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

Clustering Stacked Bars in Pandas and Matplotlib

Introduction

This article addresses the problem of creating stacked bar plots with clustered bars for multiple dataframes sharing the same index and columns. The goal is to have clustered stacked bars per index, ensuring clarity in the visualization.

Using Pandas and Matplotlib

The solution leverages the capabilities of Pandas and Matplotlib libraries. Here's the code:

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

def plot_clustered_stacked(dfall, labels=None, title=&quot;multiple stacked bar plot&quot;):
    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)

    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(&quot;/&quot; * 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=&quot;/&quot; * i))

    l1 = axe.legend(h[:n_col], l[:n_col])
    if labels is not None:
        l2 = plt.legend(n, labels) 
    axe.add_artist(l1)
    return axe</code>

To use this function, simply pass in a list of dataframes and optional arguments such as labels and title. It will produce clustered stacked bars with hatches to differentiate the dataframes.

Example

Here's an example using this function:

<code class="python"># 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),
                   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;])
df3 = 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;])

# plot clustered stacked bar
plot_clustered_stacked([df1, df2, df3], [&quot;df1&quot;, &quot;df2&quot;, &quot;df3&quot;])</code>

Additional Features

You can customize the colors of the bars by passing a cmap argument:

<code class="python">plot_clustered_stacked([df1, df2, df3], [&quot;df1&quot;, &quot;df2&quot;, &quot;df3&quot;], cmap=plt.cm.viridis)</code>

Conclusion

This solution provides a flexible and convenient way to create clustered stacked bar plots. You can easily modify the code to meet the specific requirements of your data visualization.

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