How to plot a superimposed bar chart using matplotlib in python?

Question

I want to plot a bar chart or a histogram using matplotlib. I don't want a stacked bar plot, but a superimposed barplot of two lists of data, for instance I have the following two lists of data with me:

Some code to begin with :

import matplotlib.pyplot as plt
from numpy.random import normal, uniform

highPower   = [1184.53,1523.48,1521.05,1517.88,1519.88,1414.98,1419.34,
              1415.13,1182.70,1165.17]
lowPower    = [1000.95,1233.37, 1198.97,1198.01,1214.29,1130.86,1138.70,
               1104.12,1012.95,1000.36]

plt.hist(highPower, bins=10, histtype='stepfilled', normed=True,
         color='b', label='Max Power in mW')
plt.hist(lowPower, bins=10, histtype='stepfilled', normed=True,
         color='r', alpha=0.5, label='Min Power in mW')

I want to plot these two lists against the number of values in the two lists such that I am able to see the variation per reading.

Answer 1

You can produce a superimposed bar chart using plt.bar() with the alpha keyword as shown below.

The alpha controls the transparency of the bar.

N.B. when you have two overlapping bars, one with an alpha < 1, you will get a mixture of colours. As such the bar will appear purple even though the legend shows it as a light red. To alleviate this I have modified the width of one of the bars, this way even if your powers should change you will still be able to see both bars.

plt.xticks can be used to set the location and format of the x-ticks in your graph.

import matplotlib.pyplot as plt
import numpy as np

width = 0.8

highPower   = [1184.53,1523.48,1521.05,1517.88,1519.88,1414.98,
               1419.34,1415.13,1182.70,1165.17]
lowPower    = [1000.95,1233.37, 1198.97,1198.01,1214.29,1130.86,
               1138.70,1104.12,1012.95,1000.36]

indices = np.arange(len(highPower))

plt.bar(indices, highPower, width=width, 
        color='b', label='Max Power in mW')
plt.bar([i+0.25*width for i in indices], lowPower, 
        width=0.5*width, color='r', alpha=0.5, label='Min Power in mW')

plt.xticks(indices+width/2., 
           ['T{}'.format(i) for i in range(len(highPower))] )

plt.legend()

plt.show()

Answer 2

Building on @Ffisegydd's answer, if your data is in a Pandas DataFrame, this should work nicely:

def overlapped_bar(df, show=False, width=0.9, alpha=.5,
                   title='', xlabel='', ylabel='', **plot_kwargs):
    """Like a stacked bar chart except bars on top of each other with transparency"""
    xlabel = xlabel or df.index.name
    N = len(df)
    M = len(df.columns)
    indices = np.arange(N)
    colors = ['steelblue', 'firebrick', 'darksage', 'goldenrod', 'gray'] * int(M / 5. + 1)
    for i, label, color in zip(range(M), df.columns, colors):
        kwargs = plot_kwargs
        kwargs.update({'color': color, 'label': label})
        plt.bar(indices, df[label], width=width, alpha=alpha if i else 1, **kwargs)
        plt.xticks(indices + .5 * width,
                   ['{}'.format(idx) for idx in df.index.values])
    plt.legend()
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    if show:
        plt.show()
    return plt.gcf()

And then in a python command line:

low = [1000.95, 1233.37, 1198.97, 1198.01, 1214.29, 1130.86, 1138.70, 1104.12, 1012.95, 1000.36]
high = [1184.53, 1523.48, 1521.05, 1517.88, 1519.88, 1414.98, 1419.34, 1415.13, 1182.70, 1165.17]
df = pd.DataFrame(np.matrix([high, low]).T, columns=['High', 'Low'],
                  index=pd.Index(['T%s' %i for i in range(len(high))],
                  name='Index'))
overlapped_bar(df, show=False)

Answer 3

It is actually simpler than the answers all over the internet make it appear.

a = range(1,10)
b = range(4,13)
ind = np.arange(len(a))

fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(x=ind, height=a, width=0.35,align='center')
ax.bar(x=ind, height=b, width=0.35/3,  align='center')

plt.xticks(ind, a)

plt.tight_layout()
plt.show()