How to create clustered stacked bar plots using Pandas and Matplotlib or Seaborn?

Creating Clustered Stacked Bar Plots

Problem:

Consider two dataframes, df1 and df2, with the same index but potentially different columns, where each row represents a category and each column represents a metric. The goal is to create clustered stacked bar plots where the bars for each category are grouped together, and the bars for each dataframe are stacked on top of each other.

Solution Using Pandas and Matplotlib:

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

def plot_clustered_stacked(df_list, labels=None, title="Clustered Stacked Bar Plot"):
    n_dataframes = len(df_list)
    n_columns = len(df_list[0].columns) 
    n_index = len(df_list[0].index)
    
    fig, ax = plt.subplots()

    # Iterate through each dataframe
    for i, df in enumerate(df_list):
        # Plot the bars for the current dataframe
        df.plot(kind="bar", 
                 ax=ax, 
                 linewidth=0,
                 stacked=True,
                 legend=False, 
                 grid=False)

    # Adjust the position and width of the bars
    for df, j in zip(df_list, range(n_dataframes)):
        for n, rect in enumerate(ax.patches):
            if rect.get_y() == 0:
                # Stacked bar for dataframe df
                rect.set_x(rect.get_x() + j / float(n_dataframes))
                rect.set_width(1 / float(n_dataframes))

    # Set the x-axis labels and ticks
    ax.set_xticks(np.arange(0, n_index) + 0.5)
    ax.set_xticklabels(df.index)

    # Add a legend for the dataframes
    plt.legend([df.stack(level=0).index[0] for df in df_list], labels)

    # Set the plot title
    ax.set_title(title)

# Create example dataframes
df1 = pd.DataFrame(np.random.rand(4, 3), index=["A", "B", "C", "D"], columns=["x", "y", "z"])
df2 = pd.DataFrame(np.random.rand(4, 3), index=["A", "B", "C", "D"], columns=["x", "y", "z"])

# Plot the clustered stacked bar plot
plot_clustered_stacked([df1, df2], labels=["df1", "df2"])</code>

Solution Using Seaborn and Pandas:

<code class="python">import seaborn as sns

# Concatenate the dataframes into a single dataframe with a wide format
df = pd.concat([df1.reset_index().melt(id_vars=["index"]), 
                 df2.reset_index().melt(id_vars=["index"])])

# Plot the clustered stacked bar plot
g = sns.FacetGrid(data=df, col="variable", hue="index")
g.map_dataframe(sns.barplot, order=df["index"].unique())</code>

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