jiskta 0.5.1

Python SDK for the Jiskta Climate Data API

jiskta-python

Python SDK for the Jiskta Climate Data API — query historical air quality data (NO₂, PM2.5, PM10, O₃) and ERA5 meteorological data via a simple Python interface.

Install

pip install jiskta

Pandas is optional but recommended:

pip install "jiskta[pandas]"

Quick start

from jiskta import JisktaClient

client = JisktaClient(api_key="sk_live_...")

# Daily NO₂ and PM2.5 over Paris in 2023
df = client.query(
    lat=(48.7, 49.0),
    lon=(2.2, 2.5),
    start="2023-01",
    end="2023-12",
    variables=["no2", "pm2p5"],
    aggregate="daily",
)
print(df.head())
#         lat    lon        date  no2_mean  pm2p5_mean
# 0  48.7500  2.250  2023-01-01     12.34        8.21
# ...

All parameters

df = client.query(
    lat=(lat_min, lat_max),      # bounding box
    lon=(lon_min, lon_max),
    start="YYYY-MM-DD",          # or "YYYY-MM"
    end="YYYY-MM-DD",
    variables=["no2"],          # no2 | pm2p5 | pm10 | o3
    aggregate="daily",           # hourly | daily | monthly | annual
                                 # area_hourly | area_daily | area_monthly
                                 # diurnal | exceedance | percentile
    threshold=40.0,              # µg/m³  (exceedance mode)
    percentile=95,               # 0-100  (percentile mode)
)

Summary statistics (no DataFrame)

result = client.stats(
    lat=(48.7, 49.0),
    lon=(2.2, 2.5),
    start="2023-01",
    end="2023-01",
    variables=["no2"],
)
print(result["output"])

Error handling

from jiskta import JisktaClient, AuthError, InsufficientCreditsError, RateLimitError, JisktaError

try:
    df = client.query(...)
except AuthError:
    print("Invalid API key")
except InsufficientCreditsError:
    print("Buy more credits at https://jiskta.com/pricing")
except RateLimitError:
    print("Server busy, retry later")
except JisktaError as e:
    print(f"API error {e.status_code}: {e}")

Named area queries

Skip lat/lon by passing a named region:

df = client.query(
    area="paris",
    start="2023-01",
    end="2023-12",
    variables=["no2"],
    aggregate="daily",
)

result = client.stats(area="belgium", start="2023-01", end="2023-12")

Supported names depend on the API (e.g. "paris", "france", "belgium").

New aggregates

# Statistical aggregates
df = client.query(..., aggregate="max")       # daily/monthly max
df = client.query(..., aggregate="min")       # daily/monthly min
df = client.query(..., aggregate="stddev")    # standard deviation
df = client.query(..., aggregate="cumulative")  # running total (e.g. precipitation)

# Analytical aggregates
df = client.query(..., aggregate="seasonal")  # DJF/MAM/JJA/SON means
df = client.query(..., aggregate="trend")     # linear trend per grid cell

Wind variables

wind_speed and wind_dir are derived ERA5 variables (computed from u10/v10):

df = client.query(
    lat=(48.7, 49.0), lon=(2.2, 2.5),
    start="2023-01", end="2023-12",
    variables=["wind_speed", "wind_dir"],
    aggregate="daily",
)

Polygon-masked queries

Pass any GeoJSON Polygon or MultiPolygon geometry to restrict results to cells whose centres fall inside the polygon:

mask = {
    "type": "Polygon",
    "coordinates": [[[2.2, 48.7], [2.5, 48.7], [2.5, 49.0], [2.2, 49.0], [2.2, 48.7]]],
}

df = client.query_with_mask(
    lat_min=48.7, lat_max=49.0,
    lon_min=2.2, lon_max=2.5,
    start="2023-01",
    end="2023-12",
    variables=["no2"],
    aggregate="daily",
    mask=mask,
)

Advanced options

df = client.query(
    lat=(48.7, 49.0), lon=(2.2, 2.5),
    start="2023-01", end="2023-12",
    variables=["no2"],
    sort_by="no2_mean",       # sort CSV output by column
    sort_dir="desc",          # "asc" or "desc"
    unit="ppb",               # convert output units
    round=2,                  # decimal places
    dry_run=True,             # cost estimate only — no query executed
    missing_null=True,        # empty string for missing cells
    include_polygon=True,     # include area_polygon GeoJSON in raw response
)

License

MIT