Comparing R vs. Python graphing capabilities for time series data

 I used a simple time series data set on the number of touch downs for seven different quarterbacks achieved over the years.  The x-axes of the graphs are the quarterbacks' respective age.  The y-axes are their respective cumulative number of touch downs.  

You can see the complete presentation at the link below: 

R vs. Python comparison

And, I compare the two software using different types of graphs, including:

1) Time series graph of a single variable (the number of touch downs for one single quarterback);

2) Time series graph of multiple variables (including all 7 quarterbacks); and 

3) Facet graphs when you generate a separate graph for each of the quarterbacks. 

For the first two types of graphs, the two software were pretty competitive.  R was a bit more efficient in generating legends almost automatically.  Meanwhile, constructing a legend using Python was a lot longer and manual.  But, otherwise the respective Python graphs were pretty competitive with the R ones in terms of look and feel.  And, the coding difficulty (besides the legend bit) was fairly similar. 

When it came to Facet graphs, there was no comparison.  R was far easier and better.  Python facet graph capabilities appear more structured for scatter plots and not so much for time series plots.  Doing the latter in Python was truly a miserable experience.  And, the result was so poor relative to the R facet graphs, that I don't even dare to show them here.  I show them within the presentation link above.  With superior Python coding skills, maybe facet-time series graphs are doable.  But, be warned.  There is high hurdle rate there in terms of coding skills.  

Here is a multi variables regular Python graph that came out very well.

 

Here is the comparable R graph that came out equally well. 

 

Here is an R facet graph that came out very well.