Loading the data

The parsed dataset lives in the project’s GitHub repo (rebuilt from the IMD workbook by a monthly, validated pipeline). imdtrack.load() downloads that pre-parsed data — no Excel scraping on your machine.

import imdtrack as imd

bt = imd.load()                 # pre-parsed dataset from GitHub (cached locally)
bt.observations                 # tidy DataFrame: one row per 3-hourly fix
bt.storms                       # one summary row per storm
bt.remarks                      # free-text landfall / weakening notes
bt.to_xarray()                  # (storm, step) xarray.Dataset

bt = imd.load(update=True)      # re-download only if the repo published new data

Selecting a storm

storm() accepts a storm_id or a name (case-insensitive):

bt.storm("2020-001")            # by id
bt.storm("amphan")              # by name

Where the data comes from

By default imd.load() pulls the pre-parsed parquet from GitHub. Two other sources are available:

imd.load(source="imd")          # fetch + parse the IMD workbook yourself
                                # (needs the [pipeline] extra: openpyxl)
imd.load(path="best_tracks.xlsx")   # parse a local workbook

Cache & configuration

Downloads are cached under ~/.cache/imdtrack (override with IMDTRACK_CACHE). Point the loader at a local committed dataset with IMDTRACK_DATA_DIR (used by the docs build and offline work), or a fork’s data repo with IMDTRACK_REPO.