niimpy.exploration.missingness module¶
- niimpy.exploration.missingness.missing_data_format(question, keep_values=False)[source]¶
Returns a series of timestamps in the right format to allow missing data visualization .
- Parameters
- question: Dataframe
- niimpy.exploration.missingness.missing_noise(database, subject, start=None, end=None)[source]¶
Returns a Dataframe with the estimated missing data from the ambient noise sensor.
NOTE: This function aggregates data by day.
- Parameters
- database: Niimpy database
- user: string
- start: datetime, optional
- end: datetime, optional
- Returns
- avg_noise: Dataframe
- niimpy.exploration.missingness.screen_missing_data(database, subject, start=None, end=None)[source]¶
Returns a DataFrame contanining the percentage (range [0,1]) of loss data calculated based on the transitions of screen status. In general, if screen_status(t) == screen_status(t+1), we declared we have at least one missing point.
- Parameters
- database: Niimpy database
- user: string
- start: datetime, optional
- end: datetime, optional
- Returns
- count: Dataframe