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Update documentation (#868)
Co-authored-by: daniil-quix <133032822+daniil-quix@users.noreply.github.com>
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docs/api-reference/dataframe.md

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class StreamingDataFrame()
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```
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`StreamingDataFrame` is the main object you will use for ETL work.
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def stream_id() -> str
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```
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An identifier of the data stream this StreamingDataFrame
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manipulates in the application.
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metadata: bool = False) -> "StreamingDataFrame"
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```
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Apply a function to transform the value and return a new value.
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metadata: bool = False) -> "StreamingDataFrame"
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```
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Apply a function to mutate value in-place or to perform a side effect
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metadata: bool = False) -> "StreamingDataFrame"
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```
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Filter value using provided function.
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) -> "StreamingDataFrame"
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```
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"Groups" messages by re-keying them via the provided group_by operation
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def contains(keys: Union[str, list[str]]) -> StreamingSeries
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```
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Check if keys are present in the Row value.
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key: Optional[Callable[[Any], Any]] = None) -> "StreamingDataFrame"
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```
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Produce current value to a topic. You can optionally specify a new key.
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```
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Set a new timestamp based on the current message value and its metadata.
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```
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Set new message headers based on the current message value and metadata.
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def print(pretty: bool = True, metadata: bool = False) -> "StreamingDataFrame"
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```
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Print out the current message value (and optionally, the message metadata) to
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```
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Print a table with the most recent records.
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def compose(sink: Optional[VoidExecutor] = None) -> dict[str, VoidExecutor]
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```
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Compose all functions of this StreamingDataFrame into one big closure.
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A shorthand to test `StreamingDataFrame` with provided value
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Create a time-based tumbling window transformation on this StreamingDataFrame.
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Create a count-based tumbling window transformation on this StreamingDataFrame.
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Create a time-based sliding window transformation on this StreamingDataFrame.
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Fill missing values in the message value with a constant value.
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Concatenate two StreamingDataFrames together and return a new one.
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