Transitioning to Python for Data Analysis

After many years of struggling with R, I have now made the transition to Python for data analysis. Python notebooks have also been a revelation, and there is no turning back now.

A few useful resources for those looking to take the plunge:

• 10 Minutes to Pandas: Good introduction with basic code examples.

• Pandas Cookbook: Excellent introduction with sample Python notebooks you can work through yourself. I especially enjoyed the notebooks for analyzing New York City 311 calls.

• Pandas, MatPlotLib, and Numpy Cheat Sheets: Excellent, concise cheat sheets. Print them out and keep them handy.

• Official Pandas Docs: Not the easiest for beginners, but handy nonetheless.

• What’s New in Pandas: New stuff appears here.

• MatplotLib: Plotting in Python. So much easier than R!

• Seaborn: statistical data visualization: For when MatPlotLib doesn’t cut it.

• Plot.ly: now fully open source; easily create interactive plots and visualization.

And, for those looking for a quick, simple mash up of pandas and plotly, served via flask, a basic hello world application: https://github.com/ecerami/hello_flask.