# Postgresql calculate difference between rows

For calculating differences between subsequent rows in PostgreSQL, **leverage LAG() within window functions**. Subtract the value of the preceding row from the current row's value:

** diff** yields the

**net change in**across consecutive rows, ordered by

`value`

**.**

`id`

## Unleashing the power of LAG()

### The magic of LAG

The key to understanding LAG lies in its ability to peer into the past, i.e., to tap into data from an earlier row, without invoking a self-join. A window, like `OVER (ORDER BY column)`

, is like a magnifying glass ensuring our data is sorted and ready for computation.

### About the first row

The pioneer row, having no ancestors, can create a hiccup in our cool calculations. Giving it a default step-sibling value, such as 0 or `null`

, ensures uniformity.

### Watch out for stumbling stones

Just like you don't want to trip on an unseen pebble, beware of unsorted data or false gaps! The order column - `id`

in our case, should dance to the rhythm of your data sequence for accurate difference computation.

## Nitty-gritty query crafting

### The need for a sequence

It's like arranging your sock drawer – sorting matters. A timestamp for time series data, an auto-incrementing ID, or any column that lays down a logical progression is your friend in the `ORDER BY`

clause of the `LAG`

function.

### Leaping across intervals

Need to count the stars weekly or tally the cookie jars monthly? Manipulate your `ORDER BY`

clause to ride on the back of `LAG(value, n)`

, with `n`

being the rows to look backward.

### Custom built defaults

Inject a contextually-aware default for the first row's difference. It elevates the relevancy of your result and gives your analysis that extra credibility.

## Extending usage scenarios

### Reversing the trend with LEAD

Occasionally, you might desire to be a sort of time-travelling data wizard, comparing a row with the next one. `LEAD`

is your magic spell here; it's a mirror reflection of `LAG`

, peeping into the future.

### Mastering partitions

Dabbling with a multi-category dataset? `PARTITION BY`

is your superpower to distinguish calculations by each category.

### Nullifying NULLs

`NULL`

values have been known to play spoilsport in difference calculations. Be proactive by ensuring the domain of non-null values, or dabble in some logic to tame `NULL`

values in your calculations.

Was this article helpful?