Correlation, Sampling & Pipe

Compute correlation and covariance matrices, draw random samples, and chain operations with Pipe

Learn how to analyze relationships between numeric columns with Corr and Cov, draw reproducible random samples with Sample, and compose operations fluently with Pipe.

Overview

Operation	Method	Description
Correlation	`Corr()`	Pairwise Pearson correlation matrix
Covariance	`Cov()`	Pairwise sample covariance matrix
Sampling	`Sample(n, seed...)`	Random rows without replacement
Chaining	`Pipe(fn)`	Apply a custom function in a pipeline

Sample Data

All examples use this DataFrame:

Temp	Sales
20	100
25	150
30	200
35	180
40	220

Setup Code

package main

import (
    "fmt"
    "log"

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

func main() {
    gp := gpandas.GoPandas{}

    df, _ := gp.DataFrame(
        []string{"Temp", "Sales"},
        []gpandas.Column{
            {20.0, 25.0, 30.0, 35.0, 40.0},
            {100.0, 150.0, 200.0, 180.0, 220.0},
        },
        map[string]any{"Temp": gpandas.FloatCol{}, "Sales": gpandas.FloatCol{}},
    )

    // Examples follow...
}

Corr

Computes the pairwise Pearson correlation matrix over the numeric columns. The result is a square DataFrame whose columns and index are the numeric column names.

Function Signature

func (df *DataFrame) Corr() (*DataFrame, error)

Correlations use rows where both columns are non-null (pairwise complete). A pair with fewer than two overlapping values, or with zero variance, yields NaN.

Example

corr, err := df.Corr()
if err != nil {
    log.Fatalf("Corr failed: %v", err)
}
fmt.Println(corr.String())

+--------------------+--------------------+
| Temp               | Sales              |
+--------------------+--------------------+
| 1                  | 0.9101698143253768 |
| 0.9101698143253768 | 0.9999999999999998 |
+--------------------+--------------------+
[2 rows x 2 columns]

The diagonal is 1 (each column correlates perfectly with itself), and the off-diagonal shows a strong positive correlation between Temp and Sales.

Cov

Computes the pairwise sample covariance matrix (ddof=1), structured like Corr.

Function Signature

func (df *DataFrame) Cov() (*DataFrame, error)

Example

cov, _ := df.Cov()
fmt.Println(cov.String())

+-------+-------+
| Temp  | Sales |
+-------+-------+
| 62.5  | 337.5 |
| 337.5 | 2200  |
+-------+-------+
[2 rows x 2 columns]

The diagonal holds each column's variance (e.g., Temp variance is 62.5).

Note: To visualize a correlation matrix as a heatmap, pass the result of Corr() to PlotHeatmap. See Plotting Charts.

Sample

Returns n rows selected at random without replacement, in random order. An optional seed makes the selection deterministic.

Function Signature

func (df *DataFrame) Sample(n int, seed ...int64) (*DataFrame, error)

Example

s, err := df.Sample(3, 7) // deterministic with seed 7
if err != nil {
    log.Fatalf("Sample failed: %v", err)
}
fmt.Println(s.String())

+------+-------+
| Temp | Sales |
+------+-------+
| 30   | 200   |
| 20   | 100   |
| 40   | 220   |
+------+-------+
[3 rows x 2 columns]

Calling Sample(3, 7) again returns the same rows; omit the seed for a new random sample each time. Index labels of the selected rows are preserved.

Pipe

Applies a custom function to the DataFrame and returns its result, enabling fluent pipelines of reusable steps.

Function Signature

func (df *DataFrame) Pipe(fn func(*DataFrame) (*DataFrame, error)) (*DataFrame, error)

Example

result, err := df.Pipe(func(d *dataframe.DataFrame) (*dataframe.DataFrame, error) {
    return d.Filter("Sales", dataframe.GreaterThan, 150.0).Result()
})
if err != nil {
    log.Fatalf("Pipe failed: %v", err)
}
fmt.Println(result.String())

+------+-------+
| Temp | Sales |
+------+-------+
| 30   | 200   |
| 35   | 180   |
| 40   | 220   |
+------+-------+
[3 rows x 2 columns]

Pipe is equivalent to calling the function directly, but reads naturally when several steps are composed:

result, err := df.
    Pipe(normalize).
    Pipe(addFeatures)

Error	Cause	Solution
"no numeric columns"	`Corr`/`Cov` on a non-numeric DataFrame	Ensure numeric columns exist
"n ... must be in range"	`Sample(n)` with n > row count	Use n between 0 and the row count
"fn must not be nil"	`Pipe(nil)`	Provide a function

Thread Safety

These operations read under a read lock and return new DataFrames, leaving the original unchanged.

Correlation, Sampling & Pipe

Overview

Sample Data

Setup Code

Corr

Function Signature

Example

Cov

Function Signature

Example

Sample

Function Signature

Example

Pipe

Function Signature

Example

Analysis Flow

Error Handling

Common Errors

Thread Safety

See Also

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