The link you provided is a good resource, but shows the whole thing being done in matplotlib.pyplot and uses .subplots() to get to the axes. While I’ve done this before, I keep searching for ways to just use the built-into-pandas .plot() function as much as possible. To me it can simplify the code and makes it easier to leverage DataFrame goodness.
There do seem to be a number of things that aren’t easy to do fully inside the parameters of df.plot() by itself, though. Luckily it returns an matplotlib.AxesSubplot, which opens up a much larger range of possibilities.
I copied your data above into a DataFrame:
df = pd.read_clipboard(quotechar="'")
It looks sort-of like:
A B C
0 1 2 'name 1'
1 2 3 'name 2'
2 3 5 'name 3'
But, of course, much better in non table-crippled html. (Maybe SO will fix this one day).
Then all I had to do was:
ax = df.A.plot(xticks=df.index, rot=90)
ax.set_xticklabels(df.C)
If you are using IPython/Jupyter and %matplotlib inline then both of those need to be in the same cell. I had forgotten that at first and spent quite a bit of time trying to figure what was going wrong.
You can do it all using the ax variable:
ax = df.A.plot()
ax.set_xticks(df.index)
ax.set_xticklabels(df.C, rotation=90)
but, as I mentioned, I haven’t found a way to the xticklabels inside the df.plot() function parameters, which would make it possible to do this all in a single line.
The extra step to rotate the xtick labels may be extraneous in this example, but came in handy in the one I was working on when looking for this answer.
And, of course, you can plot both A and B columns together even easier:
ax = df.plot()
ax.set_xticks(df.index)
ax.set_xticklabels(df.C, rotation=90)
