Datasets / Northern Territory · school locations
Northern Territory 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
Northern Territory · school locations: 272 rows, 16 columns. The directory identifier and sector classify source records; missing coordinates are still counted.
272 rows in, 2 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
2 rows out of 272, 2 columns. First 2 shown.
| group | schools |
|---|---|
| Government | 219 |
| Non-government | 53 |