argopandas

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The goal of argopandas is to provide seamless access to Argo NetCDF files using a pandas DataFrame-based interface. It is a Python port of the argodata package for R. The package is under heavy development and we would love feedback on the interface or anything else about the package!

Installation

You can install the argopandas package using pip.

pip install argopandas

The package depends on pandas, numpy, netCDF4, and pyarrow, which install automatically if using pip or you can install also your favourite Python package manager. The argopandas package requires Python 3.6 or later.

Examples

The intended interface for most usage is contained in the argopandas module. You can import this as argo for pretty-looking syntax:

import argopandas as argo

The global indexes are available via argo.prof, argo.meta, argo.tech, argo.traj, argo.bio_prof, argo.synthetic_prof, and argo.bio_traj.

argo.meta.head(5)

                       file  profiler_type institution     date_update
0  aoml/13857/13857_meta.nc            845          AO  20181011200014
1  aoml/13858/13858_meta.nc            845          AO  20181011200015
2  aoml/13859/13859_meta.nc            845          AO  20181011200025
3  aoml/15819/15819_meta.nc            845          AO  20181011200016
4  aoml/15820/15820_meta.nc            845          AO  20181011200018

By defaut, downloads are lazily cached from the Ifremer https mirror. You can use argo.url_mirror() or argo.file_mirror() with argo.set_default_mirror() to point argopandas at your favourite copy of Argo.

To get Argo data from one or more NetCDF files, subset the indexes and use one of the table accessors to download, cache, and read variables aligned along common dimensions. The accessor you probably want is the .levels accessor from the argo.prof index:

argo.prof.head(5).levels[['PRES', 'TEMP']]

                                                          PRES       TEMP
file                              N_PROF N_LEVELS
aoml/13857/profiles/R13857_001.nc 0      0           11.900000  22.235001
                                         1           17.000000  21.987000
                                         2           22.100000  21.891001
                                         3           27.200001  21.812000
                                         4           32.299999  21.632000
...                                                        ...        ...
aoml/13857/profiles/R13857_005.nc 0      102        976.500000   4.527000
                                         103        986.700012   4.527000
                                         104        996.799988   4.533000
                                         105       1007.000000   4.487000
                                         106       1017.200012   4.471000

[551 rows x 2 columns]

You can get data from every variable in an Argo NetCDF file using one of these accessors. The variables grouped in each are aligned along the same dimensions and are documented together in the Argo user’s manual.

  • All indexes have a .info accessor that contains length-one variables that aren’t aligned along any dimensions

  • argo.prof: argo.prof.levels, arog.prof.prof, argo.prof.calib, argo.prof.param, and argo.prof.history

  • argo.traj: argo.traj.cycle, argo.traj.measurement, argo.traj.param, and argo.traj.history

  • argo.tech: argo.tech.tech_param

  • argo.meta: argo.meta.config_param, argo.meta.missions, argo.meta.trans_system, argo.meta.positioning_system, argo.meta.launch_config_param, argo.meta.sensor, and argo.meta.param

Once you have a data frame you do anything you’d do with a regular pd.DataFrame(), like plot your data using the built-in plot method:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
for label, df in argo.prof.head(5).levels.groupby('file'):
    df.plot(x='TEMP', y = 'PRES', ax=ax, label=label)
ax.invert_yaxis()
_images/README_8_0.png

You can access all the index files for a particular float using argo.float(), which lazily filters all the indexes for a particular float ID.

float_obj = argo.float(13857)
float_obj.meta.info

Reading 1 file

                                   DATA_TYPE FORMAT_VERSION HANDBOOK_VERSION  file
aoml/13857/13857_meta.nc 0  Argo meta-data             3.1              1.2

                             DATE_CREATION     DATE_UPDATE PLATFORM_NUMBER  file
aoml/13857/13857_meta.nc 0  20181011200014  20181011200014        13857

                                                                          PTT  file
aoml/13857/13857_meta.nc 0  09335                                         ...

                                                              PLATFORM_FAMILY  file
aoml/13857/13857_meta.nc 0  FLOAT                                         ...

                                               PLATFORM_TYPE  file
aoml/13857/13857_meta.nc 0  PALACE

                                                               PLATFORM_MAKER  file
aoml/13857/13857_meta.nc 0  WRC                                           ...

                            ... LAUNCH_QC      START_DATE START_DATE_QC  file                        ...
aoml/13857/13857_meta.nc 0  ...      b'1'  19970719163000          b'1'

                              STARTUP_DATE STARTUP_DATE_QC  file
aoml/13857/13857_meta.nc 0  19970719103000            b'1'

                                         DEPLOYMENT_PLATFORM  file
aoml/13857/13857_meta.nc 0  R/V Seward Johnson

                                        DEPLOYMENT_CRUISE_ID  file
aoml/13857/13857_meta.nc 0  97-03

                                              DEPLOYMENT_REFERENCE_STATION_ID  file
aoml/13857/13857_meta.nc 0  CTD 108                                       ...

                           END_MISSION_DATE END_MISSION_STATUS
file
aoml/13857/13857_meta.nc 0                                 NaN

[1 rows x 43 columns]

Contents:

Indices and tables