Cape Cod - Detailed step-by-step

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.

Detailed step-by-step workflow

This example workflow takes a deeper dive into some of the expanded functionality to examine results for different water quality parameters in Cape Cod

Install and import 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

[2]:

import dataretrieval.wqp as wqp
from harmonize_wq import wrangle
from harmonize_wq import location
from harmonize_wq import harmonize
from harmonize_wq import visualize
from harmonize_wq import clean

Download location data using dataretrieval

[3]:

# Read geometry for Area of Interest from geojson file url and plot
aoi_url = 'https://github.com/jbousquin/test_notebook/raw/main/temperature_data/NewEngland.geojson'
aoi_gdf = wrangle.as_gdf(aoi_url)  # Already 4326 standard
aoi_gdf.plot()

[3]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_9_1.png

[4]:

# Build query with characteristicNames and the AOI extent
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)

[5]:

# Query stations (can be slow)
stations, site_md = wqp.what_sites(**query)

[6]:

# Rows and columns for results
stations.shape

[6]:

(11369, 37)

[7]:

# First 5 rows
stations.head()

[7]:

	OrganizationIdentifier	OrganizationFormalName	MonitoringLocationIdentifier	MonitoringLocationName	MonitoringLocationTypeName	MonitoringLocationDescriptionText	HUCEightDigitCode	DrainageAreaMeasure/MeasureValue	DrainageAreaMeasure/MeasureUnitCode	ContributingDrainageAreaMeasure/MeasureValue	...	AquiferName	LocalAqfrName	FormationTypeText	AquiferTypeName	ConstructionDateText	WellDepthMeasure/MeasureValue	WellDepthMeasure/MeasureUnitCode	WellHoleDepthMeasure/MeasureValue	WellHoleDepthMeasure/MeasureUnitCode	ProviderName
0	USGS-MA	USGS Massachusetts Water Science Center	USGS-010965305	MERRIMACK R NR TYNGSBOROUGH BRIDGE TYNGSBOROUG...	Stream	NaN	1070006.0	4070.00	sq mi	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
1	USGS-MA	USGS Massachusetts Water Science Center	USGS-01096544	STONY BROOK AT SCHOOL STREET AT CHELMSFORD, MA	Stream	NaN	1070006.0	41.57	sq mi	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
2	USGS-MA	USGS Massachusetts Water Science Center	USGS-01096546	STONY BROOK AT CHELMSFORD, MA	Stream	NaN	1070006.0	43.60	sq mi	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
3	USGS-MA	USGS Massachusetts Water Science Center	USGS-01096548	STONY BROOK AT N CHELMSFORD, MA	Stream	NaN	1070006.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS
4	USGS-MA	USGS Massachusetts Water Science Center	USGS-01096550	MERRIMACK RIVER ABOVE LOWELL, MA	Stream	NaN	1070006.0	3900.00	sq mi	3900.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NWIS

5 rows × 37 columns

[8]:

# Columns used for an example row
stations.iloc[0][['HorizontalCoordinateReferenceSystemDatumName', 'LatitudeMeasure', 'LongitudeMeasure']]

[8]:

HorizontalCoordinateReferenceSystemDatumName        NAD83
LatitudeMeasure                                 42.677389
LongitudeMeasure                               -71.421056
Name: 0, dtype: object

[9]:

# Harmonize location datums to 4326 (Note we keep intermediate columns using intermediate_columns=True)
stations_gdf = location.harmonize_locations(stations, outEPSG=4326, intermediate_columns=True)

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/clean.py:356: FutureWarning: Logical ops (and, or, xor) between Pandas objects and dtype-less sequences (e.g. list, tuple) are deprecated and will raise in a future version. Wrap the object in a Series, Index, or np.array before operating instead.
  cond_notna = mask & (df_out["QA_flag"].notna())  # Mask cond and not NA
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/clean.py:360: FutureWarning: Logical ops (and, or, xor) between Pandas objects and dtype-less sequences (e.g. list, tuple) are deprecated and will raise in a future version. Wrap the object in a Series, Index, or np.array before operating instead.
  df_out.loc[mask & (df_out["QA_flag"].isna()), "QA_flag"] = flag
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/clean.py:360: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'LatitudeMeasure: Imprecise: lessthan3decimaldigits' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[mask & (df_out["QA_flag"].isna()), "QA_flag"] = flag
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/clean.py:356: FutureWarning: Logical ops (and, or, xor) between Pandas objects and dtype-less sequences (e.g. list, tuple) are deprecated and will raise in a future version. Wrap the object in a Series, Index, or np.array before operating instead.
  cond_notna = mask & (df_out["QA_flag"].notna())  # Mask cond and not NA
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/clean.py:360: FutureWarning: Logical ops (and, or, xor) between Pandas objects and dtype-less sequences (e.g. list, tuple) are deprecated and will raise in a future version. Wrap the object in a Series, Index, or np.array before operating instead.
  df_out.loc[mask & (df_out["QA_flag"].isna()), "QA_flag"] = flag

[10]:

# Every function has a dostring to help understand input/output and what it does
location.harmonize_locations?

[11]:

# Rows and columns for results after running the function (5 new columns, only 2 new if intermediate_columns=False)
stations_gdf.shape

[11]:

(11369, 42)

[12]:

# Example results for the new columns
stations_gdf.iloc[0][['geom_orig', 'EPSG', 'QA_flag', 'geom', 'geometry']]

[12]:

geom_orig         (-71.4210556, 42.67738889)
EPSG                                  4269.0
QA_flag                                  NaN
geom         POINT (-71.4210556 42.67738889)
geometry     POINT (-71.4210556 42.67738889)
Name: 0, dtype: object

[13]:

# geom and geometry look the same but geometry is a special datatype
stations_gdf['geometry'].dtype

[13]:

<geopandas.array.GeometryDtype at 0x7ffa7f5e5760>

[14]:

# Look at the different QA_flag flags that have been assigned,
# e.g., for bad datums or limited decimal precision
set(stations_gdf.loc[stations_gdf['QA_flag'].notna()]['QA_flag'])

[14]:

{'HorizontalCoordinateReferenceSystemDatumName: Bad datum OTHER, EPSG:4326 assumed',
 'HorizontalCoordinateReferenceSystemDatumName: Bad datum UNKWN, EPSG:4326 assumed',
 'LatitudeMeasure: Imprecise: lessthan3decimaldigits',
 'LatitudeMeasure: Imprecise: lessthan3decimaldigits; LongitudeMeasure: Imprecise: lessthan3decimaldigits',
 'LongitudeMeasure: Imprecise: lessthan3decimaldigits'}

[15]:

# Map it
stations_gdf.plot()

[15]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_21_1.png

[16]:

# Clip to area of interest
stations_clipped = wrangle.clip_stations(stations_gdf, aoi_gdf)

[17]:

# Map it
stations_clipped.plot()

[17]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_23_1.png

[18]:

# How many stations now?
len(stations_clipped)

[18]:

[19]:

# To save the results to a shapefile
#import os
#path = ''  #specify the path (folder/directory) to save it to
#stations_clipped.to_file(os.path.join(path,'CapeCod_stations.shp'))

Retrieve Characteristic Data

[20]:

