Big Tech Finance
Big Tech company financial data for FY 2023 — revenue, profit, growth rate, and headcount.
import polars as pl
import polarise
from polarise.datasets import get_finance_data
df = get_finance_data()
Revenue gradient
{ cmap="reds" · built-in }
Visualise revenue distribution with a sequential colormap:
(df.style()
.gradient("Revenue", cmap="reds")
.fashion_minimal()
.title("Revenue Distribution")
.show()
)
Revenue Distribution
| Company | Revenue | Profit | Growth | Employees_k |
|---|---|---|---|---|
| Apple | 383.3 | 97.0 | 7.8 | 161 |
| Microsoft | 211.9 | 72.4 | 6.9 | 221 |
| 307.4 | 73.8 | 8.7 | 182 | |
| Amazon | 574.8 | 30.4 | 11.8 | 1541 |
| Meta | 134.9 | 39.1 | 16.4 | 67 |
Highlight best performer + bar chart
(df.style()
.highlight_max("Profit", fill="gold")
.bar("Revenue")
.fashion_grid()
.show()
)
| Company | Revenue | Profit | Growth | Employees_k |
|---|---|---|---|---|
| Apple | 383.3 | 97.0 | 7.8 | 161 |
| Microsoft | 211.9 | 72.4 | 6.9 | 221 |
| 307.4 | 73.8 | 8.7 | 182 | |
| Amazon | 574.8 | 30.4 | 11.8 | 1541 |
| Meta | 134.9 | 39.1 | 16.4 | 67 |
Cross-column condition + formatting
Highlight revenue for companies with growth above 12%, and format numbers:
(df.style()
.highlight_when("Revenue", when=pl.col("Growth") > 12, then_fill="lightgreen")
.format({"Revenue": "{:.0f}B", "Growth": "{:+.1f}%"})
.fashion_zebra()
.title("High-Growth Companies", subtitle="Green = Growth > 12%")
.show()
)
High-Growth Companies
Green = Growth > 12%
| Company | Revenue | Profit | Growth | Employees_k |
|---|---|---|---|---|
| Apple | 383B | 97.0 | +7.8% | 161 |
| Microsoft | 212B | 72.4 | +6.9% | 221 |
| 307B | 73.8 | +8.7% | 182 | |
| Amazon | 575B | 30.4 | +11.8% | 1541 |
| Meta | 135B | 39.1 | +16.4% | 67 |