# day\_count

## Usage

{% hint style="info" %}
This calculator allow the user to count specific days (monday = 0, … sunday = 6) between 2 dates.
{% endhint %}

This calculator can be used with the following method:

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

Examples:

* Find the number of sundays between 2 dates.
* Find the number of weekend days between 2 dates.

***

## 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;">begin\_date</mark>* \
  The name of the column containing the lower bound for days counting.
* *<mark style="color:blue;">end\_date</mark>* \
  The name of the column containing the upper bound for days counting.
* *<mark style="color:blue;">day\_idx</mark>* \
  The list of days to count, using their position in week. 0: Monday, …, 6: Sunday.

***

## Examples

1. Using a promo sales dataset with the begin date of the promo (`begin_date_promo`) and the end date of the promo (`end_date_promo`), the store is closed on Sunday. The user want to find the number of Sunday within the promo time frame.

   ```yaml
   calculated_cols:
     count_sunday:
       method: day_count
       input_columns:
       - begin_date_promo
       - end_date_promo
       output_columns:
       - count_sunday
       params:
         begin_col: begin_date_promo
         end_col: end_date_promo
         day_idx:
         - 6
   ```

   **Input :**

   | begin\_date\_promo | end\_date\_promo |
   | ------------------ | ---------------- |
   | 2024-01-01         | 2024-01-14       |

   **Output :**

   | begin\_date\_promo | end\_date\_promo | count\_sunday |
   | ------------------ | ---------------- | ------------- |
   | 2024-01-01         | 2024-01-14       | 2             |
2. With the same use case but now the store is closed on Monday and Sunday. So now the user want to find the number of Monday and Sunday within the different promo time frame.

   ```yaml
   calculated_cols:
     count_sunday:
       method: day_count
       input_columns:
       - begin_date_promo
       - end_date_promo
       output_columns:
       - count_offday
       params:
         begin_col: begin_date_promo
         end_col: end_date_promo
         day_idx:
         - 0
         - 6
   ```

   Input :

   | begin\_date\_promo | end\_date\_promo |
   | ------------------ | ---------------- |
   | 2024-01-01         | 2024-01-14       |

   Output :

   | begin\_date\_promo | end\_date\_promo | count\_offday |
   | ------------------ | ---------------- | ------------- |
   | 2024-01-01         | 2024-01-14       | 4             |


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