# Column mapping

This section applies both to Dashboards and Profiles.

If you prefer a video version, watch this tutorial:

{% embed url="<https://www.youtube.com/watch?v=MiSl73LRj5I&t=7s&ab_channel=EvidentlyAI>" %}

### Column mapping

If the `column_mapping` is not specified or set as `None`, we use the default mapping strategy:

* All features with numeric types (np.number) will be treated as numerical. All datetime features (np.datetime64) will be treated as datetimes. All others will be treated as categorical.
* The column with **'id'** name will be treated as an ID column.
* The column with **'datetime'** name will be treated as a datetime column.
* The column with **'target'** name will be treated as a target function.
* The column with **'prediction'** name will be treated as a model prediction.

ID, datetime, target, and prediction are utility columns. Requirements are different depending on the report type:

* For the **Data Drift** report, these columns are not required. If you specify id, target, and prediction, they will be excluded from the data drift report. However, starting from the version [0.1.51.dev0](https://github.com/evidentlyai/evidently/releases/tag/v0.1.51.dev0) only id column will be excluded from the data drift report; if target or prediction is specified it will remain in the report. If you specify the datetime, it will be used in data plots.
* For the **Target Drift** reports, we expect either the target or the prediction column or both. ID and datetime are optional.
* For **Model Performance** reports, both the target and the prediction column are required. ID and datetime are optional.
* For **Data Quality** report, these columns are not required. If you you specify target and datetime they will be used in data plots.

You can create a `ColumnMapping` object to specify whether your dataset includes the utility columns and split the features into numerical and categorical types. Also you could specify datetime types. If **datetime** expects that you pass main datetime column bounded with objects, **datetime\_feature\_names** expects all others date columns (example churn task: datetime: 'date\_of\_curn', datetime\_feature\_names = \['lust\_call\_date', 'join\_date'])

```python
from evidently.pipeline.column_mapping import ColumnMapping

column_mapping = ColumnMapping()

column_mapping.target = 'y' #'y' is the name of the column with the target function
column_mapping.prediction = 'pred' #'pred' is the name of the column(s) with model predictions
column_mapping.id = None #there is no ID column in the dataset
column_mapping.datetime = 'date' #'date' is the name of the column with datetime 

column_mapping.numerical_features = ['temp', 'atemp', 'humidity'] #list of numerical features
column_mapping.categorical_features = ['season', 'holiday'] #list of categorical features
```

**NOTE: Column names in Probabilistic Classification**

The tool expects your `DataFrame(s)` to contain columns with the names matching the ones from the ‘prediction’ list. Each column should include information about the predicted probability \[0;1] for the corresponding class.

```python
column_mapping = ColumnMapping()

column_mapping.prediction = ['class_name1', 'class_name2', 'class_name3',]
```

**NOTE: Column order in Binary Classification**

For binary classification, class order matters. The tool expects that the target (so-called positive) class is the **first** in the `column_mapping.prediction` list.

**NOTE: task parameter in Data Quality**

To build the report correctly we should define classification from regression problem. There is a case when we can’t do it for sure: multiclass problem with a lot of classes encoded by numbers looks like regression problem too. In such cases, you should specify the **task** parameter. It accepts two values: 'regression' and 'classification'.

```python
column_mapping = ColumnMapping()

column_mapping.target = 'y'
column_mapping.task = 'classification'
```

If you don't specify it we use a simple strategy:

if the target has a numeric type and number of unique values > 5: task == ‘regression’

in all other cases task == ‘classification’


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