# Now query for results
query['dataProfile'] = 'narrowResult'
res_narrow, md_narrow = wqp.get_results(**query)

[21]:

df = res_narrow
df

[21]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	ResultDetectionQuantitationLimitUrl	LaboratoryAccreditationIndicator	LaboratoryAccreditationAuthorityName	TaxonomistAccreditationIndicator	TaxonomistAccreditationAuthorityName	LabSamplePreparationUrl	ProviderName	ActivityStartDateTime	AnalysisStartDateTime	AnalysisEndDateTime
0	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.90100001	1901-08-09	09:00:00	EST	USGS-01116500	NWIS-66459391	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NWIS	1901-08-09 14:00:00+00:00	NaT	NaT
1	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.90100001	1901-08-09	09:00:00	EST	USGS-01116500	NWIS-66459396	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NWIS	1901-08-09 14:00:00+00:00	NaT	NaT
2	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.90100001	1901-08-09	09:00:00	EST	USGS-01116500	NWIS-66459398	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NWIS	1901-08-09 14:00:00+00:00	NaT	NaT
3	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.95300021	1952-10-21	09:00:00	EST	USGS-01104500	NWIS-66461873	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NWIS	1952-10-21 14:00:00+00:00	NaT	NaT
4	USGS-MA	USGS Massachusetts Water Science Center	nwisma.01.95300023	1952-10-24	11:45:00	EST	USGS-01108000	NWIS-66461945	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NWIS	1952-10-24 16:45:00+00:00	NaT	NaT
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
577222	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_DUCK_W_19560816_731708	1956-08-16	NaN	NaN	11NPSWRD_WQX-CACO_DUCK_W	STORET-740598866	NaN	NaN	...	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET	NaT	NaT	NaT
577223	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_DUCK_W_19560616_731704	1956-06-16	NaN	NaN	11NPSWRD_WQX-CACO_DUCK_W	STORET-740598862	NaN	NaN	...	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET	NaT	NaT	NaT
577224	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_DUCK_W_19560701_731705	1956-07-01	NaN	NaN	11NPSWRD_WQX-CACO_DUCK_W	STORET-740598863	NaN	NaN	...	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET	NaT	NaT	NaT
577225	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_GREAT_W_19550816_731703	1955-08-16	NaN	NaN	11NPSWRD_WQX-CACO_GREAT_W	STORET-740649462	NaN	NaN	...	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET	NaT	NaT	NaT
577226	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_SLOUGH_19520816_731702	1952-08-16	NaN	NaN	11NPSWRD_WQX-CACO_SLOUGH	STORET-740745800	NaN	Not Detected	...	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET	NaT	NaT	NaT

577227 rows × 81 columns

[22]:

# Map number of usable results at each station
gdf_count = visualize.map_counts(df, stations_clipped)
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)

[22]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_29_1.png

Harmonize Characteristic Results

Two options for functions to harmonize characteristics: harmonize_all() or harmonize_generic(). harmonize_all runs functions on all characteristics and lets you specify how to handle errors harmonize_generic runs functions only on the characteristic specified with char_val and lets you also choose output units, to keep intermediate columns and to do a quick report summarizing changes.

[23]:

# See Documentation
#harmonize_WQP.harmonize_all?
#harmonize_WQP.harmonize?

secchi disk depth

[24]:

# Each harmonize function has optional params, e.g., char_val is the characticName column value to use so we can send the entire df.
# Optional params: units='m', char_val='Depth, Secchi disk depth', out_col='Secchi', report=False)

# We start by demonstrating on secchi disk depth (units default to m, keep intermediate fields, see report)
df = harmonize.harmonize(df, "Depth, Secchi disk depth", errors="ignore", intermediate_columns=True, report=True)

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/clean.py:360: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'ResultMeasureValue: "BV" result cannot be used' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[mask & (df_out["QA_flag"].isna()), "QA_flag"] = flag
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: '%' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(1.9812, 'meter')> <Quantity(3.9624, 'meter')>
 <Quantity(3.5052, 'meter')> ... <Quantity(17.0, 'meter')>
 <Quantity(16.0, 'meter')> <Quantity(7.8, 'meter')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count    8961.000000
mean       -5.920774
std       298.744632
min     -9999.000000
25%         1.400000
50%         2.000000
75%         3.962400
max        27.000000
dtype: float64
Unusable results: 2231
Usable results with inferred units: 8
Results outside threshold (0.0 to 1786.5470213513204): 16

../_images/notebooks_Harmonize_CapeCod_Detailed_34_2.png

The threshold is based on standard deviations and is currently only used in the histogram.

[25]:

# Look at a table of just Secchi results and focus on subset of columns
cols = ['MonitoringLocationIdentifier', 'ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Units']
sechi_results = df.loc[df['CharacteristicName']=='Depth, Secchi disk depth', cols + ['Secchi']]
sechi_results

[25]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Units	Secchi
11317	11NPSWRD_WQX-SAMA_MEQ76_SB03	6.5	ft	NaN	ft	1.9811999999999999 meter
11334	11NPSWRD_WQX-SAMA_MEQ76_SB06	13.0	ft	NaN	ft	3.9623999999999997 meter
11376	11NPSWRD_WQX-SAMA_MEQ76_SB01	11.5	ft	NaN	ft	3.5051999999999994 meter
11401	11NPSWRD_WQX-SAMA_MEQ76_SB03	20.0	ft	NaN	ft	6.095999999999999 meter
11415	11NPSWRD_WQX-SAMA_MEQ76_SB05	11.0	ft	NaN	ft	3.3527999999999993 meter
...	...	...	...	...	...	...
577222	11NPSWRD_WQX-CACO_DUCK_W	10	m	NaN	m	10.0 meter
577223	11NPSWRD_WQX-CACO_DUCK_W	17	m	NaN	m	17.0 meter
577224	11NPSWRD_WQX-CACO_DUCK_W	16	m	NaN	m	16.0 meter
577225	11NPSWRD_WQX-CACO_GREAT_W	7.8	m	NaN	m	7.8 meter
577226	11NPSWRD_WQX-CACO_SLOUGH	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN

11192 rows × 6 columns

[26]:

# Look at unusable(NAN) results
sechi_results.loc[df['Secchi'].isna()]

[26]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Units	Secchi
27448	11113300-02-IPB	BV	m	ResultMeasureValue: "BV" result cannot be used	m	NaN
32889	11113300-07A-MER	BV	m	ResultMeasureValue: "BV" result cannot be used	m	NaN
99878	11113300-GRTKINSD	=3.4	m	ResultMeasureValue: "=3.4" result cannot be used	m	NaN
100885	11113300-CUBSDND	=1.7	m	ResultMeasureValue: "=1.7" result cannot be used	m	NaN
100978	11113300-CUBLDVLD	=1.4	m	ResultMeasureValue: "=1.4" result cannot be used	m	NaN
...	...	...	...	...	...	...
563277	11NPSWRD_WQX-CACO_GREAT_W	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN
563944	11NPSWRD_WQX-CACO_GREAT_W	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN
565619	11113300-DARLITD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
569087	11113300-WORSALD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
577226	11NPSWRD_WQX-CACO_SLOUGH	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN

2231 rows × 6 columns

[27]:

# look at the QA flag for first row from above
list(sechi_results.loc[df['Secchi'].isna()]['QA_flag'])[0]

[27]:

'ResultMeasureValue: "BV" result cannot be used'

[28]:

# All cases where there was a QA flag
sechi_results.loc[df['QA_flag'].notna()]

[28]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Units	Secchi
27448	11113300-02-IPB	BV	m	ResultMeasureValue: "BV" result cannot be used	m	NaN
32889	11113300-07A-MER	BV	m	ResultMeasureValue: "BV" result cannot be used	m	NaN
99878	11113300-GRTKINSD	=3.4	m	ResultMeasureValue: "=3.4" result cannot be used	m	NaN
100885	11113300-CUBSDND	=1.7	m	ResultMeasureValue: "=1.7" result cannot be used	m	NaN
100978	11113300-CUBLDVLD	=1.4	m	ResultMeasureValue: "=1.4" result cannot be used	m	NaN
...	...	...	...	...	...	...
563277	11NPSWRD_WQX-CACO_GREAT_W	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN
563944	11NPSWRD_WQX-CACO_GREAT_W	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN
565619	11113300-DARLITD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
569087	11113300-WORSALD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
577226	11NPSWRD_WQX-CACO_SLOUGH	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN

1273 rows × 6 columns

If both value and unit are missing nothing can be done, a unitless (NaN) value is assumed as to be in default units but a QA_flag is added

[29]:

# Aggregate secchi data by station
visualize.station_summary(sechi_results, 'Secchi')

[29]:

	MonitoringLocationIdentifier	cnt	mean
0	11113300-ANGSDND	45	3.765500
1	11113300-ARLSALD	3	3.400000
2	11113300-BARKIND	1	2.100000
3	11113300-BAYKIND	1	1.900000
4	11113300-BEADERD	46	3.666304
...	...	...	...
983	WWMD_VA-SH1	2	1.500000
984	WWMD_VA-SH2	2	1.800000
985	WWMD_VA-SR6A	1	0.500000
986	WWMD_VA-WF2	1	0.600000
987	WWMD_VA-WR5	1	2.000000

988 rows × 3 columns

[30]:

# Map number of usable results at each station
gdf_count = visualize.map_counts(sechi_results, stations_clipped)
gdf_count.plot(column='cnt', cmap='Blues', legend=True, scheme='quantiles', legend_kwds=legend_kwds)

