Cape Cod - Simple workflow

Standardize, clean and wrangle Water Quality Portal data in Cape Cod into more analytic-ready formats using the harmonize_wq package

US EPA’s Water Quality Portal (WQP) aggregates water quality, biological, and physical data provided by many organizations and has become an essential resource with tools to query and retrieval data using python or R. Given the variety of data and variety of data originators, using the data in analysis often requires data cleaning to ensure it meets the required quality standards and data wrangling to get it in a more analytic-ready format. Recognizing the definition of analysis-ready varies depending on the analysis, the harmonixe_wq package is intended to be a flexible water quality specific framework to help: - Identify differences in data units (including speciation and basis) - Identify differences in sampling or analytic methods - Resolve data errors using transparent assumptions - Reduce data to the columns that are most commonly needed - Transform data from long to wide format

Domain experts must decide what data meets their quality standards for data comparability and any thresholds for acceptance or rejection.

Simple workflow

This example steps through a typical workflow in Cape Cod to demonstrate commonly used functionality

Install the required libraries

[1]:

import sys
#!python -m pip uninstall harmonize-wq --yes
# Use pip to install the package from pypi or the latest from github
#!{sys.executable} -m pip install harmonize-wq
# For latest dev version
#!{sys.executable} -m pip install git+https://github.com/USEPA/harmonize-wq.git@new_release_0-3-8

Create dataretrieval query using a polygon for Area Of Interest

[2]:

from harmonize_wq import wrangle

/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/dataretrieval/nadp.py:44: UserWarning: GDAL not installed. Some functions will not work.
  warnings.warn('GDAL not installed. Some functions will not work.')

[3]:

# Read geometry for Area of Interest from geojson file url
# NOTE: alternatively you can direct it to a local shapefile
aoi_url = 'https://github.com/jbousquin/test_notebook/raw/main/temperature_data/NewEngland.geojson'

[4]:

# Map aoi (geojson is WGS1984 standard)
wrangle.as_gdf(aoi_url).plot()

[4]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Simple_10_1.png

The Area Of Interest is made up of many small polygons. The query will be built from the total extent of these polygons, but alternatively could be restricted to the bounding box for just one such polygon.

[5]:

# Bounding box string for total extent
bBox = wrangle.get_bounding_box(aoi_url)
# Bounding box string for specific polygon by index
#bBox = wrangle.get_bounding_box(aoi_url, idx=0)

[6]:

# Build query
query = {'characteristicName': ['Phosphorus',
                                'Temperature, water',
                                'Depth, Secchi disk depth',
                                'Dissolved oxygen (DO)',
                                'Salinity',
                                'pH',
                                'Nitrogen',
                                'Conductivity',
                                'Organic carbon',
                                'Chlorophyll a',
                                'Turbidity',
                                'Sediment',
                                'Fecal Coliform',
                                'Escherichia coli']}
query['bBox'] = bBox
query['dataProfile'] = 'narrowResult'

Retrieve data

Stations will be retrieved using the query criteria seperately after results. md_narrow is metadata documenting the query not used here but meant for reproducibility

[7]:

import dataretrieval.wqp as wqp

[8]:

# Query for results
# Note: large quieries like this can take up a lot of RAM and may give a DtypeWarning,
# set low_memory=False or provide dataTypes for columns to use less memory.
res_narrow, md_narrow = wqp.get_results(**query)

/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/dataretrieval/wqp.py:83: DtypeWarning: Columns (8,10,13,15,17,19,20,21,22,23,28,31,32,33,34,36,38,60,63,64,65,66,67,68,69,70,71,72) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv(StringIO(response.text), delimiter=',')

[9]:

