Working with Argo data

argopy not only get you easy access to Argo data, it also aims to help you work with it.

In the following documentation sections, you will see how to:

• manipulate Argo data from a xarray.Dataset with argo accessor methods,

• compute new data from an Argo xarray.Dataset with argo accessor methods,

• save Argo data from a xarray.Dataset with argo accessor methods,

• visualize Argo data, whether it is a xarray.Dataset or pandas.DataFrame profile index,

You can also refer to the documentation on Tools for experts and operators.

About argopy data model

By default argopy will provide users with a xarray.Dataset or pandas.DataFrame.

For your own analysis, you may prefer to switch from one to the other. This is all built in argopy, with the DataFetcher.to_dataframe() and DataFetcher.to_xarray() methods.

In [1]: from argopy import DataFetcher

In [2]: DataFetcher().profile(6902746, 34).to_dataframe()
Out[2]: 
          CYCLE_NUMBER DATA_MODE  ... LONGITUDE                TIME
N_POINTS                          ...                              
0                   34         D  ...   -58.119 2017-12-20 06:58:00
1                   34         D  ...   -58.119 2017-12-20 06:58:00
2                   34         D  ...   -58.119 2017-12-20 06:58:00
3                   34         D  ...   -58.119 2017-12-20 06:58:00
4                   34         D  ...   -58.119 2017-12-20 06:58:00
...                ...       ...  ...       ...                 ...
104                 34         D  ...   -58.119 2017-12-20 06:58:00
105                 34         D  ...   -58.119 2017-12-20 06:58:00
106                 34         D  ...   -58.119 2017-12-20 06:58:00
107                 34         D  ...   -58.119 2017-12-20 06:58:00
108                 34         D  ...   -58.119 2017-12-20 06:58:00

[109 rows x 18 columns]

Note that internally, argopy also work with pyarrow.Table.