Introduction to Python and Its Applications

Introduction to Python and Its Applications For Data Science ?

Introduction to Python and Its Applications For Data Science

Introduction to Python and Its Applications

Introduction to Python Programming ?

If you want to learn to program, it’s also a great starter language because of the huge global community and wealth of documentation. In fact, several different surveys in 2019 found that over 80% of data professionals worldwide use Python. Python is useful for many situations, including data science, AI and machine learning, web development, and IoT devices like the Raspberry Pi. Large organizations that use Python heavily include IBM, Wikipedia, Google, Yahoo!, CERN, NASA, Facebook, Amazon, Instagram, Spotify, and Reddit. Python is a powerful general-purpose programming language that can do a lot of things. 

It is widely supported by a global community and shepherded by the Python Software Foundation. 

What is Python ?

Python is a high-level general-purpose programming language that can be applied to many different classes of problems. It has a large, standard library that provides tools suited to many different tasks, including but not limited to databases, automation, web scraping, text processing, image processing, machine learning, and data analytics. 

Application of Python in Data science/AI/ML?

Introduction to Python and Its Applications
Introduction to Python and Its Applications


TensorFlow is a python library for data science is. TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. It’s used across various scientific fields.


NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them.


SciPy (Scientific Python) is another free and open-source Python library for data science that is extensively used for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It’s extensively used for scientific and technical computations, because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.


Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provides fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very easily and intuitively.


Matplotlib has powerful yet beautiful visualizations. It’s a plotting library for Python with around 26,000 comments on GitHub and a very vibrant community of about 700 contributors. Because of the graphs and plots that it produces, it’s extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications.


Similar to TensorFlow, Keras is another popular library that is used extensively for deep learning and neural network modules. Keras supports both the TensorFlow and Theano backends,


Next in the list of the top Python libraries for data science comes Scikit-learn, a machine learning library that provides almost all the machine learning algorithms you might need. Scikit-learn is designed to be interpolated into NumPy and SciPy.


Next in the list of top python libraries for data science is PyTorch, which is a Python-based scientific computing package that uses the power of graphics processing units. PyTorch is one of the most commonly preferred deep learning research platforms built to provide maximum flexibility and speed.


The next known python library for data science is Scrapy. Scrapy is one of the most popular, fast, open-source web crawling frameworks written in Python. It is commonly used to extract the data from the web page with the help of selectors based on XPath.


BeautifulSoup – the next python library for data science. This is another popular Python library most commonly known for web crawling and data scraping. Users can collect data that are available on some website without a proper CSV or API, and BeautifulSoup can help them scrape it and arrange it into the required format.

Another great selling point is the Python community, which has a well-documented history of paving the way for diversity and inclusion efforts in the tech industry as a whole. The Python language has a code of conduct executed by the Python Software Foundation that seeks to ensure safety and inclusion for all online and in-person python communities.

Python And Its Pro & Cons- Click Here

Aarju Raj


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