weathervault

Obtain Polars DataFrames of historical weather data from NOAA's National Centers for Environmental Information (NCEI) Integrated Surface Database (ISD).

Features

download and decode weather station data with a simple API
get data as efficient Polars DataFrames ready for analysis
locate met stations by name, location, and elevation
automatic conversion from UTC to local station time
support different temperature units (Celsius, Fahrenheit, and Kelvin)
optional local data file caching with automatic cache detection
access to the global ISD network of weather stations (29,000+ stations)

Installation

pip install weathervault

Quick Start

import weathervault as wv

# Get weather data for LaGuardia Airport (New York) for 2024
weather = wv.get_weather_data("725030-14732", years=2024)

weather.head()

Output:

shape: (5, 10)
┌──────────────┬─────────────────────────┬───────┬───────────┬───────┬─────┬──────┬────────────┬──────────┬────────────┐
│ id           ┆ time                    ┆ temp  ┆ dew_point ┆ rh    ┆ wd  ┆ ws   ┆ atmos_pres ┆ ceil_hgt ┆ visibility │
│ ---          ┆ ---                     ┆ ---   ┆ ---       ┆ ---   ┆ --- ┆ ---  ┆ ---        ┆ ---      ┆ ---        │
│ str          ┆ datetime[μs, tz]        ┆ f64   ┆ f64       ┆ f64   ┆ i32 ┆ f64  ┆ f64        ┆ i32      ┆ i32        │
╞══════════════╪═════════════════════════╪═══════╪═══════════╪═══════╪═════╪══════╪════════════╪══════════╪════════════╡
│ 725030-14732 ┆ 2024-01-01 00:51:00 EST ┆ 12.8  ┆ 7.8       ┆ 72.1  ┆ 200 ┆ 4.6  ┆ 1016.9     ┆ 2438     ┆ 16093      │
│ 725030-14732 ┆ 2024-01-01 01:51:00 EST ┆ 12.2  ┆ 7.2       ┆ 71.8  ┆ 190 ┆ 5.1  ┆ 1016.5     ┆ 2743     ┆ 16093      │
│ ...          ┆ ...                     ┆ ...   ┆ ...       ┆ ...   ┆ ... ┆ ...  ┆ ...        ┆ ...      ┆ ...        │
└──────────────┴─────────────────────────┴───────┴───────────┴───────┴─────┴──────┴────────────┴──────────┴────────────┘

Finding Weather Stations

Convenient Shorthand Syntax

Use wv.country and wv.state for clean, readable code with IDE autocomplete:

# Search by country using convenient shorthand
de_stations = wv.search_stations(country_code=wv.country.DE)
de_stations.head()

Output:

shape: (5, 9)
┌──────────────┬──────────────────────────────────┬─────────────┬───────────────┬───────┬───────┬────────┬────────┬────────┐
│ id           ┆ name                             ┆ country     ┆ state         ┆ icao  ┆ lat   ┆ lon    ┆ elev   ┆ begin  │
│ ---          ┆ ---                              ┆ ---         ┆ ---           ┆ ---   ┆ ---   ┆ ---    ┆ ---    ┆ ---    │
│ str          ┆ str                              ┆ str         ┆ str           ┆ str   ┆ f64   ┆ f64    ┆ f64    ┆ str    │
╞══════════════╪══════════════════════════════════╪═════════════╪═══════════════╪═══════╪═══════╪════════╪════════╪════════╡
│ 100040-99999 ┆ JAN MAYEN                        ┆ NORWAY      ┆ null          ┆ ENJA  ┆ 70.93 ┆ -8.67  ┆ 9.0    ┆ 19210… │
│ 100050-99999 ┆ TWENTHE                          ┆ NETHERLANDS ┆ null          ┆ EHTW  ┆ 52.27 ┆ 6.89   ┆ 35.0   ┆ 19460… │
│ 100070-99999 ┆ FRIGG                            ┆ NORWAY      ┆ null          ┆ null  ┆ 59.9  ┆ 2.25   ┆ 47.0   ┆ 19780… │
│ 100080-99999 ┆ EKOFISK                          ┆ NORWAY      ┆ null          ┆ EKOF  ┆ 56.55 ┆ 3.22   ┆ 30.0   ┆ 19760… │
│ 100100-99999 ┆ HEIDRUN                          ┆ NORWAY      ┆ null          ┆ null  ┆ 65.33 ┆ 7.32   ┆ 222.0  ┆ 19940… │
└──────────────┴──────────────────────────────────┴─────────────┴───────────────┴───────┴───────┴────────┴────────┴────────┘

