git:20260617.39bd919 to git:20260705.7cbc9ad

10 added, 0 removed. Audit A to A.

---

name: data-charts

description: "Analyze tabular data and render charts (bar, line, scatter, pie, histogram) to PNG/SVG with pandas + matplotlib. Use whenever the user wants to plot, chart, graph, or visualize data from a CSV/JSON/Excel file or inline numbers."

license: MIT (Code Buddy original skill)

nativeEngine:

triggers:

- chart

- plot

- graph

- visualize

- visualization

- histogram

- bar chart

- scatter plot

- line chart

- pie chart

- graphique

- diagramme

- trace

---

# Analyze data & render charts with pandas + matplotlib

Turn tabular data (CSV/JSON/Excel/inline) into a real chart image using the

open-source **pandas** + **matplotlib** libraries. Work in **visible steps** and

verify the image was written before reporting.

+ ## Getting the data when it's NOT in a file (e.g. "France GDP over 10 years")

+ When the user asks to chart something you must look up rather than read from a

+ file, gather the numbers FIRST with your normal tools — your own knowledge, or a

+ quick `web_search` — then **hard-code them as inline Python lists**. Do **NOT**

+ fetch from an external API *inside* the Python script (`urllib`/`requests`):

+ sandboxed network calls routinely time out (the 30 s command limit), which makes

+ you give up and fall back to a much worse ASCII/text "chart". Inline numbers →

+ matplotlib → a real PNG is fast and reliable. Only read from the network when the

+ user explicitly gives you a data URL and it's known-fast.

## Preflight (extras-tier libs)

These libs are optional and not in the default build. Always check first:

```bash

python3 - <<'PY'

try:

import pandas, matplotlib # noqa: F401

except ModuleNotFoundError as e:

raise SystemExit(

f"{e.name} not installed. Run `npm run prepare:python:extras` in cowork/ "

"to enable the data-charts skill (pandas + matplotlib)."

)

print("data-charts deps OK")

```

## Workflow (one step at a time)

1. **Load** the data with pandas (`read_csv` / `read_json` / `read_excel`), or

build a `DataFrame` from inline numbers.

2. **Inspect**: print `df.head()` and `df.describe()` (or the shape) so the data

is visibly understood before plotting.

3. **Plot**: use the **non-interactive Agg backend** (`matplotlib.use("Agg")`,

set before importing pyplot — there is no display), build the chart, add a

title + axis labels, and `savefig(path, dpi=144, bbox_inches="tight")`.

4. **Verify**: `assert os.path.getsize(path) > 0` and print the path + size.

5. **Report** the saved image path and a one-line insight from the data.

## Quality conventions

- Always set `matplotlib.use("Agg")` **before** `import matplotlib.pyplot` — the

app has no X display; the interactive backend would crash.

- Label axes and give every chart a title; use a legend when >1 series.

- Prefer `tight_layout()` / `bbox_inches="tight"` so labels aren't clipped.

- Save PNG by default (144 dpi); SVG when the user wants a vector.

## Example

```bash

python3 - <<'PY'

import os

import pandas as pd

import matplotlib

matplotlib.use("Agg") # headless — set before pyplot

import matplotlib.pyplot as plt

df = pd.read_csv("sales.csv") # columns: month, revenue

print(df.head()); print(df.describe())

fig, ax = plt.subplots(figsize=(8, 4.5))

ax.bar(df["month"], df["revenue"], color="#3b82f6")

ax.set_title("Monthly revenue"); ax.set_xlabel("Month"); ax.set_ylabel("Revenue ($)")

fig.tight_layout()

out = "revenue.png"

fig.savefig(out, dpi=144, bbox_inches="tight")

assert os.path.getsize(out) > 0, "chart image is empty"

print(f"wrote {out} ({os.path.getsize(out)} bytes); peak month = "

f"{df.loc[df['revenue'].idxmax(), 'month']}")

```

## Notes

- The chart image is an artifact — surface its path so the GUI can preview it.

- For quick stats without a chart, `df.describe()` / `df.groupby(...).agg(...)`.

- To put a chart *into* an Excel/Word/PowerPoint file, hand the saved PNG to the

`xlsx`/`docx`/`pptx` create skills.