Datasets / Victoria · school locations
Victoria directory coverage
Counts describe source directory records, not school quality or complete worldwide coverage. Smaller groups are retained as Other when needed.
How it decided
Victoria · school locations: 2,294 rows, 16 columns. The directory identifier and sector classify source records; missing coordinates are still counted.
2,294 rows in, 3 out across 2 columns. Counts describe source directory records, not school quality or complete worldwide coverage. Smaller groups are retained as Other when needed.
bar. A count comparison describes coverage without inventing achievement scores or attendance boundaries.
Palette, spacing, axis titles and legend placement applied. 0 issues found.
The transformation
This is the code, not a description of it — the chart above was drawn from what it returns, and the notebook download runs the same lines against the same file.
result = df.groupby('sector', dropna=False).size().reset_index(name='schools').rename(columns={'sector':'group'})
result = result.sort_values('schools', ascending=False).reset_index(drop=True)
if len(result) > 15:
result = pd.concat([result.head(15), pd.DataFrame([{'group':'Other','schools':int(result.iloc[15:]['schools'].sum())}])], ignore_index=True)What it charted
3 rows out of 2,294, 2 columns. First 3 shown.
| group | schools |
|---|---|
| Government | 1570 |
| Catholic | 495 |
| Independent | 229 |