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I.e., vertically concatenates two datasets, appending the rows of the second to the end of the first. When the two datasets contain different columns, the join parameter controls whether only the common columns are kept (inner), or all columns (outer). In the latter case, rows will have missing values (NaNs), where a column only existed in one of the two datasets.

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Usage

The following example shows how the step can be used in a recipe.

Examples

  • Example 1
  • Signature
To append the rows of dataset ds_right to the dataset ds_left, keeping all columns from both datasets:
append_rows(ds_left, ds_right) -> (ds_out)

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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").
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ds_left
dataset
required
An input dataset.
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ds_right
dataset
required
A second dataset whose rows to append below the original dataset (ds_left).
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result
dataset
required
A dataset containing the rows of both ds_left, and ds_right, as well as an aditional column original_index indicating the index of each row in its original dataset.

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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

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join
string
default:"outer"
required
Whether to do concatenate using an “inner” or “outer” join of columns. When "inner", only common columns will be kept. When "outer", all columns will be kept.Values must be one of the following:
  • inner
  • outer
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