How to Create Clustered Stacked Bar Plots in Python with Pandas and Matplotlib?

Creating Clustered Stacked Bar Plots

Problem:

You have multiple data frames with identical columns and index and want to create a stacked bar plot with data from each data frame clustered together.

Solution using Pandas and Matplotlib:

1. Combine the data frames into a single data frame.

2. Use the plot_clustered_stacked() function to create the stacked bar plot. This function takes the following arguments:

   • dfall: A list of data frames to be plotted.
   • labels: A list of names for the data frames (optional).
   • title: The title of the plot (optional).
   • H: The hatch pattern to use for each data frame (optional).

Code:

<code class="python">import pandas as pd
import matplotlib.cm as cm
import numpy as np
import matplotlib.pyplot as plt

def plot_clustered_stacked(dfall, labels=None, title=&quot;multiple stacked bar plot&quot;,  H=&quot;/&quot;, **kwargs):
    n_df = len(dfall)
    n_col = len(dfall[0].columns)
    n_ind = len(dfall[0].index)
    axe = plt.subplot(111)

    for df in dfall : # for each data frame
        axe = df.plot(kind=&quot;bar&quot;,
                      linewidth=0,
                      stacked=True,
                      ax=axe,
                      legend=False,
                      grid=False,
                      **kwargs)  # make bar plots

    h,l = axe.get_legend_handles_labels() # get the handles we want to modify
    for i in range(0, n_df * n_col, n_col): # len(h) = n_col * n_df
        for j, pa in enumerate(h[i:i+n_col]):
            for rect in pa.patches: # for each index
                rect.set_x(rect.get_x() + 1 / float(n_df + 1) * i / float(n_col))
                rect.set_hatch(H * int(i / n_col)) #edited part
                rect.set_width(1 / float(n_df + 1))

    axe.set_xticks((np.arange(0, 2 * n_ind, 2) + 1 / float(n_df + 1)) / 2.)
    axe.set_xticklabels(df.index, rotation = 0)
    axe.set_title(title)

    # Add invisible data to add another legend
    n=[]
    for i in range(n_df):
        n.append(axe.bar(0, 0, color=&quot;gray&quot;, hatch=H * i))

    l1 = axe.legend(h[:n_col], l[:n_col], loc=[1.01, 0.5])
    if labels is not None:
        l2 = plt.legend(n, labels, loc=[1.01, 0.1])
    axe.add_artist(l1)
    return axe

# create fake dataframes
df1 = pd.DataFrame(np.random.rand(4, 5),
                   index=[&quot;A&quot;, &quot;B&quot;, &quot;C&quot;, &quot;D&quot;],
                   columns=[&quot;I&quot;, &quot;J&quot;, &quot;K&quot;, &quot;L&quot;, &quot;M&quot;])
df2 = pd.DataFrame(np.random.rand(4, 5),</code>

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