chore: import upstream snapshot with attribution

docs/md/explanation/table/schema.md

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+# Schema and column types
+
+The mapping of a `Table`'s column names to data types is referred to as a
+`schema`. Each column has a unique name and a single data type, one of
+
+-   `float`
+-   `integer`
+-   `boolean`
+-   `date`
+-   `datetime`
+-   `string`
+
+A `Table` schema is fixed at construction, either by explicitly passing a schema
+dictionary to the `Client::table` method, or by passing _data_ to this method
+from which the schema is _inferred_ (if CSV or JSON format) or inherited (if
+Arrow).
+
+## Type inference
+
+When passing CSV or JSON data to the `Client::table` constructor, the type of
+each column is inferred automatically. In some cases, the inference algorithm
+may not return exactly what you'd like. For example, a column may be interpreted
+as a `datetime` when you intended it to be a `string`, or a column may have no
+values at all (yet), as it will be updated with values from a real-time data
+source later on. In these cases, create a `table()` with a _schema_.
+
+Once the `Table` has been created, further `Table::update` calls will perform
+limited type _coercion_ based on the schema. While _coercion_ works similarly to
+_inference_, in that input data may be parsed based on the expected column type,
+`Table::update` will not _change_ the column's type further. For example, a
+number literal `1234` would be _inferred_ as an `"integer"`, but _in the context
+of an `Table::update` call on a known `"string"` column_, this will be parsed as
+the _string_ `"1234"`.
+
+## `date` and `datetime` inference
+
+Various string representations of `date` and `datetime` format columns can be
+_inferred_ as well _coerced_ from strings if they match one of Perspective's
+internal known datetime parsing formats, for example
+[ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) (which is also the format
+Perspective will _output_ these types for CSV).