Introduction (Thirty second pitch)
The python-sa-gwdata repository hosts a Python package called sa_gwdata
which provides an easy way to use code to access groundwater data in
South Australia which is otherwise available via the websites Groundwater Data
(also known as WaterConnect) and “Water Data SA”.
Commonly-used data is available in bulk as pandas DataFrames, and comprehensive data is available via the web services which run in the background on Groundwater Data.
Comprehensive documentation is also being worked on which will eventually provide a data dictionary, tutorials on how to find and access data, some tutorials on how you might use the data (although that won’t be a long-term focus), and comprehensive documentation for technically-minded users on the Python functions and underlying web services that are implemented here.
Requirements
Python >= 3.8
Required: pandas>0.24.1, requests, platformdirs, pyarrow, and pyshp -
these are installed automatically with pip install python-sa-gwdata.
Recommended but optional dependencies: shapely, contextily, geopandas.
These are best installed with conda/mamba if you are using that: conda/mamba install -c conda-forge geopandas shapely contextily
Install
You can install sa_gwdata the usual way with pip install python-sa-gwdata
This will install and/or update the Python package sa_gwdata.
Usage
You can locate wells by plain-text search for well identifiers:
>>> import sa_gwdata
>>> wells = sa_gwdata.find_wells("ADE206, ADE207, 7022-11315")
>>> wells
[Well(unit_hyphen='7022-11315'), Well(obs_no='ADE206'), Well(obs_no='ADE207')]
These sa_gwdata.Well objects are important because they can be used to
query a variety of other functions that let you obtain data.
For example let’s download water levels:
>>> df = sa_gwdata.water_levels(wells)
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 75 entries, 0 to 74
Data columns (total 21 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 dh_no 75 non-null int64
1 network 74 non-null object
2 unit_long 75 non-null int64
3 aquifer 75 non-null object
4 easting 75 non-null float64
5 northing 75 non-null float64
6 zone 75 non-null int64
7 unit_hyphen 75 non-null object
8 obs_no 74 non-null object
9 obs_date 75 non-null datetime64[ns]
10 dtw 68 non-null float64
11 swl 66 non-null float64
12 rswl 66 non-null float64
13 pressure 16 non-null float64
14 temperature 2 non-null float64
15 dry_ind 0 non-null float64
16 anomalous_ind 75 non-null object
17 pump_ind 75 non-null object
18 measured_during 75 non-null object
19 data_source 75 non-null object
20 comments 22 non-null object
dtypes: datetime64[ns](1), float64(8), int64(3), object(9)
memory usage: 12.4+ KB