Easily load U.S. Climate Reference Network (USCRN) data.

With uscrn, fetching and loading years of data for all USCRN sites¹ takes just one line of code².

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 engine³ with pandas v2.1+.

df.to_parquet("uscrn_2019_hourly.parquet", engine="pyarrow")

Conda install example⁴:

conda create -n crn -c conda-forge python=3.11 joblib numpy pandas pyyaml requests xarray pyarrow netcdf4
conda activate crn
pip install --no-deps uscrn