The rise of Python can be connected to the rise of interest in data science.Python’s popularity in data science, machine learning, web development and Artificial Intelligence are probably the main driver of its fast growth.
PYPL PopularitY of Programming Language
The PYPL PopularitY of Programming Language Index is created by analyzing how often language tutorials are searched on Google.
Worldwide, Python is the most popular language, Python grew the most in the last 5 years (13.2%) and PHP lost the most (-6.3%)
There’s still an important question here, though. Plenty of other programming languages, like SQL and R, can be useful in the field of data science. Why are so many people choosing Python?
One major factor is Python’s versatility. There are over 125,000 third-party Python libraries. These libraries make Python more useful for specific purposes, from the traditional (e.g. web development, text processing) to the cutting edge (e.g. AI and machine learning). For example, a biologist might use the Biopython library to aid their work with genetic sequencing.
Python libraries you can’t live without
Requests. The most famous http library written by kenneth reitz. It’s a must have for every python developer.
Scrapy. If you are involved in webscraping then this is a must have library for you. After using this library you won’t use any other.
wxPython. A gui toolkit for python. I have primarily used it in place of tkinter. You will really love it.
Pillow. A friendly fork of PIL (Python Imaging Library). It is more user friendly than PIL and is a must have for anyone who works with images.
SQLAlchemy. A database library. Many love it and many hate it. The choice is yours.
BeautifulSoup. I know it’s slow but this xml and html parsing library is very useful for beginners.
Twisted. The most important tool for any network application developer. It has a very beautiful api and is used by a lot of famous python developers.
NumPy. How can we leave this very important library ? It provides some advance math functionalities to python.
SciPy. When we talk about NumPy then we have to talk about scipy. It is a library of algorithms and mathematical tools for python and has caused many scientists to switch from ruby to python.
matplotlib. A numerical plotting library. It is very useful for any data scientist or any data analyzer.
Pygame. Which developer does not like to play games and develop them ? This library will help you achieve your goal of 2d game development.
Pyglet. A 3d animation and game creation engine. This is the engine in which the famous python port of minecraft was made
pyQT. A GUI toolkit for python. It is my second choice after wxpython for developing GUI’s for my python scripts.
pyGtk. Another python GUI library. It is the same library in which the famous Bittorrent client is created.
Scapy. A packet sniffer and analyzer for python made in python.
pywin32. A python library which provides some useful methods and classes for interacting with windows.
nltk. Natural Language Toolkit – I realize most people won’t be using this one, but it’s generic enough. It is a very useful library if you want to manipulate strings. But it’s capacity is beyond that. Do check it out.
nose. A testing framework for python. It is used by millions of python developers. It is a must have if you do test driven development.
SymPy. SymPy can do algebraic evaluation, differentiation, expansion, complex numbers, etc. It is contained in a pure Python distribution.
IPython. I just can’t stress enough how useful this tool is. It is a python prompt on steroids. It has completion, history, shell capabilities, and a lot more. Make sure that you take a look at it.
A sting of Python in my daily life
Additionally, Python has become a go-to language for data analysis. With data-focused libraries like pandas, NumPy, and matplotlib, anyone familiar with Python’s syntax and rules can deploy it as a powerful tool to process, manipulate, and visualize data.
If you want to do something simple, it’s probably one line [of code]. If you want to do something really complicated, you also have that very fine level of control.
Given its versatility and applicability to data analysis, a skill that gets more important every day, it’s become clear to me that Python is here to stay. So if data literacy is one of my priorities, should I get started with Python?
“I’m not looking to learn Python to become a data scientist or even a software engineer. Rather, my goal is to improve my grasp of data analysis, use programming skills for web development purposes, and prep for any other technical demands my career might throw my way.”
Increasingly, the people who are seeking out programming knowledge are not looking to become full-time software developers or data scientists. Instead, these are working professionals who are using programming skills to get better at their jobs—marketers, project managers, and entrepreneurs who are looking for an edge, and who simply don’t have time to learn a new language for every purpose.
Python is appealing to those of us in non-technical fields because it puts data analysis, an increasingly important skill in the business world, within arm’s reach, without being too demanding.
“Data driven decision making is increasing in popularity. While in the past years, analysts would use software like Excel to analyze data, while only academics would turn to SPSS, Stata, etc., now things are changing,” according to Forbes.
It’s become clear to me that Python is the Swiss Army Knife of programming languages—a versatile tool that can useful in just about any career.
It’s ideal for novice software engineers, marketers, business analysts, bankers, and anyone else who wants to do more with data.