Uniqueness
Uniqueness(dataset, column_list=None, filter_condition_dict=None, dataset_filter_by_data_type=None, duckdb_connection=None, decimal_places=2)
¶
Calculate uniqueness metrics for specified columns in a dataset using DuckDB.
This function analyzes the uniqueness of values in specified columns by calculating distinct counts, total counts, and the percentage of distinct values. It can filter columns by explicitly provided column names AND/OR by data type(s).
Example
Consider a column 'products' with values: ["apple", "banana", "apple", "orange", "banana", "mango"]
Uniqueness calculation: - Distinct values: "apple", "banana", "orange", "mango" (count = 4) - Total values: 6 - Percentage distinct = (4/6) * 100 = 66.67%
Returns:
Type | Description |
---|---|
List[Dict[str, Union[str, int, float, dict, None]]]
|
List[Dict[str, Union[str, int, float, dict, None]]]: A list of dictionaries with the following keys: - column_name (str): Name of the analyzed column - distinct_count (int): Count of distinct values - total_count (int): Total count of values - uniqueness_percentage (float): Percentage of values that are unique - table_name (str): Name of the analyzed table - execution_timestamp_utc (str): Timestamp of execution in UTC - filter_conditions (dict|None): Applied filter conditions if any - filtered_by_data_type (list|None): Data types used for filtering if any |
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset
|
Any
|
Input dataset that can be either: - A DataFrame (pandas, polars) or other DuckDB-compatible data structure - A string representing an existing table name in the DuckDB connection When providing a DataFrame along with duckdb_connection, the DataFrame will be registered as a temporary table in that connection. |
required |
column_list
|
Optional[List[str]]
|
List of column names to analyze. Can be used together with dataset_filter_by_data_type. Defaults to None. |
None
|
filter_condition_dict
|
Optional[Dict[str, Union[str, int, float]]]
|
Dictionary of filter conditions to apply before calculating uniqueness. Format: {'column_name': value}. Supports string, integer, and float values. Example: {'category': 'electronics', 'price': 100}. Defaults to None. |
None
|
dataset_filter_by_data_type
|
Optional[List[str]]
|
Data type(s) to filter columns. Can be a single type as string or list of types. Can be used together with column_list. The function will analyze all columns of these data types. Case-insensitive. Defaults to None. Example: ['VARCHAR'] or ['VARCHAR', 'INTEGER'] |
None
|
duckdb_connection
|
Optional[DuckDBPyConnection]
|
Existing DuckDB connection. If None, a new connection will be created and closed after execution. Can be used with either a table name string or a DataFrame input. Defaults to None. |
None
|
decimal_places
|
int
|
Number of decimal places to round the uniqueness percentage. Defaults to 2. |
2
|
Source code in src/whistlingduck/analyzers/Uniqueness.py
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