# 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"

=== "Python 3.8 и выше"

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

=== "Python 3.10 и выше"

    ```Python hl_lines="1  2  11-15"
    {!> ../../../docs_src/extra_data_types/tutorial001_py310.py!}
    ```

```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!}

      sink.writeInt(errorCode.httpCode)
      sink.flush()
    }
  }

  /** The maximum size of bytes that may be sent in a single call to [data]. */
  fun maxDataLength(): Int = maxFrameSize

  /**
   * `source.length` may be longer than the max length of the variant's data frame. Implementations
   * must send multiple frames as necessary.
   *
   * @param source the buffer to draw bytes from. May be null if byteCount is 0.

    ```Python hl_lines="3  77-79"
    {!> ../../../docs_src/security/tutorial003.py!}
    ```

### Check the password

At this point we have the user data from our database, but we haven't checked the password.

Let's put that data in the Pydantic `UserInDB` model first.

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

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

which is a fitness factor for a lossless data compression.

Pre-compressed input typically compresses in excess of 2GiB/s per core,
so performance impact should be minimal even if precompressed data is re-compressed.
Decompressing incompressible data has no significant performance impact.

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

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")`

* **Kodlaması Hızlı**: Geliştirme hızını yaklaşık %200 ile %300 aralığında arttırır. *
* **Daha az hata**: İnsan (geliştirici) kaynaklı hataları yaklaşık %40 azaltır. *
* **Sezgisel**: Muhteşem bir editör desteği. Her yerde <abbr title="Otomatik Tamamlama: auto-complete, autocompletion, IntelliSense">otomatik tamamlama</abbr>. Hata ayıklama ile daha az zaman harcayacaksınız.