docs: add section about Frame type annotations
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@@ -351,7 +351,7 @@ You can use it like any other generic type and it will provide type checking for
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caption: [Simple column type definition],
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)
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==== `Frame`
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==== `Frame` <frame-type>
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The `Frame` type is a super-powered generic type used to represent a `pandas.DataFrame` object.
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In place of type arguments, `Frame` accepts a schema, i.e. a series of column definitions.
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@@ -558,6 +558,7 @@ A simple annotation declaration, without assigning a value, is enough to declare
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)
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Because unpacking is not supported, assigning to multiple values is also not handled by the type checker.
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For more information about type annotations, see @type-annotations
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== Arithmetic
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@@ -633,7 +634,7 @@ Conditional statements are checked relatively strictly by Midas. The test expres
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Simple forms of `for` loops can be used, that is using a single variable and iterating over an object implementing the `__getitem__` method. Like above in @if-else, leaking variables from inside the loop is ignored.
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The `for`-`else` statements are not supported. `while` loops are also not not supported.
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`for`-`else` statements are not supported. `while` loops are also not supported.
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== Functions
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@@ -741,6 +742,35 @@ There may be some cases where the cost of checking a value at runtime is simply
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If the value passed to `cast` or `unsafe_cast` is a literal (e.g. an integer, a string, a list of literals, etc.), the assertion is evaluated _at compile-time_ and no runtime assertion is generated.
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== Annotations / Type Hints <type-annotations>
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Vanilla Python already lets you use type hints to specify the type of variables and function parameters.
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Midas use them to type check your code. Additionally, it allows you to use a special syntax to define a `Frame` types directly in these annotations.
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Because these annotations are not interpretable by Python, your integrated type checker might complain loudly about them being invalid.
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A workaround is to silence it by adding a type comment at the end of the line, as shown in @silence-errors.
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#figure(
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```python
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var: Frame[name: str, age: float] # type: ignore # noqa: F821
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```,
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caption: [MyPy's and Pylance's complaints about custom type annotation can be silenced with type comments],
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) <silence-errors>
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=== Frame type annotation
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The syntax is similar to how you can define frame types in the Midas language (see @frame-type). The only difference is that types can only be name references; you cannot inline constraint types.
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The example of @python-frame-type shows how you can annotate a dataframe with some columns directly in Python.
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#figure(
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```python
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df: Frame[name: Name, age: float, height: Length[Meter]] = ...
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```,
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caption: [Frame type annotation in Python],
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) <python-frame-type>
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= Commands <commands>
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#TODO
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