# evidently.calculations
## Subpackages
* [evidently.calculations.stattests package](/reference/api-reference/evidently.calculations/evidently.calculations.stattests.md)
* [Submodules](/reference/api-reference/evidently.calculations/evidently.calculations.stattests.md#submodules)
* [anderson\_darling\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.anderson_darling_stattest)
* [chisquare\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.chisquare_stattest)
* [cramer\_von\_mises\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.cramer_von_mises_stattest)
* [`CramerVonMisesResult`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.cramer_von_mises_stattest.CramerVonMisesResult)
* [energy\_distance module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.energy_distance)
* [epps\_singleton\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.epps_singleton_stattest)
* [fisher\_exact\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.fisher_exact_stattest)
* [g\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.g_stattest)
* [hellinger\_distance module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.hellinger_distance)
* [jensenshannon module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.jensenshannon)
* [kl\_div module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.kl_div)
* [ks\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.ks_stattest)
* [mann\_whitney\_urank\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.mann_whitney_urank_stattest)
* [psi module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.psi)
* [registry module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.registry)
* [`StatTest`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTest)
* [`StatTest.allowed_feature_types`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTest.allowed_feature_types)
* [`StatTest.default_threshold`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTest.default_threshold)
* [`StatTest.display_name`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTest.display_name)
* [`StatTest.func`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTest.func)
* [`StatTest.name`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTest.name)
* [`StatTestInvalidFeatureTypeError`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTestInvalidFeatureTypeError)
* [`StatTestNotFoundError`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTestNotFoundError)
* [`StatTestResult`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTestResult)
* [`StatTestResult.actual_threshold`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTestResult.actual_threshold)
* [`StatTestResult.drift_score`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTestResult.drift_score)
* [`StatTestResult.drifted`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.StatTestResult.drifted)
* [`get_stattest()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.get_stattest)
* [`register_stattest()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.registry.register_stattest)
* [t\_test module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.t_test)
* [tvd\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.tvd_stattest)
* [utils module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.utils)
* [`generate_fisher2x2_contingency_table()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.utils.generate_fisher2x2_contingency_table)
* [`get_binned_data()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.utils.get_binned_data)
* [`get_unique_not_nan_values_list_from_series()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.utils.get_unique_not_nan_values_list_from_series)
* [`permutation_test()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.utils.permutation_test)
* [wasserstein\_distance\_norm module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.wasserstein_distance_norm)
* [z\_stattest module](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#module-evidently.calculations.stattests.z_stattest)
* [`proportions_diff_z_stat_ind()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.z_stattest.proportions_diff_z_stat_ind)
* [`proportions_diff_z_test()`](https://docs-old.evidentlyai.com/reference/api-reference/pages/Rb3BF2Y7DDvjAuK838aV#evidently.calculations.stattests.z_stattest.proportions_diff_z_test)
## Submodules
## classification\_performance module
### class ConfusionMatrix(labels: Sequence\[Union\[str, int]], values: list)
Bases: `object`
#### Attributes:
**labels : Sequence\[Union\[str, int]]**
**values : list**
### class DatasetClassificationQuality(accuracy: float, precision: float, recall: float, f1: float, roc\_auc: Optional\[float] = None, log\_loss: Optional\[float] = None, tpr: Optional\[float] = None, tnr: Optional\[float] = None, fpr: Optional\[float] = None, fnr: Optional\[float] = None, rate\_plots\_data: Optional\[Dict] = None, plot\_data: Optional\[Dict] = None)
Bases: `object`
#### Attributes:
**accuracy : float**
**f1 : float**
**fnr : Optional\[float] = None**
**fpr : Optional\[float] = None**
**log\_loss : Optional\[float] = None**
**plot\_data : Optional\[Dict] = None**
**precision : float**
**rate\_plots\_data : Optional\[Dict] = None**
**recall : float**
**roc\_auc : Optional\[float] = None**
**tnr : Optional\[float] = None**
**tpr : Optional\[float] = None**
### class PredictionData(predictions: pandas.core.series.Series, prediction\_probas: Optional\[pandas.core.frame.DataFrame], labels: List\[Union\[str, int]])
Bases: `object`
#### Attributes:
**labels : List\[Union\[str, int]]**
**prediction\_probas : Optional\[DataFrame]**
**predictions : Series**
### calculate\_confusion\_by\_classes(confusion\_matrix: ndarray, class\_names: Sequence\[Union\[str, int]])
Calculate metrics:
* TP (true positive)
* TN (true negative)
* FP (false positive)
* FN (false negative)\
for each class from confusion matrix.
