Skip to main content
If you request a number of rows greater than the dataframe length, it will return the original dataframe instead.

​
Usage

The following examples show how the step can be used in a recipe.

Examples

  • Example 1
  • Example 2
  • Example 3
  • Signature
This draws a sample of 12.000 random rows from the original dataset:
filter_sample(ds, {"n_samples": 12000}) -> (ds_sampled)

​
Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
​
ds_in
dataset
required
An input dataset to filter.
​
ds_out
dataset
required
A new dataset containing a random sample of the original rows.

​
Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

​
n_samples
[number, integer]
required
Number of rows to sample. How many random rows to pick from the original dataset (without replacement). If the value is greater than 1, it will be interpreted as a count of desired rows. If it is smaller than 1, it will be interpreted as a proportion of the entire dataset.
  • number
  • integer
​
{_}
number
number.Values must be in the following range:
0 < {_} < 1
​
by
string (ds_in.column)
Sample independently in these groups. If a column is specified here, the sampling will be applied separately within each group defined by the unique values in this column. Combining this with a count of rows to pick (rather than a proportion), allows this step to balance the dataset, leading to an (approximately) equal number of rows within each group.
​
seed
[number, null]
A value used to initialize the random number generator, making it deterministic (reproducible).
⌘I