# evidently.metrics.data\_integrity
## Submodules
## column\_missing\_values\_metric module
### class ColumnMissingValues(number\_of\_rows: int, different\_missing\_values: Dict\[Any, int], number\_of\_different\_missing\_values: int, number\_of\_missing\_values: int, share\_of\_missing\_values: float)
Bases: `object`
Statistics about missing values in a column
#### Attributes:
**different\_missing\_values : Dict\[Any, int]**
**number\_of\_different\_missing\_values : int**
**number\_of\_missing\_values : int**
**number\_of\_rows : int**
**share\_of\_missing\_values : float**
### class ColumnMissingValuesMetric(column\_name: str, missing\_values: Optional\[list] = None, replace: bool = True)
Bases: [`Metric`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.Metric)\[`ColumnMissingValuesMetricResult`]
Count missing values in a column.
Missing value is a null or NaN value.
Calculate an amount of missing values kinds and count for such values.\
NA-types like numpy.NaN, pandas.NaT are counted as one type.
You can set you own missing values list with missing\_values parameter.\
Value None in the list means that Pandas null values will be included in the calculation.
If replace parameter is False - add defaults to user’s list.\
If replace parameter is True - use values from missing\_values list only.
#### Attributes:
**DEFAULT\_MISSING\_VALUES = \['', inf, -inf, None]**
**column\_name : str**
**missing\_values : frozenset**
#### Methods:
**calculate(data:** [**InputData**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.InputData)**)**
### class ColumnMissingValuesMetricRenderer(color\_options: Optional\[[ColorOptions](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)] = None)
Bases: [`MetricRenderer`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/M9iMLhGad2yNU1Kd04Dv#evidently.renderers.base_renderer.MetricRenderer)
#### Attributes:
**color\_options :** [**ColorOptions**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)
#### Methods:
**render\_html(obj: ColumnMissingValuesMetric)**
**render\_json(obj: ColumnMissingValuesMetric)**
### class ColumnMissingValuesMetricResult(column\_name: str, current: ColumnMissingValues, reference: Optional\[ColumnMissingValues] = None)
Bases: `object`
#### Attributes:
**column\_name : str**
**current : ColumnMissingValues**
**reference : Optional\[ColumnMissingValues] = None**
## column\_regexp\_metric module
### class ColumnRegExpMetric(column\_name: str, reg\_exp: str, top: int = 10)
Bases: [`Metric`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.Metric)\[`DataIntegrityValueByRegexpMetricResult`]
Count number of values in a column matched or not by a regular expression (regexp)
#### Attributes:
**column\_name : str**
**reg\_exp : str**
**top : int**
#### Methods:
**calculate(data:** [**InputData**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.InputData)**)**
### class ColumnRegExpMetricRenderer(color\_options: Optional\[[ColorOptions](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)] = None)
Bases: [`MetricRenderer`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/M9iMLhGad2yNU1Kd04Dv#evidently.renderers.base_renderer.MetricRenderer)
#### Attributes:
**color\_options :** [**ColorOptions**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)
#### Methods:
**render\_html(obj: ColumnRegExpMetric)**
**render\_json(obj: ColumnRegExpMetric)**
### class DataIntegrityValueByRegexpMetricResult(column\_name: str, reg\_exp: str, top: int, current: DataIntegrityValueByRegexpStat, reference: Optional\[DataIntegrityValueByRegexpStat] = None)
Bases: `object`
#### Attributes:
**column\_name : str**
**current : DataIntegrityValueByRegexpStat**
**reference : Optional\[DataIntegrityValueByRegexpStat] = None**
**reg\_exp : str**
**top : int**
### class DataIntegrityValueByRegexpStat(number\_of\_matched: int, number\_of\_not\_matched: int, number\_of\_rows: int, table\_of\_matched: Dict\[str, int], table\_of\_not\_matched: Dict\[str, int])
Bases: `object`