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/mapclassify/classifiers.py:1653: UserWarning: Not enough unique values in array to form 5 classes. Setting k to 4.
  self.bins = quantile(y, k=k)

[30]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_42_2.png

[31]:

# Map average results at each station
gdf_avg = visualize.map_measure(sechi_results, stations_clipped, 'Secchi')
gdf_avg.plot(column='mean', cmap='OrRd', legend=True)

[31]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_43_1.png

Temperature

The default error=’raise’, makes it so that there is an error when there is a dimensionality error (i.e. when units can’t be converted). Here we would get the error: DimensionalityError: Cannot convert from ‘count’ (dimensionless) to ‘degree_Celsius’ ([temperature])

[32]:

#'Temperature, water'
# errors=‘ignore’, invalid dimension conversions will return the NaN.
df = harmonize.harmonize(df, 'Temperature, water', intermediate_columns=True, report=True, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(2.0, 'degree_Celsius')> <Quantity(1.0, 'degree_Celsius')>
 <Quantity(16.5, 'degree_Celsius')> ... <Quantity(4.0, 'degree_Celsius')>
 <Quantity(1.5, 'degree_Celsius')> <Quantity(3.9, 'degree_Celsius')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count    151125.000000
mean         16.311906
std           7.273072
min          -9.830000
25%          11.260000
50%          17.300000
75%          21.910000
max         910.000000
dtype: float64
Unusable results: 226
Usable results with inferred units: 217
Results outside threshold (0.0 to 59.95033855767852): 477

../_images/notebooks_Harmonize_CapeCod_Detailed_46_2.png

[33]:

# Look at what was changed
cols = ['MonitoringLocationIdentifier', 'ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Temperature', 'Units']
temperature_results = df.loc[df['CharacteristicName']=='Temperature, water', cols]
temperature_results

[33]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Temperature	Units
0	USGS-01116500	2.0	deg C	NaN	2.0 degree_Celsius	degC
5	USGS-01104200	1.0	deg C	NaN	1.0 degree_Celsius	degC
13	USGS-01105500	16.5	deg C	NaN	16.5 degree_Celsius	degC
14	USGS-01100000	17.0	deg C	NaN	17.0 degree_Celsius	degC
15	USGS-01108500	17.0	deg C	NaN	17.0 degree_Celsius	degC
...	...	...	...	...	...	...
577207	11NPSWRD_WQX-CACO_GULL	4.8	deg C	NaN	4.8 degree_Celsius	degC
577208	11NPSWRD_WQX-CACO_GULL	9.1	deg C	NaN	9.1 degree_Celsius	degC
577209	11NPSWRD_WQX-CACO_GULL	4	deg C	NaN	4.0 degree_Celsius	degC
577210	11NPSWRD_WQX-CACO_GULL	1.5	deg C	NaN	1.5 degree_Celsius	degC
577211	11NPSWRD_WQX-CACO_GULL	3.9	deg C	NaN	3.9 degree_Celsius	degC

151351 rows × 6 columns

[34]:

# Examine deg F
temperature_results.loc[df['ResultMeasure/MeasureUnitCode'] == 'deg F']

[34]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Temperature	Units
11300	11NPSWRD_WQX-SAMA_MEQ76_SB01	59	deg F	NaN	15.000000000000057 degree_Celsius	degF
11302	11NPSWRD_WQX-SAMA_MEQ76_SB02	60	deg F	NaN	15.5555555555556 degree_Celsius	degF
11307	11NPSWRD_WQX-SAMA_MEQ76_SB02	58	deg F	NaN	14.444444444444457 degree_Celsius	degF
11310	11NPSWRD_WQX-SAMA_MEQ76_SB03	62	deg F	NaN	16.666666666666686 degree_Celsius	degF
11316	11NPSWRD_WQX-SAMA_MEQ76_SB03	60	deg F	NaN	15.5555555555556 degree_Celsius	degF
...	...	...	...	...	...	...
576586	11NPSWRD_WQX-SAIR_DMF11_HS1	56	deg F	NaN	13.333333333333371 degree_Celsius	degF
576587	11NPSWRD_WQX-SAIR_DMF11_OS3	62	deg F	NaN	16.666666666666686 degree_Celsius	degF
576588	11NPSWRD_WQX-SAIR_DMF11_OS4	61	deg F	NaN	16.111111111111143 degree_Celsius	degF
576594	11NPSWRD_WQX-SAIR_DMF11_S2	43	deg F	NaN	6.111111111111143 degree_Celsius	degF
576595	11NPSWRD_WQX-SAIR_DMF11_OS1	59	deg F	NaN	15.000000000000057 degree_Celsius	degF

695 rows × 6 columns

In the above we can see examples where the results were in deg F and in the result field they’ve been converted into degree_Celsius

[35]:

# Examine missing units
temperature_results.loc[df['ResultMeasure/MeasureUnitCode'].isna()]

[35]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Temperature	Units
28268	USGS-414654070002901	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
383203	11113300-HHPS056	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
383209	11113300-HHPS055	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
383213	11113300-HHPS061	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
461985	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
...	...	...	...	...	...	...
462721	AQUINNAH-SQUIB # 33	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
462722	AQUINNAH-SQUIB # 33	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
462723	AQUINNAH-SQUIB # 33	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
462734	AQUINNAH-SQUIB # 33	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
470083	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC

221 rows × 6 columns

We can see where the units were missing, the results were assumed to be in degree_Celsius already

[36]:

# This is also noted in the QA_flag field
list(temperature_results.loc[df['ResultMeasure/MeasureUnitCode'].isna(), 'QA_flag'])[0]

[36]:

'ResultMeasureValue: missing (NaN) result; ResultMeasure/MeasureUnitCode: MISSING UNITS, degC assumed'

[37]:

# Look for any without usable results
temperature_results.loc[df['Temperature'].isna()]

[37]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Temperature	Units
28268	USGS-414654070002901	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
80417	11NPSWRD_WQX-SAIR_SWC_SR3	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC
104811	11113300-HHPS073	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
108592	11113300-HHPS058	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
108641	11113300-HHPS071	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
...	...	...	...	...	...	...
435657	11113300-HHPS072	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
435687	11113300-HHPS073	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
523147	11NPSWRD_WQX-SAIR_SWC_SR6	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC
523872	11NPSWRD_WQX-SAIR_SWC_SR7	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC
526312	11NPSWRD_WQX-SAIR_SWC_SRT9	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC

226 rows × 6 columns

[38]:

# Aggregate temperature data by station
visualize.station_summary(temperature_results, 'Temperature')

[38]:

	MonitoringLocationIdentifier	cnt	mean
0	11113300-00-SPB	21	10.909524
1	11113300-00F-KLY	3	20.233333
2	11113300-01-BAK	10	20.600000
3	11113300-01-BVR	1	21.000000
4	11113300-01-CTP	39	24.438462
...	...	...	...
6563	WWMD_VA-WWE1149PI	1	18.800000
6564	WWMD_VA-WWE1150PI	1	21.300000
6565	WWMD_VA-WWE1151PI	2	20.700000
6566	WWMD_VA-WWE1152PI	1	20.000000
6567	WWMD_VA-WWE1153PI	1	20.800000

6568 rows × 3 columns

[39]:

# Map number of usable results at each station
gdf_count = visualize.map_counts(temperature_results, stations_clipped)
gdf_count.plot(column='cnt', cmap='Blues', legend=True, scheme='quantiles', legend_kwds=legend_kwds)

[39]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_55_1.png

[40]:

# Map average results at each station
gdf_avg = visualize.map_measure(temperature_results, stations_clipped, 'Temperature')
gdf_avg.plot(column='mean', cmap='OrRd', legend=True)

[40]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_56_1.png

Dissolved Oxygen (DO)

[41]:

# look at Dissolved oxygen (DO), but this time without intermediate fields
df = harmonize.harmonize(df, 'Dissolved oxygen (DO)')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(8.0, 'milligram / liter')> <Quantity(8.0, 'milligram / liter')>
 <Quantity(10.0, 'milligram / liter')> ...
 <Quantity(10.0, 'milligram / liter')>
 <Quantity(10.0, 'milligram / liter')>
 <Quantity(9.0, 'milligram / liter')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

Note: Imediately when we run a harmonization function without the intermediate fields they’re deleted.