* **Returns**
a dict like:
```default
{
"class_1_name": {
"tp": 1,
"tn": 5,
"fp": 0,
"fn": 3,
},
"class_1_name": {
"tp": 1,
"tn": 5,
"fp": 0,
"fn": 3,
},
}
```
### calculate\_matrix(target: Series, prediction: Series, labels: List\[Union\[str, int]])
### calculate\_metrics(column\_mapping: [ColumnMapping](https://docs-old.evidentlyai.com/reference/api-reference/pages/D9Oc6VIiKDafnPidUy9b#evidently.pipeline.column_mapping.ColumnMapping), confusion\_matrix: ConfusionMatrix, target: Series, prediction: PredictionData)
### calculate\_pr\_table(binded)
### collect\_plot\_data(prediction\_probas: DataFrame)
### get\_prediction\_data(data: DataFrame, data\_columns: [DatasetColumns](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns), pos\_label: Optional\[Union\[str, int]], threshold: float = 0.5)
Get predicted values and optional prediction probabilities from source data.\
Also take into account a threshold value - if a probability is less than the value, do not take it into account.
Return and object with predicted values and an optional prediction probabilities.
### k\_probability\_threshold(prediction\_probas: DataFrame, k: Union\[int, float])
### threshold\_probability\_labels(prediction\_probas: DataFrame, pos\_label: Union\[str, int], neg\_label: Union\[str, int], threshold: float)
Get prediction values by probabilities with the threshold apply
## data\_drift module
Methods and types for data drift calculations.
### class ColumnDataDriftMetrics(column\_name: str, column\_type: str, stattest\_name: str, drift\_score: float, drift\_detected: bool, threshold: float, current\_distribution: [Distribution](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.visualizations.Distribution), reference\_distribution: [Distribution](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.visualizations.Distribution), current\_small\_distribution: Optional\[list] = None, reference\_small\_distribution: Optional\[list] = None, current\_scatter: Optional\[Dict\[str, list]] = None, x\_name: Optional\[str] = None, plot\_shape: Optional\[Dict\[str, float]] = None, current\_correlations: Optional\[Dict\[str, float]] = None, reference\_correlations: Optional\[Dict\[str, float]] = None)
Bases: `object`
One column drift metrics.
