StandardDeviationPop
StandardDeviationPop(dataset, column_list=None, filter_condition_dict=None, dataset_filter_by_data_type=None, duckdb_connection=None, decimal_places=4)
¶
Calculate population standard deviation for specified numeric columns in a dataset using DuckDB.
This function computes the population standard deviation for specified numeric columns using DuckDB's STDDEV_POP function. It can analyze either explicitly specified columns or filter columns by data type(s), with optional filtering conditions on the dataset.
Example
Consider a column 'salaries' with values: [50000, 60000, 55000, 65000, 52000] Standard deviation = sqrt(sum((x - mean)^2) / N) Result ≈ 5505.43
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 - stddev_value (float): Calculated population standard deviation - 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
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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 numeric 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 standard deviation. Format: {'column_name': value}. Supports string, integer, and float values. Example: {'department': 'IT', 'age': 30}. 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 numeric columns of these data types. Case-insensitive. Defaults to None. Example: ['INTEGER'] or ['INTEGER', 'DOUBLE'] |
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 standard deviation values. Defaults to 4. |
4
|
Raises:
Type | Description |
---|---|
ValueError
|
If any of the following conditions are met: - Neither column_list nor dataset_filter_by_data_type is provided - decimal_places is negative - Invalid column names in column_list - No columns found matching the specified data types - Non-numeric columns specified for standard deviation calculation - Invalid filter conditions - Failed to register or access the dataset |
Source code in src/whistlingduck/analyzers/StandardDeviationPop.py
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