# Creating Metadata

This guide will walk you through creating the metadata using the Python API.

## Auto Detect Metadata

Once you have loaded your data into Python, you can auto-detect your actual data.

### detect\_from\_dataframe

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

**Parameters**:

* (required) `data`: Your pandas DataFrame object that contains the data
* `table_name`: A string describing the name of your table. SDV will use the table name when referring to your table in the metadata, as well as any warnings or descriptive error messages.
  * (default) By default, we'll name your data table `'table'`
* `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 keys
  * (default) `'primary_only'`: Infer the primary keys in the table
  * `None`: Do not infer any primary keys. You can manually add these later.

**Output** A [Metadata](https://docs.sdv.dev/sdv/concepts/metadata) object that descibes the data

```python
from sdv.metadata import Metadata

metadata = Metadata.detect_from_dataframe(
    data=my_dataframe,
    table_name='hotel_guests')
```

## 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 it so it accurately represents your data.

**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='start_date',
    sdtype='datetime',
    datetime_format='%Y-%m-%d')
    
metadata.update_column(
    column_name='user_cell',
    sdtype='phone_number',
    pii=True)
    
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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