Statistics about matched by a regular expression values in a column for one dataset
#### Attributes:
**number\_of\_matched : int**
**number\_of\_not\_matched : int**
**number\_of\_rows : int**
**table\_of\_matched : Dict\[str, int]**
**table\_of\_not\_matched : Dict\[str, int]**
## column\_summary\_metric module
### class CategoricalCharacteristics(number\_of\_rows: int, count: int, unique: Optional\[int], unique\_percentage: Optional\[float], most\_common: Optional\[object], most\_common\_percentage: Optional\[float], missing: Optional\[int], missing\_percentage: Optional\[float], new\_in\_current\_values\_count: Optional\[int] = None, unused\_in\_current\_values\_count: Optional\[int] = None)
Bases: `object`
#### Attributes:
**count : int**
**missing : Optional\[int]**
**missing\_percentage : Optional\[float]**
**most\_common : Optional\[object]**
**most\_common\_percentage : Optional\[float]**
**new\_in\_current\_values\_count : Optional\[int] = None**
**number\_of\_rows : int**
**unique : Optional\[int]**
**unique\_percentage : Optional\[float]**
**unused\_in\_current\_values\_count : Optional\[int] = None**
### class ColumnSummary(column\_name: str, column\_type: str, reference\_characteristics: Union\[NumericCharacteristics, CategoricalCharacteristics, DatetimeCharacteristics, NoneType], current\_characteristics: Union\[NumericCharacteristics, CategoricalCharacteristics, DatetimeCharacteristics], plot\_data: DataQualityPlot)
Bases: `object`
#### Attributes:
**column\_name : str**
**column\_type : str**
**current\_characteristics : Union\[NumericCharacteristics, CategoricalCharacteristics, DatetimeCharacteristics]**
**plot\_data : DataQualityPlot**
**reference\_characteristics : Optional\[Union\[NumericCharacteristics, CategoricalCharacteristics, DatetimeCharacteristics]]**
### class ColumnSummaryMetric(column\_name: str)
Bases: [`Metric`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.Metric)\[`ColumnSummary`]
#### Methods:
**calculate(data:** [**InputData**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.InputData)**)**
**static map\_data(stats:** [**FeatureQualityStats**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ICxNS1GschXEUCV60dnZ#evidently.calculations.data_quality.FeatureQualityStats)**)**
### class ColumnSummaryMetricRenderer(color\_options: Optional\[[ColorOptions](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)] = None)
Bases: [`MetricRenderer`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/M9iMLhGad2yNU1Kd04Dv#evidently.renderers.base_renderer.MetricRenderer)
#### Attributes:
**color\_options :** [**ColorOptions**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)
#### Methods:
**render\_html(obj: ColumnSummaryMetric)**
**render\_json(obj: ColumnSummaryMetric)**
### class DataByTarget(data\_for\_plots: Dict\[str, Dict\[str, Union\[list, pandas.core.frame.DataFrame]]], target\_name: str, target\_type: str)
Bases: `object`
#### Attributes:
**data\_for\_plots : Dict\[str, Dict\[str, Union\[list, DataFrame]]]**
**target\_name : str**
**target\_type : str**
### class DataInTime(data\_for\_plots: Dict\[str, pandas.core.frame.DataFrame], freq: str, datetime\_name: str)
Bases: `object`
#### Attributes:
**data\_for\_plots : Dict\[str, DataFrame]**
**datetime\_name : str**
**freq : str**
### class DataQualityPlot(bins\_for\_hist: Dict\[str, pandas.core.frame.DataFrame], data\_in\_time: Optional\[DataInTime], data\_by\_target: Optional\[DataByTarget], counts\_of\_values: Optional\[Dict\[str, pandas.core.frame.DataFrame]])
Bases: `object`
#### Attributes:
**bins\_for\_hist : Dict\[str, DataFrame]**
**counts\_of\_values : Optional\[Dict\[str, DataFrame]]**
**data\_by\_target : Optional\[DataByTarget]**
**data\_in\_time : Optional\[DataInTime]**
### class DatetimeCharacteristics(number\_of\_rows: int, count: int, unique: Optional\[int], unique\_percentage: Optional\[float], most\_common: Optional\[object], most\_common\_percentage: Optional\[float], missing: Optional\[int], missing\_percentage: Optional\[float], first: Optional\[str], last: Optional\[str])
Bases: `object`
