Make a heatmap with the four metrics (revenue, orders, ad_spend, returns) as rows and the months as columns, where each row is scaled to 0–1 (min becomes 0, max becomes 1) so the rows are comparable. Use the month names as column labels.
import pandas as pd
monthly = pd.DataFrame({
"month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
"revenue": [420, 380, 510, 560, 600, 580, 640, 700, 660, 820, 950, 1100],
"orders": [210, 190, 240, 260, 275, 270, 290, 310, 300, 360, 410, 470],
"ad_spend": [60, 55, 70, 75, 80, 78, 85, 90, 88, 110, 130, 150],
"returns": [12, 10, 15, 14, 18, 16, 17, 20, 19, 25, 30, 34],
})
import seaborn as sns
import matplotlib.pyplot as pltDraw the chart. It is checked on what it shows: the data, the title and axis labels, the legend and the category names. Size and fonts don't matter.
Each hint gives a little more help. Try on your own first.
Run your code to see the output here.