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A package to access meteorological data from Environment Canada

Project description

Environment Canada (env_canada)

PyPI version Snyk rating

This package provides access to various data sources published by Environment and Climate Change Canada.

Weather Observations and Forecasts

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 specific station code of the form AB/s0000123 based on those listed in this CSV file. For example:

import asyncio

from env_canada import ECWeather

ec_en = ECWeather(coordinates=(50, -100))
ec_fr = ECWeather(station_id='ON/s0000430', language='french')

# current conditions

# daily forecasts

# hourly forecasts

# alerts

Weather Radar

ECRadar provides Environment Canada meteorological radar imagery.

import asyncio

from env_canada import ECRadar

radar_coords = ECRadar(coordinates=(50, -100))

# Conditions Available
animated_gif =
latest_png =

Air Quality Health Index (AQHI)

ECAirQuality provides Environment Canada air quality data.

import asyncio

from env_canada import ECAirQuality

aqhi_coords = ECAirQuality(coordinates=(50, -100))

# Data available

Water Level and Flow

ECHydro provides Environment Canada hydrometric data.

import asyncio

from env_canada import ECHydro

hydro_coords = ECHydro(coordinates=(50, -100))

# Data available

Historical Weather Data

ECHistorical 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, 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 =, 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)

# metadata describing the station

# historical weather data, in dictionary form

# 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, get_historical_stations
from datetime import datetime

coordinates = ['48.508333', '-68.467667']

stations = pd.DataFrame(, 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)))


#yield an XML formated str. 
# For more options, use ec.to_xml(*arg, **kwargs) with pandas options
#yield an CSV formated str.
# For more options, use ec.to_csv(*arg, **kwargs) with pandas options

In 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

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