# Data Filtering

Data filtering refers to the process of choosing a smaller part of your dataset and using that subset for viewing or analysis.

Filtering may be used to:

• Look at results for a particular period of time.
• Exclude erroneous or "bad" observations from an analysis.
• Extract erroneous or "bad" observations from an analysis for manual (by data stewards)/ augmented (AI) Data Quality Management.

re_data provides the following macros for filtering data. Check out the list of currently available filters and let us know if you could use some different ones on Slack 😊 or Github.

### filter_remove_duplicates​

#### (source code)​

Arguments:

• relation: dbt model to perform the filtering on
• unique_cols: List of columns that uniquely identify each row
• sort_columns: Order in which we want to sort the partitioned rows. e.g. (created_at DESC, created_at ASC to choose the latest or earliest row based on the timestamp column

Return type: table with filtered rows

This macro allows you to remove duplicate rows from a dbt model based on certain conditions.

  id |  status      |   updated_at    |--------------------------------------+ 1   |  pending     |    13:00:45     | 2   |  completed   |    13:05:23     | 1   |  completed   |    13:10:35     | 2   |  pending     |    13:04:49     | 3   |  completed   |    13:30:00     | => select id, status, updated_at from {{ re_data.filter_remove_duplicates(ref('duplicated'), ['id'], ['updated_at desc']) }} duplicates -- After filtering, the resulting rows are:  id |  status      |   updated_at    |--------------------------------------+ 1   |  completed   |    13:10:35     | 2   |  completed   |    13:05:23     | 3   |  completed   |    13:30:00     |

### filter_get_duplicates​

#### (source code)​

Arguments:

• relation: dbt base model to perform the filtering on
• unique_cols: List of columns that uniquely identify each row
• sort_columns: Order in which we want to sort the partitioned rows. e.g. (created_at DESC, created_at ASC to choose the latest or earliest row based on the timestamp column

Return type: table with duplicate rows

along with the fields of the base model the macro returns duplication context in new fields: re_data_duplicates_count - total number of duplicates with the same current key set re_data_duplicate_row_number - number of current duplicate row inside the group of duplicates with the same current key set

This macro allows you to get duplicate rows from a dbt model based on certain conditions.

  id |  status      |   updated_at    |--------------------------------------+ 1   |  pending     |    13:00:45     | 2   |  completed   |    13:05:23     | 1   |  completed   |    13:10:35     | 2   |  pending     |    13:04:49     | 3   |  completed   |    13:30:00     | => select id, status, updated_at,       re_data_duplicate_group_row_count,        re_data_duplicate_group_row_number    from {{ re_data.filter_get_duplicates( ref('duplicated') , ['id'], ['updated_at desc']) }}  duplicates -- After filtering, the resulting rows are: id | updated_at |  status   | re_data_duplicate_group_row_count | re_data_duplicate_group_row_number----+------------+-----------+-----------------------------------+------------------------------------  1 | 13:10:35   | completed |                                 2 |                                  1  1 | 13:00:45   | pending   |                                 2 |                                  2  2 | 13:05:23   | completed |                                 2 |                                  1  2 | 13:04:49   | pending   |                                 2 |                                  2