#### Attributes:
**count : int**
**first : Optional\[str]**
**last : Optional\[str]**
**missing : Optional\[int]**
**missing\_percentage : Optional\[float]**
**most\_common : Optional\[object]**
**most\_common\_percentage : Optional\[float]**
**number\_of\_rows : int**
**unique : Optional\[int]**
**unique\_percentage : Optional\[float]**
### class NumericCharacteristics(number\_of\_rows: int, count: int, mean: Union\[float, int, NoneType], std: Union\[float, int, NoneType], min: Union\[float, int, NoneType], p25: Union\[float, int, NoneType], p50: Union\[float, int, NoneType], p75: Union\[float, int, NoneType], max: Union\[float, int, NoneType], unique: Optional\[int], unique\_percentage: Optional\[float], missing: Optional\[int], missing\_percentage: Optional\[float], infinite\_count: Optional\[int], infinite\_percentage: Optional\[float], most\_common: Union\[float, int, NoneType], most\_common\_percentage: Optional\[float])
Bases: `object`
#### Attributes:
**count : int**
**infinite\_count : Optional\[int]**
**infinite\_percentage : Optional\[float]**
**max : Optional\[Union\[float, int]]**
**mean : Optional\[Union\[float, int]]**
**min : Optional\[Union\[float, int]]**
**missing : Optional\[int]**
**missing\_percentage : Optional\[float]**
**most\_common : Optional\[Union\[float, int]]**
**most\_common\_percentage : Optional\[float]**
**number\_of\_rows : int**
**p25 : Optional\[Union\[float, int]]**
**p50 : Optional\[Union\[float, int]]**
**p75 : Optional\[Union\[float, int]]**
**std : Optional\[Union\[float, int]]**
**unique : Optional\[int]**
**unique\_percentage : Optional\[float]**
## dataset\_missing\_values\_metric module
### class DatasetMissingValues(different\_missing\_values: Dict\[Any, int], number\_of\_different\_missing\_values: int, different\_missing\_values\_by\_column: Dict\[str, Dict\[Any, int]], number\_of\_different\_missing\_values\_by\_column: Dict\[str, int], number\_of\_missing\_values: int, share\_of\_missing\_values: float, number\_of\_missing\_values\_by\_column: Dict\[str, int], share\_of\_missing\_values\_by\_column: Dict\[str, float], number\_of\_rows: int, number\_of\_rows\_with\_missing\_values: int, share\_of\_rows\_with\_missing\_values: float, number\_of\_columns: int, columns\_with\_missing\_values: List\[str], number\_of\_columns\_with\_missing\_values: int, share\_of\_columns\_with\_missing\_values: float)
Bases: `object`
Statistics about missed values in a dataset
#### Attributes:
**columns\_with\_missing\_values : List\[str]**
**different\_missing\_values : Dict\[Any, int]**
**different\_missing\_values\_by\_column : Dict\[str, Dict\[Any, int]]**
**number\_of\_columns : int**
**number\_of\_columns\_with\_missing\_values : int**
**number\_of\_different\_missing\_values : int**
**number\_of\_different\_missing\_values\_by\_column : Dict\[str, int]**
**number\_of\_missing\_values : int**
**number\_of\_missing\_values\_by\_column : Dict\[str, int]**
**number\_of\_rows : int**
**number\_of\_rows\_with\_missing\_values : int**
**share\_of\_columns\_with\_missing\_values : float**
**share\_of\_missing\_values : float**
**share\_of\_missing\_values\_by\_column : Dict\[str, float]**
**share\_of\_rows\_with\_missing\_values : float**
### class DatasetMissingValuesMetric(missing\_values: Optional\[list] = None, replace: bool = True)
Bases: [`Metric`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.Metric)\[`DatasetMissingValuesMetricResult`]
Count missing values in a dataset.
Missing value is a null or NaN value.
Calculate an amount of missing values kinds and count for such values.\
NA-types like numpy.NaN, pandas.NaT are counted as one type.
You can set you own missing values list with missing\_values parameter.\
Value None in the list means that Pandas null values will be included in the calculation.
If replace parameter is False - add defaults to user’s list.\
If replace parameter is True - use values from missing\_values list only.
