morpheus.utils.column_info

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Functions

`column_listjoin`(df, col_name)	Returns the array series `df[col_name]` as flattened string series.
`create_increment_col`(df, column_name[, ...])	Create a new integer column counting unique occurrences of values in `column_name` grouped per-day using the timestamp values in `timestamp_column` and then grouping by `groupby_column` returning incrementing values starting at `1`.
`process_dataframe`(df_in, input_schema)	Applies colmn transformations as defined by `input_schema`

Classes

`BoolColumn`(name, dtype, input_name[, ...])	Subclass of `RenameColumn`, adds the ability to map a set custom values as boolean values.
`ColumnInfo`(name, dtype)	Defines a single column and type-cast.
`CustomColumn`(name, dtype, process_column_fn)	Subclass of `ColumnInfo`, defines a column to be computed by a user-defined function `process_column_fn`.
`DataFrameInputSchema`([json_columns, ...])	Defines the schema specifying the columns to be included in the output `DataFrame`.
`DateTimeColumn`(name, dtype, input_name)	Subclass of `RenameColumn`, specific to casting UTC localized datetime values.
`IncrementColumn`(name, dtype, input_name, ...)	Subclass of `DateTimeColumn`, counts the unique occurrences of a value in `groupby_column` over a specific time window `period` based on dates in the `input_name` field.
`RenameColumn`(name, dtype, input_name)	Subclass of `ColumnInfo`, adds the ability to also perform a rename.
`StringCatColumn`(name, dtype, input_columns, sep)	Subclass of `ColumnInfo`, concatenates values from multiple columns into a new string column separated by `sep`.
`StringJoinColumn`(name, dtype, input_name, sep)	Subclass of `RenameColumn`, converts incoming `list` values to string by joining by `sep`.

column_listjoin(df, col_name)[source]

Returns the array series df[col_name] as flattened string series.

create_increment_col(df, column_name, groupby_column='username', timestamp_column='timestamp')[source]

Create a new integer column counting unique occurrences of values in column_name grouped per-day using the timestamp values in timestamp_column and then grouping by groupby_column returning incrementing values starting at 1.

process_dataframe(df_in, input_schema)[source]

Applies colmn transformations as defined by input_schema