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Documentation Index

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

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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)

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Parameters

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dataframe
DataFrame
required
DataFrame containing spatial data to be sampled. Must contain a geometry column.
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fraction
float
required
Sampling rate between 0 and 1
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partition_count
int
required
Number of partitions to divide the data into. If not a perfect square, the number of partitions
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geometry
Optional[str]
Column containing the geometry data. Default is “geometry”
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seed
int
required
Seed for sampling the data

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Returns

The input DataFrame sampled down to the specified rate.

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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)