[42]:

# Look at what was changed
cols = ['MonitoringLocationIdentifier', 'ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'DO']
do_res = df.loc[df['CharacteristicName']=='Dissolved oxygen (DO)', cols]
do_res

[42]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	DO
2617	11NPSWRD_WQX-SAMA_EPG_SH13	8.0	mg/L	NaN	8.0 milligram / liter
2624	11NPSWRD_WQX-SAMA_EPG_SH18	8.0	mg/L	NaN	8.0 milligram / liter
2627	11NPSWRD_WQX-SAMA_EPG_SH3	10.0	mg/L	NaN	10.0 milligram / liter
2629	11NPSWRD_WQX-SAMA_EPG_SH1	9.0	mg/L	NaN	9.0 milligram / liter
2635	11NPSWRD_WQX-SAMA_EPG_SH7	4.0	mg/L	NaN	4.0 milligram / liter
...	...	...	...	...	...
576585	11NPSWRD_WQX-SAIR_DMF11_S1	8	mg/L	NaN	8.0 milligram / liter
576589	11NPSWRD_WQX-SAIR_DMF11_S3	10	mg/L	NaN	10.0 milligram / liter
576590	11NPSWRD_WQX-SAIR_DMF11_S4	10	mg/L	NaN	10.0 milligram / liter
576592	11NPSWRD_WQX-SAIR_DMF11_S5	10	mg/L	NaN	10.0 milligram / liter
576597	11NPSWRD_WQX-SAIR_DMF11_S3	9	mg/L	NaN	9.0 milligram / liter

95156 rows × 5 columns

[43]:

do_res.loc[do_res['ResultMeasure/MeasureUnitCode']!='mg/l']

[43]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	DO
2617	11NPSWRD_WQX-SAMA_EPG_SH13	8.0	mg/L	NaN	8.0 milligram / liter
2624	11NPSWRD_WQX-SAMA_EPG_SH18	8.0	mg/L	NaN	8.0 milligram / liter
2627	11NPSWRD_WQX-SAMA_EPG_SH3	10.0	mg/L	NaN	10.0 milligram / liter
2629	11NPSWRD_WQX-SAMA_EPG_SH1	9.0	mg/L	NaN	9.0 milligram / liter
2635	11NPSWRD_WQX-SAMA_EPG_SH7	4.0	mg/L	NaN	4.0 milligram / liter
...	...	...	...	...	...
576585	11NPSWRD_WQX-SAIR_DMF11_S1	8	mg/L	NaN	8.0 milligram / liter
576589	11NPSWRD_WQX-SAIR_DMF11_S3	10	mg/L	NaN	10.0 milligram / liter
576590	11NPSWRD_WQX-SAIR_DMF11_S4	10	mg/L	NaN	10.0 milligram / liter
576592	11NPSWRD_WQX-SAIR_DMF11_S5	10	mg/L	NaN	10.0 milligram / liter
576597	11NPSWRD_WQX-SAIR_DMF11_S3	9	mg/L	NaN	9.0 milligram / liter

93859 rows × 5 columns

Though there were no results in %, the conversion from percent saturation (%) to mg/l is special. This equation is being improved by integrating tempertaure and pressure instead of assuming STP (see DO_saturation())

[44]:

# Aggregate data by station
visualize.station_summary(do_res, 'DO')

[44]:

	MonitoringLocationIdentifier	cnt	mean
0	11113300-00F-KLY	3	4.040000
1	11113300-01-BAK	10	5.697000
2	11113300-01-BVR	1	6.700000
3	11113300-01-CTP	37	6.945135
4	11113300-01-GOL	1	4.400000
...	...	...	...
2875	WWMD_VA-SR5	1	10.810000
2876	WWMD_VA-WF2	2	6.800000
2877	WWMD_VA-WI1	1	9.510000
2878	WWMD_VA-WR2X	2	6.000000
2879	WWMD_VA-WR5	1	8.355000

2880 rows × 3 columns

[45]:

# Map number of usable results at each station
gdf_count = visualize.map_counts(do_res, stations_clipped)
gdf_count.plot(column='cnt', cmap='Blues', legend=True, scheme='quantiles', legend_kwds=legend_kwds)

[45]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_64_1.png

[46]:

# Map average results at each station
gdf_avg = visualize.map_measure(do_res, stations_clipped, 'DO')
gdf_avg.plot(column='mean', cmap='OrRd', legend=True)

[46]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_65_1.png

pH

[47]:

# pH, this time looking at a report
df = harmonize.harmonize(df, "pH", errors="ignore", report=True)

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'mV' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(6.7, 'dimensionless')> <Quantity(7.1, 'dimensionless')>
 <Quantity(6.3, 'dimensionless')> ... <Quantity(6.6, 'dimensionless')>
 <Quantity(6.8, 'dimensionless')> <Quantity(7.1, 'dimensionless')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count    107912.000000
mean          6.683628
std           4.078693
min          -3.124705
25%           5.870000
50%           6.800000
75%           7.670000
max         788.000000
dtype: float64
Unusable results: 4814
Usable results with inferred units: 86358
Results outside threshold (0.0 to 31.15578654092979): 14

../_images/notebooks_Harmonize_CapeCod_Detailed_67_2.png

Note the warnings that occur when a unit is not recognized by the package. These occur even when report=False. Future versions could include these as defined units for pH, but here it wouldn’t alter results.

[48]:

df.loc[df['CharacteristicName']=='pH', ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'pH']]

[48]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	pH
1	6.7	std units	NaN	6.7 dimensionless
3	7.1	std units	NaN	7.1 dimensionless
4	6.3	std units	NaN	6.3 dimensionless
6	5.7	std units	NaN	5.7 dimensionless
7	6.9	std units	NaN	6.9 dimensionless
...	...	...	...	...
577119	6.6	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.6 dimensionless
577126	6.9	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.9 dimensionless
577150	6.6	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.6 dimensionless
577165	6.8	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.8 dimensionless
577192	7.1	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	7.1 dimensionless

112726 rows × 4 columns

‘None’ is uninterpretable and replaced with NaN, which then gets replaced with ‘dimensionless’ since pH is unitless

Salinity

[49]:

# Salinity
df = harmonize.harmonize(df, 'Salinity', report=True, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/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/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'deg C' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(31.0, 'Practical_Salinity_Units')>
 <Quantity(27.0, 'Practical_Salinity_Units')>
 <Quantity(33.0, 'Practical_Salinity_Units')> ...
 <Quantity(31.0, 'Practical_Salinity_Units')>
 <Quantity(32.0, 'Practical_Salinity_Units')>
 <Quantity(27.0, 'Practical_Salinity_Units')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count    61120.000000
mean        23.209709
std         20.530382
min        -30.000000
25%         13.160000
50%         30.000000
75%         31.500000
max       4003.482834
dtype: float64
Unusable results: 228
Usable results with inferred units: 1
Results outside threshold (0.0 to 146.39200087840806): 3

../_images/notebooks_Harmonize_CapeCod_Detailed_72_2.png

[50]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Salinity']
df.loc[df['CharacteristicName']=='Salinity', cols]

[50]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Salinity
2620	31.0	ppth	NaN	31.0 Practical_Salinity_Units
2623	27.0	ppth	NaN	27.0 Practical_Salinity_Units
2625	33.0	ppth	NaN	33.0 Practical_Salinity_Units
2631	30.0	ppth	NaN	30.0 Practical_Salinity_Units
2633	32.5	ppth	NaN	32.5 Practical_Salinity_Units
...	...	...	...	...
576576	32.5	ppth	NaN	32.5 Practical_Salinity_Units
576584	31.5	ppth	NaN	31.5 Practical_Salinity_Units
576591	31.0	ppth	NaN	31.0 Practical_Salinity_Units
576593	32.0	ppth	NaN	32.0 Practical_Salinity_Units
576596	27.0	ppth	NaN	27.0 Practical_Salinity_Units

61348 rows × 4 columns

Nitrogen

[51]:

