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Version: Python

Time operations cheat sheet

Formats​

hh:mm:ss​

Deephaven time tables use timestamps in the format of a Duration to specify ticking intervals.

from deephaven import time_table

result = time_table("PT1S")

yyyy-mm-ddT:hh:mm:ss.[millis|micros|nanos] TZ​

Deephaven time tables may also use the yyyy-mm-ddT:hh:mm:ss.[millis|micros|nanos] TZ format to specify a start date.

TZ represents the time zone. A few examples are:

  • UTC
  • ET (America/New York)
  • PT (America/Los Angeles)
from deephaven import time_table

result = time_table("PT1S", "2021-08-06T13:21:00 ET")

Convert string to Deephaven date-time object​

Deephaven supports converting a string to a Deephaven date-time object via the to_j_instant or to_j_zdt method.

Strings should be in the yyyy-mm-ddT:hh:mm:ss.[millis|micros|nanos] TZ format.

from deephaven.time import to_j_instant

datetime_object = to_j_instant("2021-07-04T08:00:00 ET")

Date-time columns​

Deephaven tables have built-in support for Deephaven date-time objects via the datetime_col method.

from deephaven.time import to_j_instant
from deephaven import new_table
from deephaven.column import datetime_col

first_time = to_j_instant("2021-07-04T08:00:00 ET")
second_time = to_j_instant("2021-09-06T12:30:00 ET")
third_time = to_j_instant("2021-12-25T21:15:00 ET")

result = new_table([
    datetime_col("DateTimes", [first_time, second_time, third_time])
])

Timestamp comparison​

Deephaven's filtering has support for timestamp comparisons.

note

Query strings require single quotes ' around timestamps.

from deephaven.time import to_j_instant
from deephaven import new_table
from deephaven.column import datetime_col

first_time = to_j_instant("2021-07-04T08:00:00 ET")
second_time = to_j_instant("2021-09-06T12:30:00 ET")
third_time = to_j_instant("2021-12-25T21:15:00 ET")
result = new_table([
    datetime_col("DateTimes", [first_time, second_time, third_time])
])
filtered = result.where(filters=["DateTimes >= '2021-09-06T12:30:00 ET'"])

See our How to use filters guide for more information.

Time zones​

from deephaven.time import dh_time_zone, to_j_instant

system_tz = dh_time_zone()

print(system_tz)

See our full list of time zones

Downsampling temporal data via time binning​

Downsampling time series data may be accomplished by calculating binning-intervals for time values and using appropriate aggregation methods, grouped by the binned interval.

from deephaven.time import to_j_instant
from deephaven import new_table
from deephaven.column import datetime_col, int_col
from deephaven import agg

first_time = to_j_instant("2021-09-06T12:29:58 ET")
second_time = to_j_instant("2021-09-06T12:30:00 ET")
third_time = to_j_instant("2021-09-06T12:30:01 ET")
fourth_time = to_j_instant("2021-09-06T12:30:05 ET")
fifth_time = to_j_instant("2021-09-06T12:30:09 ET")
data_table = new_table([
    datetime_col("DateTimes", [first_time, second_time, third_time, fourth_time, fifth_time]),
    int_col("DataCol", [0, 1, 2, 3, 4]),
])
binned_table = data_table.update_view(formulas=["UpperBin=upperBin(DateTimes, 5 * SECOND)", "LowerBin=lowerBin(DateTimes, 5 * SECOND)"])

grouped_upper = binned_table.agg_by([agg.first(cols=["FirstDateTime=DateTimes"])], by=["UpperBin"])

grouped_lower = binned_table.agg_by([agg.first(cols=["FirstDateTime=DateTimes"])], by=["LowerBin"])