# Search by US state
ca_stations = wv.search_stations(state=wv.state.CA)
ca_stations.head()

Output:

shape: (5, 9)
┌──────────────┬─────────────────────────────┬─────────┬───────┬───────┬───────┬──────────┬───────┬────────┐
│ id           ┆ name                        ┆ country ┆ state ┆ icao  ┆ lat   ┆ lon      ┆ elev  ┆ begin  │
│ ---          ┆ ---                         ┆ ---     ┆ ---   ┆ ---   ┆ ---   ┆ ---      ┆ ---   ┆ ---    │
│ str          ┆ str                         ┆ str     ┆ str   ┆ str   ┆ f64   ┆ f64      ┆ f64   ┆ str    │
╞══════════════╪═════════════════════════════╪═════════╪═══════╪═══════╪═══════╪══════════╪═══════╪════════╡
│ 690020-99999 ┆ NORTH ISLAND                ┆ US      ┆ CA    ┆ KNZY  ┆ 32.7  ┆ -117.21  ┆ 8.0   ┆ 19730… │
│ 690150-23174 ┆ TWENTYNINE PALMS            ┆ US      ┆ CA    ┆ KNXP  ┆ 34.3  ┆ -116.15  ┆ 626.0 ┆ 19420… │
│ 690190-93134 ┆ IMPERIAL BEACH              ┆ US      ┆ CA    ┆ KNRS  ┆ 32.57 ┆ -117.12  ┆ 6.0   ┆ 19580… │
│ 690230-23188 ┆ EDWARDS AFB                 ┆ US      ┆ CA    ┆ KEDW  ┆ 34.9  ┆ -117.88  ┆ 702.0 ┆ 19420… │
│ 690240-99999 ┆ VANDENBERG SFB              ┆ US      ┆ CA    ┆ KVBG  ┆ 34.73 ┆ -120.57  ┆ 112.0 ┆ 19730… │
└──────────────┴─────────────────────────────┴─────────┴───────┴───────┴───────┴──────────┴───────┴────────┘

Search by Location (lat/long Bounding Box)

# Search by coordinates (latitude/longitude range)
nyc_stations = wv.search_stations(
    lat_range=(40.5, 41.0),
    lon_range=(-74.5, -73.5)
)
nyc_stations

Output:

shape: (8, 9)
┌──────────────┬───────────────────────────────┬─────────┬───────┬───────┬───────┬─────────┬───────┬────────┐
│ id           ┆ name                          ┆ country ┆ state ┆ icao  ┆ lat   ┆ lon     ┆ elev  ┆ begin  │
│ ---          ┆ ---                           ┆ ---     ┆ ---   ┆ ---   ┆ ---   ┆ ---     ┆ ---   ┆ ---    │
│ str          ┆ str                           ┆ str     ┆ str   ┆ str   ┆ f64   ┆ f64     ┆ f64   ┆ str    │
╞══════════════╪═══════════════════════════════╪═════════╪═══════╪═══════╪═══════╪═════════╪═══════╪════════╡
│ 725020-14734 ┆ NEWARK LIBERTY INTERNATIONAL  ┆ US      ┆ NJ    ┆ KEWR  ┆ 40.7  ┆ -74.17  ┆ 4.0   ┆ 19730… │
│ 725025-14734 ┆ NEWARK LIBERTY INTERNATIONAL  ┆ US      ┆ NJ    ┆ KEWR  ┆ 40.7  ┆ -74.17  ┆ 4.0   ┆ 19510… │
│ 725030-14732 ┆ LA GUARDIA AIRPORT            ┆ US      ┆ NY    ┆ KLGA  ┆ 40.78 ┆ -73.88  ┆ 3.0   ┆ 19730… │
│ 725033-14732 ┆ LA GUARDIA AIRPORT            ┆ US      ┆ NY    ┆ KLGA  ┆ 40.78 ┆ -73.88  ┆ 3.0   ┆ 19380… │
│ 725037-94728 ┆ JOHN F KENNEDY INTL AP        ┆ US      ┆ NY    ┆ KJFK  ┆ 40.64 ┆ -73.78  ┆ 3.4   ┆ 19730… │
│ 744860-94728 ┆ JOHN F KENNEDY INTL AP        ┆ US      ┆ NY    ┆ KJFK  ┆ 40.64 ┆ -73.78  ┆ 3.4   ┆ 19491… │
│ 999999-14732 ┆ NEW YORK/LA GUARDIA           ┆ US      ┆ NY    ┆ null  ┆ 40.78 ┆ -73.88  ┆ 3.4   ┆ 19310… │
│ 999999-94728 ┆ NEW YORK/JFK                  ┆ US      ┆ NY    ┆ null  ┆ 40.64 ┆ -73.78  ┆ 3.4   ┆ 19310… │
└──────────────┴───────────────────────────────┴─────────┴───────┴───────┴───────┴─────────┴───────┴────────┘