#### Attributes:
**column\_name : str**
**column\_type : str**
**current\_correlations : Optional\[Dict\[str, float]] = None**
**current\_distribution :** [**Distribution**](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.visualizations.Distribution)
**current\_scatter : Optional\[Dict\[str, list]] = None**
**current\_small\_distribution : Optional\[list] = None**
**drift\_detected : bool**
**drift\_score : float**
**plot\_shape : Optional\[Dict\[str, float]] = None**
**reference\_correlations : Optional\[Dict\[str, float]] = None**
**reference\_distribution :** [**Distribution**](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.visualizations.Distribution)
**reference\_small\_distribution : Optional\[list] = None**
**stattest\_name : str**
**threshold : float**
**x\_name : Optional\[str] = None**
### class DatasetDrift(number\_of\_drifted\_columns: int, dataset\_drift\_score: float, dataset\_drift: bool)
Bases: `object`
Dataset drift calculation results
#### Attributes:
**dataset\_drift : bool**
**dataset\_drift\_score : float**
**number\_of\_drifted\_columns : int**
### class DatasetDriftMetrics(number\_of\_columns: int, number\_of\_drifted\_columns: int, share\_of\_drifted\_columns: float, dataset\_drift: bool, drift\_by\_columns: Dict\[str, ColumnDataDriftMetrics], options: [DataDriftOptions](https://docs-old.evidentlyai.com/reference/api-reference/pages/ZW3IQh73672cpd3xySyJ#evidently.options.data_drift.DataDriftOptions), dataset\_columns: [DatasetColumns](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns))
Bases: `object`
#### Attributes:
**dataset\_columns :** [**DatasetColumns**](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns)
**dataset\_drift : bool**
**drift\_by\_columns : Dict\[str, ColumnDataDriftMetrics]**
**number\_of\_columns : int**
**number\_of\_drifted\_columns : int**
**options :** [**DataDriftOptions**](https://docs-old.evidentlyai.com/reference/api-reference/pages/ZW3IQh73672cpd3xySyJ#evidently.options.data_drift.DataDriftOptions)
**share\_of\_drifted\_columns : float**
### ensure\_prediction\_column\_is\_string(\*, prediction\_column: Optional\[Union\[str, Sequence]], current\_data: DataFrame, reference\_data: DataFrame, threshold: float = 0.5)
Update dataset by predictions type:
* if prediction column is None or a string, no dataset changes
* (binary classification) if predictions is a list and its length equals 2
set predicted\_labels column by threshold
* (multi label classification) if predictions is a list and its length is greater than 2
set predicted\_labels from probability values in columns by prediction column
* **Returns**
prediction column name.
### get\_dataset\_drift(drift\_metrics, drift\_share=0.5)
### get\_drift\_for\_columns(\*, current\_data: DataFrame, reference\_data: DataFrame, dataset\_columns: [DatasetColumns](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns), data\_drift\_options: [DataDriftOptions](https://docs-old.evidentlyai.com/reference/api-reference/pages/ZW3IQh73672cpd3xySyJ#evidently.options.data_drift.DataDriftOptions), drift\_share\_threshold: Optional\[float] = None, columns: Optional\[List\[str]] = None)
### get\_one\_column\_drift(\*, current\_data: DataFrame, reference\_data: DataFrame, column\_name: str, options: [DataDriftOptions](https://docs-old.evidentlyai.com/reference/api-reference/pages/ZW3IQh73672cpd3xySyJ#evidently.options.data_drift.DataDriftOptions), dataset\_columns: [DatasetColumns](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns), column\_type: Optional\[str] = None)
## data\_integration module
### get\_number\_of\_all\_pandas\_missed\_values(dataset: DataFrame)
Calculate the number of missed - nulls by pandas - values in a dataset
### get\_number\_of\_almost\_constant\_columns(dataset: DataFrame, threshold: float)
Calculate the number of almost constant columns in a dataset
### get\_number\_of\_almost\_duplicated\_columns(dataset: DataFrame, threshold: float)
Calculate the number of almost duplicated columns in a dataset
### get\_number\_of\_constant\_columns(dataset: DataFrame)
Calculate the number of constant columns in a dataset
### get\_number\_of\_duplicated\_columns(dataset: DataFrame)
Calculate the number of duplicated columns in a dataset
### get\_number\_of\_empty\_columns(dataset: DataFrame)
Calculate the number of empty columns in a dataset
## data\_quality module
Methods for overall dataset quality calculations - rows count, a specific values count, etc.
