The hydropandas module is a Python package for reading timeseries data into DataFrames.
The Hydropandas package allows users to manipulate data using all of the wonderful features included in pandas extented with custom methods and attributes related to the timeseries. The hydropandas module extends pandas.DataFrame with extra functionality and stores metadata related to the type of measurements.
Install the module with pip:
pip install hydropandas
For some functionality additional packages are required:
pastastore: for reading or storing data from PastaStore
folium: for interactive maps
flopy: for reading data from MODFLOW models
xarray: for loading data from REGIS
For installing in development mode, clone the repository and install by
pip install -e . from the module root directory.
If you have trouble installing hydropandas, refer to the Dependencies section below.
Importing a single DINO csv file:
import hydropandas as hpd fname = './tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv' gw = hpd.GroundwaterObs.from_dino(fname=fname, verbose=True)
Or for a zipfile:
import hydropandas as hpd dinozip = './tests/data/2019-Dino-test/dino.zip' dino_gw = hpd.ObsCollection.from_dino(dirname=dinozip, subdir='Grondwaterstanden_Put', suffix='1.csv', ObsClass=hpd.GroundwaterObs, keep_all_obs=False, verbose=False)
The Obs class
The Obs class holds the measurements and metadata for one timeseries. There are currently 5 specific Obs classes for different types of measurements:
- GroundwaterObs: for groundwater measurements
- GroundwaterQualityObs: for groundwater quality measurements
- WaterlvlObs: for surface water level measurements
- ModelObs: for hydropandas from a MODFLOW model
- KnmiObs: for (daily) KNMI hydropandas
Each of these Obs classes is essentially a pandas DataFrame with additional methods and attributes related to the type of measurement that it holds. The classes also contain specific methods to read data from specific sources.
The ObsCollection class
The ObsCollection class, as the name implies, represents a collection of Obs classes, e.g. 10 timeseries of the groundwater level in a certain area. The ObsCollection is also a pandas DataFrame in which each timeseries is stored in a different row. Each row contains metadata (e.g. latitude and longitude of the observation point) and the Obs object (DataFrame) that holds the measurements. It is recommended to let an ObsCollection contain only one Obs type, e.g. to create an ObsCollection for 10 GroundwaterObs, and a separate ObsCollection for 5 KnmiObs.
Like the Obs class, the ObsCollection class contains a bunch of methods for reading data from different sources. See the next section for supported data sources.
Supported data sources
Currently supported datasources that can be read:
- FEWS PI-XML
- DINO csv
- WISKI csv
- Artesia Fieldlogger for Android and iOS
- Pastas projects (deprecated)
- Pastastore, for managing Pastas timeseries and models
- PyStore, a fast datastore for pandas timeseries
- Arctic, a timeseries / dataframe database that sits atop MongoDB
- KNMI data
- MODFLOW groundwater models
- IMOD groundwater models
ObsCollection can be exported to:
- Artesia Fieldlogger
- Pastas projects (deprecated)
Hydropandas (indirectly) uses some packages that cannot be installed
pip on Windows. These packages are:
If you do not have these packages already it is recommended to first try
installing them with
conda install <pkg>. Otherwise, read the instructions
below how to install them manually.
Download the packages from Christoph Gohlke's website. Use CTRL+F to find the download link on the page. Be sure to download the correct version of the package. The Python version should match your Python version. Also the architecture should match (i.e. 64bits vs 32bits). For example:
This is the GDAL version for Python 3.8 (as can be seen from the cp38 in the name), for 64-bits Python (as derived from the amd64 in the name).
Once you have downloaded the correct files, navigate to the directory in which you saved your downloads. Now type the following commands (the order is important):
pip install GDAL-3.1.4-cp38-cp38-win_amd64.whl
pip install Fiona-1.8.17-cp38-cp38-win_amd64.whl
pip install Shapely-1.7.1-cp38-cp38-win_amd64.whl
pip install python_snappy-0.5.4-cp38-cp38-win_amd64.whl
pip install fastparquet-0.4.1-cp38-cp38-win_amd64.whl
After you've done this you can install hydropandas using
pip install hydropandas.
- Onno Ebbens, Artesia
- Ruben Caljé, Artesia
- Davíd Brakenhoff, Artesia