Search by Name or Country

# Search by station name
airports = wv.search_stations(name="tokyo international")
airports

Output:

shape: (2, 9)
┌──────────────┬─────────────────────────────┬─────────┬───────┬───────┬───────┬────────┬───────┬────────┐
│ id           ┆ name                        ┆ country ┆ state ┆ icao  ┆ lat   ┆ lon    ┆ elev  ┆ begin  │
│ ---          ┆ ---                         ┆ ---     ┆ ---   ┆ ---   ┆ ---   ┆ ---    ┆ ---   ┆ ---    │
│ str          ┆ str                         ┆ str     ┆ str   ┆ str   ┆ f64   ┆ f64    ┆ f64   ┆ str    │
╞══════════════╪═════════════════════════════╪═════════╪═══════╪═══════╪═══════╪════════╪═══════╪════════╡
│ 476710-99999 ┆ TOKYO INTERNATIONAL AIRPORT ┆ JAPAN   ┆ null  ┆ RJTT  ┆ 35.55 ┆ 139.78 ┆ 4.0   ┆ 19730… │
│ 476770-99999 ┆ TOKYO INTERNATIONAL AIRPORT ┆ JAPAN   ┆ null  ┆ RJTT  ┆ 35.55 ┆ 139.78 ┆ 4.0   ┆ 19530… │
└──────────────┴─────────────────────────────┴─────────┴───────┴───────┴───────┴────────┴───────┴────────┘

# Search by country name
uk_stations = wv.search_stations(country="United Kingdom")
uk_stations.head()

Output:

shape: (5, 9)
┌──────────────┬──────────────────────┬────────────────┬───────┬───────┬───────┬────────┬───────┬────────┐
│ id           ┆ name                 ┆ country        ┆ state ┆ icao  ┆ lat   ┆ lon    ┆ elev  ┆ begin  │
│ ---          ┆ ---                  ┆ ---            ┆ ---   ┆ ---   ┆ ---   ┆ ---    ┆ ---   ┆ ---    │
│ str          ┆ str                  ┆ str            ┆ str   ┆ str   ┆ f64   ┆ f64    ┆ f64   ┆ str    │
╞══════════════╪══════════════════════╪════════════════╪═══════╪═══════╪═══════╪════════╪═══════╪════════╡
│ 030050-99999 ┆ LERWICK              ┆ UNITED KINGDOM ┆ null  ┆ ENSL  ┆ 60.13 ┆ -1.18  ┆ 82.0  ┆ 19310… │
│ 030350-99999 ┆ KIRKWALL AIRPORT     ┆ UNITED KINGDOM ┆ null  ┆ EGPA  ┆ 58.95 ┆ -2.9   ┆ 26.0  ┆ 19730… │
│ 030510-99999 ┆ STORNOWAY            ┆ UNITED KINGDOM ┆ null  ┆ EGPO  ┆ 58.22 ┆ -6.32  ┆ 9.0   ┆ 19730… │
│ 030600-99999 ┆ CAPE WRATH           ┆ UNITED KINGDOM ┆ null  ┆ null  ┆ 58.63 ┆ -5.0   ┆ 122.0 ┆ 19310… │
│ 030630-99999 ┆ WICK                 ┆ UNITED KINGDOM ┆ null  ┆ EGPC  ┆ 58.45 ┆ -3.08  ┆ 36.0  ┆ 19730… │
└──────────────┴──────────────────────┴────────────────┴───────┴───────┴───────┴────────┴───────┴────────┘

