Load `U.S. Climate Reference Network <https://www.ncei.noaa.gov/access/crn/>`__ (USCRN) data.
Project description
uscrn
Easily load U.S. Climate Reference Network (USCRN) data.
With uscrn
, fetching and loading years of data for all USCRN sites[^a] takes just one line of code[^b].
Example:
import uscrn
df = uscrn.get_data(2019, "hourly", n_jobs=6) # pandas.DataFrame
ds = uscrn.to_xarray(df) # xarray.Dataset, with soil depth dimension if applicable (hourly, daily)
Both df
(pandas) and ds
(xarray) include dataset and variable metadata.
For df
, these are in df.attrs
and can be preserved by
writing to Parquet with the PyArrow engine[^d] with
pandas v2.1+.
df.to_parquet("uscrn_2019_hourly.parquet", engine="pyarrow")
Conda install example[^c]:
conda create -n crn -c conda-forge python=3.10 joblib numpy pandas pyyaml requests xarray pyarrow netcdf4
conda activate crn
pip install --no-deps uscrn
[^a]: Use uscrn.load_meta()
to load the site metadata table.
[^b]: Not counting the import
statement...
[^c]: uscrn
is not yet on conda-forge.
[^d]: Or the fastparquet engine with fastparquet v2024.2.0+.
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