Python, the programming language, is considered the Swiss Army knife of the coding world. Unlike programming languages like R, it supports structured programming, functional programming patterns, and object-oriented programming. The Python community considers it the second-best language for programming. Python is an all-in-one, unified language capable of handling running embedded systems, data mining, and website construction. Moreover, this is an easy language to pick up and can be learned by taking an online Python for Data Science course.
Python was used at ForecastWatch to write a parser to collect forecasts from different websites, in an integrated engine to mine data, and in the website code to present the outcomes. Earlier, PHP was used to develop websites until the company realized that dealing with a single language was easier. According to the Fast Company Magazine article, in 2014, Facebook selected Python for data analysis as it was increasingly global.
Python preferred over data science tools
Python becomes Pythonic when the code is written naturally. It has many other features that attract the data science community. Being a data science tool, Python helps to explore the concepts of machine learning in the best way possible. Machine Learning is all about probability, mathematical optimization, and statistics, which are all made easy by Python.
Easy to learn
Python is the popular data analysis tool. It is ahead of SQL and SAS and comes next to R with 35% of data analysts using it.
What drives developers to Python is that it is easy to learn and code. It promotes an easy-to-understand syntax especially when compared to other data science languages, such as R and thereby leads to a shorter learning curve.
Today, Jupyter is a tool used for writing code and text within a web page’s context. It helps data scientists and engineers work in a collaborative manner. The code works on a server and you get results in HTML and integrated into your writing page. If you want to run it freeBSD 10.2 for a notebook server, you need to follow three simple steps.
0. install python3
- install pip3
- install Juypter
- generate the config: jupyter notebook –generate-config
Python has emerged as a scalable language compared to R and is faster to use than Matlab and Stata. Even YouTube has migrated to Python due to its scalability that lies in its flexibility during problem-solving situations. Skilled data scientists in various industries use this language to develop various types of applications successfully.
Data science libraries
The reason for growing success of Python is the availability of data science libraries for aspiring candidates. These libraries have been upgraded continuously. The constraints that developers faced a year ago are now treated successfully with Python.
Many libraries are available to perform data analysis, here’s an important one to start with:
NumPy is important to perform scientific computing with Python. It encompasses an assortment of high-level mathematical functions to operate on multi-dimensional arrays and matrices.
SciPy works in association with NumPy arrays and offers effective routines for numerical integration and up-gradation.
Pandas, also developed on top of NumPy, delivers data structures and operations to change numerical tables and time series.
Matplotlib is a 2D plotting library. It offers data visualizations in the form of histograms, power spectra, bar charts, and scatterplots with minimal coding lines.
Developed on NumPy, SciPy, and Matplotlib, Scikit-learn acts as a machine learning library that leads to classification, regression, and clustering algorithms that involve support vector machines, logistic regression, naive Bayes, random forests, and gradient boosting.
The growth of Python is due to its ecosystem. Today, an increased number of volunteers are developing Python libraries as Python has extended its reach to the data science community. This helps develop advanced tools and processes in Python.
The community helps Python aspirants look for relevant solutions to their coding problems. Moreover, code mentor and stack flow are available to find the right answers to questions.
Python offers many visualization options. Matplotlib is the base for the development of libraries like Seaborn, pandas plotting, and ggplot. Developers can understand data, develop charts, graphical plot and develop web-ready plots with the help of data visualization packages.
Python is easy, simple, powerful, and innovative due to its broader usage in a variety of contexts, some of which are not associated with data science. R is an optimized environment for data analysis, but it is difficult to learn.
It’s only one way to shape the debate: considering it as a zero-sum game. The fact is that understanding both tools and utilizing them as per their respective strengths can refine you as a data scientist. Every data scientist should be versatile and should stay at the top of their game.