Data Create Viz

data-create-viz

When to use

Use this when you need one polished static chart — for a report, slide, or export — where clarity and correct chart-type choice matter more than interactivity.

Not for: an explorable multi-chart tool with filters (use data-build-dashboard), deciding whether a difference is significant (use data-statistical-analysis), or answering the underlying question (use data-analyze).

Method

1. Name the message. Every chart makes one point. Write it as a sentence; the title should state that point, not just label axes.
2. Choose the chart type by data relationship:
   • Trend over time → line.
   • Comparison across categories → horizontal bar, sorted by value.
   • Part-to-whole → stacked bar, or (rarely, ≤4 slices) donut — never a pie for many slices.
   • Distribution → histogram or box/violin.
   • Correlation → scatter, with trend line only if a relationship is claimed.
   • Composition over time → stacked area. Decision point: if two encodings both fit, pick the one that puts the message on a position axis (bar/line) over area/angle.
3. Build in Python (matplotlib/seaborn). Sort bars, start value axes at zero for bars, label directly instead of relying on legends.
4. Design for legibility: high contrast, minimal gridlines, no chartjunk, colorblind-safe palette, readable fonts at final scale.
5. Annotate the key point (peak, inflection, outlier) so the message is unmissable.
6. Export at high resolution (PNG 2x, plus SVG when scalable output helps).

Example

Message: "Region West drives 40% of revenue." A pie of 8 regions hides it; a sorted horizontal bar makes West's dominance instant.

import matplotlib.pyplot as plt
d = df.sort_values("revenue")
plt.barh(d.region, d.revenue)
plt.title("West drives 40% of revenue")  # title = the point
plt.xlim(0, None)                          # bars start at zero
plt.tight_layout(); plt.savefig("rev_by_region@2x.png", dpi=200)

Pitfalls

• Titling with the metric ("Revenue by region") instead of the takeaway.
• Truncating a bar's value axis so a small difference looks huge.
• Pie/donut with many slices, forcing readers to compare angles.
• Encoding series in a red/green palette that colorblind viewers cannot separate.

Output format

Script:  runnable Python
Image:   exported file(s), descriptive filename (e.g. rev_by_region@2x.png)
Caption: one line stating the single takeaway
Source:  data source, filters, time window

Verify axes, units, and totals before exporting. If the chosen type would mislead, fix it and say why.