docs/integrations/block-integrations/sampling.md
The Data Sampling block is a tool for selecting a subset of data from a larger dataset using various sampling methods.
This block takes a dataset as input and returns a smaller sample of that data based on specified criteria. It supports multiple sampling methods, allowing users to choose the most appropriate technique for their needs.
The block processes the input data and applies the chosen sampling method to select a subset of items. It can work with different data structures and supports data accumulation for scenarios where data is received in batches.
| Input | Description |
|---|---|
| Data | The dataset to sample from. This can be a single dictionary, a list of dictionaries, or a list of lists. |
| Sample Size | The number of items to select from the dataset. |
| Sampling Method | The technique used to select the sample. Options include random, systematic, top, bottom, stratified, weighted, reservoir, and cluster sampling. |
| Accumulate | A flag indicating whether to accumulate data before sampling. This is useful for scenarios where data is received in batches. |
| Random Seed | An optional value to ensure reproducible random sampling. |
| Stratify Key | The key to use for stratified sampling (required when using the stratified sampling method). |
| Weight Key | The key to use for weighted sampling (required when using the weighted sampling method). |
| Cluster Key | The key to use for cluster sampling (required when using the cluster sampling method). |
| Output | Description |
|---|---|
| Sampled Data | The selected subset of the input data. |
| Sample Indices | The indices of the sampled items in the original dataset. |
A data scientist working with a large customer dataset wants to create a representative sample for analysis. They could use this Data Sampling block to select a smaller subset of customers using stratified sampling, ensuring that the sample maintains the same proportions of different customer segments as the full dataset.