# Look at initial results (input)
df = res_narrow
df

[9]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	AnalysisEndTime/TimeZoneCode	ResultLaboratoryCommentCode	ResultLaboratoryCommentText	ResultDetectionQuantitationLimitUrl	LaboratoryAccreditationIndicator	LaboratoryAccreditationAuthorityName	TaxonomistAccreditationIndicator	TaxonomistAccreditationAuthorityName	LabSamplePreparationUrl	ProviderName
0	CRWA	Charles River Watershed Association (Massachus...	CRWA-CYN20130809ROBTemp01	2013-08-09	11:14:33	EST	CRWA-ROB	STORET-591631481	130809111433.0	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
1	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_HX30_21_7/22/2013_SFW_0.01	2013-07-22	11:01:00	EDT	11NPSWRD_WQX-CACO_HX30_21	STORET-986369728	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
2	MASSDEP	Massachusetts Department of Environmental Prot...	MASSDEP-LB-5142	2013-07-30	14:20:00	EDT	MASSDEP-W2173	STORET-762955917	NaN	NaN	...	NaN	NaN	NaN	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET
3	IRWA	Ipswich River Watershed Association (Volunteer)	IRWA-HB:20130630120000:FM	2013-06-30	12:00:00	EDT	IRWA-HB	STORET-853064665	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
4	CRWA	Charles River Watershed Association (Massachus...	CRWA-VMM20131217609SEC02	2013-12-17	07:40:00	EST	CRWA-609S	STORET-872379847	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
416607	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400495	2024-01-08	12:00:00	EST	USGS-01098530	NWIS-126836828	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
416608	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989046	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
416609	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989056	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
416610	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989063	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
416611	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989064	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS

416612 rows × 78 columns

Harmonize data using defaults

[10]:

from harmonize_wq import harmonize
from harmonize_wq import location
from harmonize_wq import visualize

[11]:

# Harmonize it and look at how it changed (output)
# Note: 'ignore' unit dimensionality errors will warn when they are encountered and replace with NaN
# Note: depending on the Pint version this may cause a UnitStrippedWarning
df_harmonized = harmonize.harmonize_all(df, errors='ignore')
df_harmonized

/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'ug/cm2' UNDEFINED UNIT for Chlorophyll
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'ppb' UNDEFINED UNIT for Chlorophyll
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'ug/m3' UNDEFINED UNIT for Chlorophyll
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'None' UNDEFINED UNIT for Secchi
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'MPN/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'CFU/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: '%' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'cfu/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'MPN/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'CFU/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'cfu/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'cm3/g' UNDEFINED UNIT for Nitrogen
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/pandas/core/construction.py:616: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  data = np.array(data, copy=copy)
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/pandas/core/construction.py:616: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  data = np.array(data, copy=copy)

2 Phosphorus sample fractions not in frac_dict
2 Phosphorus sample fractions not in frac_dict found in expected domains, mapped to "Other_Phosphorus"

/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/basis.py:154: UserWarning: Mismatched ResultTemperatureBasisText: updated from 25 deg C to @25C (units)
  warn(f"Mismatched {flag}", UserWarning)
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'deg C' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'g / H2O' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/harmonize.py:149: UserWarning: Bad Turbidity unit: count
  warn(f"Bad Turbidity unit: {unit}")
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")

[11]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	Carbon	Phosphorus	TP_Phosphorus	TDP_Phosphorus	Other_Phosphorus	Salinity	Sediment	Temperature	Turbidity	pH
0	CRWA	Charles River Watershed Association (Massachus...	CRWA-CYN20130809ROBTemp01	2013-08-09	11:14:33	EST	CRWA-ROB	STORET-591631481	130809111433.0	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	23.01 degree_Celsius	NaN	NaN
1	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_HX30_21_7/22/2013_SFW_0.01	2013-07-22	11:01:00	EDT	11NPSWRD_WQX-CACO_HX30_21	STORET-986369728	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4.2 Nephelometric_Turbidity_Units	NaN
2	MASSDEP	Massachusetts Department of Environmental Prot...	MASSDEP-LB-5142	2013-07-30	14:20:00	EDT	MASSDEP-W2173	STORET-762955917	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	IRWA	Ipswich River Watershed Association (Volunteer)	IRWA-HB:20130630120000:FM	2013-06-30	12:00:00	EDT	IRWA-HB	STORET-853064665	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	CRWA	Charles River Watershed Association (Massachus...	CRWA-VMM20131217609SEC02	2013-12-17	07:40:00	EST	CRWA-609S	STORET-872379847	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
416607	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400495	2024-01-08	12:00:00	EST	USGS-01098530	NWIS-126836828	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.9 Nephelometric_Turbidity_Units	NaN
416608	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989046	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.9 degree_Celsius	NaN	NaN
416609	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989056	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.4 dimensionless
416610	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989063	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
416611	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	EST	USGS-01097050	NWIS-126989064	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.7 Nephelometric_Turbidity_Units	NaN