# Nitrogen
df = harmonize.harmonize(df, 'Nitrogen', report=True)

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/basis.py:343: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'as N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[mask, basis_col] = basis
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:484: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '['as N' 'as N' 'as N' ... nan nan nan]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  self.df[c_mask] = basis.basis_from_method_spec(self.df[c_mask])
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'cm3/g' UNDEFINED UNIT for Nitrogen
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/pandas/core/construction.py:627: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  data = np.asarray(data)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/pandas/core/construction.py:627: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  data = np.asarray(data)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(1900.0, 'milligram / liter')>
 <Quantity(2800.0, 'milligram / liter')>
 <Quantity(1100.0, 'milligram / liter')> ...
 <Quantity(1.7892171, 'milligram / liter')>
 <Quantity(0.2243001, 'milligram / liter')>
 <Quantity(0.1150221, 'milligram / liter')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/domains.py:277: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  sub_df[cols[2]] = sub_df[cols[2]].fillna(sub_df[cols[1]])  # new_fract
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/domains.py:277: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  sub_df[cols[2]] = sub_df[cols[2]].fillna(sub_df[cols[1]])  # new_fract
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/domains.py:277: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  sub_df[cols[2]] = sub_df[cols[2]].fillna(sub_df[cols[1]])  # new_fract
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/domains.py:277: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  sub_df[cols[2]] = sub_df[cols[2]].fillna(sub_df[cols[1]])  # new_fract

-Usable results-
count    2508.000000
mean        6.264310
std       104.802798
min         0.000800
25%         0.078000
50%         0.264375
75%         1.030000
max      2800.000000
dtype: float64
Unusable results: 243
Usable results with inferred units: 0
Results outside threshold (0.0 to 635.0810968269222): 6

../_images/notebooks_Harmonize_CapeCod_Detailed_75_2.png

[52]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Nitrogen']
df.loc[df['CharacteristicName']=='Nitrogen', cols]

[52]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Nitrogen
17836	.19	%	NaN	1900.0 milligram / liter
17837	.28	%	NaN	2800.0000000000005 milligram / liter
17838	NaN	%	ResultMeasureValue: missing (NaN) result	NaN
17848	.11	%	NaN	1100.0 milligram / liter
17854	.28	%	NaN	2800.0000000000005 milligram / liter
...	...	...	...	...
526271	109.59	umol	NaN	1.5353559 milligram / liter
526424	25.06	umol	NaN	0.3510906 milligram / liter
526436	127.71	umol	NaN	1.7892171 milligram / liter
526483	16.01	umol	NaN	0.2243001 milligram / liter
526484	8.21	umol	NaN	0.1150221 milligram / liter

2751 rows × 4 columns

Conductivity

[53]:

# Conductivity
df = harmonize.harmonize(df, 'Conductivity', report=True, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(327.0, 'microsiemens / centimeter')>
 <Quantity(127.0, 'microsiemens / centimeter')>
 <Quantity(345.0, 'microsiemens / centimeter')> ...
 <Quantity(433.0, 'microsiemens / centimeter')>
 <Quantity(444.0, 'microsiemens / centimeter')>
 <Quantity(424.0, 'microsiemens / centimeter')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count     13563.000000
mean      27191.912855
std       18509.783617
min           0.000000
25%       16480.300000
50%       30669.300000
75%       41824.300000
max      992000.000000
dtype: float64
Unusable results: 82
Usable results with inferred units: 0
Results outside threshold (0.0 to 138250.6145569042): 3

../_images/notebooks_Harmonize_CapeCod_Detailed_78_2.png

[54]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Conductivity']
df.loc[df['CharacteristicName']=='Conductivity', cols]

[54]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Conductivity
96800	327	uS/cm	NaN	327.0 microsiemens / centimeter
96803	127	uS/cm	NaN	127.0 microsiemens / centimeter
96972	345	uS/cm	NaN	345.0 microsiemens / centimeter
96974	348	uS/cm	NaN	348.0 microsiemens / centimeter
96977	344	uS/cm	NaN	344.0 microsiemens / centimeter
...	...	...	...	...
482620	610	uS/cm	NaN	610.0 microsiemens / centimeter
482724	150	uS/cm	NaN	150.0 microsiemens / centimeter
483348	433	uS/cm	NaN	433.0 microsiemens / centimeter
484074	444	uS/cm	NaN	444.0 microsiemens / centimeter
484703	424	uS/cm	NaN	424.0 microsiemens / centimeter

13645 rows × 4 columns

Chlorophyll a

[55]:

# Chlorophyll a
df = harmonize.harmonize(df, 'Chlorophyll a', report=True)

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'ppb' UNDEFINED UNIT for Chlorophyll
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'ug/m3' UNDEFINED UNIT for Chlorophyll
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'ug/cm2' UNDEFINED UNIT for Chlorophyll
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(0.0027, 'milligram / liter')>
 <Quantity(0.0017, 'milligram / liter')>
 <Quantity(0.000233, 'milligram / liter')> ...
 <Quantity(1.46, 'milligram / liter')>
 <Quantity(0.04, 'milligram / liter')>
 <Quantity(4.22, 'milligram / liter')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count    14059.00000
mean         0.17631
std          1.80612
min         -0.00240
25%          0.00180
50%          0.00350
75%          0.00730
max         92.90000
dtype: float64
Unusable results: 204
Usable results with inferred units: 9
Results outside threshold (0.0 to 11.01303017093192): 64

../_images/notebooks_Harmonize_CapeCod_Detailed_81_2.png

[56]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Chlorophyll']
df.loc[df['CharacteristicName']=='Chlorophyll a', cols]

[56]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Chlorophyll
12043	2.70	mg/m2	NaN	0.002700000000000001 milligram / liter
12044	1.70	mg/m2	NaN	0.0017000000000000003 milligram / liter
12057	0.233	mg/m2	NaN	0.00023300000000000008 milligram / liter
12064	0.200	mg/m2	NaN	0.00020000000000000006 milligram / liter
12084	0.912	mg/m2	NaN	0.0009120000000000003 milligram / liter
...	...	...	...	...
556627	7.28	ug/m3	ResultMeasure/MeasureUnitCode: 'ug/m3' UNDEFIN...	7.28 milligram / liter
556628	12.51	ug/m3	ResultMeasure/MeasureUnitCode: 'ug/m3' UNDEFIN...	12.51 milligram / liter
556629	1.46	ug/m3	ResultMeasure/MeasureUnitCode: 'ug/m3' UNDEFIN...	1.46 milligram / liter
556630	0.04	ug/m3	ResultMeasure/MeasureUnitCode: 'ug/m3' UNDEFIN...	0.04 milligram / liter
556631	4.22	ug/m3	ResultMeasure/MeasureUnitCode: 'ug/m3' UNDEFIN...	4.22 milligram / liter

14263 rows × 4 columns

Organic Carbon

[57]:

# Organic carbon (%)
df = harmonize.harmonize(df, 'Organic carbon', report=True, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'umol/L * H2O' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(5.5, 'milligram / liter')> <Quantity(8.5, 'milligram / liter')>
 <Quantity(7.0, 'milligram / liter')> ...
 <Quantity(9.3, 'milligram / liter')> <Quantity(0.2, 'milligram / liter')>
 <Quantity(0.4, 'milligram / liter')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count      6196.000000
mean       5240.393408
std       35437.445450
min      -90000.000000
25%           1.800000
50%           4.900000
75%           8.872500
max      530000.000000
dtype: float64
Unusable results: 89
Usable results with inferred units: 0
Results outside threshold (0.0 to 217865.06610661917): 40

../_images/notebooks_Harmonize_CapeCod_Detailed_84_2.png

[58]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Carbon']
df.loc[df['CharacteristicName']=='Organic carbon', cols]

[58]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Carbon
6538	5.5	mg/l	NaN	5.5 milligram / liter
7245	8.5	mg/l	NaN	8.5 milligram / liter
8296	7.0	mg/l	NaN	7.0 milligram / liter
8591	4.3	mg/l	NaN	4.3 milligram / liter
8754	4.9	mg/l	NaN	4.9 milligram / liter
...	...	...	...	...
563970	63900	ug/g	NaN	63900.000000000015 milligram / liter
564619	15200	ug/g	NaN	15200.000000000004 milligram / liter
570862	9.3	mg/l	NaN	9.3 milligram / liter
575466	0.2	mg/l	NaN	0.2 milligram / liter
575467	0.4	mg/l	NaN	0.4 milligram / liter

6285 rows × 4 columns

Turbidity (NTU)

[59]:

# Turbidity (NTU)
df = harmonize.harmonize(df, 'Turbidity', report=True, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/harmonize.py:149: UserWarning: Bad Turbidity unit: count
  warn(f"Bad Turbidity unit: {unit}")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(7.5701, 'Nephelometric_Turbidity_Units')>
 <Quantity(18.9773, 'Nephelometric_Turbidity_Units')>
 <Quantity(570.7023, 'Nephelometric_Turbidity_Units')> ...
 <Quantity(1.9, 'Nephelometric_Turbidity_Units')>
 <Quantity(1.7, 'Nephelometric_Turbidity_Units')>
 <Quantity(2.0, 'Nephelometric_Turbidity_Units')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count    34827.000000
mean        15.519654
std         71.703547
min       -999.000000
25%          1.250000
50%          2.500000
75%          5.700000
max       4100.000000
dtype: float64
Unusable results: 3418
Usable results with inferred units: 275
Results outside threshold (0.0 to 445.740934981733): 215

../_images/notebooks_Harmonize_CapeCod_Detailed_87_2.png

[60]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Turbidity']
df.loc[df['CharacteristicName']=='Turbidity', cols]

[60]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Turbidity
290	1.0	mg/l SiO2	NaN	7.5701 Nephelometric_Turbidity_Units
523	1	JTU	NaN	18.9773 Nephelometric_Turbidity_Units
531	30	JTU	NaN	570.7023 Nephelometric_Turbidity_Units
535	2	JTU	NaN	38.0023 Nephelometric_Turbidity_Units
540	7	JTU	NaN	133.1273 Nephelometric_Turbidity_Units
...	...	...	...	...
567067	4.0	mg/l SiO2	NaN	30.378500000000003 Nephelometric_Turbidity_Units
573181	2.3	NTU	NaN	2.3 Nephelometric_Turbidity_Units
575929	1.9	NTU	NaN	1.9 Nephelometric_Turbidity_Units
575977	1.7	NTU	NaN	1.7 Nephelometric_Turbidity_Units
575982	2	NTU	NaN	2.0 Nephelometric_Turbidity_Units

38245 rows × 4 columns

Sediment

[61]:

# Sediment
df = harmonize.harmonize(df, 'Sediment', report=False, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'g / H2O' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:134: FutureWarning: The behavior of array concatenation with empty entries is deprecated. In a future version, this will no longer exclude empty items when determining the result dtype. To retain the old behavior, exclude the empty entries before the concat operation.
  return pandas.concat(lst_series).sort_index()

[62]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Sediment']
df.loc[df['CharacteristicName']=='Sediment', cols]

[62]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Sediment
246472	0.0024	g	NaN	NaN
246484	0.0034	g	NaN	NaN
246497	0.0016	g	NaN	NaN
246506	0.0046	g	NaN	NaN
246513	0.0016	g	NaN	NaN
...	...	...	...	...
365519	0.0049	g	NaN	NaN
368318	0.0070	g	NaN	NaN
368593	0.0093	g	NaN	NaN
371133	0.0020	g	NaN	NaN
371227	0.0049	g	NaN	NaN

4410 rows × 4 columns

Phosphorus

Note: must be merged w/ activities (package runs query by site if not already merged)

[63]:

# Phosphorus
df = harmonize.harmonize(df, 'Phosphorus', errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/pandas/core/construction.py:627: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  data = np.asarray(data)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/pandas/core/construction.py:627: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  data = np.asarray(data)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/pandas/core/construction.py:627: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  data = np.asarray(data)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'umol/L * H2O' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(0.56, 'milligram / liter')>
 <Quantity(0.89, 'milligram / liter')>
 <Quantity(1.7, 'milligram / liter')> ...
 <Quantity(0.01, 'milligram / liter')>
 <Quantity(0.011, 'milligram / liter')>
 <Quantity(0.007, 'milligram / liter')>]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

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

Note: warnings for unexpected characteristic fractions. Fractions are each seperated out into their own result column.

[64]:

# All Phosphorus
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'TDP_Phosphorus']
df.loc[df['Phosphorus'].notna(), cols]

[64]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	TDP_Phosphorus
1194	0.56	mg/l PO4	NaN	NaN
1290	0.89	mg/l PO4	NaN	NaN
1310	1.7	mg/l PO4	NaN	NaN
1355	0.28	mg/l PO4	NaN	NaN
1400	0.07	mg/l PO4	NaN	NaN
...	...	...	...	...
575998	0.024	mg/L	NaN	NaN
576642	0.01	mg/L	NaN	NaN
576644	0.01	mg/L	NaN	NaN
576738	0.011	mg/L	NaN	NaN
576768	0.007	mg/L	NaN	NaN

19507 rows × 4 columns

[65]:

# Total phosphorus
df.loc[df['TP_Phosphorus'].notna(), cols]

[65]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	TDP_Phosphorus
1194	0.56	mg/l PO4	NaN	NaN
1290	0.89	mg/l PO4	NaN	NaN
1310	1.7	mg/l PO4	NaN	NaN
1355	0.28	mg/l PO4	NaN	NaN
1400	0.07	mg/l PO4	NaN	NaN
...	...	...	...	...
575998	0.024	mg/L	NaN	NaN
576642	0.01	mg/L	NaN	NaN
576644	0.01	mg/L	NaN	NaN
576738	0.011	mg/L	NaN	NaN
576768	0.007	mg/L	NaN	NaN

14234 rows × 4 columns

[66]:

# Total dissolved phosphorus
df.loc[df['TDP_Phosphorus'].notna(), cols]

[66]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	TDP_Phosphorus
1483	0.080	mg/l as P	NaN	0.08 milligram / liter
1488	0.040	mg/l as P	NaN	0.04 milligram / liter
1493	0.290	mg/l as P	NaN	0.29 milligram / liter
1499	0.290	mg/l as P	NaN	0.29 milligram / liter
1503	0.110	mg/l as P	NaN	0.11 milligram / liter
...	...	...	...	...
493033	0.03	mg/L	NaN	0.03 milligram / liter
493221	0.05	mg/L	NaN	0.05 milligram / liter
493229	0.02	mg/L	NaN	0.02 milligram / liter
493296	0.03	mg/L	NaN	0.03 milligram / liter
575465	0.07	mg/l as P	NaN	0.07 milligram / liter

4857 rows × 4 columns

[67]:

# All other phosphorus sample fractions
df.loc[df['Other_Phosphorus'].notna(), cols]

[67]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	TDP_Phosphorus
27579	140	mg/kg as P	NaN	NaN
27580	570	mg/kg as P	NaN	NaN
27583	100	mg/kg as P	NaN	NaN
27584	630	mg/kg as P	NaN	NaN
27586	190	mg/kg as P	NaN	NaN
...	...	...	...	...
571217	1300	mg/kg as P	NaN	NaN
571218	800	mg/kg as P	NaN	NaN
571219	2100	mg/kg as P	NaN	NaN
571220	1900	mg/kg as P	NaN	NaN
571221	1700	mg/kg as P	NaN	NaN

416 rows × 4 columns

Bacteria

Some equivalence assumptions are built-in where bacteria counts that are not equivalent are treated as such because there is no standard way to convert from one to another.

Fecal Coliform

[68]:

# Known unit with bad dimensionality ('Colony_Forming_Units * milliliter')
df = harmonize.harmonize(df, 'Fecal Coliform', report=True, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'CFU/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'cfu/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'MPN/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(1.0, 'Colony_Forming_Units / milliliter')>
 <Quantity(4200.0, 'Colony_Forming_Units / milliliter')>
 <Quantity(410.0, 'Colony_Forming_Units / milliliter')> ... nan nan nan]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count      2462.000000
mean        910.512591
std        6103.365912
min           0.000000
25%          24.000000
50%          93.000000
75%         400.000000
max      250000.000000
dtype: float64
Unusable results: 5815
Usable results with inferred units: 1
Results outside threshold (0.0 to 37530.70806108734): 7

../_images/notebooks_Harmonize_CapeCod_Detailed_103_2.png

[69]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Fecal_Coliform']
df.loc[df['CharacteristicName']=='Fecal Coliform', cols]

[69]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Fecal_Coliform
2	1	cfu/100ml	NaN	1.0 Colony_Forming_Units / milliliter
1463	4200	cfu/100ml	NaN	4200.0 Colony_Forming_Units / milliliter
1538	410	cfu/100ml	NaN	410.0 Colony_Forming_Units / milliliter
1545	10000	cfu/100ml	NaN	10000.0 Colony_Forming_Units / milliliter
1547	10000	cfu/100ml	NaN	10000.0 Colony_Forming_Units / milliliter
...	...	...	...	...
575994	400	#/100mL	NaN	NaN
575995	300	#/100mL	NaN	NaN
575996	24000	#/100mL	NaN	NaN
576797	180	#/100mL	NaN	NaN
577117	NaN	#/100mL	ResultMeasureValue: missing (NaN) result	NaN

