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]:

(11313, 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]:

(11313, 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 0x7f4370ed5d30>

[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	...	AnalysisEndTime/TimeZoneCode	ResultLaboratoryCommentCode	ResultLaboratoryCommentText	ResultDetectionQuantitationLimitUrl	LaboratoryAccreditationIndicator	LaboratoryAccreditationAuthorityName	TaxonomistAccreditationIndicator	TaxonomistAccreditationAuthorityName	LabSamplePreparationUrl	ProviderName
0	BRC	Blackstone River Coalition (Volunteer)	BRC-C-02-02-020:20131012040800:FM:0.2083333333...	2013-10-12	04:08:00	EST	BRC-C-02-02-020	STORET-1039097035	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
1	OARS	OARS - For the Assabet Sudbury & Concord River...	OARS-CND-161:20130818:0651:FM:1	2013-08-18	06:51:00	EDT	OARS-CND-161	STORET-838568413	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
2	OARS	OARS - For the Assabet Sudbury & Concord River...	OARS-SUD-064:20130721:0610:FM:1	2013-07-21	06:10:00	EDT	OARS-SUD-064	STORET-838568309	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
3	CRWA	Charles River Watershed Association (Massachus...	CRWA-CYN20130809ROBTemp01	2013-08-09	11:14:33	EST	CRWA-ROB	STORET-591631481	130809111433	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
4	WTGHA	Wompanoag Tribe of Gay Head Aquinnah (Tribal)	WTGHA-m41:20131017:FM:B	2013-10-17	NaN	NaN	WTGHA-M41	STORET-1041401688	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	STORET
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
575758	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	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET
575759	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	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET
575760	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	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET
575761	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	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET
575762	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	...	NaN	NaN	NaN	https://www.waterqualitydata.us/data/providers...	NaN	NaN	NaN	NaN	NaN	STORET

575763 rows × 78 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: missing (NaN) result' 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(3.25, 'meter')> <Quantity(4.25, 'meter')>
 <Quantity(3.5, '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 1786.54702135132): 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
48	11113300-GRTKINSD	3.25	m	NaN	m	3.25 meter
179	11113300-BEADERD	4.25	m	NaN	m	4.25 meter
318	11113300-ANGSDND	3.5	m	NaN	m	3.5 meter
698	11113300-GRTKINSD	2.25	m	NaN	m	2.25 meter
740	11113300-GRTKINSD	4.75	m	NaN	m	4.75 meter
...	...	...	...	...	...	...
575758	11NPSWRD_WQX-CACO_DUCK_W	10	m	NaN	m	10.0 meter
575759	11NPSWRD_WQX-CACO_DUCK_W	17	m	NaN	m	17.0 meter
575760	11NPSWRD_WQX-CACO_DUCK_W	16	m	NaN	m	16.0 meter
575761	11NPSWRD_WQX-CACO_GREAT_W	7.8	m	NaN	m	7.8 meter
575762	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
24555	NARS_WQX-NLA_MA-10007	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
38414	11113300-ISLDERD	=4.25	m	ResultMeasureValue: "=4.25" result cannot be used	m	NaN
38813	11113300-ROBHUDD	=2.25	m	ResultMeasureValue: "=2.25" result cannot be used	m	NaN
39063	11113300-ROBHUDD	=3	m	ResultMeasureValue: "=3" result cannot be used	m	NaN
39483	11113300-LONDVLD	=2.675	m	ResultMeasureValue: "=2.675" result cannot be ...	m	NaN
...	...	...	...	...	...	...
491732	11113300-MILSALD	=2	m	ResultMeasureValue: "=2" result cannot be used	m	NaN
491747	11113300-LONPELNHD	=2.35	m	ResultMeasureValue: "=2.35" result cannot be used	m	NaN
492845	11113300-DARLITD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
499100	11113300-WORSALD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
575762	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: missing (NaN) result; ResultMeasure/MeasureUnitCode: MISSING UNITS, m assumed'

