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