#Pandas plot rename x value code
#Pandas plot rename x value series

You can do it all using the ax variable: ax = df.A.plot()īut, 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. I had forgotten that at first and spent quite a bit of time trying to figure what was going wrong.

If you are using IPython/Jupyter and %matplotlib inline then both of those need to be in the same cell. Extra labels listed don’t throw an error.

#Pandas plot rename x value series

Labels not contained in a dict / Series will be left as-is. Function / dict values must be unique (1-to-1). a.txt plot Now I want to hide some parts of the data, ie I want to set all y-values for an interval x to 0 or better hide them completely, the rest of the plot shouldnt disappear. rename (mapper None,, index None, columns None, axis None, copy None, inplace False, level None, errors 'ignore') source Alter axes labels. Then all I had to do was: ax = df.A.plot(xticks=df.index, rot=90) I am importing data from a.txt file and plot it, that is working. I copied your data above into a DataFrame: df = pd.read_clipboard(quotechar=''')īut, of course, much better in non table-crippled html. Luckily it returns an matplotlib.AxesSubplot, which opens up a much larger range of possibilities. ax df.A.plot(xticksdf.index, rot90) ax.setxticklabels(df.C) If you are using IPython/Jupyter and matplotlib inline then both of those need to be in the same cell. 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.

#Pandas plot rename x value code

To me it can simplify the code and makes it easier to leverage DataFrame goodness. withhue function will plot percentages on the bar graphs if you have the 'hue' parameter in your plots. While I've done this before, I keep searching for ways to just use the built-into-pandas. You can follow these steps so that you can see the count and percentages on top of the bars in your plot. The link you provided is a good resource, but shows the whole thing being done in matplotlib.pyplot and uses.