WHAT IS THE IMPORTANCE OF PYTHON IN DATA SCIENCE?
- What is Python?
- History of Python
- Importance of Python in Data Science
- How Python is the best than other programming languages
- How to use Python 3
- Web development
- Data Science/Data Analysis
- Scripting
What is Python?
Python is a high-level, interpreted, multi-purpose, open-source, and object-oriented programming language that allows us to work easily, quickly, and effectively. Python includes high-level dynamic data types, modules, exceptions, and classes. Python is supported by well-established data platforms and processing frameworks that help analyze data simply and efficiently.
In other words, Python is a powerful programming language and really easy to find out. It is a simple and efficient high-level data structure. It is also an effective approach to object-oriented programming language. The elegant syntax and dynamic typing of Python make it an ideal language for scripting. It is a rapid application development in many areas and most of the platforms in Data Science.
We get the Python interpreter and an extensive library from the various sources and platforms. Python modules, programs, and tools are freely distributed among many third parties. The Python interpreter is easy to function and data type implemented in C and C++. It is also suitable for customizable applications as an extension language. Above all, Python can read offline because of its hands-on experience.
The Python Standard Library provides standard objects and modules. Python gives a very good idea of the language's style and net-worthy features. You can write python's modules and programs after reading it. You are also able to learn various python library modules in Data Science that describe in the python standard library.
History of Python
When Guido van Rossum was reading the BBC comedy series Monty Python’s Flying Circus, he started implementing python. Guido van Rossum found the language Python in the late 1980s at Centrum Wiskunde & Informatica (CWI) in the Netherlands. As a result, he chose a short and unique name called Python.
He released Python 2.0 on 16 October 2000. It was introduced with many features with a cycle-detecting garbage collector and Unicode. Again Python 3.0 was released on 3 December 2008 with a major revision of the language.
"I have this hope that there is a better way, Higher-level tools that actually let you see the structure of the software more clearly will be of tremendous value."
Guido van Rossum
Importance of Python in Data Science
- Relatively easy to learn and write
- Dynamically typed
- Object-oriented Programming
- Multi-paradigm and multipurpose Programming language
- Vast numbers of libraries, functions, and modules
- The free and open-source community
- Easily extensible features
- Interpreted language
How Python is the best programming language than other
When you work more on computers, you always like to automate some tasks. For example, replace and search a bunch of text files, or rearrange and rename a bunch of photos in a complicated way.
As a professional software developer, you know the usual write, compile, test, and recompile cycle is too slow working with C/C++/Java libraries. In addition, the C/C++/Java program takes a lot of time to get even the first draft program. But Python is simple to use and available on Windows, Mac OS X, and UNIX operating systems. It also works fast comparatively above programming languages.
Python may be a real programming language that offering more structure and support for giant programs. Pythons programs are comparatively shorter than C, C++, or Java programs. So Python is the best programming language than others.
You can split your program into modules that will be reused in other Python programs. A large collection of standard modules can be used as the basis of your programs. For example, start learning a python programming language. These modules provide file I/O, system calls, sockets, and interfaces like TK.
Compilation and linking are not needed during program development in python. That helps you to save time during program development. So, it is called a handy desk calculator.
How to use Python 3
Python 2 and Python 3 are the two important Python versions. They are quite different. Python 3 is more semantically correct than Python 2. As a result, the importance of Python 3 in data science is necessary.
We use the Python programming language (latest Python3) for the 3 main popular applications. These are
- Web development
- Data Science (Data Analysis, Data visualization, Machine Learning applications)
- Scripting
Web development
The importance of Python is also visible in web development in Data Science. Django and Flask are based on python and have recently been very popular for web development. These are created server-side code(back-end code) in python that runs on your server, devices, and browser(front-end code).
Data Science (Data Analysis, Data visualization, Machine Learning applications)
Python is the best programming language to learn Data Science or to be a Data Scientist. As we know, python is a very good language for data analysis. Data mining, data processing, Data modeling, and data visualization in Data analysis are the most popular steps in Python. Python Deals with each stage of the Data Analysis Process by using these Libraries. The importance of Python is its collection of libraries that can be used in Data Science to perform better.
Scrapy and BeautifulSoup
A data Analyst uses a library like Scrapy and BeautifulSoup for Data mining. We can build a program and collect structured data from the web using Scrapy a python framework. We pull data out of XML and HTML files using BeautifulSoup, a library of Python.
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Numpy
Numpy is a library of Python that offers numerical value as a mathematical function and linear algebra.
Pandas
Pandas is a python library for cleaning, exploring, manipulating, and analyzing, data.
SciPy
SciPy is a scientific library for python that depends on Numpy for fast N-dimensional array manipulation.
Matplotlib
Matplotlib is a big library that creating a develop quality plot, animated, and visualizations in Python. It customizes line style, font, axes properties, and embeds some file formats.
Scikit Learn
Scikit Learn is an efficient tool for machine learning in Data science for prediction. Scikit Learn is used in 3 categories. These are classification, Regression, and Clustering.
- We apply Scikit learn for spam detection, and image recognization using SVM, nearest neighbors, random forest, and more in Classification.
- In Regression, We use SVR, nearest neighbors, random forest, and more for Drug response and stock price to predict continuous value.
- Clustering in Scikit learn offers automatic grouping experiments and customer segmentation of similar objects into sets using k-Means, spectral clustering, mean-shift, and more.
TensorFlow
You can see the importance of Python when you will use the Tensorflow library in Data Science projects. TensorFlow library uses to do numerical computations that develop and train models using Python. Google has created TensorFlow a machine learning framework to design, build, and train deep learning models. TensorFlow is an open-source Machine learning library that focuses on the training of deep neural networks. It is a mathematics library that is based on data flow and differentiable programming to build a neural network.
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