
- Data cleaning and manipulation
Matplotlib is a graphics library for Python that has a community of over 700 contributors. It produces graphs and charts that can be used to visualize data, as well as an object-oriented API for embedding graphics in applications.
One of the most popular choices for data science, Matplotlib has a variety of applications. It can be used for correlation analysis of variables, to visualize the confidence intervals of models and the distribution of data to obtain insights and to detect deviations using a scatter plot.
Here are some of the key features of Matplotlib for data science:
- It can be a substitute for MATLAB
- Free and open source
- Supports dozens of backends and source types
- Low memory consumption
Scikit-learn is another great Python library for data science. The machine learning library provides various useful machine learning algorithms and is designed to be interpolated into SciPy and NumPy.
Scikit-learn includes gradient enhancement, DBSCAN, random forests within the classification, regression, clustering methods, and support vector machines.
The Python library is often used for applications such as clustering, classification, model selection, regression, and dimension reduction.
Here are some of the key features of Scikit-learn for data science:
- Data classification and modeling
- Data processing
- Choice of model
- End-to-end machine learning algorithms
Keras is a very popular Python library, often used for deep learning modules and neural networks, similar to TensorFlow. The library supports both TensorFlow and Theano servers, making it a great choice for those who don’t want to commit too much to TensorFlow.
The open source library provides you with all the tools you need to build models, analyze datasets, and visualize graphs, and includes pre-labeled datasets that can be directly imported and loaded. The Keras library is modular, extensible and flexible, making it an easy-to-use option for beginners. It also offers one of the widest ranges for data types.
Keras is often sought after for deep learning models that come with pre-trained weights and they can be used to make predictions or to extract its features without creating or training your own model.
Here are some of Keras’ main characteristics for data science:
- Development of nerve layers
- Data aggregation
- Activation functions and costs
- Models for deep learning and machine learning
Scrapy is one of Python’s most famous data science libraries. Fast and open source Web crawling Python frames are often used to retrieve data from a web page using XPath-based selectors.
The library has a wide range of applications, including being used to build crawl programs that retrieve structured data from the web. It is also used to collect data from the API and allows users to write universal codes that can be reused to build and scale large robots.
Here are some of the key features of Scrapy for data science:
- Lightweight and open source
- Robust web scraping library
- Retrieves data from online pages with XPath selectors
- Built-in support
Towards the end of our list is PyTorch, which is another top Python library for data science. The Python-based science computer package relies on the power of graphics processors and is often chosen as a research platform for deep learning with maximum flexibility and speed.
Created by Facebook’s AI research team in 2016, PyTorch’s best features include its high execution speed, which it can achieve even when working with heavy graphics. It is very flexible, capable of running on simple processors or CPU and GPU.
Here are some of the key features of PyTorch for data science:
- Data set control
- Extremely flexible and fast
- Development of models for deep learning
- Statistical distribution and operations
10. Beautiful soup
Closing our list of the 10 best Python data science libraries is BeautifulSoup, which is most commonly used for web crawling and data scraping. With BeautifulSoup, users can collect data that is available on a website without the appropriate CSV or API. At the same time, the Python library helps to scrape the data and arrange it in the required format.
BeautifulSoup also has an established support community and comprehensive documentation that allows for easy learning.
Here are some of the key features of BeautifulSoup for data science:
- Community support
- Web crawling and data writing
- Easy to use
- Collect data without a suitable CSV or API