Python is today's most popular programming language. When it comes to tackling data science tasks and hurdles, Python never ceases to astound its users. The majority of data scientists already use Python programming on a daily basis and it is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance programming language, and it has many more advantages. Programmers use Python libraries on daily basic to solve the challenges in Python.
By using Python libraries programmers used to create machine learning, data visualization, deep learning, Artificial Intelligence, data science, data manipulation, other applications. Let's begin with a quick overview of the Python programming language before diving right into the most popular Python libraries.
What is the definition of a library?
A library is a collection of pre-combined programmes that can be used to minimise coding time in an iterative approach. They're especially handy for accessing pre-written commonly used codes rather than having to write them from scratch every time. These are a collection of reusable resources, similar to physical libraries, which means that each library has a root source. The numerous open-source libraries available in Python are built on this foundation.
let’s start discussing on types of Python Libraries.
TensorFlow
TensorFlow is one of the best Python libraries which is capable to perform high task with 35,000 comments to do numerical computation framework and a thriving community of over 1,500 contributors and employed in a variety of scientific domains. It is a framework mainly used for building and executing tensor-based calculations. TensorFlow are computational objects that are partially defined but eventually emit a value. Google created and maintains it, and it's open source under the Apache 2.0 licence. Although there is access to the underlying C++ API, the API is ostensibly for the Python programming language.
Features
1. TensorFlow is an open-source library which allows faster calculations in Machine learning.
2. Simple to run
3. It has very fast Debugging skills
4. Effective
5. Experimentation is very easy and scalable
6. Flexible
2. NumPy
NumPy (numerical Python) is a collection of multidimensional array objects and routines for manipulating them. It is designed to allows you to conduct mathematical and logical operations on arrays. NumPy is a Python scripting language. 'Numerical Python' is what it stands for. NumPy is a Python ibrary that contains different multidimensional array objects as well as a collection of array for routines. Jim Hugunin created Numeric, the forerunner of NumPy. Numarray, a new package with some extra features, was also created. Numpy Package is created by Travis Oliphant in 2005 by combining the functionality of Numarray with the Numeric package in an effective way.
Features
1. Performance will be very fast with N-dimensional array object
2. Integrating code from c/c++
3. Multidimensional container
4. Broadcasting functions
3. Pandas
In the data science life cycle, Pandas (Python data analysis) is a requirement. Along with NumPy in matplotlib, it is the most popular and commonly used Python package for data research. It is frequently used for data analysis and cleansing, with about 17,00 comments on GitHub and a community of 1,200 contributors. Pandas provides rapid and adaptable data structures, such as data frame CDs, that make working with structured data intuitive.
Features
1. Fast an efficient DataFrame object
2. Loading data into in-memory from various file formats
3. Data alignment
4. Integrated Handling
5. Data alignment and integrated handling
4. SciPy
Another free and open-source Python library for data science that is widely used for high-level computations is SciPy (Scientific Python). On GitHub, SciPy has over 19,000 comments and a community of about 600 contributors. Because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations, it is widely used for scientific and technical computations.
Features
1. Collections of alogorithms and functions on the Numpy
2. Efficient commands for data manipulation
3. High visualization
4. In-built functions for equations
5. Flexible and reliable
5. Matplotlib
Matplotlib's visualizations are both powerful and elegant. It's a Python charting package with over 26,000 GitHub comments and a thriving community of roughly 700 developers. It's widely used for data visualization because of the graphs and charts it generates. It also has an object-oriented API for integrating the charts into applications.
Matplotlib is most sophisticated data visualization tools among all the libraries
Features
1) Colored labels
2) Aspect ratio of the axes box
3) Generate complex with semantic ways
4) 3D plots supporting minor ticks
5) Label and ticks
Some of the Advantages of Python
1. Fast Learning
2. Easy to Read and write
3. Increased productivity
4. Flexibility
5. Reliability
Wrapping Up
There are many additional useful Python libraries that should be investigated in addition to these Python libraries for data science. If you want to learn and master data science with Python as a next step, enrol in Learnbay's Data Science Certification Course in Chennai
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