![df.plot rename x ticks df.plot rename x ticks](https://i.stack.imgur.com/6xfG1.png)
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.
#DF.PLOT RENAME X TICKS INSTALL#
To run the app below, run pip install dash, click 'Download' to get the code and run python app.py. I had forgotten that at first and spent quite a bit of time trying to figure what was going wrong. Dash is the best way to build analytical apps in Python using Plotly figures. If you are using IPython/Jupyter and %matplotlib inline then both of those need to be in the same cell. Then all I had to do was: ax = df.A.plot(xticks=df.index, rot=90) I copied your data above into a DataFrame: df = pd.read_clipboard(quotechar="'")īut, of course, much better in non table-crippled html.
![df.plot rename x ticks df.plot rename x ticks](https://i.stack.imgur.com/4a6li.png)
Share Improve this answer answered at 16:35 tdy 27. The code goes as follows: import matplotlib.pyplot as plt x bygender 'Gender' y bygender 'Value' plt.bar (x, y, label 'Proportion', color '468499') plt.title (' respondents accepting violence against women, by gender') plt.xlabel ('Gender') plt.ylabel ('Proportion ') plt.legend () plt.show () This gives me the plot, no problem. And, of course, you can plot both A and B columns together even easier: ax df.plot() ax.setxticks(df.index) ax.setxticklabels(df. Luckily it returns an matplotlib.AxesSubplot, which opens up a much larger range of possibilities. df.plot (y'A').setxticks (df.index, df.C) Note that plt.xticks always had a labels param, so this change just unifies the Axes and pyplot APIs. 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. 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. For achieving data reporting process from pandas perspective the plot() method in pandas library is used. On top of extensive data processing the need for data reporting is also among the major factors that drive the data world.
![df.plot rename x ticks df.plot rename x ticks](https://i.stack.imgur.com/EymoF.png)
If you don’t specify labels, the first tick will take the value of the first index entered. Use ax.set (xticks a, b, c) or ax.setxticks ( a, b, c) to choose specific places to add ticks at indices. To customize X-axis ticks, we can use tick. If you don’t specify anything, all ticks will be placed automatically. To me it can simplify the code and makes it easier to leverage DataFrame goodness. Introduction to Pandas ot() The following article provides an outline for Pandas ot(). To customize X-axis ticks in Matplotlib, we can change the ticks length and width. While I've done this before, I keep searching for ways to just use the built-into-pandas. To remove the ticks on the x-axis, tickparams () method accepts an attribute named bottom, and we can set its value to False and pass it as a parameter inside the tickparams () function. The link you provided is a good resource, but shows the whole thing being done in matplotlib.pyplot and uses.