This package provides access to various data sources published by Environment and Climate Change Canada.
Important
If you're using the library in a Jupyter notebook, replace asyncio.run(...) with await ... in the examples below. For example:
asyncio.run(ec_en.update())becomes
await ec_en.update()ECWeather provides current conditions and forecasts. It automatically determines which weather station to use based on latitude/longitude provided. It is also possible to specify a station code in multiple flexible formats:
- Full format:
"AB/s0000123"(province code and full station ID) - Station ID only:
"s0000123"(station ID without province - province is resolved automatically) - Numeric only:
"123"(just the station number - province is resolved automatically)
Station codes are based on those listed in this CSV file. For example:
import asyncio from env_canada import ECWeather # Using coordinates (automatic station selection) ec_coords = ECWeather(coordinates=(50, -100)) # Using station ID - multiple formats supported: ec_full = ECWeather(station_id="ON/s0000430", language="french") # Full format ec_station = ECWeather(station_id="s0000430") # Station ID only ec_numeric = ECWeather(station_id="430") # Numeric only asyncio.run(ec_coords.update()) # current conditions ec_coords.conditions # daily forecasts ec_coords.daily_forecasts # hourly forecasts ec_coords.hourly_forecasts # alerts (categorised dict with warnings, watches, advisories, statements, endings) ec_coords.alerts # raw WFS alert feature properties (includes text, area, confidence, impact, etc.) ec_coords.alert_featuresNote
As of version 0.11.0, ECWeather automatically handles Environment Canada's new timestamped weather file URL structure (effective June 2025). The library dynamically discovers the most recent weather files, ensuring continued functionality during Environment Canada's infrastructure changes.
ECAlerts provides direct access to Environment Canada weather alerts via the GeoMet WFS Current-Alerts layer. It returns richer data than the alerts embedded in ECWeather, including full bilingual alert text, affected area names, risk colour, confidence, and impact. ECWeather.update() uses ECAlerts internally, so both ECWeather.alerts and ECWeather.alert_features are automatically populated.
Use ECAlerts directly when you only need alerts (no weather conditions or forecasts):
import asyncio from env_canada import ECAlerts ec_alerts = ECAlerts(coordinates=(50, -100)) asyncio.run(ec_alerts.update()) # categorised alerts dict — same structure as ECWeather.alerts # keys: warnings, watches, advisories, statements, endings ec_alerts.alerts # list of raw WFS feature property dicts, one per alert polygon ec_alerts.alert_featuresEach entry in alerts[category]["value"] is a dict with these keys:
| Key | Description |
|---|---|
title | Alert name (title-cased) |
date | Publication datetime (ISO 8601 UTC) |
expiryTime | Expiration datetime (ISO 8601 UTC) |
alertColourLevel | Risk colour (e.g. "Yellow", "Red") |
text | Full alert text |
area | Affected geographic area name |
status | Alert status (e.g. "active", "ended") |
confidence | Confidence level (e.g. "Likely") |
impact | Impact level (e.g. "High") |
alert_code | Short alert type code (e.g. "WS") |
ECRadar provides Environment Canada meteorological radar imagery.
import asyncio from env_canada import ECRadar radar_coords = ECRadar(coordinates=(50, -100)) # Conditions Available animated_gif = asyncio.run(radar_coords.get_loop()) latest_png = asyncio.run(radar_coords.get_latest_frame())ECMap provides Environment Canada WMS weather map imagery with support for various meteorological layers.
import asyncio from env_canada import ECMap # Create a map with rain radar layer map_coords = ECMap(coordinates=(50, -100), layer="rain") # Get the latest image with the specified layer latest_png = asyncio.run(map_coords.get_latest_frame()) # Get an animated GIF with the specified layer animated_gif = asyncio.run(map_coords.get_loop()) # Customize the map appearance custom_map = ECMap( coordinates=(50, -100), layer="snow", width=1200, height=800, radius=300, layer_opacity=80, legend=True, timestamp=True, language="french", )Available layers include:
rain: Precipitation rain radarsnow: Precipitation snow radarprecip_type: Surface precipitation type
Additional configuration options:
width/height: Image dimensions (default: 800x800)radius: Map radius in km around coordinates (default: 200km)layer_opacity: Layer transparency 0-100% (default: 65%)legend: Show legend (default: True)timestamp: Show timestamp (default: True)language: "english" or "french" (default: "english")
Note: ECMap automatically discovers available legend styles from Environment Canada's WMS capabilities, ensuring compatibility with any future style changes.
