Fetching Argo meta-data¶

Since the Argo measurements dataset is quite complex, it comes with a collection of index files, or lookup tables with meta data. These index help you determine what you can expect before retrieving the full set of measurements. argopy has a specific fetcher for index:

In [1]: from argopy import IndexFetcher as ArgoIndexFetcher

You can use the Index fetcher with the region or float access points, similarly to data fetching:

In [2]: idx = ArgoIndexFetcher(src='localftp').float(2901623).load()

In [3]: idx.index
Out[3]: 
                                       file  ...                             profiler
0    nmdis/2901623/profiles/R2901623_000.nc  ...  Provor, Seabird conductivity sensor
1   nmdis/2901623/profiles/R2901623_000D.nc  ...  Provor, Seabird conductivity sensor
2    nmdis/2901623/profiles/R2901623_001.nc  ...  Provor, Seabird conductivity sensor
3    nmdis/2901623/profiles/R2901623_002.nc  ...  Provor, Seabird conductivity sensor
4    nmdis/2901623/profiles/R2901623_003.nc  ...  Provor, Seabird conductivity sensor
..                                      ...  ...                                  ...
93   nmdis/2901623/profiles/R2901623_092.nc  ...  Provor, Seabird conductivity sensor
94   nmdis/2901623/profiles/R2901623_093.nc  ...  Provor, Seabird conductivity sensor
95   nmdis/2901623/profiles/R2901623_094.nc  ...  Provor, Seabird conductivity sensor
96   nmdis/2901623/profiles/R2901623_095.nc  ...  Provor, Seabird conductivity sensor
97   nmdis/2901623/profiles/R2901623_096.nc  ...  Provor, Seabird conductivity sensor

[98 rows x 11 columns]

Alternatively, you can use argopy.IndexFetcher.to_dataframe().

See Fetching methods for a list of all methods available for the Index fetcher.