Grouped Bar graph Pandas

Question

I have a table in a pandas DataFrame named df:

+--- -----+------------+-------------+----------+------------+-----------+
|avg_views| avg_orders | max_views   |max_orders| min_views  |min_orders |
+---------+------------+-------------+----------+------------+-----------+
| 23       | 123       |   135       | 500      |    3       |    1      |
+---------+------------+-------------+----------+------------+-----------+ 

What I am looking for now is to plot a grouped bar graph which shows me (avg, max, min) of views and orders in one single bar chart.

i.e on x axis there would be Views and orders separated by a distance and 3 bars of (avg, max, min) for views and similarly for orders.

I have attached a sample bar graph image, just to know how the bar graph should look.

Green color should be for avg, yellow for max and pink for avg.

I took the following code from setting spacing between grouped bar plots in matplotlib but it is not working for me:

plt.figure(figsize=(13, 7), dpi=300)

groups = [[23, 135, 3], [123, 500, 1]]
group_labels = ['views', 'orders']
num_items = len(group_labels)
ind = np.arange(num_items)
margin = 0.05
width = (1. - 2. * margin) / num_items

s = plt.subplot(1, 1, 1)
for num, vals in enumerate(groups):
    print 'plotting: ', vals
    # The position of the xdata must be calculated for each of the two data 
    # series.
    xdata = ind + margin + (num * width)
    # Removing the "align=center" feature will left align graphs, which is 
    # what this method of calculating positions assumes.
    gene_rects = plt.bar(xdata, vals, width)
s.set_xticks(ind + 0.5)
s.set_xticklabels(group_labels)

plotting: [23, 135, 3] ... ValueError: shape mismatch: objects cannot be broadcast to a single shape

Answer 1

Using pandas:

import pandas as pd

groups = [[23,135,3], [123,500,1]]
group_labels = ['views', 'orders']

# Convert data to pandas DataFrame.
df = pd.DataFrame(groups, index=group_labels).T

# Plot.
pd.concat(
    [
        df.mean().rename('average'), 
        df.min().rename('min'), 
        df.max().rename('max')
    ],
    axis=1,
).plot.bar()