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# count\_rows\_by\_keys

## Usage

{% hint style="info" %}
This calculator allows you to count the number of occurrences that share the same value across defined columns. This calculator must be stored in model to be used.
{% endhint %}

This calculator can be used with the following method:

<mark style="color:red;">**`count_rows_by_key`**</mark>

Examples:

* The user wants to count the number of products per promotional event.
* The user wants to count the number of products per department.

***

## Main Parameters

{% hint style="success" %}
**The bold options** represent the default values when the parameters are optional.
{% endhint %}

* *<mark style="color:blue;">input\_columns</mark>* \
  list of columns used as input of the calculators: The list of columns that will be used to fill the output column.
* *<mark style="color:blue;">output\_columns</mark>* \
  list of columns added by the calculators : Name of the filled column added to the dataset.
* *<mark style="color:blue;">global</mark>* *(true, **false)*** \
  Should this calculator be performed before data splitting during training for cross-validation
* *<mark style="color:blue;">steps</mark>* \[optionnal] *(**training, prediction**, postprocessing*)\
  List of steps in a pipeline where columns from this calculator are added to the data. Note that when the training option is listed, the calculator is actually added during preprocessing.
* *<mark style="color:blue;">store\_in\_model</mark>* \[optionnal] *(true, **false)*** \
  Please indicate whether the "calculated" columns by the calculator should be stored in the model or not to avoid recalculating them during prediction. This is only relevant if the calculated columns are added to both training and prediction. Without this parameter, the values will not be stored in the model. The following parameters only make sense if this parameter is set to *true*.
* *<mark style="color:blue;">stored\_columns</mark>* \[required if *<mark style="color:blue;">store\_in\_model</mark> is true*] \
  List indicating the columns to be stored among the *<mark style="color:blue;">output\_columns</mark>*.
* *<mark style="color:blue;">stored\_keys</mark>* \[required if *<mark style="color:blue;">store\_in\_model</mark> is true*] \
  List indicating the columns to use for identifying the correct values to join on the data for prediction among the stored values (logically, they are to be chosen from the *<mark style="color:blue;">input\_columns</mark>*).

***

## Specific Parameters

* *<mark style="color:blue;">keys\_to\_count</mark>*\
  Columns for which we want to compute cardinality.
* *<mark style="color:blue;">group\_by</mark>*\
  Columns used for grouping keys to count.

***

## Examples

1. The user wants to count the number of products (item\_id) per promotional event (event\_id) :

```yaml
calculated_cols:
  items_per_event:
    method: count_rows_by_keys
    input_columns:
    - item_id
    - event_id
    output_columns:
    - nb_items_per_event
    params:
      keys_to_count:
      - item_id
      group_by:
      - event_id
      store_in_model: True
      stored_keys:
      - event_id
```


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