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Working with dates, times and timezones

Working with dates, times, and timezones can be hard.This guide aims to clarify how to work with these with PredictHQ data.

Event start and end local time

The Events API provides event start and end times in UTC and in the event local time format. The date and time fields available are:

  • The fields that provide local time are start_local, end_local, and predicted_end_local. These fields have the local time end date of the event in ISO 8601 format. This is the local time in the time zone at the event location. See the Events API Response Fields for more details on these fields.

  • The fields start, end, and predicted_end have date values in UTC format.

Note that the date fields in the Events API Query Parameters are in UTC. You can use the tz suffix to specify a time zone when querying using the date filters.

Previous guide on converting to local time

The Events API provides dates and times of events in UTC and in the local time where the event is occurring. If you want to convert the dates into another time zone use the Python example in this section.

The following is an example of converting UTC time to local time using the pytz library in Python:

from datetime import datetime
import pytz

# Example dates for an event retrieved from Events API.
# The rest of the event data has been stripped out for brevity.
event = {
  "start": "2023-10-23T13:00:00Z",
  "end": "2023-10-24T12:59:59Z",
  "timezone": "Australia/Sydney",
}

# Function to convert a UTC datetime string to a local datetime string using the given timezone
def convert_to_local(date_str, timezone_str):
    utc_dt = datetime.fromisoformat(date_str.replace("Z", "+00:00"))
    target_tz = pytz.timezone(timezone_str)
    local_dt = utc_dt.astimezone(target_tz)
    return local_dt.isoformat()

event["start_tz_converted"] = convert_to_local(event["start"], event["timezone"])
event["end_tz_converted"] = convert_to_local(event["end"], event["timezone"])

print(event)

Dates are a little more complex and the rest of this guide helps you understand how we represent dates in our data.

Date concepts

Internally, we have the concept of different date types for events. We don't expose these date types directly, but they surface indirectly and this guide demonstrates how to understand dates, times, and timezones on events. The different date types we refer to internally are:

  • Fixed Date

  • Fixed Time

  • Floating Date

Fixed Date

This concept refers to events that are known to happen on a certain date (including multi-day events) but the start and end times are not known. We represent these events in UTC, and they have a local start time of 00:00:00 and local end time of 23:59:59 as well as a known timezone, as in the following example:

The event in the earlier example is happening on Tue, 24 Oct 2023 (a single day) and using the convert_to_local function from the earlier Python code produces:

The start_local and end_local fields show the event spans the entire day of October 24th, 2023 in the Australia/Sydney timezone.

Fixed Time

Refers to events covering an exact time range. The start and end times are known (or predicted). We represent these events in UTC, and they have a timezone, as in this example:

Using the same convert_to_local function from earlier we get:

Showing the event is scheduled from 7:00 PM to 8:30 PM on November 9th, 2023 in the Australia/Melbourne timezone.

Floating Date

Refers to events that happen on a particular date regardless of timezone. E.g., USA Independence Day is 4th of July regardless of timezone. The way we represent this concept is by setting the timezone to null as in the following example:

When the timezone field is null like this you don't need to convert the start/end times.

Predicted End Times

Many events don't have scheduled end times, for many of these events we provide a predicted_end_time field (in UTC) and a predicted_end_local (in the time zone of the location of the event). When the start and end values are exactly the same, we know the start time but not the scheduled end time, so we provide a predicted_end value, as in this example:

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