#### Attributes:
**DEFAULT\_MISSING\_VALUES = \['', inf, -inf, None]**
**missing\_values : frozenset**
#### Methods:
**calculate(data:** [**InputData**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.InputData)**)**
### class DatasetMissingValuesMetricRenderer(color\_options: Optional\[[ColorOptions](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)] = None)
Bases: [`MetricRenderer`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/M9iMLhGad2yNU1Kd04Dv#evidently.renderers.base_renderer.MetricRenderer)
#### Attributes:
**color\_options :** [**ColorOptions**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)
#### Methods:
**render\_html(obj: DatasetMissingValuesMetric)**
**render\_json(obj: DatasetMissingValuesMetric)**
### class DatasetMissingValuesMetricResult(current: DatasetMissingValues, reference: Optional\[DatasetMissingValues] = None)
Bases: `object`
#### Attributes:
**current : DatasetMissingValues**
**reference : Optional\[DatasetMissingValues] = None**
## dataset\_summary\_metric module
### class DatasetSummary(target: Optional\[str], prediction: Optional\[Union\[str, Sequence\[str]]], date\_column: Optional\[str], id\_column: Optional\[str], number\_of\_columns: int, number\_of\_rows: int, number\_of\_missing\_values: int, number\_of\_categorical\_columns: int, number\_of\_numeric\_columns: int, number\_of\_datetime\_columns: int, number\_of\_constant\_columns: int, number\_of\_almost\_constant\_columns: int, number\_of\_duplicated\_columns: int, number\_of\_almost\_duplicated\_columns: int, number\_of\_empty\_rows: int, number\_of\_empty\_columns: int, number\_of\_duplicated\_rows: int, columns\_type: dict, nans\_by\_columns: dict, number\_uniques\_by\_columns: dict)
Bases: `object`
Columns information in a dataset
#### Attributes:
**columns\_type : dict**
**date\_column : Optional\[str]**
**id\_column : Optional\[str]**
**nans\_by\_columns : dict**
**number\_of\_almost\_constant\_columns : int**
**number\_of\_almost\_duplicated\_columns : int**
**number\_of\_categorical\_columns : int**
**number\_of\_columns : int**
**number\_of\_constant\_columns : int**
**number\_of\_datetime\_columns : int**
**number\_of\_duplicated\_columns : int**
**number\_of\_duplicated\_rows : int**
**number\_of\_empty\_columns : int**
**number\_of\_empty\_rows : int**
**number\_of\_missing\_values : int**
**number\_of\_numeric\_columns : int**
**number\_of\_rows : int**
**number\_uniques\_by\_columns : dict**
**prediction : Optional\[Union\[str, Sequence\[str]]]**
**target : Optional\[str]**
### class DatasetSummaryMetric(almost\_duplicated\_threshold: float = 0.95, almost\_constant\_threshold: float = 0.95)
Bases: [`Metric`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.Metric)\[`DatasetSummaryMetricResult`]
Common dataset(s) columns/features characteristics
#### Attributes:
**almost\_constant\_threshold : float**
**almost\_duplicated\_threshold : float**
#### Methods:
**calculate(data:** [**InputData**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/PNL2TuiArYt5TU085k7F#evidently.metrics.base_metric.InputData)**)**
### class DatasetSummaryMetricRenderer(color\_options: Optional\[[ColorOptions](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)] = None)
Bases: [`MetricRenderer`](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/M9iMLhGad2yNU1Kd04Dv#evidently.renderers.base_renderer.MetricRenderer)
#### Attributes:
**color\_options :** [**ColorOptions**](https://docs-old.evidentlyai.com/reference/api-reference/evidently.metrics/pages/ZW3IQh73672cpd3xySyJ#evidently.options.color_scheme.ColorOptions)
#### Methods:
**render\_html(obj: DatasetSummaryMetric)**
**render\_json(obj: DatasetSummaryMetric)**
### class DatasetSummaryMetricResult(almost\_duplicated\_threshold: float, current: DatasetSummary, reference: Optional\[DatasetSummary] = None)
Bases: `object`
#### Attributes:
**almost\_duplicated\_threshold : float**
**current : DatasetSummary**
**reference : Optional\[DatasetSummary] = None**
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