416612 rows × 98 columns

[12]:

# Get harmonized stations clipped to the Area of Interest
stations_gdf, stations, site_md = location.get_harmonized_stations(query, aoi=aoi_url)

[13]:

# Map number of usable results at each station
gdf_count = visualize.map_counts(df_harmonized, stations_gdf)
legend_kwds={"fmt": "{:.0f}", 'bbox_to_anchor':(1, 0.75)}
gdf_count.plot(column='cnt', cmap='Blues', legend=True, scheme='quantiles', legend_kwds=legend_kwds)

[13]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Simple_23_1.png

[14]:

# Map average temperature results at each station
gdf_temperature = visualize.map_measure(df_harmonized, stations_gdf, 'Temperature')
gdf_temperature.plot(column='mean', cmap='OrRd', legend=True)

[14]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Simple_24_1.png

Clean additional columns of data

[15]:

from harmonize_wq import clean

[16]:

df_cleaned = clean.datetime(df_harmonized)  # datetime
df_cleaned = clean.harmonize_depth(df_cleaned)  # Sample depth

/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/dataretrieval/utils.py:87: UserWarning: Warning: 132776 incomplete dates found, consider setting datetime_index to False.
  warnings.warn(

Reduce data to the columns that are most commonly needed

There are many columns in the dataframe that are characteristic specific, that is they have different values for the same sample depending on the characteristic. To ensure one result for each sample after the transformation of the data these columns must either be split, generating a new column for each characteristic with values, or moved out from the table if not being used.

[17]:

# Split single QA column into multiple by characteristic (rename the result to preserve these QA_flags)
df_expanded = wrangle.split_col(df_cleaned)
df_expanded

[17]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	QA_Nitrogen	QA_E_coli	QA_Carbon	QA_Salinity	QA_Fecal_Coliform	QA_pH	QA_Conductivity	QA_Secchi	QA_DO	QA_Temperature
0	CRWA	Charles River Watershed Association (Massachus...	CRWA-CYN20130809ROBTemp01	2013-08-09	11:14:33	-0500	CRWA-ROB	STORET-591631481	130809111433.0	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_HX30_21_7/22/2013_SFW_0.01	2013-07-22	11:01:00	-0400	11NPSWRD_WQX-CACO_HX30_21	STORET-986369728	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	MASSDEP	Massachusetts Department of Environmental Prot...	MASSDEP-LB-5142	2013-07-30	14:20:00	-0400	MASSDEP-W2173	STORET-762955917	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	IRWA	Ipswich River Watershed Association (Volunteer)	IRWA-HB:20130630120000:FM	2013-06-30	12:00:00	-0400	IRWA-HB	STORET-853064665	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	MASSDEP	Massachusetts Department of Environmental Prot...	MASSDEP-SM-4452	2013-09-25	09:05:00	-0400	MASSDEP-W0696	STORET-762964497	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
416607	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400495	2024-01-08	12:00:00	-0500	USGS-01098530	NWIS-126836828	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
416608	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	-0500	USGS-01097050	NWIS-126989046	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
416609	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	-0500	USGS-01097050	NWIS-126989056	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
416610	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	-0500	USGS-01097050	NWIS-126989063	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
416611	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.02400639	2024-02-12	08:45:00	-0500	USGS-01097050	NWIS-126989064	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

380739 rows × 115 columns

[18]:

# Split table into main with columns of interest and characteristic specific columns/metadata
main_df, chars_df = wrangle.split_table(df_expanded)

[19]:

# Drop empty columns (QA columns without flags)
main_df_small = main_df.dropna(axis=1, how='all')

Transform data from long to wide format

[20]:

transformed_df = wrangle.collapse_results(main_df_small)

Results are collapsed by retaining the first result that isn’t NAN. There can be several reasons for multiple results for the same parameter/characteristic sampled at the same station, time and by the same organization. The collapse_results function assumes the user has already reviewed the quality of all results and narrowed down instances of multiple results to only the desired/best/highest quality result before running this function.