morpheus.messages.message_meta.AppShieldMessageMeta#

class AppShieldMessageMeta(**kwargs)[source]#

Bases: MessageMeta

This class extends MessageMeta to also hold source corresponding to batched metadata.

Parameters:

dfpd.DataFrame: Input rows in dataframe.
sourcestr: Determines which source generated the snapshot messages.

Attributes:

count: Returns the number of messages in the batch.
df

Methods

`copy_ranges`(ranges)	Perform a copy of the current message instance for the given `ranges` of rows.
`ensure_sliceable_index`()	Replaces the index in the underlying dataframe if the existing one is not unique and monotonic.
`get_data`([columns])	Return column values from the underlying DataFrame.
`get_meta_range`(mess_offset, message_count[, ...])	Return column values from `morpheus.pipeline.messages.MessageMeta.df` from the specified start offset until the message count.
`get_slice`(start, stop)	Returns a new MessageMeta with only the rows specified by start/stop.
`has_sliceable_index`()	Returns True if the underlying DataFrame's index is unique and monotonic.
`set_data`(columns, value)	Set column values to the underlying DataFrame.

copy_dataframe
get_column_names
mutable_dataframe

copy_ranges(ranges)[source]#

Perform a copy of the current message instance for the given ranges of rows.

Parameters:

rangestyping.List[typing.Tuple[int, int]]: Rows to include in the copy in the form of [(`start_row, stop_row),…]` The stop_row isn’t included. For example to copy rows 1-2 & 5-7 ranges=[(1, 3), (5, 8)]

Returns:

MessageMeta: A new MessageMeta with only the rows specified by ranges.

property count: int#

Returns the number of messages in the batch.

Returns:

int: number of messages in the MessageMeta.df.

ensure_sliceable_index()[source]#

Replaces the index in the underlying dataframe if the existing one is not unique and monotonic. The old index will be preserved in a column named _index_{old_index.name}. If has_sliceable_index() == true, this is a no-op.

Returns:

str: The name of the column with the old index or None if no changes were made

get_data(columns=None)[source]#

Return column values from the underlying DataFrame.

Parameters:

columnstyping.Union[None, str, typing.List[str]]: Input column names. Returns all columns if None is specified. When a string is passed, a Series is returned. Otherwise, a Dataframe is returned.

Returns:

Series or Dataframe: Column values from the dataframe.

get_meta_range( mess_offset, message_count, columns=None, )[source]#

Return column values from morpheus.pipeline.messages.MessageMeta.df from the specified start offset until the message count.

Parameters:

mess_offsetint: Offset into the metadata batch.
message_countint: Messages count.
columnstyping.Union[None, str, typing.List[str]]: Input column names. Returns all columns if None is specified. When a string is passed, a Series is returned. Otherwise a Dataframe is returned.

Returns:

Series or Dataframe: Column values from the dataframe.

get_slice(start, stop)[source]#

Returns a new MessageMeta with only the rows specified by start/stop.

Parameters:

startint: Start offset address.
stopint: Stop offset address.

Returns:

MessageMeta: A new MessageMeta with sliced offset and count.

has_sliceable_index()[source]#

Returns True if the underlying DataFrame’s index is unique and monotonic. Sliceable indices have better performance since a range of rows can be specified by a start and stop index instead of requiring boolean masks.

Returns:

bool

set_data(columns, value)[source]#

Set column values to the underlying DataFrame.

Parameters:

columnstyping.Union[None, str, typing.List[str]]: Input column names. Sets the value for the corresponding column names. If None is specified, all columns will be used. If the column does not exist, a new one will be created.
valueAny: Value to apply to the specified columns. If a single value is passed, it will be broadcast to all rows. If a Series or Dataframe is passed, rows will be matched by index.