McKinsey Charts
Drop-in chart builders for python-pptx. Charts are inserted as native PowerPoint chart objects — your audience can edit the data, change the colors, copy the chart to their own deck. No images, no screenshots.
The Three Charts
| Type | When to use | Example |
|------|-------------|---------|
| bar_callout | One number is the story. TAM, market size, headcount, anything where you want to anchor on a single highlighted bar with a big callout. | "RTD coffee TAM will hit $42.5B by 2028" |
| stacked_bar_over_time | Composition over time. Revenue by segment, usage by feature, headcount by function. Shows both total growth and mix shift. | "Revenue grew 3x, but enterprise segment grew 7x" |
| waterfall | Bridging two numbers. Revenue walk, cost walk, headcount changes, any "start → adds → subtracts → end" story. | "FY24 → FY25 revenue bridge: $80M → $112M" |
Design choices (intentional, not configurable)
- Title is a claim, not a label. "RTD market will hit $42.5B by 2028" beats "Market Size."
- One highlight color, everything else grey. McKinsey decks don't rainbow. The chart points at one thing.
- No gridlines, no chart border, no legend unless multi-series. Less ink → more signal.
- Source line at the bottom in light grey italic. Always include it.
- Single font (Inter) at consistent sizes. Title 20pt, axis 10pt, source 9pt.
How to use
from pptx import Presentation
from pptx.util import Inches
from charts import add_bar_callout, add_stacked_bar_over_time, add_waterfall, new_deck
prs = new_deck() # 16:9 with title slide layout
slide = prs.slides.add_slide(prs.slide_layouts[6]) # blank
add_bar_callout(
slide,
title="RTD coffee TAM will hit $42.5B by 2028",
categories=["2023", "2024", "2025", "2026", "2027", "2028"],
values=[28.1, 30.8, 33.6, 36.5, 39.4, 42.5],
highlight_index=5, # which bar to highlight (last one here)
callout="$42.5B\n2028 TAM",
y_label="USD, billions",
source="Euromonitor 2025; Mintel; team analysis",
)
prs.save("output.pptx")
Same pattern for the other two:
add_stacked_bar_over_time(
slide,
title="Enterprise segment now drives 62% of revenue, up from 18% in 2021",
categories=["2021", "2022", "2023", "2024", "2025"],
series=[
("SMB", [12, 14, 15, 16, 18]),
("Mid-market", [8, 12, 16, 22, 28]),
("Enterprise", [4, 10, 22, 38, 74]),
],
highlight_series="Enterprise",
y_label="Revenue, $M",
source="Internal financials; FY21–FY25",
)
add_waterfall(
slide,
title="FY24 → FY25 revenue bridge: $80M → $112M, with new logos doing the heavy lifting",
labels=["FY24", "New logos", "Expansion", "Churn", "Price", "FY25"],
values=[80.0, 24.0, 12.0, -8.0, 4.0, 112.0],
kinds=["start", "pos", "pos", "neg", "pos", "total"],
y_label="Revenue, $M",
source="Internal financials; FY24–FY25",
)
Test it
python3 test_charts.py
open test_output.pptx
The test script generates one slide per chart type with realistic sample data. Open it in PowerPoint or Keynote and right-click any chart → "Edit Data" to confirm it's a native chart, not an image.
When NOT to use this skill
- You need a distribution (use a histogram or box plot — not a McKinsey staple).
- You need a scatter / quadrant (the 2x2 / portfolio map is a different skill).
- You're showing >5 series stacked (split into small multiples instead — one chart can't carry that load).