Spatially Stratified Sampling Python Module

Spatially stratified sampling partitions the data into a grid and randomly samples each partition independently.

`spatially_stratified_sample`

Spatially stratified sampling of a DataFrame containing spatial data. Each dimension will be rounded to the nearest integer.

def spatially_stratified_sample(dataframe: DataFrame, fraction: float, partition_count: int, geometry: Optional[str], seed: int)

Parameters

DataFrame

required

DataFrame containing spatial data to be sampled. Must contain a geometry column.

float

required

Sampling rate between 0 and 1

int

required

Number of partitions to divide the data into. If not a perfect square, the number of partitions

Optional[str]

Column containing the geometry data. Default is “geometry”

int

required

Seed for sampling the data

Returns

The input DataFrame sampled down to the specified rate.

Usage Examples

from spatially_stratified_sampling import *

# Example usage of spatially_stratified_sample
result = spatially_stratified_sample(dataframe=example_value, fraction=example_value, partition_count=example_value)

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Spatially Stratified Sampling Scala Object

⌘I

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Spatially Stratified Sampling Python Module

`spatially_stratified_sample`

Parameters

Returns

Usage Examples

​spatially_stratified_sample

​Parameters

​Returns

​Usage Examples

`spatially_stratified_sample`

Parameters

Returns

Usage Examples