How to plot segmented bar chart (stacked bar graph) with Python?

Question

cat = {'A':1, 'B':2, 'C':3}
dog = {'A':2, 'B':2, 'C':4}
owl = {'A':3, 'B':3, 'C':3}

Suppose I have 3 dictionary, each containing pairs of (subcategory, count). How can I plot a segmented bar chart (i.e stacked bar graph) using Python with x being 3 categories (cat, dog, owl) and y being proportion (of each subcategory)? What I have in mind looks like this:

Answer 1

Using Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

def plot_stacked_bar(data, series_labels, category_labels=None, 
                     show_values=False, value_format="{}", y_label=None, 
                     colors=None, grid=False, reverse=False, 
                     savefig=False,save_figname="Stacked_Bar.png"):
    
    """
    Plots a stacked bar chart with the data and labels provided.

    Keyword arguments:
    data            -- 2-dimensional numpy array or nested list
                       containing data for each series in rows
    series_labels   -- list of series labels (these appear in
                       the legend)
    category_labels -- list of category labels (these appear
                       on the x-axis)
    show_values     -- If True then numeric value labels will 
                       be shown on each bar
    value_format    -- Format string for numeric value labels
                       (default is "{}")
    y_label         -- Label for y-axis (str)
    colors          -- List of color labels
    grid            -- If True display grid
    reverse         -- If True reverse the order that the
                       series are displayed (left-to-right
                       or right-to-left)
    savefig         -- If true export plot to png
    save_figname    -- Filename to save figure
    """

    ny = len(data[0])
    ind = list(range(ny))

    axes = []
    cum_size = np.zeros(ny)

    data = np.array(data)

    if reverse:
        data = np.flip(data, axis=1)
        category_labels = reversed(category_labels)

    for i, row_data in enumerate(data):
        color = colors[i] if colors is not None else None
        axes.append(plt.bar(ind, row_data, bottom=cum_size, 
                            label=series_labels[i], color=color))
        cum_size += row_data

    if category_labels:
        plt.xticks(ind, category_labels)

    if y_label:
        plt.ylabel(y_label)

    plt.legend()

    if grid:
        plt.grid()

    if show_values:
        for axis in axes:
            for bar in axis:
                w, h = bar.get_width(), bar.get_height()
                plt.text(bar.get_x() + w/2, bar.get_y() + h/2, 
                         value_format.format(h), ha="center", 
                         va="center")
    
    if savefig:
        plt.savefig(save_figname)

Data Setup

cat = {'A':1, 'B':2, 'C':3}
dog = {'A':2, 'B':2, 'C':4}
owl = {'A':3, 'B':3, 'C':3}

from collections import defaultdict

dd = defaultdict(list)

# you can list as many input dicts as you want here
for d in (cat, dog, owl): 
    for key, value in d.items():
        dd[key].append(value)

# print(list(dd.values()))

data = list(dd.values())
series_labels  = list(dd.keys())

category_labels = ["Cat", "Dog", "Owl"]

Plotting

plt.figure(figsize=(5, 8))

plot_stacked_bar(
    data, 
    series_labels, 
    category_labels=category_labels, 
    show_values=True, 
    value_format="{:.1f}",
    colors=['tab:blue', 'tab:orange', 'tab:green'],
    y_label="Count",
    savefig=True
)
plt.show()

Figure