Python is considered the most popular programming language used by data scientists on a daily basis. As an object-oriented, high-performance, and open-source language has revolutionised solving data-related problems and tasks like data frame manipulation, data visualization, and the like. It is also widely used in multiple types of Machine Learning. Python comes with numerous useful libraries for data science that developers widely use to solve issues.
The Python community creates and maintains these libraries, which may be installed via package managers like pip. They are simply imported into Python scripts upon installation, enabling programmers to make full use of their capabilities and features.
Why Are Python Libraries Important?
Python libraries have multiple use cases and are widely used because they are:-
- Reusable: Python libraries enable developers to reuse code developed by others to do specific tasks or address specific issues. This saves programmers a lot of time and effort because they aren't required to write code from the scratch for each project.
- Highly efficient: Python modules are frequently optimised for speed, allowing developers to complete complicated jobs fast and efficiently. This can result in shorter development times and improved application performance.
- Standardised: Python libraries provide a consistent collection of tools and functions on which developers may rely. This makes project collaboration easy because everyone is utilising the same tools and methodologies.
- Supported: Python libraries have a huge and active community that provides assistance and contributes to their development. This can assist developers in solving difficulties fast and learning from the experiences of others.
- Innovative: Python libraries frequently provide state-of-the-art features and premium functionality that may be leveraged to develop creative apps. This can assist developers in staying miles ahead and developing solutions that satisfy changing corporate demands.
5 Most Widely Used Python Libraries
There are dozens of readily accessible Python libraries that cover a wide variety of functionalities including data analysis, web development, scientific computing, artificial intelligence, machine learning, and others. Here is a list of the top 5 Python libraries:-
Pillow
Pillow is a well-known open-source library that enables programmers to manipulate images. It is a counterpart of PIL (Python Imaging Library) based on the OOPS concepts in programming and supports a broad range of image file formats such as GIF, JPEG, PNG, BMP, WEBP, and TIFF. It represents and manipulates pictures by using classes and objects. Developers may use Pillow to do image processing operations like cropping, filtering, resizing, and modifying colours.
Features:-
- Image metadata support
- Easy conversion of image format
- Seamless integration with different Python libraries
Applications:-
- Image processing
- Image enhancement
- Image analysis
- Image file handling
- Web development
- Data visualization
NumPy
NumPy (Numerical Python) is the foundational Python module used in numerical computation and comprises a strong N-dimensional array object. With around 18,000 comments on GitHub, it receives a massive amount of community support via an active group of 700 contributors. It is an array-processing general-purpose software that offers high-performance arrays (multidimensional objects), and tools for manipulating them.
Features:-
- Provides quick functions precompiled for numerical routines
- Provides better efficiency with array-oriented computing
- Encourages object-oriented strategies
- Allows for more compact and quick calculations via Vectorisation
Applications:-
- Used widely in data analysis.
- Generates a strong N-dimensional array.
- Formulates the foundation of different libraries like sci-kit-learn and SciPy.
- When used with SciPy and matplotlib, it helps replace MATLAB.
Pandas
Pandas (Python data analysis) is an essential component of data science and is the most popular and commonly used Python package for data research. It is widely utilised in data analysis and cleansing and is supported by an active GitHub community of around 1,200 contributors. It is popularly used for data frame manipulation and offers quick and dynamic data structures like data frame CDs, that work well with structured data.
Features:-
- Fluent syntax and extensive functionality allow users to work with missing data.
- Allows users to write their own function and execute it on a series of data.
- A high level of abstraction
- It includes high-level data structures and tools for data manipulation.
Applications:-
- Data wrangling and cleansing
- Data frame manipulation
- ETL (extract, transform, load) processes for data transformation and storage.
- Academic and commercial applications like statistics, neurology, and economics.
- Time-series-specific functions like linear regression, moving window, date range creation, and date shifting.
Keras
Keras is a high-functioning neural network API that is written in Python and runs on top of various ML frameworks, like Theano, TensorFlow, or CNTK. It is a popular library that is widely used for various types of Machine Learning, neural network modules, and deep learning. This Python library supports the backends of both Theano and TensorFlow, making it a decent choice.
Features:-
- An abundance of prelabeled datasets that can be used to import and load directly.
- Has a vast number of parameters and integrated layers used for building, configuring, training, and evaluating neural networks.
Applications
- Extensive creation of predictions
- Easy extraction of characteristics
- Image classification
- Natural language processing
- Time-series analysis
- Speech and audio recognition
Matplotlib
Matplotlib's visualisations are both powerful and elegant. As a plotting library for Python, it has vast community support on GitHub with over 26,000 comments and over 700 developers. It is widely used for data visualisation because it helps generate graphs and plots. It also has an object-oriented API for embedding such graphs into applications.
Features:-
Can be used as a MATLAB substitute
- Supports dozens of backends and output types, and can be used regardless of which operating system or output format is preferred.
- Pandas may be used as MATLAB API wrappers to control MATLAB like a cleaner.
- Low memory utilisation
- Enhanced runtime performance
Applications:-
- Correlation evaluation of variables
- Display the models' 95% confidence intervals.
- Outlier detection
- Visualise data distribution to acquire fast insights.
Conclusion
To summarise, Python's vast ecosystem of libraries covers a wide range of use cases, ranging from data analysis and data visualisation to ML and web development. With these libraries, developers have the ease of simply adding significant functionalities to their apps rather than implementing them from scratch.
Having in-depth knowledge of Python and its libraries is key to becoming an expert in this field. To learn more about Python libraries and their uses, you can consider joining a professional course. If you are looking for a reliable online program, you can join the course offered by Imarticus Learning. Their top-tier Postgraduate Program In Data Science And Analytics will give you the knowledge and skills necessary to move forward in this career field.
