# Extra Data Types

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

## Other data types

    ```Python hl_lines="1  3  12-16"
    {!> ../../../docs_src/extra_data_types/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="1  3  12-16"
    {!> ../../../docs_src/extra_data_types/tutorial001_an_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="1  3  13-17"
    {!> ../../../docs_src/extra_data_types/tutorial001_an.py!}
    ```

=== "Python 3.10+ non-Annotated"

* `datetime.datetime`:
    * Встроенный в Python `datetime.datetime`.
    * В запросах и ответах будет представлен как `str` в формате ISO 8601, например: `2008-09-15T15:53:00+05:00`.
* `datetime.date`:
    * Встроенный в Python `datetime.date`.
    * В запросах и ответах будет представлен как `str` в формате ISO 8601, например: `2008-09-15`.
* `datetime.time`:
    * Встроенный в Python `datetime.time`.

```Python hl_lines="1  3  12-16"
{!../../../docs_src/extra_data_types/tutorial001.py!}
```

Note que os parâmetros dentro da função tem seu tipo de dados natural, e você pode, por exemplo, realizar manipulações normais de data, como:

```Python hl_lines="18-19"
{!../../../docs_src/extra_data_types/tutorial001.py!}

- Large numbers (outside of the signed 64-bit range) are not yet supported.
- The Date [functions](https://docs.aws.amazon.com/AmazonS3/latest/dev/s3-glacier-select-sql-reference-date.html) `DATE_ADD`, `DATE_DIFF`, `EXTRACT` and `UTCNOW` along with type conversion using `CAST` to the `TIMESTAMP` data type are currently supported.

### Remotely Inspecting backend data

e.g.:
To download `2021/taxes.csv` archived in `financial.zip` and stored under a bucket named `company-data`, you can issue a GET request using the following path 'company-data/financial.zip/2021/taxes.csv`

## Contents properties

    "Content-Length": "380",
    "Content-Type": "application/octet-stream",
    "User-Agent": "MinIO (linux; amd64) minio-go/v7.0.15 mc/DEVELOPMENT.2021-10-06T23-39-34Z",
    "X-Amz-Content-Sha256": "STREAMING-AWS4-HMAC-SHA256-PAYLOAD",
    "X-Amz-Date": "20211008T004636Z",

Lambda

MinIO's Object Lambda implementation allows for transforming your data to serve unique data format requirements for each application. For example, a dataset created by an ecommerce application might include personally identifiable information (PII). When the same data is processed for analytics, PII should be redacted. However, if the same dataset is used for a marketing campaign, you might need to enrich the data with additional details, such as information from the customer loyalty database....

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` یعنی:

    کلید ها و مقادیر دیکشنری `second_user_data` را مستقیما به عنوان ارگومان های key-value بفرست، که معادل است با : `User(id=4, name="Mary", joined="2018-11-30")`

### پشتیبانی ویرایشگر