Size
Size(dataset, column_list=None, filter_condition_dict=None, dataset_filter_by_data_type=None, duckdb_connection=None, decimal_places=2)
¶
Calculate size metrics for a dataset using DuckDB.
This function calculates the total number of rows in a dataset, with optional filtering and column validation capabilities.
Example
Consider a dataset with 1000 rows and a filter condition {'category': 'electronics'}
Size calculation: - Total rows without filter: 1000 - Total rows after filter: 200 (assuming 200 rows match the filter)
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, one per column, with the following keys: - column_name (str): Name of the analyzed column - row_count (int): Total number of rows for this column after filtering - 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 |
required |
column_list
|
Optional[List[str]]
|
List of column names to validate. |
None
|
filter_condition_dict
|
Optional[Dict[str, Union[str, int, float]]]
|
Dictionary of filter conditions to apply before calculating size. |
None
|
dataset_filter_by_data_type
|
Optional[List[str]]
|
Data type(s) to filter columns. |
None
|
duckdb_connection
|
Optional[DuckDBPyConnection]
|
Existing DuckDB connection. |
None
|
decimal_places
|
int
|
Number of decimal places for rounding. |
2
|
Source code in src/whistlingduck/analyzers/Size.py
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