Get All Station Metadata

# Get metadata for all 29,000+ stations
all_stations = wv.get_station_metadata()
all_stations.head()

Output:

shape: (5, 9)
┌──────────────┬───────────────────────────┬─────────┬───────┬───────┬────────┬─────────┬────────┬────────┐
│ id           ┆ name                      ┆ country ┆ state ┆ icao  ┆ lat    ┆ lon     ┆ elev   ┆ begin  │
│ ---          ┆ ---                       ┆ ---     ┆ ---   ┆ ---   ┆ ---    ┆ ---     ┆ ---    ┆ ---    │
│ str          ┆ str                       ┆ str     ┆ str   ┆ str   ┆ f64    ┆ f64     ┆ f64    ┆ str    │
╞══════════════╪═══════════════════════════╪═════════╪═══════╪═══════╪════════╪═════════╪════════╪════════╡
│ 007005-99999 ┆ CWOS 07005                ┆ null    ┆ null  ┆ null  ┆ null   ┆ null    ┆ null   ┆ 19710… │
│ 007011-99999 ┆ CWOS 07011                ┆ null    ┆ null  ┆ null  ┆ null   ┆ null    ┆ null   ┆ 19730… │
│ 007018-99999 ┆ WXPOD 7018                ┆ null    ┆ null  ┆ null  ┆ 0.0    ┆ 0.0     ┆ 7018.0 ┆ 20110… │
│ 007026-99999 ┆ WXPOD 7026                ┆ null    ┆ null  ┆ null  ┆ 0.0    ┆ 0.0     ┆ 7026.0 ┆ 20050… │
│ 007070-99999 ┆ WXPOD 7070                ┆ null    ┆ null  ┆ null  ┆ 0.0    ┆ 0.0     ┆ 7070.0 ┆ 20050… │
└──────────────┴───────────────────────────┴─────────┴───────┴───────┴────────┴─────────┴────────┴────────┘

Weather Data Columns

The returned DataFrame includes:

Column	Description
`id`	Station identifier (USAF-WBAN format)
`time`	Observation time (local time by default)
`temp`	Air temperature (°C)
`dew_point`	Dew point temperature
`rh`	Relative humidity (%)
`wd`	Wind direction (degrees, 0-360)
`ws`	Wind speed (m/s)
`atmos_pres`	Sea level pressure (hPa)
`ceil_hgt`	Ceiling height (m, 22000 = unlimited)
`visibility`	Visibility (m, max 16000 = 10 miles)

Advanced Usage

Include Station Metadata

Add station information (name, country, state, ICAO, coordinates, elevation) to each observation:

weather = wv.get_weather_data(
    "725030-14732",
    years=2024,
    incl_stn_info=True
)
weather[["time", "temp", "name", "country"]].head()

Output:

shape: (5, 4)
┌─────────────────────────┬───────┬────────────────────┬─────────┐
│ time                    ┆ temp  ┆ name               ┆ country │
│ ---                     ┆ ---   ┆ ---                ┆ ---     │
│ datetime[μs, tz]        ┆ f64   ┆ str                ┆ str     │
╞═════════════════════════╪═══════╪════════════════════╪═════════╡
│ 2024-01-01 00:51:00 EST ┆ 12.8  ┆ LA GUARDIA AIRPORT ┆ US      │
│ 2024-01-01 01:51:00 EST ┆ 12.2  ┆ LA GUARDIA AIRPORT ┆ US      │
│ 2024-01-01 02:51:00 EST ┆ 11.7  ┆ LA GUARDIA AIRPORT ┆ US      │
│ 2024-01-01 03:51:00 EST ┆ 11.1  ┆ LA GUARDIA AIRPORT ┆ US      │
│ 2024-01-01 04:51:00 EST ┆ 10.6  ┆ LA GUARDIA AIRPORT ┆ US      │
└─────────────────────────┴───────┴────────────────────┴─────────┘

Temperature Units

# Get temperatures in Fahrenheit
weather = wv.get_weather_data("725030-14732", years=2024, temp_unit="f")
weather[["time", "temp", "dew_point"]].head()

Output:

shape: (5, 3)
┌─────────────────────────┬───────┬───────────┐
│ time                    ┆ temp  ┆ dew_point │
│ ---                     ┆ ---   ┆ ---       │
│ datetime[μs, tz]        ┆ f64   ┆ f64       │
╞═════════════════════════╪═══════╪═══════════╡
│ 2024-01-01 00:51:00 EST ┆ 55.0  ┆ 46.0      │
│ 2024-01-01 01:51:00 EST ┆ 54.0  ┆ 45.0      │
│ 2024-01-01 02:51:00 EST ┆ 53.1  ┆ 44.6      │
│ 2024-01-01 03:51:00 EST ┆ 52.0  ┆ 43.9      │
│ 2024-01-01 04:51:00 EST ┆ 51.1  ┆ 43.3      │
└─────────────────────────┴───────┴───────────┘

Supported values: "c"/"celsius", "f"/"fahrenheit", "k"/"kelvin".

