docs: add Column and Frame to manual
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@@ -333,6 +333,46 @@ The _body_ of a generic type, i.e. the right-hand side of the definition, can co
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caption: [Type parameters in a generic type's body],
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)
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=== `Column` / `Frame` types
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To provide useful type-checking for data engineers, Midas offers two special types: `Column` and `Frame`.
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Their goal is to help type check Pandas' `Series` and `DataFrame` respectively.
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==== `Column`
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The `Column` type is a generic type used to represent a `pandas.Series` object.
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You can use it like any other generic type and it will provide type checking for some common methods and attributes offered by Pandas.
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#figure(
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```midas
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type Temperature = float
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alias Temperatures = Column[Temperature]
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```,
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caption: [Simple column type definition],
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)
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==== `Frame`
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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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@simple-frame show how you can define a simple frame type with 3 columns:
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- `name`: a column of `Name` values
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- `age`: a column of `int` values
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- `height`: a column of `float where _ >= 0` values
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Notice that you don't need to specify `Column` types.
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#figure(
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```midas
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type Name = str where len(_) != 0
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alias Data = Frame[
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name: Name,
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age: int,
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height: float where _ >= 0
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]
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```,
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) <simple-frame>
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#pagebreak()
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== Extend Statement <extend-stmt>
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@@ -79,6 +79,13 @@ contexts:
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- match: \b(where)\b
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scope: keyword.other.midas
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set: constraint
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- match: "Frame"
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scope: entity.name.type
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push:
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- match: \[
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push: frame-schema
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- match: $
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pop: true
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- match: "{{identifier}}"
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scope: entity.name.type
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- match: $
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@@ -190,3 +197,15 @@ contexts:
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- match: '\)'
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pop: true
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frame-schema:
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- include: frame-column
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- match: \]
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# scope: punctuation.section.block.end
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pop: true
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frame-column:
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- match: "{{identifier}}"
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scope: variable.other.member
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- match: ":"
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push: type-expr
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