### class ColumnCorrelations(column\_name: str, kind: str, values: [Distribution](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.visualizations.Distribution))
Bases: `object`
#### Attributes:
**column\_name : str**
**kind : str**
**values :** [**Distribution**](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.visualizations.Distribution)
### class DataQualityGetPlotData()
Bases: `object`
#### Methods:
**calculate\_data\_by\_target(curr: DataFrame, ref: Optional\[DataFrame], feature\_name: str, feature\_type: str, target\_name: str, target\_type: str, merge\_small\_cat: Optional\[int] = 5)**
**calculate\_data\_in\_time(curr: DataFrame, ref: Optional\[DataFrame], feature\_name: str, feature\_type: str, datetime\_name: str, merge\_small\_cat: Optional\[int] = 5)**
**calculate\_main\_plot(curr: DataFrame, ref: Optional\[DataFrame], feature\_name: str, feature\_type: str, merge\_small\_cat: Optional\[int] = 5)**
### class DataQualityPlot(bins\_for\_hist: Dict\[str, pandas.core.frame.DataFrame])
Bases: `object`
#### Attributes:
**bins\_for\_hist : Dict\[str, DataFrame]**
### class DataQualityStats(rows\_count: int, num\_features\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, cat\_features\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, datetime\_features\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, target\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, prediction\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None)
Bases: `object`
#### Attributes:
**cat\_features\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None**
**datetime\_features\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None**
**num\_features\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None**
**prediction\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None**
**rows\_count : int**
**target\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None**
#### Methods:
**get\_all\_features()**
### class FeatureQualityStats(feature\_type: str, number\_of\_rows: int = 0, count: int = 0, infinite\_count: Optional\[int] = None, infinite\_percentage: Optional\[float] = None, missing\_count: Optional\[int] = None, missing\_percentage: Optional\[float] = None, unique\_count: Optional\[int] = None, unique\_percentage: Optional\[float] = None, percentile\_25: Optional\[float] = None, percentile\_50: Optional\[float] = None, percentile\_75: Optional\[float] = None, max: Optional\[Union\[int, float, bool, str]] = None, min: Optional\[Union\[int, float, bool, str]] = None, mean: Optional\[float] = None, most\_common\_value: Optional\[Union\[int, float, bool, str]] = None, most\_common\_value\_percentage: Optional\[float] = None, std: Optional\[float] = None, most\_common\_not\_null\_value: Optional\[Union\[int, float, bool, str]] = None, most\_common\_not\_null\_value\_percentage: Optional\[float] = None, new\_in\_current\_values\_count: Optional\[int] = None, unused\_in\_current\_values\_count: Optional\[int] = None)
Bases: `object`
Class for all features data quality metrics store.
A type of the feature is stored in feature\_type field.\
Concrete stat kit depends on the feature type. Is a metric is not applicable - leave None value for it.
Metrics for all feature types:
```
- feature type - cat for category, num for numeric, datetime for datetime features
- count - quantity of a meaningful values (do not take into account NaN values)
- missing_count - quantity of meaningless (NaN) values
- missing_percentage - the percentage of the missed values
- unique_count - quantity of unique values
- unique_percentage - the percentage of the unique values
- max - maximum value (not applicable for category features)
- min - minimum value (not applicable for category features)
- most_common_value - the most common value in the feature values
- most_common_value_percentage - the percentage of the most common value
- most_common_not_null_value - if most_common_value equals NaN - the next most common value. Otherwise - None
- most_common_not_null_value_percentage - the percentage of most_common_not_null_value if it is defined.
If most_common_not_null_value is not defined, equals None too.
```
Metrics for numeric features only:
```
- infinite_count - quantity infinite values (for numeric features only)
- infinite_percentage - the percentage of infinite values (for numeric features only)
- percentile_25 - 25% percentile for meaningful values
- percentile_50 - 50% percentile for meaningful values
- percentile_75 - 75% percentile for meaningful values
- mean - the sum of the meaningful values divided by the number of the meaningful values
- std - standard deviation of the values
```
Metrics for category features only:
* new\_in\_current\_values\_count - quantity of new values in the current dataset after the reference
```
Defined for reference dataset only.
```
* new\_in\_current\_values\_count - quantity of values in the reference dataset that not presented in the current
Defined for reference dataset only.