ECAirQuality provides Environment Canada air quality data.
import asyncio from env_canada import ECAirQuality aqhi_coords = ECAirQuality(coordinates=(50, -100)) asyncio.run(aqhi_coords.update()) # Data available aqhi_coords.current aqhi_coords.forecastsECHydro provides Environment Canada hydrometric data.
import asyncio from env_canada import ECHydro hydro_coords = ECHydro(coordinates=(50, -100)) asyncio.run(hydro_coords.update()) # Data available hydro_coords.measurementsECHistorical provides historical daily weather data. The ECHistorical object is instantiated with a station ID, year, language, format (one of xml or csv) and granularity (hourly, daily data). Once updated asynchronously, historical weather data is contained with the station_data property. If xml is requested, station_data will appear in a dictionary form. If csv is requested, station_data will contain a CSV-readable buffer. For example:
import asyncio from env_canada import ECHistorical from env_canada.ec_historical import get_historical_stations # search for stations, response contains station_ids coordinates = [53.916944, -122.749444] # [lat, long] # coordinates: [lat, long] # radius: km # limit: response limit, value one of [10, 25, 50, 100] # The result contains station names and ID values. stations = asyncio.run(get_historical_stations(coordinates, radius=200, limit=100)) ec_en_xml = ECHistorical(station_id=31688, year=2020, language="english", format="xml") ec_fr_xml = ECHistorical(station_id=31688, year=2020, language="french", format="xml") ec_en_csv = ECHistorical(station_id=31688, year=2020, language="english", format="csv") ec_fr_csv = ECHistorical(station_id=31688, year=2020, language="french", format="csv") # timeframe argument can be passed to change the granularity # timeframe=1 hourly (need to create of for every month in that case, use ECHistoricalRange to handle it automatically) # timeframe=2 daily (default) ec_en_xml = ECHistorical( station_id=31688, year=2020, month=1, language="english", format="xml", timeframe=1 ) ec_en_csv = ECHistorical( station_id=31688, year=2020, month=1, language="english", format="csv", timeframe=1 ) asyncio.run(ec_en_xml.update()) asyncio.run(ec_en_csv.update()) # metadata describing the station ec_en_xml.metadata # historical weather data, in dictionary form ec_en_xml.station_data # csv-generated responses return csv-like station data import pandas as pd df = pd.read_csv(ec_en_csv.station_data)ECHistoricalRange provides historical weather data within a specific range and handles the update by itself.
The ECHistoricalRange object is instantiated with at least a station ID and a daterange. One could add language, and granularity (hourly, daily (default)).
The data can then be used as pandas DataFrame, XML (requires pandas >=1.3.0) and csv
For example :
import pandas as pd import asyncio from env_canada import ECHistoricalRange from env_canada.ec_historical import get_historical_stations from datetime import datetime coordinates = ["48.508333", "-68.467667"] stations = pd.DataFrame( asyncio.run( get_historical_stations( coordinates, start_year=2022, end_year=2022, radius=200, limit=100 ) ) ).T ec = ECHistoricalRange( station_id=int(stations.iloc[0, 2]), timeframe="daily", daterange=(datetime(2022, 7, 1, 12, 12), datetime(2022, 8, 1, 12, 12)), ) ec.get_data() # yield an XML formated str. # For more options, use ec.to_xml(*arg, **kwargs) with pandas options ec.xml # yield an CSV formated str. # For more options, use ec.to_csv(*arg, **kwargs) with pandas options ec.csvIn this example ec.df will be:
| Date/Time | Longitude (x) | Latitude (y) | Station Name | Climate ID | Year | Month | Day | Data Quality | Max Temp (°C) | Max Temp Flag | Min Temp (°C) | Min Temp Flag | Mean Temp (°C) | Mean Temp Flag | Heat Deg Days (°C) | Heat Deg Days Flag | Cool Deg Days (°C) | Cool Deg Days Flag | Total Rain (mm) | Total Rain Flag | Total Snow (cm) | Total Snow Flag | Total Precip (mm) | Total Precip Flag | Snow on Grnd (cm) | Snow on Grnd Flag | Dir of Max Gust (10s deg) | Dir of Max Gust Flag | Spd of Max Gust (km/h) | Spd of Max Gust Flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-07-02 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 7 | 2 | 22,8 | 12,5 | 17,7 | 0,3 | 0 | 0 | 26 | 37 | ||||||||||||||||
| 2022-07-03 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 7 | 3 | 21,7 | 10,1 | 15,9 | 2,1 | 0 | 0,4 | 28 | 50 | ||||||||||||||||
| … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … |
| 2022-07-31 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 7 | 31 | 23,5 | 14,1 | 18,8 | 0 | 0,8 | 0 | 23 | 31 | ||||||||||||||||
| 2022-08-01 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 8 | 1 | 23 | 15 | 19 | 0 | 1 | 0 | 21 | 35 |
One should note that july 1st is excluded as the time provided contains specific hours, so it yields only data after or at exactly the time provided.
To have all the july 1st data in that case, one can provide a datarange without time: datetime(2022, 7, 7) instead of datetime(2022, 7, 1, 12, 12)
The code is available under terms of MIT License