Overview : History of Python
Python was developed by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands in the late 1980s, as an improvement to the ABC programming language. Its implementation started in late 1989 and was first released in 20 February 1991. Guido Van Rossum managed this open source project until 12 July 2018, when he announced his permanent break (exit) from his responsibilities as lead developer of the project.1. Python 2.0 was released on 16 October 2000, with many major new features, including a cycle-detecting garbage collector (in addition to reference counting) for memory management and support for Unicode. Decision makers of Python project decided to discontinue support for Python 2 post January 01, 2020. It's not maintained further anymore.
2. Python 3.0 was released on 3 December 2008. It was a major revision of the language that is not completely backward-compatible.
Latest version of Python 3.10.1 was released on 6 December 2021
Is Python free?
Python is an open source programming language. It means it is available for free and you don't need to purchase license for using it. You can also get future updates for free as open source developers contribute to the future development of Python.Popularity of Python
In PyPL popularity of programming language index, Python leads with market share of 29% share. This popularity index compares several programming languages by analyzing how often language tutorials are searched on Google. In another survey, Python was named most in-demand coding language for 2022. If you don't belive in these surveys, just visit websites of popular job portals and search 'Python' as a skill which is mentioned in different job postings. You would find out a tons of job postings for this skill-set.- YouTube
- Dropbox
- Disqus
Python Uses
It is widely used as a high-level programming language for general-purpose programming. You can use it for various purposes. Some of them are as follows -- Web / Dashboard Building
- Advanced Analytics
- Artificial Intelligence
- Natural Language Processing
- Basic and Descriptive Analytics
- Web Scrapping
- API Building
How to install Python?
There are two ways to download and install Python- Download Anaconda. It comes with Python software along with preinstalled popular libraries.
- Download Python from its official website. You have to manually install libraries.
Recommended : Go for first option and download anaconda. It saves a lot of time in learning and coding Python
25+ Python Tutorials for Data Manipulation
Go ahead and visit the links below and follow them in order. These tutorials were designed for learning python with real-world problem statements and use cases.- Data Structures in Python
- How to install Python Package
- Import Data in Python
- 15 ways to read CSV file
- How to create Dummy Data
- Data Manipulation with Pandas - Learn with 50 Examples
- Python : 10 Ways to Filter Pandas DataFrame
- How to drop one or multiple columns from Pandas Dataframe
- How to rename columns in Pandas Dataframe
- How to use variable in a query in pandas
- String Functions in Python with Examples
- A Complete Guide to Python DateTime Functions
- NumPy Tutorial with Exercises
- Loops in Python explained with examples
- Python Lambda Function with Examples
- Python list comprehension : Learn by Examples
- Python Dictionary Comprehension with Examples
- Matplotlib Tutorial : Learn by Examples
- Object Oriented Programming in Python : Learn by Examples
- What are *args and **kwargs and How to use them
- PIP connection Error : SSL CERTIFICATE VERIFY FAILED
- Run Python in R
- Run SAS in Python without Installation
- Translating Web Page while Scraping
- Wish Christmas with Python and R
- Fix : Only size-1 arrays can be converted to Python scalars
Data Science with Python Tutorials
The following tutorials are for learning advanced analytics with Python. It ranges from basic statistics to advanced concepts of machine learning.- A Complete Guide to Linear Regression in Python
- Logistic Regression in Python
- Random Forest in Python
- Preprocessing steps of model building
- Calculate KS Statistic with Python
- Precision Recall Curve Simplified
- Case Study : Sentiment analysis using Python
- Identify Person, Place and Organisation in content using Python
- Loan Amortisation Schedule using R and Python
Other Tutorials
FAQs
It's a misconception that Python requires high-performance computers and laptops. Python works well even if you have old laptop with poor configuration like 4GB RAM and i3 processor. If you need to handle large datasets, you would obviously need more system memory with powerful processor. In nutshell, laptop with not-so-high configuration is enough for practice python programming but needs high-performance system for memory intensive operations.
Python is used for a variety of purposes ranging from API building to Artificial Intelligence. You should focus on basics of Python and then you can jump to advanced programming concepts. There are several packages in Python for Data Science which are easy to use but you need to have prior knowledge of statistics. If you already possess intermediate knowledge of statistics, these packages would be a piece of cake for you. Make your schedule and follow it with no cheat. You can learn basic of python in a week if you spend 8 hours per day with a lot of practice. Once done, start using different statistics algorithms using Python. You can make use of the publicly available datasets for practice
Python is open-source only and available for free. The only difference you may see is different editors for coding Python. Some of the popular ones are Spyder, Microsoft Visual Studio, PyCharm etc. Some of the popular notebooks are Jupyter, Google Colab, Azure Notebooks. They ease writing codes with beautiful interface but programming and concepts of Python are exactly same everywhere so you should focus on learning Python rather than the different editors. You will be asked about programming questions in interviews. Interviewers don't care much about editor you use.
You can follow the links above and put your question related to the topic in the comment section. You can also sign-up on Stackoverflow and ask your questions