Multiple Years

# Get multiple years of data
weather = wv.get_weather_data(
    "725030-14732",
    years=[2022, 2023, 2024]
)
weather[["time", "temp"]].head()

Output:

shape: (5, 2)
┌─────────────────────────┬───────┐
│ time                    ┆ temp  │
│ ---                     ┆ ---   │
│ datetime[μs, tz]        ┆ f64   │
╞═════════════════════════╪═══════╡
│ 2022-01-01 00:51:00 EST ┆ 2.2   │
│ 2022-01-01 01:51:00 EST ┆ 1.7   │
│ 2022-01-01 02:51:00 EST ┆ 1.1   │
│ 2022-01-01 03:51:00 EST ┆ 0.6   │
│ 2022-01-01 04:51:00 EST ┆ 0.0   │
└─────────────────────────┴───────┘

Hourly Resampling

# Resample to regular hourly intervals (fills missing hours with None)
weather = wv.get_weather_data(
    "725030-14732",
    years=2024,
    make_hourly=True
)
weather[["time", "temp", "ws"]].head(10)

Output:

shape: (10, 3)
┌─────────────────────────┬───────┬──────┐
│ time                    ┆ temp  ┆ ws   │
│ ---                     ┆ ---   ┆ ---  │
│ datetime[μs, tz]        ┆ f64   ┆ f64  │
╞═════════════════════════╪═══════╪══════╡
│ 2024-01-01 00:00:00 EST ┆ null  ┆ null │
│ 2024-01-01 01:00:00 EST ┆ 12.8  ┆ 4.6  │
│ 2024-01-01 02:00:00 EST ┆ 12.2  ┆ 5.1  │
│ 2024-01-01 03:00:00 EST ┆ 11.7  ┆ 4.6  │
│ 2024-01-01 04:00:00 EST ┆ 11.1  ┆ 3.6  │
│ 2024-01-01 05:00:00 EST ┆ 10.6  ┆ 3.6  │
│ 2024-01-01 06:00:00 EST ┆ 10.0  ┆ 2.6  │
│ 2024-01-01 07:00:00 EST ┆ 10.0  ┆ 3.6  │
│ 2024-01-01 08:00:00 EST ┆ 10.0  ┆ 4.1  │
│ 2024-01-01 09:00:00 EST ┆ 10.6  ┆ 4.1  │
└─────────────────────────┴───────┴──────┘

Keep UTC Time

# Don't convert to local time (keep UTC time)
weather = wv.get_weather_data(
    "725030-14732",
    years=2024,
    convert_to_local=False
)
weather[["time", "temp"]].head()

Output:

shape: (5, 2)
┌─────────────────────────┬───────┐
│ time                    ┆ temp  │
│ ---                     ┆ ---   │
│ datetime[μs, UTC]       ┆ f64   │
╞═════════════════════════╪═══════╡
│ 2024-01-01 05:51:00 UTC ┆ 12.8  │
│ 2024-01-01 06:51:00 UTC ┆ 12.2  │
│ 2024-01-01 07:51:00 UTC ┆ 11.7  │
│ 2024-01-01 08:51:00 UTC ┆ 11.1  │
│ 2024-01-01 09:51:00 UTC ┆ 10.6  │
└─────────────────────────┴───────┘

Smart Data Caching

Cache downloaded files for offline use or faster repeated access:

# Cache in current directory
weather = wv.get_weather_data(
    "725030-14732",
    years=2024,
    cache_dir="."
)
weather[["time", "temp"]].head()

Output:

shape: (5, 2)
┌─────────────────────────┬───────┐
│ time                    ┆ temp  │
│ ---                     ┆ ---   │
│ datetime[μs, tz]        ┆ f64   │
╞═════════════════════════╪═══════╡
│ 2024-01-01 00:51:00 EST ┆ 12.8  │
│ 2024-01-01 01:51:00 EST ┆ 12.2  │
│ 2024-01-01 02:51:00 EST ┆ 11.7  │
│ 2024-01-01 03:51:00 EST ┆ 11.1  │
│ 2024-01-01 04:51:00 EST ┆ 10.6  │
└─────────────────────────┴───────┘

Weathervault automatically checks:

Specified cache_dir= (if provided)
Current working directory (for existing cached files)

and if no relevant data files are found, they are downloaded from NOAA.

