PctChange / Computation Layer

Calculate the percentage change between current and previous values in a numeric column. Similar to pandas.pct_change() or R's diff()/lag() percentage calculations.

Mathematical form: $y_{i} = \frac{x _{i} - x _{i - n}}{x _{i - n}} \times 100$ where $n$ is the shift amount (ShiftBy) and $i$ is the row index.

Common applications:

Financial analysis (stock returns, growth rates)
Economic indicators (GDP growth, inflation rates)
Sales performance (year-over-year growth)
Population dynamics (growth rates)
Market analysis (market share changes)
Trend analysis (sequential changes)
Performance metrics (improvement rates)

Note: First n rows (where n is ShiftBy) will be null. Division by zero results in null values.

Table

Transforms

[, ...]

Select

column

The numeric column to compute percentage changes for. Typical inputs:

Price series (stock prices, commodity prices)
Measurement sequences (sensor readings)
Performance metrics (sales figures, KPIs)
Quantity series (inventory levels, counts)

ShiftBy

i64

Number of rows to look back for comparison. Common values:

1: Adjacent row comparison (default, sequential change)
4: Quarter-over-quarter comparison (for quarterly data)
12: Year-over-year comparison (for monthly data)
52: Year-over-year comparison (for weekly data) Must be positive.

Name for the new column. If not provided, the system generates a unique name. If AsColumn matches an existing column, the existing column is replaced. The name should follow valid column naming conventions.