Alerts¶

flowtask.events.events.alerts ¶

Alert ¶

Alert(*args, **kwargs)

Bases: Notification, AbstractEvent

get_function ¶

get_function(payload)

Get the function name, function object and parameters from the payload.

recursive_lookup ¶

recursive_lookup(d, target_key)

Recursively finds the first dictionary that contains a given key inside a nested dictionary.

:param d: Dictionary to search :param target_key: Key to find ("result" by default) :return: The dictionary containing the key, or None if not found

colfunctions ¶

average ¶

average(df, desc, column_name, threshold, deviation=2, allow_below=False, allow_above=False)

average.

Parameters:

Name	Type	Description	Default
`df`	`DataFrame`	Dataframe.	required
`desc`	`Any`	Description of the DataFrame.	required
`colname`	`str`	Column Name.	required
`threshold`	`Union[int, float]`	Threshold value.	required
`deviation`	`Union[int, float]`	percent of deviation from the threshold	`2`
`allow_below`	`bool`	how many percent below the threshold is allowed	`False`
`allow_above`	`bool`	how many percent above the threshold is allowed	`False`

Returns:

Type	Description
`tuple`

between ¶

between(df, desc, column_name, values)

Check if the values in a DataFrame column are between the given min and max values.

Args: - df (pd.DataFrame): The DataFrame to check. - desc (Any): The description (usually from df.describe()) of the DataFrame. - column_name (str): The name of the column to check. - values (tuple): A tuple containing the (min, max) values.

column_size ¶

column_size(df, desc, column_name, min_length, max_length)

Check if all values in a string column have lengths within the specified range.

Parameters:

Name	Type	Description	Default
`df`	`DataFrame`	The DataFrame to check.	required
`desc`	`Any`	Ignored, used for compatibility.	required
`column_name`	`str`	The name of the column to check.	required
`min_length`	`int`	The minimum length allowed for strings.	required
`max_length`	`int`	The maximum length allowed for strings.	required

Returns:

Name	Type	Description
`tuple`	`tuple`	(column_name, min_length, max_length, True/False)

count_nulls ¶

count_nulls(df, desc, column_name, value)

Check if the number of non-null values in a column is greater than a given threshold.

Parameters:

Name	Type	Description	Default
`-`	`df (pd.DataFrame`	The DataFrame to check.	required
`-`	`desc (Any`	The description (usually from df.describe()) of the DataFrame.	required
`-`	`column_name (str`	The name of the column to check.	required
`-`	`min_length (int`	The minimum number of non-null values required.	required

Returns:

Name	Type	Description
`tuple`	`tuple`	(min_length, True/False)

equal ¶

equal(df, desc, column_name, values)

Check if all values in a DataFrame column are within the provided list of strings.

Args: - df (pd.DataFrame): The DataFrame to check. - desc (Any): The description (usually from df.describe()) of the DataFrame. - column_name (str): The name of the column to check. - values (tuple): A tuple containing the allowed strings.

not_null ¶

not_null(df, desc, column_name)

Check if a DataFrame column contains only non-null values.

Parameters:

Name	Type	Description	Default
`-`	`df (pd.DataFrame`	The DataFrame to check.	required
`-`	`desc (Any`	The description (usually from df.describe()) of the DataFrame.	required
`-`	`column_name (str`	The name of the column to check.	required

Returns:

Name	Type	Description
`tuple`		(column_name, True/False)

functions ¶

average ¶

average(data, column, threshold, deviation, allow_below=False)

average.

Calculates the average of a value compared with a threshold. Args: data (dict): extract column from data. column (str): column to calculate. threshold (float): value to be used for threshold deviation (float): max deviation acceptable for threshold allow_below (bool): if True, the threshold is not evaluated on minimum values.

between ¶

between(data, column, value)

Checks if the actual value in the specified column is between two given values.

Parameters:

Name	Type	Description	Default
`data`	`dict`	Dictionary containing the column data.	required
`column`	`str`	Column name to evaluate.	required
`value`	`list`	A list containing two values: [min_value, max_value].	required

Returns:

Name	Type	Description
`tuple`	`Tuple[str, Union[int, float], bool]`	(column, actual_value, True/False)

equal ¶

equal(data, column, value)

Check if the actual value on a specified column in the data is equal to the given threshold value.

gt ¶

gt(data, column, value)

Check if the actual value on a specified column in the data is greater than to the given threshold value.

has_columns ¶

has_columns(data, column='columns', value=[])

Check if the actual value on a specified column in the data is equal to the given threshold value.

lt ¶

lt(data, column, value)

Check if the actual value on a specified column in the data is less than to the given threshold value.

max_value ¶

max_value(data, column, value)

Checks if the actual value of a specified column in the data is less than or equal to the given threshold value.

Parameters:

Name	Type	Description	Default
`data`	`dict`	Dictionary containing the data to be checked.	required
`column`	`str`	Name of the column in the data whose value needs to be checked.	required
`value`	`Union[int, float]`	The threshold value. The actual value in the data	required

Returns:

Name	Type	Description
`tuple`	`Tuple[str, Union[int, float], bool]`	A tuple containing: - column (str): Name of the column that was checked. - actual_value (Union[int, float]): The actual value from the data for the specified column. - val (bool): True if the actual value is less than or equal to the threshold, False otherwise.

min_value ¶

min_value(data, column, value)

Checks if the actual value of a specified column in the data is greater than or equal to the given threshold value.

Parameters:

Name	Type	Description	Default
`data`	`dict`	Dictionary containing the data to be checked.	required
`column`	`str`	Name of the column in the data whose value needs to be checked.	required
`value`	`Union[int, float]`	The threshold value. The actual value in the data should be greater than or equal to this.	required

Returns:

Name	Type	Description
`tuple`	`Tuple[str, Union[int, float], bool]`	A tuple containing: - column (str): Name of the column that was checked. - actual_value (Union[int, float]): The actual value from the data for the specified column. - val (bool): True if the actual value is greater than or equal to the threshold, False otherwise.

missing_columns ¶

missing_columns(data, column='columns', value=[])

Check if all required columns exist in the 'columns' field of the given data.

:param data: Dictionary containing the structure with "columns". :param column: Name of the column to check (by default: "columns"). :param value: List of columns to check. :return: Tuple (checked_key, missing_columns, bool)