- Regular Aggregate Mode
It computes the count, mean, std, min, percentiles, and max for numeric columns.
Default statistics include: "count", "mean", "std", "min", "percentile", "max".
If describe() is used on the output of any DataFrame API or groupby(), then it is used in regular aggregate mode. - Time Series Aggregate Mode
It computes max, mean, min, std, median, mode, and percentiles for numeric columns.
Default statistics include: 'max', 'mean', 'min', 'std'
If describe() is used on the output of groupby_time(), then it is used in time series aggregate mode, where time series aggregates are used to calculate the statistics.
Examples Prerequisite
See Example Setup to set up the environment for the following examples.
Example: Get the basic statistics
Get the basic statistics for time series aggregation for all the numeric columns, use default settings. This example returns max, mean, min and std values.
>>> ocean_buoys_grpby.describe()
temperature salinity
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid func
('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2 0 max 100 55
mean 54.75 55
min 10 55
std 51.674 0
1 max 79 55
mean 74.5 55
min 70 55
std 3.937 0
2 max 82 55
mean 81 55
min 80 55
std 1 0
44 max 56 55
mean 48.077 55
min 43 55
std 5.766 0
Example: Get the verbose statistics
Get the verbose statistics for time series aggregation for all the numeric columns, use default settings. This example returns max, mean, min, std, median, mode, 25th, 50th and 75th percentile.
>>> ocean_buoys_grpby.describe(verbose=True)
temperature salinity
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid func
('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2 0 25% 10 55
50% 54.5 55
75% 99.25 55
max 100 55
mean 54.75 55
median 54.5 55
min 10 55
mode 10 55
std 51.674 0
1 25% 71.25 55
50% 74.5 55
75% 77.75 55
max 79 55
mean 74.5 55
median 74.5 55
min 70 55
mode 71 55
mode 72 55
mode 77 55
mode 78 55
mode 79 55
mode 70 55
std 3.937 0
2 25% 80.5 55
50% 81 55
75% 81.5 55
max 82 55
mean 81 55
median 81 55
min 80 55
mode 80 55
mode 81 55
mode 82 55
std 1 0
44 25% 43 55
50% 43 55
75% 53 55
max 56 55
mean 48.077 55
median 43 55
min 43 55
mode 43 55
std 5.766 0
Example: Get the basic statistics, consider only unique values
Get the basic statistics for time series aggregation for all the numeric columns, consider only unique values. This example returns max, mean, min and std values.
>>> ocean_buoys_grpby.describe(distinct=True)
temperature salinity
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid func
('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2 0 max 100 55
mean 69.667 55
min 10 55
std 51.675 None
1 max 79 55
mean 74.5 55
min 70 55
std 3.937 None
2 max 82 55
mean 81 55
min 80 55
std 1 None
44 max 56 55
mean 52.2 55
min 43 55
std 5.263 None
Example: Get the verbose statistics, select non-default percentiles
Get the verbose statistics for time series aggregation for all the numeric columns. In this example, you select non-default percentiles 33rd and 66th. This example returns max, mean, min, std, median, mode, 33rd, and 66th percentile.
>>> ocean_buoys_grpby.describe(verbose=True, percentiles=[0.33, 0.66])
temperature salinity
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid func
('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2 0 33% 10 55
66% 97.22 55
max 100 55
mean 54.75 55
median 54.5 55
min 10 55
mode 10 55
std 51.674 0
1 33% 71.65 55
66% 77.3 55
max 79 55
mean 74.5 55
median 74.5 55
min 70 55
mode 70 55
mode 71 55
mode 77 55
mode 78 55
mode 79 55
mode 72 55
std 3.937 0
2 33% 80.66 55
66% 81.32 55
max 82 55
mean 81 55
median 81 55
min 80 55
mode 80 55
mode 81 55
mode 82 55
std 1 0
44 33% 43 55
66% 53 55
max 56 55
mean 48.077 55
median 43 55
min 43 55
mode 43 55
std 5.766 0