great_expectations.expectations.metrics.util
¶
Module Contents¶
Functions¶
|
|
|
|
|
|
|
|
|
If we can’t reflect the table, use a query to at least get column names. |
|
|
|
|
|
|
|
Ensures that necessary parameters for a distribution are present and that all parameters are sensical. |
|
Helper function that returns positional arguments for a scipy distribution using a dict of parameters. |
|
Tests whether a given object is a valid continuous partition object. See Partition Objects. |
-
great_expectations.expectations.metrics.util.
sqlalchemy_psycopg2
¶
-
great_expectations.expectations.metrics.util.
snowflake
¶
-
great_expectations.expectations.metrics.util.
sa
¶
-
great_expectations.expectations.metrics.util.
sqlalchemy_redshift
¶
-
great_expectations.expectations.metrics.util.
logger
¶
-
great_expectations.expectations.metrics.util.
bigquery_types_tuple
¶
-
great_expectations.expectations.metrics.util.
SCHEMAS
¶
-
great_expectations.expectations.metrics.util.
get_sql_dialect_floating_point_infinity_value
(schema: str, negative: bool = False) → float¶
-
great_expectations.expectations.metrics.util.
get_dialect_regex_expression
(column, regex, dialect, positive=True)¶
-
great_expectations.expectations.metrics.util.
_get_dialect_type_module
(dialect=None)¶
-
great_expectations.expectations.metrics.util.
attempt_allowing_relative_error
(dialect)¶
-
great_expectations.expectations.metrics.util.
column_reflection_fallback
(selectable, dialect, sqlalchemy_engine)¶ If we can’t reflect the table, use a query to at least get column names.
-
great_expectations.expectations.metrics.util.
parse_value_set
(value_set)¶
-
great_expectations.expectations.metrics.util.
filter_pair_metric_nulls
(column_A, column_B, ignore_row_if)¶
-
great_expectations.expectations.metrics.util.
get_dialect_like_pattern_expression
(column, dialect, like_pattern, positive=True)¶
-
great_expectations.expectations.metrics.util.
validate_distribution_parameters
(distribution, params)¶ Ensures that necessary parameters for a distribution are present and that all parameters are sensical.
If parameters necessary to construct a distribution are missing or invalid, this function raises ValueError with an informative description. Note that ‘loc’ and ‘scale’ are optional arguments, and that ‘scale’ must be positive.
- Parameters
distribution (string) – The scipy distribution name, e.g. normal distribution is ‘norm’.
params (dict or list) –
The distribution shape parameters in a named dictionary or positional list form following the scipy cdf argument scheme.
params={‘mean’: 40, ‘std_dev’: 5} or params=[40, 5]
- Exceptions:
ValueError: With an informative description, usually when necessary parameters are omitted or are invalid.
-
great_expectations.expectations.metrics.util.
_scipy_distribution_positional_args_from_dict
(distribution, params)¶ Helper function that returns positional arguments for a scipy distribution using a dict of parameters.
See the cdf() function here https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html#Methods to see an example of scipy’s positional arguments. This function returns the arguments specified by the scipy.stat.distribution.cdf() for tha distribution.
- Parameters
distribution (string) – The scipy distribution name.
params (dict) – A dict of named parameters.
- Raises
AttributeError – If an unsupported distribution is provided.
-
great_expectations.expectations.metrics.util.
is_valid_continuous_partition_object
(partition_object)¶ Tests whether a given object is a valid continuous partition object. See Partition Objects.
- Parameters
partition_object – The partition_object to evaluate
- Returns
Boolean