Congratulations on completing another challenging data science project! 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.

Well done for completing today's lessons! Have a good rest. I'll see you tomorrow!