Hydropandas

A Python package for reading, analyzing, and writing hydrological time series from a pandas DataFrame, with all of its wonderful features, and extended with custom methods and attributes related to hydrological time series.
https://github.com/artesiawater/hydropandas

Category: Hydrosphere
Sub Category: Ocean and Hydrology Data Access

Keywords

data groundwater hydrology observations pandas timeseries

Keywords from Contributors

flopy geopandas groundwater-modelling hydrogeology modflow pastas arctic data-management articles poster

Last synced: about 16 hours ago
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Repository metadata

Module for loading observation data into custom DataFrames

Host: GitHub
URL: https://github.com/artesiawater/hydropandas
Owner: ArtesiaWater
License: mit
Created: 2019-06-28T09:58:59.000Z (almost 6 years ago)
Default Branch: dev
Last Pushed: 2025-04-23T07:22:53.000Z (4 days ago)
Last Synced: 2025-04-25T14:07:33.504Z (1 day ago)
Topics: data, groundwater, hydrology, observations, pandas, timeseries
Language: Python
Homepage: https://hydropandas.readthedocs.io
Size: 43.2 MB
Stars: 61
Watchers: 6
Forks: 11
Open Issues: 20
Releases: 42
Metadata Files:
- Readme: readme.md
- License: LICENSE

readme.md

HydroPandas

Hydropandas is a Python package for reading, analyzing and writing
(hydrological) timeseries data.

Introduction

The HydroPandas package allows users to store a timeseries and metadata in a
single object. This object inherits from a pandas DataFrame, with all its
wonderful features, and is extended with custom methods and attributes related
to hydrological timeseries.

The HydroPandas package also provides convenient read functions for Dutch hydrological data from:

BRO
DINO
FEWS PI-XML
KNMI
Lizard
MODFLOW groundwater models
IMOD groundwater models
Pastastore
Waterinfo
WISKI csv files

Install

Install the module with pip:

pip install hydropandas

HydroPandas requires pandas, scipy, matplotlib, tqdm, requests and colorama.

For some functionality additional packages are required:

geopandas: for dealing with shapefiles
pastastore: for reading or storing data from PastaStore
bokeh, branca, 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
typing pip install -e . from the module root directory.
For installing all the optional packages use pip install -e .[full].

Get in touch

Questions on HydroPandas ("How can I?") can be asked and answered on Github Discussions.
Bugs, feature requests and other improvements can be posted as Github Issues.
Find out how to contribute to HydroPandas at our Contribution page.

Examples

Importing a groundwater time series from the BRO using the BRO-id and the tube number:

import hydropandas as hpd
gw_bro = hpd.GroundwaterObs.from_bro("GMW000000041261", 1)

Or import all groundwater time series from the BRO within a certain extent:

oc = hpd.read_bro(extent=(117850, 118180, 439550, 439900))

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
WaterQualityObs: for groundwater quality measurements
WaterlvlObs: for surface water level measurements
ModelObs: for "observations" from a MODFLOW model
MeteoObs: for meteorological observations
PrecipitationObs: for precipitation observations, subclass of MeteoObs
EvaporationObs: for evaporation observations, subclass of MeteoObs

Each of these Obs classes is essentially a pandas DataFrame with additional
methods and attributes related to the type of measurement that it holds.
Each Obs object 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 PrecipitationObs.

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.

Authors

Onno Ebbens, Artesia
Ruben Caljé, Artesia
Davíd Brakenhoff, Artesia
Martin Vonk, Artesia

Owner metadata

Name: Artesia Water
Login: ArtesiaWater
Email:
Kind: user
Description: Artesia adviseert bij hydrologische vraagstukken. We zijn gespecialiseerd in het programmeren van modellen in Python, Matlab en andere bekende programmeertalen.
Website: http://www.artesia-water.nl
Location: Schoonhoven, The Netherlands
Twitter:
Company: Artesia Water
Icon url: https://avatars.githubusercontent.com/u/31697400?u=a5a6fc31ec93c07853dd53835936fd90c44f7483&v=4
Repositories: 13
Last ynced at: 2024-04-24T04:06:10.080Z
Profile URL: https://github.com/ArtesiaWater

GitHub Events

Total

Create event: 25
Release event: 6
Issues event: 33
Watch event: 10
Delete event: 13
Issue comment event: 64
Push event: 112
Pull request review event: 47
Pull request review comment event: 23
Pull request event: 46