8277 rows × 4 columns

Excherichia Coli

[70]:

# Known unit with bad dimensionality ('Colony_Forming_Units * milliliter')
df = harmonize.harmonize(df, 'Escherichia coli', report=True, errors='ignore')

/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:158: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.
  for bad_meas in pandas.unique(bad_measures):
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'CFUcol/100mL' UNDEFINED UNIT for E_coli
  warn("WARNING: " + problem)
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'CFU/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'count' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'cfu/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: '%' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'MPN/100mL' converted to NaN
  warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.9.25/x64/lib/python3.9/site-packages/harmonize_wq/wq_data.py:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[nan nan nan ... nan nan nan]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_out.loc[m_mask, self.out_col] = convert_unit_series(**params)

-Usable results-
count     5190.000000
mean       245.858339
std       1752.146999
min          0.000000
25%          0.500000
50%         30.000000
75%        127.750000
max      72000.000000
dtype: float64
Unusable results: 27840
Usable results with inferred units: 0
Results outside threshold (0.0 to 10758.740332329791): 14

../_images/notebooks_Harmonize_CapeCod_Detailed_106_2.png

[71]:

cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'E_coli']
df.loc[df['CharacteristicName']=='Escherichia coli', cols]

[71]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	E_coli
28967	930	MPN/100mL	NaN	NaN
29097	430	MPN/100mL	NaN	NaN
30225	430	MPN/100mL	NaN	NaN
32346	40	MPN/100mL	NaN	NaN
32355	90	MPN/100mL	NaN	NaN
...	...	...	...	...
548468	89	#/100mL	NaN	NaN
548473	59	#/100mL	NaN	NaN
548558	1	#/100mL	NaN	NaN
548697	17	#/100mL	NaN	NaN
548823	5	#/100mL	NaN	NaN

33030 rows × 4 columns

Combining Salinity and Conductivity

Convert module has various functions to convert from one unit or characteristic to another. Some of these are used within a single characteristic during harmonization (e.g. DO saturation to concentration) while others are intended to model one characteristic as an indicator of another (e.g. estimate salinity from conductivity).

Note: this should only be done after both characteristic fields have been harmonized. Results before and after should be inspected, thresholds for outliers applied, and consider adding a QA_flag for modeled data.

Explore Salinity results:

[72]:

from harmonize_wq import convert

[73]:

# First note initial Salinity info
lst = [x.magnitude for x in list(df['Salinity'].dropna())]
q_sum = sum(lst)
print('Range: {} to {}'.format(min(lst), max(lst)))
print('Results: {} \nMean: {} PSU'.format(len(lst), q_sum/len(lst)))

Range: -30.0 to 4003.4828342857154
Results: 61120
Mean: 23.20970929655375 PSU

[74]:

# Identify extreme outliers
[x for x in lst if x >3200]

[74]:

[4003.4828342857154]

Other fields like units and QA_flag may help understand what caused high values and what results might need to be dropped from consideration

[75]:

# Columns to focus on
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Salinity']

[76]:

# Look at important fields for max 5 values
salinity_series = df['Salinity'][df['Salinity'].notna()]
salinity_series.sort_values(ascending=False, inplace=True)
df[cols][df['Salinity'].isin(salinity_series[0:5])]

[76]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Salinity
65460	4980	mg/mL @25C	ResultTemperatureBasisText: updated from 25 de...	4003.4828342857154 Practical_Salinity_Units
163496	804	ppth	NaN	804.0 Practical_Salinity_Units
301168	77.6666666666667	ppt	NaN	77.6666666666667 Practical_Salinity_Units
470190	70.62	ppth	NaN	70.62 Practical_Salinity_Units
470260	71.49	ppth	NaN	71.49 Practical_Salinity_Units

Detection limits may help understand what caused low values and what results might need to be dropped or updated

[77]:

df = wrangle.add_detection(df, 'Salinity')
cols+=['ResultDetectionConditionText',
       'DetectionQuantitationLimitTypeName',
       'DetectionQuantitationLimitMeasure/MeasureValue',
       'DetectionQuantitationLimitMeasure/MeasureUnitCode']

[78]:

# Look at important fields for min 5 values (often multiple 0.0)
df[cols][df['Salinity'].isin(salinity_series[-5:])]

[78]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Salinity	ResultDetectionConditionText	DetectionQuantitationLimitTypeName	DetectionQuantitationLimitMeasure/MeasureValue	DetectionQuantitationLimitMeasure/MeasureUnitCode
23795	0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
23797	0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
23803	0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
23825	0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
23829	0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...
542778	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	Reporting limit	0.0	ppt
543228	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	Reporting limit	0.0	ppt
544507	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	Reporting limit	0.0	ppt
544730	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	Reporting limit	0.0	ppt
545537	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	Reporting limit	0.0	ppt

876 rows × 8 columns

Explore conductivity results:

[79]:

# Create series for Conductivity values
cond_series = df['Conductivity'].dropna()
cond_series

[79]:

96800     327.0 microsiemens / centimeter
96803     127.0 microsiemens / centimeter
96972     345.0 microsiemens / centimeter
96974     348.0 microsiemens / centimeter
96977     344.0 microsiemens / centimeter
                       ...
482620    610.0 microsiemens / centimeter
482724    150.0 microsiemens / centimeter
483348    433.0 microsiemens / centimeter
484074    444.0 microsiemens / centimeter
484703    424.0 microsiemens / centimeter
Name: Conductivity, Length: 13563, dtype: object

Conductivity thresholds from Freshwater Explorer: 10 > x < 5000 us/cm, use a higher threshold for coastal waters

[80]:

# Sort and check other relevant columns before converting (e.g. Salinity)
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Salinity', 'Conductivity']
df.sort_values(by=['Conductivity'], ascending=False, inplace=True)
df.loc[df['Conductivity'].notna(), cols]

[80]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Salinity	Conductivity
386985	992	mS/cm	NaN	NaN	992000.0 microsiemens / centimeter
386996	270.2	mS/cm	NaN	NaN	270200.0 microsiemens / centimeter
387616	241.1	mS/cm	NaN	NaN	241100.0 microsiemens / centimeter
474268	57700	uS/cm	NaN	NaN	57700.0 microsiemens / centimeter
347883	52.418	mS/cm	NaN	NaN	52418.0 microsiemens / centimeter
...	...	...	...	...	...
182694	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
382645	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
380818	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
380050	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
182683	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter

13563 rows × 5 columns

[81]:

# Convert values to PSU and write to Salinity
cond_series = cond_series.apply(str)  # Convert to string to convert to dimensionless (PSU)
df.loc[df['Conductivity'].notna(), 'Salinity'] = cond_series.apply(convert.conductivity_to_PSU)
df.loc[df['Conductivity'].notna(), 'Salinity']

[81]:

386985    3226.305 dimensionless
386996      269.94 dimensionless
387616      227.97 dimensionless
474268      38.468 dimensionless
347883      34.521 dimensionless
                   ...
182694       0.012 dimensionless
382645       0.012 dimensionless
380818       0.012 dimensionless
380050       0.012 dimensionless
182683       0.012 dimensionless
Name: Salinity, Length: 13563, dtype: object

Datetime

datetime() formats time using dataretrieval and ActivityStart

[82]:

# First inspect the existing unformated fields
cols = ['ActivityStartDate', 'ActivityStartTime/Time', 'ActivityStartTime/TimeZoneCode']
df[cols]

[82]:

	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode
386985	2025-08-13	10:29:00	EDT
386996	2025-08-13	10:55:00	EDT
387616	2025-09-02	09:36:00	EDT
474268	2021-06-21	NaN	NaN
347883	2022-08-09	12:58:00	EST
...	...	...	...
577222	1956-08-16	NaN	NaN
577223	1956-06-16	NaN	NaN
577224	1956-07-01	NaN	NaN
577225	1955-08-16	NaN	NaN
577226	1952-08-16	NaN	NaN

577227 rows × 3 columns

[83]:

# 'ActivityStartDate' presserves date where 'Activity_datetime' is NAT due to no time zone
df = clean.datetime(df)
df[['ActivityStartDate', 'Activity_datetime']]

[83]:

	ActivityStartDate	Activity_datetime
386985	2025-08-13	2025-08-13 14:29:00+00:00
386996	2025-08-13	2025-08-13 14:55:00+00:00
387616	2025-09-02	2025-09-02 13:36:00+00:00
474268	2021-06-21	NaT
347883	2022-08-09	2022-08-09 17:58:00+00:00
...	...	...
577222	1956-08-16	NaT
577223	1956-06-16	NaT
577224	1956-07-01	NaT
577225	1955-08-16	NaT
577226	1952-08-16	NaT

577227 rows × 2 columns

Activity_datetime combines all three time component columns into UTC. If time is missing this is NaT so a ActivityStartDate column is used to preserve date only.

