Minimum
Minimum(dataset, column_list=None, filter_condition_dict=None, dataset_filter_by_data_type=None, duckdb_connection=None, decimal_places=2)
¶
Calculate minimum values for specified numeric columns in a dataset using DuckDB.
This function analyzes numeric columns to find their minimum values. It supports filtering by column names and/or data types, and can process the data through specified conditions before calculating minimums. Only numeric-type columns (INTEGER, DECIMAL, FLOAT, etc.) will be processed.
Returns:
Type | Description |
---|---|
List[Dict[str, Union[str, float, dict, None]]]
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List[Dict[str, Union[str, float, dict, None]]]: A list of dictionaries with the following keys: - column_name (str): Name of the analyzed column - min_value (float): Minimum value found in the column, rounded to specified decimal places - 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. Only numeric columns from this list will be processed. Defaults to None. Example: ['price', 'quantity', 'weight'] |
None
|
filter_condition_dict
|
Optional[Dict[str, Union[str, int, float]]]
|
Dictionary of filter conditions to apply before calculating minimum values. Format: {'column_name': value}. Supports string, integer, and float values. Example: {'category': 'electronics', 'status': 'active'} Defaults to None. |
None
|
dataset_filter_by_data_type
|
Optional[List[str]]
|
Data type(s) to filter columns. Can be used together with column_list. The function will analyze all numeric columns that match these data types. Case-insensitive. Defaults to None. Example: ['INTEGER'] or ['DECIMAL', 'FLOAT'] |
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 minimum values. Applies to all numeric results. Defaults to 2. Example: With decimal_places=2, 10.543 becomes 10.54 |
2
|
Raises:
Type | Description |
---|---|
ValueError
|
In the following cases: - Neither column_list nor dataset_filter_by_data_type is provided - Invalid column names in column_list or filter_condition_dict - No columns match the specified data types - Failed to register or access the dataset - Non-numeric columns specified for analysis |
TypeError
|
If decimal_places is not an integer |
Source code in src/whistlingduck/analyzers/Minimum.py
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