Today we've seen how to grab some raw data and create some interesting charts using Pandas and Matplotlib. We've
- used .groupby() to explore the number of posts and entries per programming language
- converted strings to Datetime objects with to_datetime() for easier plotting
- reshaped our DataFrame by converting categories to columns using .pivot()
- used .count() and isna().values.any() to look for NaN values in our DataFrame, which we then replaced using .fillna()
- created (multiple) line charts using .plot() with a for-loop
- styled our charts by changing the size, the labels, and the upper and lower bounds of our axis.
- added a legend to tell apart which line is which by colour
- smoothed out our time-series observations with .rolling().mean() and plotted them to better identify trends over time.