# Type variables（generics）

**Type variables** or **generics**,Allow us to write flexible, reusable code by creating placeholder types that can that can stand for different types and different contexts。Especially in classes and functions。

The author used the TypeVar from Python's typing module。

```python
from typing import TypeVar, Generic

#T can be any type
T = TypeVar("T")

#N can be int or float
N = TypeVar("N", int, float)
```

* Since float can handle int in python ,N = TypeVar("N", int, float),"int"in the code is redundant?

{% code overflow="wrap" %}

```GPT4

Python's type system treats `int` and `float` as distinct types, even though a float can represent an integer value. So, even though it's true that floats can handle integers, it's not redundant to define a `TypeVar` that allows both `int` and `float`. It indicates to the type checker (and to other programmers) that a function or class that uses `N` is designed to work with both integers and floating-point numbers.
While a float in Python can represent an integer value (like `3.0`), an int can't fully represent a float. If you attempt to assign a floating-point value to an integer, Python will truncate the decimal part, leading to loss of information.
>>> int(3.5)
3
>>> float(3)
3.0
```

{% endcode %}

**By inheriting from the Generic class, and specifying a particular type, you can create subclasses that work with specific data types.**

Let's consider an example involving a generic class `Box` that holds a single item of any type. We then create subclasses of `Box` that are specific to certain data types.

First, let's define the generic class `Box`:

<pre class="language-python"><code class="lang-python"><strong>from typing import TypeVar, Generic
</strong>
T = TypeVar('T')

class Box(Generic[T]):
    def __init__(self, item: T):
        self.item = item

    def get_item(self) -> T:
        return self.item
</code></pre>

In the code above, `Box` is a generic class parameterized by `T`. It can hold an item of any type.

Now let's create a few subclasses that inherit from `Box` and are specific to certain data types:

```python
class IntBox(Box[int]):
    pass

class StrBox(Box[str]):
    pass

class FloatBox(Box[float]):
    pass
```

Now, these subclasses `IntBox`, `StrBox`, and `FloatBox` are specialized versions of the `Box` class that are designed to work with `int`, `str`, and `float` types, respectively.

Let's see them in action:

```python
# Creating instances of specialized box classes
int_box = IntBox(10)
str_box = StrBox('hello')
float_box = FloatBox(3.14)

# Getting items from the boxes
print(int_box.get_item())  # prints: 10
print(str_box.get_item())  # prints: hello
print(float_box.get_item())  # prints: 3.14
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://combo-of-rl-and-trading.gitbook.io/rl/foundations-of-rl-with-applications-in-finance/type-variables-generics.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.