> ## Documentation Index
> Fetch the complete documentation index at: https://docs.evidentlyai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Metric generators

> How to generate multiple metrics at once.

Sometimes you need to generate multiple column-level Tests or Metrics. To simplify this, you can use metric generator helper functions.

**Pre-requisites**:

* You know how to [generate Reports](/docs/library/report).

## Imports

<Accordion title="Generate data" defaultOpen={false}>
  Use the following code to generate toy data for this guide.

  ```python theme={null}
  import pandas as pd
  import numpy as np
  from evidently import Dataset
  from evidently import DataDefinition

  np.random.seed(42)

  data = {
      "Age": np.random.randint(18, 60, size=30),
      "Salary": np.random.randint(30000, 120000, size=30),
      "Department": np.random.choice(["HR", "IT", "Finance", "Marketing", "Operations"], size=30),
      "YearsExperience": np.random.randint(1, 15, size=30),  
      "EducationLevel": np.random.choice(["High School", "Bachelor", "Master", "PhD"], size=30)  
  }

  dummy_df = pd.DataFrame(data)

  eval_data_1 = Dataset.from_pandas(
      dummy_df.iloc[:15],
      data_definition=DataDefinition()
  )
  eval_data_2 = Dataset.from_pandas(
      dummy_df.iloc[15:],
      data_definition=DataDefinition()
  )
  ```
</Accordion>

Imports

```python theme={null}
from evidently import Report
from evidently.metrics import *
from evidently.generators import ColumnMetricGenerator
```

## Metric generators

**Example 1**. Apply the selected metric (`ValueDrift`) to all columns in the dataset.

```python theme={null}
report = Report([
    ColumnMetricGenerator(ValueDrift)
])

my_eval = report.run(eval_data_1, eval_data_2)
my_eval
```

**Example 2**. Apply the selected metric (`ValueDrift`) to the listed columns in the dataset. Use `metric_kwargs` to pass any applicable metric parameters.

```python theme={null}
report = Report([
    ColumnMetricGenerator(ValueDrift, 
                          columns=["EducationLevel", "Salary"],
                          metric_kwargs={"method":"psi"}), # metric parameters
])

my_eval = report.run(eval_data_1, eval_data_2)
my_eval
```

**Example 3**. Apply the selected metric (`ValueDrift`) only to the categorical (`cat`) columns in the dataset.

```python theme={null}
report = Report([
    ColumnMetricGenerator(UniqueValueCount, 
                          column_types='cat'),  #apply to categorical columns only 
])

my_eval = report.run(eval_data_1, eval_data_2)
my_eval
```

Available:

* `num` - numerical
* `cat` - categorical
* `all` - all

## Test generators

You can use the same approach to generate Tests. Use `metric_kwargs` to pass test conditions.

**Example.** Generate the same Test for all the columns in the dataset. It will use defaults if you do not specify the test condition.

```python theme={null}
from evidently.future.tests import *

report = Report([
    ColumnMetricGenerator(MinValue, 
                          column_types='num',
                          metric_kwargs={"tests":[gt(0)]}), 
])

my_eval = report.run(eval_data_1, eval_data_2)
my_eval
```

This will apply the minimum value test to all numerical columns in the dataset and check that they are above 0.