[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
24555	NARS_WQX-NLA_MA-10007	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
38414	11113300-ISLDERD	=4.25	m	ResultMeasureValue: "=4.25" result cannot be used	m	NaN
38813	11113300-ROBHUDD	=2.25	m	ResultMeasureValue: "=2.25" result cannot be used	m	NaN
39063	11113300-ROBHUDD	=3	m	ResultMeasureValue: "=3" result cannot be used	m	NaN
39483	11113300-LONDVLD	=2.675	m	ResultMeasureValue: "=2.675" result cannot be ...	m	NaN
...	...	...	...	...	...	...
491732	11113300-MILSALD	=2	m	ResultMeasureValue: "=2" result cannot be used	m	NaN
491747	11113300-LONPELNHD	=2.35	m	ResultMeasureValue: "=2.35" result cannot be used	m	NaN
492845	11113300-DARLITD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
499100	11113300-WORSALD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
575762	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(22.18, 'degree_Celsius')> <Quantity(23.01, 'degree_Celsius')>
 <Quantity(17.39, 'degree_Celsius')> ... <Quantity(2.9, '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    150759.000000
mean         16.297022
std           7.274172
min          -9.830000
25%          11.200000
50%          17.300000
75%          21.900000
max         910.000000
dtype: float64
Unusable results: 226
Usable results with inferred units: 217
Results outside threshold (0.0 to 59.94205590817702): 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
1	OARS-CND-161	22.18	deg C	NaN	22.18 degree_Celsius	degC
3	CRWA-ROB	23.01	deg C	NaN	23.01 degree_Celsius	degC
5	WTGHA-M43	17.39	deg C	NaN	17.39 degree_Celsius	degC
8	MASSDEP-W2386	20.9	deg C	NaN	20.9 degree_Celsius	degC
12	WTGHA-S35	22.54	deg C	NaN	22.54 degree_Celsius	degC
...	...	...	...	...	...	...
575730	USGS-413831070304703	11.3	deg C	NaN	11.3 degree_Celsius	degC
575732	USGS-01100627	7.0	deg C	NaN	7.0 degree_Celsius	degC
575736	USGS-01096548	2.9	deg C	NaN	2.9 degree_Celsius	degC
575740	USGS-01098530	1.5	deg C	NaN	1.5 degree_Celsius	degC
575744	USGS-01097050	3.9	deg C	NaN	3.9 degree_Celsius	degC

150985 rows × 6 columns

[34]:

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

[34]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Temperature	Units
12632	NALMS-F865245	81	deg F	NaN	27.222222222222285 degree_Celsius	degF
31004	11113300-HOODERD	59.2	deg F	NaN	15.111111111111143 degree_Celsius	degF
31375	11113300-HOODERD	59.1	deg F	NaN	15.0555555555556 degree_Celsius	degF
31705	11113300-HOODERD	59.9	deg F	NaN	15.500000000000057 degree_Celsius	degF
32253	11113300-HOODERD	56	deg F	NaN	13.333333333333371 degree_Celsius	degF
...	...	...	...	...	...	...
562404	11NPSWRD_WQX-SAIR_DMF11_HS1	56	deg F	NaN	13.333333333333371 degree_Celsius	degF
562405	11NPSWRD_WQX-SAIR_DMF11_OS3	62	deg F	NaN	16.666666666666686 degree_Celsius	degF
562406	11NPSWRD_WQX-SAIR_DMF11_OS4	61	deg F	NaN	16.111111111111143 degree_Celsius	degF
562410	11NPSWRD_WQX-SAIR_DMF11_OS1	59	deg F	NaN	15.000000000000057 degree_Celsius	degF
562413	11NPSWRD_WQX-SAIR_DMF11_S2	43	deg F	NaN	6.111111111111143 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
226123	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
226124	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
226125	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
226126	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
226127	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
...	...	...	...	...	...	...
245324	AQUINNAH-MEN PND HC	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
526233	11113300-HHPS056	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
526406	11113300-HHPS055	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
526472	11113300-HHPS061	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
559704	USGS-414654070002901	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	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]:

'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
361	MERRIMACK_RIVER_WATERSHED_WQX-Hayden-Schofield	8	count	NaN	NaN	count
3668	MERRIMACK_RIVER_WATERSHED_WQX-Manchester	8	count	NaN	NaN	count
4193	MERRIMACK_RIVER_WATERSHED_WQX-Nina-Scarito	8	count	NaN	NaN	count
5321	MERRIMACK_RIVER_WATERSHED_WQX-Misserville	8	count	NaN	NaN	count
5731	MERRIMACK_RIVER_WATERSHED_WQX-Ferrous	8	count	NaN	NaN	count
...	...	...	...	...	...	...
526420	11113300-HHPS067	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
526440	11113300-HHPS057	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
526472	11113300-HHPS061	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC
526540	11113300-HHPS258	NO DATA	deg C	ResultMeasureValue: "NO DATA" result cannot be...	NaN	degC
559704	USGS-414654070002901	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	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
...	...	...	...
6507	WWMD_VA-WWE1149PI	1	18.800000
6508	WWMD_VA-WWE1150PI	1	21.300000
6509	WWMD_VA-WWE1151PI	2	20.700000
6510	WWMD_VA-WWE1152PI	1	20.000000
6511	WWMD_VA-WWE1153PI	1	20.800000

