# Creating Metadata

## Auto Detect Metadata

If you don't already have a metadata object, we recommend auto-detecting it based on your data.

### detect\_from\_dataframes

Use this function to automatically detect metadata from your data that you've loaded as a pandas.DataFrame objects.

**Parameters**:

* (required) `data`: Your data, represented as a dictionary. The keys are your table names and values are the pandas.DataFrame objects containing your data.
* `infer_sdtypes`: A boolean describing whether to infer the sdtypes of each column
  * (default) `True`: Infer the sdtypes of each column based on the data.
  * `False`: Do not infer the sdtypes. All columns will be marked as unknown, ready for you to manually update.
* `infer_keys`: A string describing whether to infer the primary and/or foreign keys.
  * (default) `'primary_and_foreign'`: Infer the primary keys in each table, and the foreign keys in other tables that refer to them
  * `'primary_only'`: Infer the primary keys in each table. You can manually add the foreign key relationships later.
  * `None`: Do not infer any primary or foreign keys. You can manually add these later.
* `foreign_key_inference_algorithm`: The algorithm to use when inferring the foreign key connections to primary keys
  * (default) `'column_name_match'`: Match up foreign and primary key columns that have the same names
  * ＊(default, SDV Enterprise) `'data_match'`: Match up foreign and primary key columns based on the data that they contain

**Output** A Metadata object that describes the data

```python
from sdv.metadata import Metadata

metadata = Metadata.detect_from_dataframes(
    data={
        'hotels': hotels_dataframe,
        'guests': guests_dataframe
    })
```

{% hint style="info" %}
**＊SDV Enterprise Feature.** This feature is only available for licensed, enterprise users. For more information, visit our page to [Compare SDV Features](https://docs.sdv.dev/sdv/explore/sdv-enterprise/compare-features).
{% endhint %}

## Updating Metadata

{% hint style="danger" %}
**The detected metadata is not guaranteed to be accurate or complete.** Be sure to carefully inspect the metadata and update information.

**For more information about inspecting and updating your metadata, see the** [**Metadata API reference**](https://docs.sdv.dev/sdv/concepts/metadata/metadata-api)**.**
{% endhint %}

```python
metadata.update_column(
    column_name='age',
    sdtype='numerical',
    table_name='users'
)

metadata.validate()
```

## Saving, Loading & Sharing Metadata

You can save the metadata object as a JSON file and load it again for future use.

### save\_to\_json

Use this to save the metadata object to a new JSON file that will be compatible with SDV 1.0 and beyond. We recommend you write the metadata to a new file every time you update it.

**Parameters**

* (required) `filepath`: The location of the file that will be created with the JSON metadata
* `mode`: A string describing the mode to use when creating the JSON file
  * (default) `'write'`: Write the metadata to the file, raising an error if the file already exists
  * `'overwrite'`: Write the metadata to the file, replacing the contents if the file already exists

**Output** (None) 

```python
metadata.save_to_json(filepath='my_metadata_v1.json')
```

### load\_from\_json

Use this method to load your file as a Metadata object.

**Parameters**

* (required) `filepath`: The name of the file containing the JSON metadata

**Output:** A Metadata object.

```
metadata = Metadata.load_from_json(filepath='my_metadata_v1.json')
```


---

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