Grouped bar chart of multiindex

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first of all: I'm completely new to python.

I'm trying to visualize some measured data. Each entry has a quadrant, number and sector. The original data lies in a .xlsx file. I've managed to use a .pivot_table to sort the data according to its sector. Due to overlapping, number and quadrant also have to be indexed. Now I want to plot it as a bar chart, where the bars are grouped by sector and the colors represent the quadrant.

But because number also has to be indexed, it shows up in the bar chart as a separate group. There should only be three groups, 0, i and a.

enter image description here

MWE:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

d = {'quadrant': ["0","0","0","0","0","0","I","I","I","I","I","I","I","I","I","I","I","I","II","II","II","II","II","II","II","II","II","II","II","II","III","III","III","III","III","III","III","III","III","III","III","III","IV","IV","IV","IV","IV","IV","IV","IV","IV","IV","IV","IV"], 'sector': [0,"0","0","0","0","0","a","a","a","a","a","a","i","i","i","i","i","i","a","a","a","a","a","a","i","i","i","i","i","i","a","a","a","a","a","a","i","i","i","i","i","i","a","a","a","a","a","a","i","i","i","i","i","i"], 'number': [1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6], 'Rz_m': [67.90,44.17,44.30,63.43,49.87,39.33,61.17,69.37,66.20,44.20,64.77,39.93,44.33,50.97,55.90,51.33,58.23,44.53,50.03,47.40,58.67,71.57,57.60,70.77,63.93,47.37,46.90,34.73,41.27,48.23,58.30,47.07,50.53,51.20,32.67,50.37,37.50,55.50,41.20,48.07,56.80,49.77,40.87,44.43,44.00,60.03,63.73,72.80,51.60,45.53,60.27,71.00,59.63,48.70]}

df = pd.DataFrame(data=d)

B = df.pivot_table(index=['sector','number', 'quadrant'])

B.unstack().plot.bar(y='Rz_m')
pandas bar-chart python
1 Answers

The data viz ecosystem in Python is pretty diverse and there are multiple libraries you can use to produce the same chart. Matplotlib is a very powerful library, but it's also quite low-level, meaning you often have to do a lot of preparatory work before getting to the chart, so usually you'll find people use seaborn for static visualisations, especially if there is a scientific element to them (it has built-in support for things like error bars, etc.)

Out of the box, it has a lot of chart types to support exploratory data analysis and is built on top of matplotlib. For your example, if I understood it right, it would be as simple as:

import seaborn as sns

sns.catplot(x="sector", y="Rz_m", hue="quadrant", data=df, ci=None,
                height=6, kind="bar", palette="muted")

And the output would look like this: enter image description here

Note that in your example, you missed out "" for one of the zeroes and 0 and "0" are plotted as separate columns. If you're using seaborn, you don't need to pivot the data, just feed it the df as you've defined it.

For interactive visualisations (with tooltips, zoom, pan, etc.), you can also check out bokeh.

There is an interesting wrinkle to this - how to center the nested bars on the label. By default the bars are drawn with center alignment which works fine for an odd number of columns. However, for an even number, you'd want them to be centered on the right edge. You can make a small alteration in the source code categorical.py, lines beginning 1642 like so:

# Draw the bars
offpos = barpos + self.hue_offsets[j]
barfunc(offpos, self.statistic[:, j], -self.nested_width,
        color=self.colors[j], align="edge",
        label=hue_level, **kws)

Save the .png and then change it back, but it's not ideal. Probably worth flagging up to the library maintainers.

user_44395240
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