Charts turn numbers into understanding.
Direct answer
Matplotlib creates charts in Python. Import matplotlib.pyplot as plt, call a plotting function such as plot, bar, scatter or hist, add a title and axis labels and show or save the figure.
Examples
import matplotlib.pyplot as plt
months = ["Jan", "Feb", "Mar", "Apr"]
sales = [120, 150, 90, 180]
plt.plot(months, sales, marker="o")
plt.title("Monthly sales")
plt.xlabel("Month")
plt.ylabel("Units")
plt.show()
plt.bar(months, sales)
plt.scatter([1, 2, 3, 4], [2, 4, 5, 8])
plt.hist([1, 2, 2, 3, 3, 3, 4])
Good chart habits
- Choose the right chart: line for trends, bar for comparisons, scatter for relationships.
- Label axes and add units.
- Keep it simple.
- Use colour with purpose and check accessibility.
- Save with
plt.savefig("chart.png", dpi=150).
Where Teyro fits
Teyro's Data Science for Beginners course turns this into short lessons you can play in a few minutes a day. It has 50 lessons, and you can start free.
The bottom line
A clear chart often communicates more than a table.