Last Year

Create event: 25
Release event: 6
Issues event: 33
Watch event: 10
Delete event: 13
Issue comment event: 64
Push event: 112
Pull request review event: 47
Pull request review comment event: 23
Pull request event: 46

Committers metadata

Last synced: 7 days ago

Total Commits: 1,205
Total Committers: 11
Avg Commits per committer: 109.545
Development Distribution Score (DDS): 0.448

Commits in past year: 108
Committers in past year: 7
Avg Commits per committer in past year: 15.429
Development Distribution Score (DDS) in past year: 0.269

Name	Email	Commits
OnnoEbbens	o**s@g**m	665
dbrakenhoff	d**f@a**l	274
Martin Vonk	v**t@g**m	222
Hendrik Meuwese	1****W	17
Floris van 't Klooster	6****r	8
Artesia Water	3****r	7
Ruben Caljé	r**e@a**l	6
anouksprong	a**g@v**l	2
MattBrst	4****t	2
Thomas Berends	t**s@h**m	1
Justin Jent	j**r@g**m	1

Committer domains:

artesia-water.nl: 2
vitens.nl: 1

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 113
Total pull requests: 141
Average time to close issues: 4 months
Average time to close pull requests: 5 days
Total issue authors: 18
Total pull request authors: 10
Average comments per issue: 2.04
Average comments per pull request: 0.89
Merged pull request: 131
Bot issues: 0
Bot pull requests: 0

Past year issues: 38
Past year pull requests: 59
Past year average time to close issues: 17 days
Past year average time to close pull requests: 2 days
Past year issue authors: 10
Past year pull request authors: 7
Past year average comments per issue: 1.97
Past year average comments per pull request: 0.73
Past year merged pull request: 51
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/artesiawater/hydropandas

Top Issue Authors

OnnoEbbens (53)
martinvonk (18)
dbrakenhoff (14)
HMEUW (9)
rubencalje (3)
jvansijl (2)
Florisklooster (2)
JaccoHoogewoud (2)
markvdbrink (1)
ArtemisRo (1)
thomas-wsbd (1)
anouksprong (1)
pimvansanten (1)
MattBrst (1)
tdmeij (1)

Top Pull Request Authors

OnnoEbbens (79)
dbrakenhoff (24)
martinvonk (17)
HMEUW (7)
Florisklooster (5)
rubencalje (3)
MattBrst (3)
ArtesiaWater (1)
tberends (1)
anouksprong (1)

Top Issue Labels

enhancement (19)
bug (7)
question (2)
code quality (2)
help wanted (1)
documentation (1)
duplicate (1)
wontfix (1)

Top Pull Request Labels

enhancement (14)
code quality (3)
bug (2)

Package metadata

Total packages: 1
Total downloads:
- pypi: 1,910 last-month
Total dependent packages: 3
Total dependent repositories: 1
Total versions: 37
Total maintainers: 2

pypi.org: hydropandas

Module by Artesia for loading observation data into custom DataFrames.

Homepage:
Documentation: https://hydropandas.readthedocs.io/
Licenses: The MIT License (MIT) Copyright (c) 2020-2025 O.N. Ebbens, D.A. Brakenhoff, R. Calje Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Latest release: 0.13.1 (published 4 days ago)
Last Synced: 2025-04-25T14:03:09.415Z (1 day ago)
Versions: 37
Dependent Packages: 3
Dependent Repositories: 1
Downloads: 1,910 Last month
Rankings:
- Dependent packages count: 3.156%
- Downloads: 8.785%
- Average: 11.183%
- Dependent repos count: 21.607%
Maintainers (2)
- dbrakenhoff
- Onno

Dependencies

docs/requirements.txt pypi

Ipython *
docutils <0.18
flopy *
ipykernel *
nbsphinx *
nbsphinx_link *
netCDF4 *
pastastore *
sphinx_rtd_theme *
tqdm *
xarray *

requirements.txt pypi

bokeh *
branca *
flopy *
folium *
geopandas *
ipykernel *
lxml *
matplotlib >=3.0
nbconvert *
nbformat *
netCDF4 ==1.5.7
numpy >=1.15
pandas *
pastas *
pastastore *
pyproj *
requests *
scipy >=1.2
shapely *
tqdm *
xarray *
zeep *

setup.py pypi

scipy *

Score: 14.350339386001615

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