NCToolkit is a comprehensive Python package for analyzing and post-processing netCDF data. Point it at your climate or ocean model output and subset, regrid, compute statistics and visualize it — all in a few lines of code, with Climate Data Operators doing the heavy lifting underneath.
NCToolkit turns netCDF post-processing into a short, readable chain of method calls, so you spend your time on the science, not on wrangling files.
open_data() handles single files, wildcards and lists. open_url and open_thredds handle remote and OPeNDAP data.
Built by climate and ocean modellers at Plymouth Marine Laboratory, for anyone who works with netCDF data — casual users and power users alike.
Covers 80–100% of day-to-day netCDF analysis and post-processing in one consistent, chainable API.
CDO does the heavy lifting under the hood, so operations stay fast — no CDO expertise required to use it.
Temporal, spatial, vertical, zonal and ensemble statistics, plus climatologies, anomalies and rolling windows.
Call plot() on almost any netCDF file for an automatic interactive figure, in Jupyter or a browser.
Match model output against spatiotemporal point observations at varying depths with match_points.
GDT, COARDS and CF-compliant structured grids, with limited support for unstructured grids too.
Every plot() call inspects the shape of your dataset and picks a sensible interactive chart — maps for gridded fields, time series for spatial means, heatmaps for Hovmöller-style zonal data — built on hvplot.
# install conda install -c conda-forge nctoolkit # from a Python or Jupyter session import nctoolkit as nc ds = nc.open_data("data/*.nc") ds.subset(lon=[-80, 90], lat=[50, 80]) ds.tmean("year") ds.to_nc("annual_mean.nc")
Install with conda and go from a folder of raw files to a subset, statistic or plot in a handful of lines. NCToolkit handles the CDO calls, temporary files and method chaining for you.
nc.options(cores=6).NCToolkit is a stand-alone package with no knowledge of CDO required to use it, but it plays nicely with the rest of the scientific Python ecosystem.