I'm building a visualization with Python. There I'd like to visualize fuel stops and the fuel costs of my car. Furthermore, car washes and their costs should be visualized as well as repairs. The fuel costs and laundry costs should have a higher bar depending on the costs. I created the visualization below to describe the concepts.

How to create such a visualization with matplotlib?

This is the visualization being built:

Yes, this kind of visualization is perfectly possible with matplotlib. To store the data, numpy arrays are usually very handy.

Here is some code to get you started:

import matplotlib.pyplot as plt
import numpy as nprefuel_km = np.array([0, 505.4, 1070, 1690])
refuel_cost = np.array([40.1, 50, 63, 55])carwash_km = np.array([302.0, 605.4, 901, 1331, 1788.2])
carwash_cost = np.array([35.0, 40.0, 35.0, 35.0, 35.0])repair_km = np.array([788.0, 1605.4])
repair_cost = np.array([135.0, 74.5])fig, ax = plt.subplots(figsize=(12,3))plt.scatter(refuel_km, np.full_like(refuel_km, 0), marker='o', s=100, color='lime', edgecolors='black', zorder=3, label='refuel')
plt.bar(refuel_km, refuel_cost, bottom=15, color='lime', ec='black', width=20, label='refuel cost')plt.scatter(carwash_km, np.full_like(carwash_km, 0), marker='d', s=100, color='tomato', edgecolors='black', zorder=3, label='car wash')
plt.bar(carwash_km, -carwash_cost, bottom=-15, color='tomato', ec='black', width=20, label='car wash cost')plt.scatter(repair_km, np.full_like(repair_km, 0), marker='^', s=100, color='lightblue', edgecolors='black', zorder=3, label='car repair')
#plt.bar(repair_km, -repair_cost, bottom=-15, color='lightblue', ec='black', width=20)ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('none')
ax.tick_params(axis='x', length=20)
ax.set_yticks([])  # turn off the yticks_, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
ax.set_xlim(-15, xmax)
ax.set_ylim(ymin, ymax+25) # make room for the legend
ax.text(xmax, -5, "km", ha='right', va='top', size=14)
plt.legend(ncol=5, loc='upper left')plt.tight_layout()
plt.show()