tz-canary - Time Zone Canary

In a perfect world, all time series data is time-zone-aware and stored in UTC. Sadly, we do not live in a perfect world. Time series data often lacks a time zone identifier, or worse, does not actually adhere to the time zone it claims to be in.

tz-canary inspects the Daylight Savings Time (DST) switches in a time series to infer a set of plausible time zones the data could be in. It allows you to infer the full set of plausible time zones for the data, or to validate whether a given time zone is plausible for the data.

Installation

tz-canary is available on PyPI, so you can install it just like any other Python package:

pip install tz-canary

Usage

Time zone validation

The simplest way to use tz-canary is to validate a given time zone for a time series:

import pandas as pd
from tz_canary import validate_time_zone

df = pd.read_csv("docs/data/example_data.csv", index_col="datetime", parse_dates=True)

validate_time_zone(df.index, "Europe/Amsterdam")  # will pass
validate_time_zone(df.index, "America/New_York")  # will raise ImplausibleTimeZoneError
validate_time_zone(df.index, "UTC")  # will raise ImplausibleTimeZoneError

Time zone inference

You can also get a list of all plausible time zones for a time series:

from pprint import pprint

import pandas as pd
from tz_canary import infer_time_zone

df = pd.read_csv("docs/data/example_data.csv", index_col="datetime", parse_dates=True)

plausible_time_zones = infer_time_zone(df.index)
pprint(plausible_time_zones)

# Output:
# {zoneinfo.ZoneInfo(key='Africa/Ceuta'),
#  zoneinfo.ZoneInfo(key='Arctic/Longyearbyen'),
#  zoneinfo.ZoneInfo(key='Europe/Amsterdam'),
#  ...
#  zoneinfo.ZoneInfo(key='Europe/Zurich')}

Advanced usage: inference with cached TransitionsData

When processing many time series, it can be useful to cache the transitions data used by tz-canary to infer time zones. You can do this by creating a TransitionsData object and passing it to infer_time_zone (and this also works for validate_time_zone):

import pandas as pd

from tz_canary import TransitionsData, infer_time_zone

# We create a TransitionsData object to avoid having to recompute the transitions for
#   every call to validate_time_zone.
transition_data = TransitionsData(2010, 2023)

for i in range(10):
    df = pd.read_csv(
        "docs/data/example_data.csv",  # In reality, these would be different files
        index_col="datetime",
        parse_dates=True,
    )
    plausible_time_zones = infer_time_zone(df.index, transition_data=transition_data)
    print(i, plausible_time_zones)

Development

1. Make sure you have git, git LFS, and Poetry installed.
2. Clone this repository:

   git clone https://github.com/leonoverweel/tz-canary
   cd tz-canary

3. Install the development requirements:

   poetry install --with dev

4. Install the pre-commit hooks (used for linting):

   pre-commit install

5. Run the tests:

   poetry run pytest

Making a release

1. Bump the version number in pyproject.toml and commit the change.
2. Make a new release on GitHub.
3. Build the package:

   poetry build

4. Publish the package to PyPI:

   poetry publish

Contributing

Please don't hesitate to open issues and PRs!

GitHub repository: https://github.com/leonoverweel/tz-canary.