Learn how to compute moving and cumulative statistics in GPandas. Rolling computes trailing-window aggregations, Shift offsets values, and the cumulative methods produce running totals — all essential for time-series analysis.

 

Overview

OperationMethodDescription
Rolling windowRolling(n)Trailing-window Mean, Sum, Min, Max, Std
ShiftShift(periods)Offset values up or down
CumulativeCumSum, CumMax, CumMin, CumProdRunning aggregations

These operations apply to numeric columns; non-numeric columns are passed through unchanged. All methods return a new DataFrame.

 

 

Sample Data

All examples use this DataFrame:

DaySales
Mon10
Tue20
Wed30
Thu40
Fri50

 

Setup Code 

package main

import (
    "fmt"
    "log"

    "github.com/apoplexi24/gpandas/dataframe"
    "github.com/apoplexi24/gpandas/utils/collection"
)

func main() {
    day, _ := collection.NewStringSeriesFromData(
        []string{"Mon", "Tue", "Wed", "Thu", "Fri"}, nil)
    sales, _ := collection.NewFloat64SeriesFromData(
        []float64{10, 20, 30, 40, 50}, nil)

    df := &dataframe.DataFrame{
        Columns:     map[string]collection.Series{"Day": day, "Sales": sales},
        ColumnOrder: []string{"Day", "Sales"},
        Index:       []string{"0", "1", "2", "3", "4"},
    }

    // Examples follow...
}

 

 

Rolling

Creates a fixed-size trailing window for moving aggregations.

 

Function Signature 

func (df *DataFrame) Rolling(window int) *RollingWindow
// RollingWindow methods: Mean(), Sum(), Min(), Max(), Std()

Note: A result is null for any position that does not yet have a full window of non-null values (equivalent to pandas’ default min_periods == window).

 

Rolling Mean 

result, err := df.Rolling(3).Mean()
if err != nil {
    log.Fatalf("Rolling failed: %v", err)
}
fmt.Println(result.String())
+-----+-------+
| Day | Sales |
+-----+-------+
| Mon | null  |
| Tue | null  |
| Wed | 20    |
| Thu | 30    |
| Fri | 40    |
+-----+-------+
[5 rows x 2 columns]

The first two rows are null (incomplete window). Row 2 is mean(10, 20, 30) = 20, and so on.

 

Rolling Sum 

result, _ := df.Rolling(3).Sum()
fmt.Println(result.String())
+-----+-------+
| Day | Sales |
+-----+-------+
| Mon | null  |
| Tue | null  |
| Wed | 60    |
| Thu | 90    |
| Fri | 120   |
+-----+-------+
[5 rows x 2 columns]

 

Window Mechanics 

flowchart LR
    subgraph Series["Sales"]
        V[10, 20, 30, 40, 50]
    end

    subgraph Windows["Rolling(3)"]
        W1[null - not enough data]
        W2[null - not enough data]
        W3[10,20,30]
        W4[20,30,40]
        W5[30,40,50]
    end

    V --> W1
    V --> W2
    V --> W3
    V --> W4
    V --> W5

    style Series fill:#1e293b,stroke:#3b82f6,stroke-width:2px
    style Windows fill:#1e293b,stroke:#22c55e,stroke-width:2px

 

 

Shift

Offsets all values by a number of periods. Positive periods shift downward (toward higher indices); negative periods shift upward. Vacated cells become null. Shift applies to every column, including non-numeric ones.

 

Function Signature 

func (df *DataFrame) Shift(periods int) (*DataFrame, error)

 

Example 

result, err := df.Shift(1)
if err != nil {
    log.Fatalf("Shift failed: %v", err)
}
fmt.Println(result.String())
+------+-------+
| Day  | Sales |
+------+-------+
| null | null  |
| Mon  | 10    |
| Tue  | 20    |
| Wed  | 30    |
| Thu  | 40    |
+------+-------+
[5 rows x 2 columns]

Shifting is commonly used to compute period-over-period changes (e.g., subtract the shifted column from the original).

 

 

Cumulative Operations

Compute running aggregations over each numeric column. Null cells remain null and are skipped in the accumulation.

 

Function Signatures 

func (df *DataFrame) CumSum() (*DataFrame, error)
func (df *DataFrame) CumMax() (*DataFrame, error)
func (df *DataFrame) CumMin() (*DataFrame, error)
func (df *DataFrame) CumProd() (*DataFrame, error)

 

Example 

result, err := df.CumSum()
if err != nil {
    log.Fatalf("CumSum failed: %v", err)
}
fmt.Println(result.String())
+-----+-------+
| Day | Sales |
+-----+-------+
| Mon | 10    |
| Tue | 30    |
| Wed | 60    |
| Thu | 100   |
| Fri | 150   |
+-----+-------+
[5 rows x 2 columns]

 

 

Error Handling

Common Errors

ErrorCauseSolution
“DataFrame is nil”Operating on nil DataFrameCheck DataFrame initialization
“window must be >= 1”Rolling(0) or negativeUse a window of at least 1

 

 

Thread Safety

Window operations read under a read lock and return new DataFrames, so the original is never mutated.

 

 

See Also