6512 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(4.6, 'milligram / liter')> <Quantity(9.4, 'milligram / liter')>
 <Quantity(10.3, 'milligram / liter')> ...
 <Quantity(14.16, 'milligram / liter')>
 <Quantity(10.2, 'milligram / liter')>
 <Quantity(14.39, '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
14	IRWA-HB	4.6	mg/L	NaN	4.6 milligram / liter
17	MYRWA-MEB001	9.4	mg/L	NaN	9.4 milligram / liter
19	NARS_WQX-MARO-1020	10.3	mg/L	NaN	10.3 milligram / liter
22	WTGHA-M46	24.7	mg/L	NaN	24.7 milligram / liter
23	MASSDEP-W2412	8.7	mg/L	NaN	8.7 milligram / liter
...	...	...	...	...	...
575043	HORSLEYWITTEN_WQX-MMR3A	10.65	mg/L	NaN	10.65 milligram / liter
575051	HORSLEYWITTEN_WQX-BWB2A	11.64	mg/L	NaN	11.64 milligram / liter
575055	HORSLEYWITTEN_WQX-BWB0	14.16	mg/L	NaN	14.16 milligram / liter
575058	HORSLEYWITTEN_WQX-B1	10.2	mg/L	NaN	10.2 milligram / liter
575062	HORSLEYWITTEN_WQX-BWB2A	14.39	mg/L	NaN	14.39 milligram / liter

94790 rows × 5 columns

[43]:

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

[43]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	DO
14	IRWA-HB	4.6	mg/L	NaN	4.6 milligram / liter
17	MYRWA-MEB001	9.4	mg/L	NaN	9.4 milligram / liter
19	NARS_WQX-MARO-1020	10.3	mg/L	NaN	10.3 milligram / liter
22	WTGHA-M46	24.7	mg/L	NaN	24.7 milligram / liter
23	MASSDEP-W2412	8.7	mg/L	NaN	8.7 milligram / liter
...	...	...	...	...	...
575043	HORSLEYWITTEN_WQX-MMR3A	10.65	mg/L	NaN	10.65 milligram / liter
575051	HORSLEYWITTEN_WQX-BWB2A	11.64	mg/L	NaN	11.64 milligram / liter
575055	HORSLEYWITTEN_WQX-BWB0	14.16	mg/L	NaN	14.16 milligram / liter
575058	HORSLEYWITTEN_WQX-B1	10.2	mg/L	NaN	10.2 milligram / liter
575062	HORSLEYWITTEN_WQX-BWB2A	14.39	mg/L	NaN	14.39 milligram / liter

93493 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
...	...	...	...
2819	WWMD_VA-SR5	1	10.810000
2820	WWMD_VA-WF2	2	6.800000
2821	WWMD_VA-WI1	1	9.510000
2822	WWMD_VA-WR2X	2	6.000000
2823	WWMD_VA-WR5	1	8.355000

2824 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(7.3, 'dimensionless')> <Quantity(8.16, 'dimensionless')>
 <Quantity(7.99, 'dimensionless')> ... <Quantity(7.4, 'dimensionless')>
 <Quantity(7.5, 'dimensionless')> <Quantity(7.4, '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 31.155786540929782): 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
6	7.3	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	7.3 dimensionless
9	8.16	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	8.16 dimensionless
26	7.99	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	7.99 dimensionless
43	6.61	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.61 dimensionless
45	8.23	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	8.23 dimensionless
...	...	...	...	...
575731	5.8	std units	NaN	5.8 dimensionless
575733	7.3	std units	NaN	7.3 dimensionless
575737	7.4	std units	NaN	7.4 dimensionless
575741	7.5	std units	NaN	7.5 dimensionless
575745	7.4	std units	NaN	7.4 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(30.33, 'Practical_Salinity_Units')>
 <Quantity(30.48, 'Practical_Salinity_Units')>
 <Quantity(33.3, 'Practical_Salinity_Units')> ...
 <Quantity(21.0, 'Practical_Salinity_Units')>
 <Quantity(18.7, 'Practical_Salinity_Units')>
 <Quantity(10.1, '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    60754.000000
mean        23.163723
std         20.583271
min        -30.000000
25%         13.090000
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 146.6633509697272): 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
10	30.33	ppt	NaN	30.33 Practical_Salinity_Units
51	30.48	ppt	NaN	30.48 Practical_Salinity_Units
56	33.3	ppth	NaN	33.3 Practical_Salinity_Units
73	30.91	ppt	NaN	30.91 Practical_Salinity_Units
77	0.21	ppth	NaN	0.21 Practical_Salinity_Units
...	...	...	...	...
575059	0.33	ppt	NaN	0.33 Practical_Salinity_Units
575144	4.6	PSU	NaN	4.6 Practical_Salinity_Units
575244	21	PSU	NaN	21.0 Practical_Salinity_Units
575257	18.7	PSU	NaN	18.7 Practical_Salinity_Units
575368	10.1	PSU	NaN	10.1 Practical_Salinity_Units

