creating-vis

name	creating-vis
description	Chart-type selection guide and matplotlib recipes for non-trivial plots. Load when the user asks for a chart that's not a basic bar (stacked, multi-axis, time series, etc.).

Visualisation recipes

The core conventions (out_image, out(...to_dict("records")), read_artifact, no markdown plots) are already in your main instructions. This skill is just chart-type guidance and worked recipes.

Choosing a chart type

Question shape	Chart
Compare values across categories	bar (horizontal if labels long)
Compare composition across categories	stacked bar
Show distribution	histogram or box
Trend over time	line
Two numeric variables	scatter
Part-of-whole, single category	pie (use sparingly — bar is usually better)

Default to a bar chart unless the data shape clearly says otherwise. Long labels → use horizontal bar (barh).

Recipe: stacked bar (top-N × dimension)

import matplotlib.pyplot as plt

customers = read_artifact("art_a")  # CustomerId, FirstName, LastName, TotalSpend
genre = read_artifact("art_b")      # CustomerId, Genre, Spend

pivot = (
    genre
    .merge(customers[["CustomerId", "FirstName", "LastName"]], on="CustomerId")
    .assign(name=lambda d: d["FirstName"] + " " + d["LastName"])
    .pivot_table(index="name", columns="Genre", values="Spend", aggfunc="sum", fill_value=0)
)

# Order rows by total spend so the chart reads top-down.
pivot = pivot.loc[pivot.sum(axis=1).sort_values(ascending=True).index]

fig, ax = plt.subplots(figsize=(9, 5))
pivot.plot(kind="barh", stacked=True, ax=ax)
ax.set_xlabel("Spend")
ax.set_title("Genre mix per top-10 customer")
ax.legend(loc="lower right", fontsize=8)
fig.tight_layout()
out_image(fig, title="Genre mix per top-10 customer")

Recipe: time series

import matplotlib.pyplot as plt

df = read_artifact("art_…").assign(
    period=lambda d: pd.to_datetime(d["InvoiceDate"]).dt.to_period("M").dt.to_timestamp()
)
totals = df.groupby("period")["Total"].sum().reset_index()

fig, ax = plt.subplots(figsize=(9, 4))
ax.plot(totals["period"], totals["Total"])
ax.set_title("Monthly revenue")
fig.autofmt_xdate()
fig.tight_layout()
out_image(fig, title="Monthly revenue")

Style notes

• Default matplotlib palette is fine; don't override colors unless asked.
• Always set ax.set_title(...) AND pass title= to out_image — the chip uses the latter.
• Always call fig.tight_layout() before out_image so labels aren't clipped.
• For >10 categories, group the long tail into "Other" before plotting.