Depth

Note: Data are often lacking sample depth metadata

[84]:

# Depth of sample (default units='meter')
df = clean.harmonize_depth(df)
#df.loc[df['ResultDepthHeightMeasure/MeasureValue'].dropna(), "Depth"]
df['ResultDepthHeightMeasure/MeasureValue'].dropna()

[84]:

125288    0.15
123998    0.15
115179    0.15
114053    0.15
124031    0.15
          ...
546084    0.15
546121    0.15
546124    0.15
546129    0.15
546131    0.15
Name: ResultDepthHeightMeasure/MeasureValue, Length: 24059, dtype: float64

Characteristic to Column (long to wide format)

[85]:

# Split single QA column into multiple by characteristic (rename the result to preserve these QA_flags)
df2 = wrangle.split_col(df)
df2

[85]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	ActivityStartDate	ActivityStartTime/Time	ActivityStartTime/TimeZoneCode	MonitoringLocationIdentifier	ResultIdentifier	DataLoggerLine	ResultDetectionConditionText	...	QA_Fecal_Coliform	QA_Sediment	QA_TP_Phosphorus	QA_TDP_Phosphorus	QA_Other_Phosphorus	QA_Secchi	QA_DO	QA_Temperature	QA_Chlorophyll	QA_Salinity
386985	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-552 (P3):20250813:1029:FM:WB:0.5	2025-08-13	10:29:00	EDT	SSCW-552 (P3)	STORET-1102951305	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
386996	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-P2:20250813:1055:FM:WB:0.5	2025-08-13	10:55:00	EDT	SSCW-P2	STORET-1102951334	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
387616	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-P2:20250902:0936:FM:WB:0.5	2025-09-02	09:36:00	EDT	SSCW-P2	STORET-1102951403	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
474268	NARS_WQX	EPA National Aquatic Resources Survey (NARS)	NARS_WQX-185450_2021	2021-06-21	NaN	NaN	NARS_WQX-NWC_RI-10033	STORET-1040684142	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
347883	AQUINNAH_WQX	Wampanoag Tribe of Gay Head (Aquinnah) (Tribal)	AQUINNAH_WQX-P3:202208091258:FM	2022-08-09	12:58:00	EST	AQUINNAH_WQX-P3	STORET-1057585495	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
577221	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_DUCK_W_19570701_731710	1957-07-01	NaN	NaN	11NPSWRD_WQX-CACO_DUCK_W	STORET-740598867	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
577222	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_DUCK_W_19560816_731708	1956-08-16	NaN	NaN	11NPSWRD_WQX-CACO_DUCK_W	STORET-740598866	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
577223	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_DUCK_W_19560616_731704	1956-06-16	NaN	NaN	11NPSWRD_WQX-CACO_DUCK_W	STORET-740598862	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
577224	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_DUCK_W_19560701_731705	1956-07-01	NaN	NaN	11NPSWRD_WQX-CACO_DUCK_W	STORET-740598863	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
577225	11NPSWRD_WQX	National Park Service Water Resources Division	11NPSWRD_WQX-CACO_GREAT_W_19550816_731703	1955-08-16	NaN	NaN	11NPSWRD_WQX-CACO_GREAT_W	STORET-740649462	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

522063 rows × 121 columns

[86]:

# This expands the single col (QA_flag) out to a number of new columns based on the unique characteristicNames and speciation
print('{} new columns'.format(len(df2.columns) - len(df.columns)))

15 new columns

[87]:

# Note: there are fewer rows because NAN results are also dropped in this step
print('{} fewer rows'.format(len(df)-len(df2)))

55164 fewer rows

[88]:

#Examine Carbon flags from earlier in notebook (note these are empty now because NAN is dropped)
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'Carbon', 'QA_Carbon']
df2.loc[df2['QA_Carbon'].notna(), cols]

[88]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	Carbon	QA_Carbon

Next the table is divided into the columns of interest (main_df) and characteristic specific metadata (chars_df)

[89]:

# split table into main and characteristics tables
main_df, chars_df = wrangle.split_table(df2)

[90]:

# Columns still in main table
main_df.columns

[90]:

Index(['OrganizationIdentifier', 'OrganizationFormalName',
       'ActivityIdentifier', 'MonitoringLocationIdentifier', 'ProviderName',
       'ActivityStartDateTime', 'AnalysisStartDateTime', 'AnalysisEndDateTime',
       'Secchi', 'Temperature', 'DO', 'pH', 'Salinity', 'Nitrogen',
       'Speciation', 'TOTAL NITROGEN_ MIXED FORMS', 'Conductivity',
       'Chlorophyll', 'Carbon', 'Turbidity', 'Sediment', 'Phosphorus',
       'TP_Phosphorus', 'TDP_Phosphorus', 'Other_Phosphorus', 'Fecal_Coliform',
       'E_coli', 'DetectionQuantitationLimitTypeName',
       'DetectionQuantitationLimitMeasure/MeasureValue',
       'DetectionQuantitationLimitMeasure/MeasureUnitCode',
       'Activity_datetime', 'Depth', 'QA_Nitrogen', 'QA_pH', 'QA_Turbidity',
       'QA_Conductivity', 'QA_Carbon', 'QA_E_coli', 'QA_Fecal_Coliform',
       'QA_Sediment', 'QA_TP_Phosphorus', 'QA_TDP_Phosphorus',
       'QA_Other_Phosphorus', 'QA_Secchi', 'QA_DO', 'QA_Temperature',
       'QA_Chlorophyll', 'QA_Salinity'],
      dtype='object')

[91]:

# look at main table results (first 5)
main_df.head()

[91]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	MonitoringLocationIdentifier	ProviderName	ActivityStartDateTime	AnalysisStartDateTime	AnalysisEndDateTime	Secchi	Temperature	...	QA_Fecal_Coliform	QA_Sediment	QA_TP_Phosphorus	QA_TDP_Phosphorus	QA_Other_Phosphorus	QA_Secchi	QA_DO	QA_Temperature	QA_Chlorophyll	QA_Salinity
386985	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-552 (P3):20250813:1029:FM:WB:0.5	SSCW-552 (P3)	STORET	2025-08-13 14:29:00+00:00	NaT	NaT	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
386996	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-P2:20250813:1055:FM:WB:0.5	SSCW-P2	STORET	2025-08-13 14:55:00+00:00	NaT	NaT	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
387616	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-P2:20250902:0936:FM:WB:0.5	SSCW-P2	STORET	2025-09-02 13:36:00+00:00	NaT	NaT	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
474268	NARS_WQX	EPA National Aquatic Resources Survey (NARS)	NARS_WQX-185450_2021	NARS_WQX-NWC_RI-10033	STORET	NaT	NaT	NaT	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
347883	AQUINNAH_WQX	Wampanoag Tribe of Gay Head (Aquinnah) (Tribal)	AQUINNAH_WQX-P3:202208091258:FM	AQUINNAH_WQX-P3	STORET	2022-08-09 17:58:00+00:00	NaT	NaT	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 48 columns

[92]:

# Empty columns that could be dropped (Mostly QA columns)
cols = list(main_df.columns)
x = main_df.dropna(axis=1, how='all')
[col for col in cols if col not in x.columns]

[92]:

['AnalysisStartDateTime',
 'AnalysisEndDateTime',
 'Sediment',
 'QA_Conductivity',
 'QA_Carbon',
 'QA_Sediment',
 'QA_TDP_Phosphorus',
 'QA_DO']

[93]:

# Map average results at each station
gdf_avg = visualize.map_measure(main_df, stations_clipped, 'Temperature')
gdf_avg.plot(column='mean', cmap='OrRd', legend=True)

[93]:

<Axes: >

../_images/notebooks_Harmonize_CapeCod_Detailed_144_1.png