Creating Charts with Matplotlib

Matplotlib supports several chart types. Choosing the right one depends on the question you want to answer.

Line Charts

Line charts are useful for showing trends over time or across an ordered sequence.

days = [1, 2, 3, 4, 5, 6]

website_visits = [120, 150, 135, 180, 210, 250]

plt.plot(days, website_visits, marker="o")

plt.title("Daily Website Visits")

plt.xlabel("Day")

plt.ylabel("Number of Visits")

plt.grid(True)

plt.show()

Markers highlight individual observations, while grid lines make values easier to compare.

Bar Charts

Bar charts compare values across distinct categories.

products = ["Laptop", "Phone", "Tablet"]

units_sold = [120, 250, 180]

plt.bar(products, units_sold)

plt.title("Product Sales Comparison")

plt.xlabel("Product")

plt.ylabel("Units Sold")

plt.show()

Bar charts are useful for comparing sales by product, revenue by region, or employees by department.

Histograms

Histograms show the distribution of numerical data by grouping values into intervals.

delivery_times = [20, 25, 30, 22, 35, 40, 28, 32, 45, 38, 27, 31]

plt.hist(delivery_times, bins=5, edgecolor="black")

plt.title("Distribution of Delivery Times")

plt.xlabel("Delivery Time (Minutes)")

plt.ylabel("Frequency")

plt.show()

A histogram helps reveal where observations are concentrated and whether the distribution is spread out or skewed.

Saving a Chart

Matplotlib allows charts to be saved as image files for reports and presentations.

plt.plot(months, sales)

plt.title("Monthly Sales")

plt.savefig("monthly_sales.png", dpi=300, bbox_inches="tight")

plt.show()

The dpi parameter controls image resolution, and bbox_inches="tight" helps prevent labels from being unnecessarily cropped. Save the figure before closing it.