Data Inventory

Check what data is available:

# Get the data inventory (record counts by station/year/month)
inventory = wv.get_inventory()
inventory.head()

Output:

shape: (5, 4)
┌──────────────┬──────┬───────┬────────┐
│ id           ┆ year ┆ month ┆ count  │
│ ---          ┆ ---  ┆ ---   ┆ ---    │
│ str          ┆ i32  ┆ i32   ┆ i32    │
╞══════════════╪══════╪═══════╪════════╡
│ 007005-99999 ┆ 1971 ┆ 5     ┆ 360    │
│ 007005-99999 ┆ 1971 ┆ 6     ┆ 356    │
│ 007005-99999 ┆ 1971 ┆ 7     ┆ 364    │
│ 007005-99999 ┆ 1971 ┆ 8     ┆ 363    │
│ 007005-99999 ┆ 1971 ┆ 9     ┆ 361    │
└──────────────┴──────┴───────┴────────┘

# Filter to a specific station
import polars as pl
station_inv = inventory.filter(pl.col("id") == "725030-14732")
station_inv.head()

Output:

shape: (5, 4)
┌──────────────┬──────┬───────┬───────┐
│ id           ┆ year ┆ month ┆ count │
│ ---          ┆ ---  ┆ ---   ┆ ---   │
│ str          ┆ i32  ┆ i32   ┆ i32   │
╞══════════════╪══════╪═══════╪═══════╡
│ 725030-14732 ┆ 1973 ┆ 1     ┆ 744   │
│ 725030-14732 ┆ 1973 ┆ 2     ┆ 672   │
│ 725030-14732 ┆ 1973 ┆ 3     ┆ 744   │
│ 725030-14732 ┆ 1973 ┆ 4     ┆ 720   │
│ 725030-14732 ┆ 1973 ┆ 5     ┆ 744   │
└──────────────┴──────┴───────┴───────┘

License

MIT License. See LICENSE for details.

Release files for weathervault 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for weathervault 0.2.0
File	Size	Uploaded
weathervault-0.2.0.tar.gz	56.5 kB	Jan 9, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for weathervault 0.2.0
File	Interpreter	ABI	Platform	Reset
weathervault-0.2.0-py3-none-any.whl 32.1 kB Jan 9, 2026	Python 3	none	any	Details

Total release size: 88.6 kB

Release files / weathervault-0.2.0.tar.gz

Download URL	weathervault-0.2.0.tar.gz
Size	56.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`04fef21e34902e6bf4a105f99753f9e8b335fec95e9f711c4ec8495f5e3799b8`
BLAKE2b-256 checksum How to use checksums	`28ca6e06ee426b18af95d8098af18cecda4a20f863f968a12e3c795e3e1262bf`
Upload date	Jan 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Release files / weathervault-0.2.0-py3-none-any.whl

Download URL	weathervault-0.2.0-py3-none-any.whl
Size	32.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c37e56adb5020d077f63025063f725ff97484e37839dd679c206796ad7a2bdc6`
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Upload date	Jan 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Release history Release notifications | RSS feed

This release

0.2.0 This release

Jan 9, 2026 2 release files

0.1.0

Jan 8, 2026 2 release files

weathervault 0.2.0

weathervault

Features

Installation

Quick Start

Finding Weather Stations

Convenient Shorthand Syntax

Search by Location (lat/long Bounding Box)

Search by Name or Country

Get All Station Metadata

Weather Data Columns

Advanced Usage

Include Station Metadata

Temperature Units

Multiple Years

Hourly Resampling

Keep UTC Time

Smart Data Caching

Data Inventory

License

Project links

Key dates

1 maintainer

Credits

License

Requires

Provides Extra

Tags

Classifiers

Release files for weathervault 0.2.0

Source distribution (sdist)

Built distribution (wheel)

Release files / weathervault-0.2.0.tar.gz

Release files / weathervault-0.2.0-py3-none-any.whl

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