#### Attributes:
**count : int = 0**
**feature\_type : str**
**infinite\_count : Optional\[int] = None**
**infinite\_percentage : Optional\[float] = None**
**max : Optional\[Union\[int, float, bool, str]] = None**
**mean : Optional\[float] = None**
**min : Optional\[Union\[int, float, bool, str]] = None**
**missing\_count : Optional\[int] = None**
**missing\_percentage : Optional\[float] = None**
**most\_common\_not\_null\_value : Optional\[Union\[int, float, bool, str]] = None**
**most\_common\_not\_null\_value\_percentage : Optional\[float] = None**
**most\_common\_value : Optional\[Union\[int, float, bool, str]] = None**
**most\_common\_value\_percentage : Optional\[float] = None**
**new\_in\_current\_values\_count : Optional\[int] = None**
**number\_of\_rows : int = 0**
**percentile\_25 : Optional\[float] = None**
**percentile\_50 : Optional\[float] = None**
**percentile\_75 : Optional\[float] = None**
**std : Optional\[float] = None**
**unique\_count : Optional\[int] = None**
**unique\_percentage : Optional\[float] = None**
**unused\_in\_current\_values\_count : Optional\[int] = None**
#### Methods:
**as\_dict()**
**is\_category()**
Checks that the object store stats for a category feature
**is\_datetime()**
Checks that the object store stats for a datetime feature
**is\_numeric()**
Checks that the object store stats for a numeric feature
### calculate\_category\_column\_correlations(column\_name: str, dataset: DataFrame, columns: List\[str])
For category columns calculate cramer\_v correlation
### calculate\_column\_distribution(column: Series, column\_type: str)
### calculate\_correlations(dataset: DataFrame, columns: [DatasetColumns](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns))
### calculate\_cramer\_v\_correlation(column\_name: str, dataset: DataFrame, columns: List\[str])
### calculate\_data\_quality\_stats(dataset: DataFrame, columns: [DatasetColumns](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns), task: Optional\[str])
### calculate\_numerical\_column\_correlations(column\_name: str, dataset: DataFrame, columns: List\[str])
### get\_features\_stats(feature: Series, feature\_type: str)
### get\_pairwise\_correlation(df, func: Callable\[\[Series, Series], float])
Compute pairwise correlation of columns\
:param df: initial data frame.\
:param func: function for computing pairwise correlation.
* **Returns**
Correlation matrix.
### get\_rows\_count(data: Union\[DataFrame, Series])
Count quantity of rows in a dataset
## regression\_performance module
### class ErrorWithQuantiles(error, quantile\_top, quantile\_other)
Bases: `object`
### class FeatureBias(feature\_type: str, majority: float, under: float, over: float, range: float)
Bases: `object`
#### Attributes:
**feature\_type : str**
**majority : float**
**over : float**
**range : float**
**under : float**
#### Methods:
**as\_dict(prefix)**
### class RegressionPerformanceMetrics(mean\_error: float, mean\_abs\_error: float, mean\_abs\_perc\_error: float, error\_std: float, abs\_error\_max: float, abs\_error\_std: float, abs\_perc\_error\_std: float, error\_normality: dict, underperformance: dict, error\_bias: dict)
Bases: `object`
#### Attributes:
**abs\_error\_max : float**
**abs\_error\_std : float**
**abs\_perc\_error\_std : float**
**error\_bias : dict**
**error\_normality : dict**
**error\_std : float**
**mean\_abs\_error : float**
**mean\_abs\_perc\_error : float**
**mean\_error : float**
**underperformance : dict**
### calculate\_regression\_performance(dataset: DataFrame, columns: [DatasetColumns](https://docs-old.evidentlyai.com/reference/api-reference/pages/9O7LQ1v4Gn98SSrnqpix#evidently.utils.data_operations.DatasetColumns), error\_bias\_prefix: str)
### error\_bias\_table(dataset, err\_quantiles, num\_feature\_names, cat\_feature\_names)
### error\_with\_quantiles(dataset, prediction\_column, target\_column, quantile: float)
---
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