get_diff_summary

get_diff_summary(idx, numeric=False)

Calculates summary statistics of the time differences between consecutive values in a datetime index.

Parameters

Name	Type	Description	Default
`idx`	pd.Series or pd.DateTimeIndex	The `idx` parameter can be either a pandas Series or a pandas DateTimeIndex. It represents the index values for which you want to calculate the difference summary.	required
`numeric`	bool	The `numeric` parameter is a boolean flag that indicates whether the input index should be treated as numeric or not. - If `numeric` is set to `True`, the index values are converted to integers representing the number of seconds since the Unix epoch (January 1, 1970). - If `numeric` is set to `False`, the index values are treated as datetime values. The default value of `numeric` is `False`.	`False`

Returns

Type Description

pd.DataFrame The function get_diff_summary returns a pandas DataFrame containing summary statistics including: If numeric is set to False, the column names are: - diff_min: The minimum time difference between consecutive observations in the time series as a timedelta. - diff_q25: The 25th percentile of the time difference between consecutive observations in the time series as a timedelta. - diff_median: The median time difference between consecutive observations in the time series as a timedelta. - diff_mean: The mean time difference between consecutive observations in the time series as a timedelta. - diff_q75: The 75th percentile of the time difference between consecutive observations in the time series as a timedelta. - diff_max: The maximum time difference between consecutive observations in the time series as a timedelta. If numeric is set to True, the column names are: - diff_min_seconds: The minimum time difference between consecutive observations in the time series in seconds. - diff_q25_seconds: The 25th percentile of the time difference between consecutive observations in the time series in seconds. - diff_median_seconds: The median time difference between consecutive observations in the time series in seconds. - diff_mean_seconds: The mean time difference between consecutive observations in the time series in seconds. - diff_q75_seconds: The 75th percentile of the time difference between consecutive observations in the time series in seconds. - diff_max_seconds: The maximum time difference between consecutive observations in the time series in seconds.

Type	Description
pd.DataFrame	The function `get_diff_summary` returns a pandas DataFrame containing summary statistics including: If `numeric` is set to `False`, the column names are: - `diff_min`: The minimum time difference between consecutive observations in the time series as a timedelta. - `diff_q25`: The 25th percentile of the time difference between consecutive observations in the time series as a timedelta. - `diff_median`: The median time difference between consecutive observations in the time series as a timedelta. - `diff_mean`: The mean time difference between consecutive observations in the time series as a timedelta. - `diff_q75`: The 75th percentile of the time difference between consecutive observations in the time series as a timedelta. - `diff_max`: The maximum time difference between consecutive observations in the time series as a timedelta. If `numeric` is set to `True`, the column names are: - `diff_min_seconds`: The minimum time difference between consecutive observations in the time series in seconds. - `diff_q25_seconds`: The 25th percentile of the time difference between consecutive observations in the time series in seconds. - `diff_median_seconds`: The median time difference between consecutive observations in the time series in seconds. - `diff_mean_seconds`: The mean time difference between consecutive observations in the time series in seconds. - `diff_q75_seconds`: The 75th percentile of the time difference between consecutive observations in the time series in seconds. - `diff_max_seconds`: The maximum time difference between consecutive observations in the time series in seconds.