Python is a well-known programming language used in various industries, including data science and subfields. Because of its popularity, it has over 130,000 packages for multiple applications. It is an easy-to-debug, high-performance programming language with many other benefits. It has numerous data science libraries to tackle problems used daily by data scientists.
We have compiled a list of 10 Python libraries for data science newbies who want to create Python data science apps.
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TensorFlow
The Google Brain Team developed TensorFlow is, an open-source deep-learning library. With around 35,000 comments, numerical computations and approximately 1,500 contributors, it’s used throughout various scientific fields. It’s a framework for building and conducting tensor-based analyses. Tensors are computational objects that yield a value.
Features:
- Improved graph displays for computational data
- Reduces error by 50% in neural machine learning
- Use parallel computing to run complex models.
- The same code runs on both GPUs and CPUs.
2. Pandas
In the data science life cycle, Pandas is a must-have. It’s a significant Python data science library, with NumPy in Matplotlib. It has efficient, versatile, and robust data structures, as well as management, data alignment, and advanced indexing capability. Providing rapid, adaptive, and expressive data structures enables programmers to work with labelled and relational data.
Features:
- DataFrame object is fast and convenient with customisable indexing
- Align data and handle missing data in a uniform way
- Use labels to slice, index, and subset large data sets
- Deal with missing data with fluent syntax and numerous features
- Use your created function on a collection of data
3. Scikit – Learn
Data science is incomplete without ML. Scikit-learn is a Python machine learning library with learning capabilities. It is built to work with SciPy and NumPy. It offers model selection, construction, evaluation tools, and different data pre-processing utilities.
Features:
- KMeans clustering is used to organise unlabeled data.
- Cross-validation is used for assessing the performance of supervised models using previously unseen data.
- Ensemble techniques to combine the predictions of various supervised models.
- Extract properties from picture and text data.
4. PyTorch
PyTorch is a graphics processing unit-based scientific computing program. It’s a fantastic package for machine learning research that makes it straightforward for developers to transition from theory and study to training and development.
Features:
- Get a deep learning development environment with great flexibility
- Access any level of computation
- It’s similar to TensorFlow in terms of training speed
- Dynamic visuals provide clarity to data scientists and programmers. TensorFlow has a higher learning curve than PyTorch
- PyTorch comes with valuable features like the ability to bind any module instantly
5. Scrapy
The architecture of the Scrapy framework is based on ‘spiders,’ which are self-contained crawlers. Scraping allows us to extract structured data from the internet in our machine learning model. This framework follows the “Don’t Repeat Yourself” principle in interface design. Most data scientists use it to obtain data from APIs worldwide.
Features:
- It uses features like auto-throttle rotating proxies that allow you to scrape almost undetected across the Internet.
- Scrapy’s auto-detection and encoding support make dealing with broken encoding declarations much easier.
- Has built-in middleware and extensions for handling cookies, sessions, HTTP features like authentication, caching, and crawl depth limiting
- Scrapy generates CSV, JSON, and XML feed exports.
- Built-in support for selecting and extracting data from sources using XPath or CSS expressions.
6. NumPy
NumPy (Numerical Python) is an open-source, fundamental program for Python’s scientific computing. The software includes linear algebra, Fourier transform, and matrix calculation functions and is mainly utilised for applications that require performance and resources. It contains a large set of routines for processing multidimensional arrays. Its high-level syntax makes it productive for both novice and expert developers.
Features:
- NumPy arrays can be single or multidimensional.
- Tools for incorporating C/C++ and Fortran code.
- Ability to perform functions on non-specific data types.
- Execute complex operations on linear algebra and the Fourier transform, among others.
- It broadcasts the geometry of larger arrays to smaller arrays
Where to learn:
7. SciPy
SciPy (Scientific Python) is a free and open-source library of technical and scientific computing. SciPy has an active community of about 600 contributors and nearly 19,000 comments on GitHub. It is a NumPy extension and provides handy and efficient routines for scientific calculations. It wraps high-optimized implementations written in low-level languages such as C++, C, and Fortran.
Features:
- NumPy is a Python extension that contains a collection of algorithms and routines
- High-level commands for data visualisation and manipulation
- SciPy and the image submodule are used to process multidimensional images
- Sub-packages that help with the most common Scientific Computation challenges
- Has a lot of computing power with a simple-to-use interface.
8. OpenCV
OpenCV is an application-specific Python package used in almost all data science projects. For example, OpenCV is designed to address real-time computer vision tools, software, and hardware.
OpenCV allows us to apply ML techniques to images. Still, we frequently need to preprocess and prepare the raw photos to be turned into features (data columns) that our Machine Learning Algorithms can understand and use.
Features:
- Edit images, capture and save videos.
- Detect specific objects like vehicles, faces, eyes in the videos or pictures.
- Analyse and edit videos, i.e., remove the background, estimate motions, and track objects.
- Dilation of the cascaded image to enhance edge detectability and better detect features.
Also Read: What are the Hottest Trends in Python
