Here, in this post I include 2 sessions on ‘Essential Python for Datascience’. These 2 presentations cover the most important features of the Python language with which you can hit the ground running in datascience. All the related material for these sessions can be cloned/downloaded from Github at ‘EssentialPythonForDatascience‘
1. Essential Python for Datascience -1
In this video presentation I cover basic data types like tuples,lists, dictionaries. How to get the type of a variable, subsetting and numpy arrays. Some basic operations on numpy arrays, slicing is also covered
2. Essential Python for Datascience -2
In the 2nd part I cover Pandas, pandas Series, dataframes, how to subset dataframes using iloc,loc, selection of specific columns, filtering dataframes by criteria etc. Other operations include group_by, apply,agg. Lastly I also touch upon matplotlib.
This is no means an exhaustive coverage of the multitude of features available in Python but can provide as a good starting point for those venturing into datascience with Python.
Good luck with Python!
1. My 3 video presentations on “Essential R”
2. Neural Networks: The mechanics of backpropagation
3. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
4. Deblurring with OpenCV: Weiner filter reloaded
5. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and table
To see all posts see Index of posts