Tampa Bay, FL - Simple workflow

Standardize, clean and wrangle Water Quality Portal data in Tampa Bay, FL 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 Tampa Bay, FL 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 = r'https://github.com/USEPA/Coastal_Ecological_Indicators/raw/master/DGGS_Coastal/temperature_data/TampaBay.geojson'

[4]:

# Map aoi
aoi_gdf = wrangle.as_gdf(aoi_url)
# geoJSON should be WGS1984 standard, but this one isn't
aoi_gdf.to_crs(4326, inplace=True)
aoi_gdf.plot()

[4]:

<Axes: >

../_images/notebooks_Harmonize_Tampa_Simple_10_1.png

[5]:

# 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'] = wrangle.get_bounding_box(aoi_gdf)
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

[6]:

import dataretrieval.wqp as wqp

[7]:

# 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 (9,10,13,15,17,19,22,23,28,31,32,33,36,38,58,60,61,63,64,65,70,71,73) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv(StringIO(response.text), delimiter=',')

[8]:

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

[8]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	AnalysisEndTime/TimeZoneCode	ResultLaboratoryCommentCode	ResultLaboratoryCommentText	ResultDetectionQuantitationLimitUrl	LaboratoryAccreditationIndicator	LaboratoryAccreditationAuthorityName	TaxonomistAccreditationIndicator	TaxonomistAccreditationAuthorityName	LabSamplePreparationUrl	ProviderName
0	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130612585-W	2013-06-12	11:01:00	EST	21FLHILL_WQX-585	STORET-301235413	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
1	21FLSEAS_WQX	Florida Department of Environmental Protection	21FLSEAS_WQX-481901119134	2013-11-19	14:01:00	EST	21FLSEAS_WQX-48SEAS190	STORET-310535134	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
2	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130702047-M	2013-07-02	11:01:00	EST	21FLHILL_WQX-047	STORET-300620295	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
3	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130716021	2013-07-16	11:01:00	EST	21FLHILL_WQX-021	STORET-300666279	NaN	NaN	...	NaN	NaN	NaN	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET
4	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-131216112-M	2013-12-16	12:01:00	EST	21FLHILL_WQX-112	STORET-301229196	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465643	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500762	1955-04-08	NaN	NaN	USGS-273926082304501	NWIS-6842541	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
1465644	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500762	1955-04-08	NaN	NaN	USGS-273926082304501	NWIS-6842544	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
1465645	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95800924	1957-10-21	14:05:00	EST	USGS-02306001	NWIS-6894410	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
1465646	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500782	1955-04-08	NaN	NaN	USGS-274455082253601	NWIS-6842941	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
1465647	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500782	1955-04-08	NaN	NaN	USGS-274455082253601	NWIS-6842944	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS

1465648 rows × 78 columns

Harmonize data using defaults

[9]:

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

[10]:

# 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: '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: '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/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")

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

[10]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	TOTAL NITROGEN_ MIXED FORMS	Carbon	Phosphorus	TP_Phosphorus	TDP_Phosphorus	Other_Phosphorus	Salinity	Temperature	Turbidity	pH
0	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130612585-W	2013-06-12	11:01:00	EST	21FLHILL_WQX-585	STORET-301235413	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	21FLSEAS_WQX	Florida Department of Environmental Protection	21FLSEAS_WQX-481901119134	2013-11-19	14:01:00	EST	21FLSEAS_WQX-48SEAS190	STORET-310535134	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	40.0 Practical_Salinity_Units	NaN	NaN	NaN
2	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130702047-M	2013-07-02	11:01:00	EST	21FLHILL_WQX-047	STORET-300620295	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	28.19 degree_Celsius	NaN	NaN
3	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130716021	2013-07-16	11:01:00	EST	21FLHILL_WQX-021	STORET-300666279	NaN	NaN	...	NaN	3.8 milligram / liter	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-131216112-M	2013-12-16	12:01:00	EST	21FLHILL_WQX-112	STORET-301229196	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.48 dimensionless
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465643	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500762	1955-04-08	NaN	NaN	USGS-273926082304501	NWIS-6842541	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	25.7 degree_Celsius	NaN	NaN
1465644	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500762	1955-04-08	NaN	NaN	USGS-273926082304501	NWIS-6842544	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.8 dimensionless
1465645	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95800924	1957-10-21	14:05:00	EST	USGS-02306001	NWIS-6894410	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.7 dimensionless
1465646	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500782	1955-04-08	NaN	NaN	USGS-274455082253601	NWIS-6842941	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	26.1 degree_Celsius	NaN	NaN
1465647	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500782	1955-04-08	NaN	NaN	USGS-274455082253601	NWIS-6842944	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.6 dimensionless

1465648 rows × 97 columns

[11]:

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

[12]:

# 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)

[12]:

<Axes: >

../_images/notebooks_Harmonize_Tampa_Simple_21_1.png

[13]:

# 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)

[13]:

<Axes: >

../_images/notebooks_Harmonize_Tampa_Simple_22_1.png

Clean additional columns of data

[14]:

from harmonize_wq import clean

[15]:

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: 100662 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.

[16]:

# 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

[16]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	QA_Fecal_Coliform	QA_DO	QA_E_coli	QA_Conductivity	QA_Nitrogen	QA_TP_Phosphorus	QA_TDP_Phosphorus	QA_Other_Phosphorus	QA_Turbidity	QA_Secchi
0	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130612585-W	2013-06-12	11:01:00	-0500	21FLHILL_WQX-585	STORET-301235413	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	21FLSEAS_WQX	Florida Department of Environmental Protection	21FLSEAS_WQX-481901119134	2013-11-19	14:01:00	-0500	21FLSEAS_WQX-48SEAS190	STORET-310535134	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130702047-M	2013-07-02	11:01:00	-0500	21FLHILL_WQX-047	STORET-300620295	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-130716021	2013-07-16	11:01:00	-0500	21FLHILL_WQX-021	STORET-300666279	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	21FLHILL_WQX	Environmental Protection Commission of Hillsbo...	21FLHILL_WQX-131216112-M	2013-12-16	12:01:00	-0500	21FLHILL_WQX-112	STORET-301229196	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465643	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500762	1955-04-08	NaN	NaN	USGS-273926082304501	NWIS-6842541	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1465644	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500762	1955-04-08	NaN	NaN	USGS-273926082304501	NWIS-6842544	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1465645	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95800924	1957-10-21	14:05:00	-0500	USGS-02306001	NWIS-6894410	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1465646	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500782	1955-04-08	NaN	NaN	USGS-274455082253601	NWIS-6842941	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1465647	USGS-FL	USGS Florida Water Science Center	nwisfl.01.95500782	1955-04-08	NaN	NaN	USGS-274455082253601	NWIS-6842944	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

1396992 rows × 113 columns

[17]:

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

[18]:

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

Transform data from long to wide format

[19]:

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.