SQL-problem from dataford

URL :https://dataford.io/sql/Population-Density-Analysis

WITH DensityData AS (
    SELECT
        c.city,
        c.population,
        c.area,
        ROUND(c.population / c.area) AS density
    FROM
        cities_population c
    WHERE
        c.area > 0 AND c.population > 0
)
SELECT
    city,
    population,
    area,
    density,
    MAX(density) OVER () AS max_density,
    MIN(density) OVER () AS min_density
FROM
    DensityData
ORDER BY
    density DESC;

To get the maximum and minimum densities globally and sort cities by their density, you can use a subquery or a CTE as previously suggested. Here’s a step-by-step correction:

1. Calculate Density: Calculate the density directly.
2. Filter Invalid Data: Ensure that area and population are non-zero.
3. Order by Density: Sort cities by density.

approach two:

SELECT
    city,
    population,
    area,
    ROUND(population / area) AS density,
    MAX(ROUND(population / area)) OVER () AS max_density,
    MIN(ROUND(population / area)) OVER () AS min_density
FROM
    cities_population
WHERE
    area > 0 AND population > 0
ORDER BY
    density DESC;

explanation

Population Density Calculation: The ROUND(population / area) computes the population density and rounds it to the nearest integer.Max and Min Density:

• MAX(ROUND(population / area)) OVER () finds the global maximum density across all cities.
• MIN(ROUND(population / area)) OVER () finds the global minimum density across all cities.

Non-Zero Areas and Population: The WHERE clause ensures that rows with zero area or population (which would cause division by zero or meaningless results) are excluded.Ordering: ORDER BY density DESC sorts the cities from the highest population density to the lowest, ensuring cities with the same density are grouped together.

Conclusion:

• Query 1 (with CTE) is generally better in terms of readability, maintainability, and potentially performance (due to avoiding redundant calculations).
• Query 2 might be slightly simpler in cases where performance and redundancy aren’t concerns (e.g., small datasets or very simple computations), but it’s less optimal for larger, more complex datasets.

For a larger dataset or a more complex calculation, Query 1 is the preferred approach.