Python is a general purpose language that is easy and intuitive. This gives it a relatively flat learning curve, and it increases the speed at which you can write a program. In short, you need less time to code and you have more time to play around with it!
The IPython Notebook makes it easier to work with Python and data. You can easily share notebooks with colleagues, without having them to install anything. This drastically reduces the overhead of organizing code, output and notes files. This will allow you to spend more time doing real work.
They are fast, becoming the go to for reproducible research and are a great learning resource.
I’ve been using IPython Notebooks and I really love how flexible they are and how quickly I’m able to try out new ideas.
The easiest way to proceed is to just download Anaconda from Continuum.io . It comes packaged with most of the things you will need ever.
Best resources for learning Python
- DataCamp Intro to Python for Data Science –
- Collection of 53 Free Python books – [ click free. ] . Includes all the books mentioned below.
- (Disclosure: Added by author)
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- Python: The Essential Reference ( )
- How to Think like a Computer Scientist ( )
- Learning Python – 4th Edition ( )
- Byte of Python ( )
- Beginning Python ( )
- The Python Standard Library by example ( )
- Python in a nutshell ( )
- Core Python Programming ( )
- MIT’s introductory course ( )
- Google for Education Python course:
- Data Science from Scratch: First Principles with Python
- Learning to Program Using Python, 2nd Edition
- is the best resources to learn
If you are a beginner and want to go through the syntax of Python with some good examples, refer –
Here is a brief introduction to various libraries.
- Practice the NumPy tutorial thoroughly, especially NumPy arrays. This will form a good foundation for things to come.
- Next, look at the SciPy tutorials. Go through the introduction and the basics and do the remaining ones basis your needs.
- If you guessed Matplotlib tutorials next, you are wrong! They are too comprehensive for our need here. Instead look at this ipython notebook till Line 68 (i.e. till animations)
- Finally, let us look at Pandas. Pandas provide DataFrame functionality (like R) for Python. This is also where you should spend good time practicing. Pandas would become the most effective tool for all mid-size data analysis. Start with a short introduction, 10 minutes to pandas. Then move on to a more detailed tutorial on pandas.
- We have discussed on these libraries in detail in our post Click Here
Also checkout these free online resources.
Happy Learning !!