60982 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(0.926976, 'milligram / liter')>
 <Quantity(1.571196, 'milligram / liter')>
 <Quantity(0.816144, 'milligram / liter')> ...
 <Quantity(0.238, 'milligram / liter')>
 <Quantity(0.052, 'milligram / liter')>
 <Quantity(0.119, '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 635.0810968269224): 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
877	0.926976	mg/L	NaN	0.926976 milligram / liter
953	1.571196	mg/L	NaN	1.571196 milligram / liter
960	0.816144	mg/L	NaN	0.816144 milligram / liter
1268	0.770448	mg/L	NaN	0.770448 milligram / liter
1306	0.848832	mg/L	NaN	0.848832 milligram / liter
...	...	...	...	...
575564	0.119	mg/l	NaN	0.119 milligram / liter
575622	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN
575665	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN
575671	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN
575708	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN

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(590.0, 'microsiemens / centimeter')>
 <Quantity(43500.0, 'microsiemens / centimeter')>
 <Quantity(349.0, 'microsiemens / centimeter')> ...
 <Quantity(465.6, 'microsiemens / centimeter')>
 <Quantity(694.0, 'microsiemens / centimeter')>
 <Quantity(534.0, '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
2	590	uS/cm	NaN	590.0 microsiemens / centimeter
7	43.5	mS/cm	NaN	43500.0 microsiemens / centimeter
49	349	uS/cm	NaN	349.0 microsiemens / centimeter
53	49.7	mS/cm	NaN	49700.0 microsiemens / centimeter
58	8	count	NaN	NaN
...	...	...	...	...
571919	23.15	mS/cm	NaN	23150.0 microsiemens / centimeter
571931	0.534	mS/cm	NaN	534.0 microsiemens / centimeter
571949	0.4656	mS/cm	NaN	465.6 microsiemens / centimeter
572071	0.694	mS/cm	NaN	694.0 microsiemens / centimeter
572126	0.534	mS/cm	NaN	534.0 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: '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: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:663: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[<Quantity(0.004, 'milligram / liter')>
 <Quantity(0.0055, 'milligram / liter')>
 <Quantity(0.00948, 'milligram / liter')> ...
 <Quantity(0.0007, 'milligram / liter')>
 <Quantity(0.0011, 'milligram / liter')>
 <Quantity(0.0007, '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 11.013030170931922): 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
13	4.0	mg/m3	NaN	0.004000000000000001 milligram / liter
288	5.50	ug/L	NaN	0.0055 milligram / liter
393	9.48	ug/L	NaN	0.00948 milligram / liter
713	3.0	mg/m3	NaN	0.003000000000000001 milligram / liter
1013	NaN	mg/m3	ResultMeasureValue: missing (NaN) result	NaN
...	...	...	...	...
575448	0.5	ug/l	NaN	0.0005 milligram / liter
575455	2.5	ug/l	NaN	0.0025 milligram / liter
575468	0.7	ug/l	NaN	0.0007 milligram / liter
575501	1.1	ug/l	NaN	0.0011 milligram / liter
575556	0.7	ug/l	NaN	0.0007 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(7.05, 'milligram / liter')>
 <Quantity(7.57, 'milligram / liter')>
 <Quantity(3.5, 'milligram / liter')> ...
 <Quantity(4.1, 'milligram / liter')>
 <Quantity(7.79, 'milligram / liter')>
 <Quantity(5.79, '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
1007	7.05	mg/L	NaN	7.05 milligram / liter
1146	7.57	mg/L	NaN	7.57 milligram / liter
1199	3.5	mg/L	NaN	3.5 milligram / liter
3158	3.8	mg/L	NaN	3.8 milligram / liter
4020	3.7	mg/L	NaN	3.7 milligram / liter
...	...	...	...	...
575552	7.01	mg/l	NaN	7.01 milligram / liter
575621	5.96	mg/l	NaN	5.96 milligram / liter
575664	4.10	mg/l	NaN	4.1 milligram / liter
575670	7.79	mg/l	NaN	7.79 milligram / liter
575707	5.79	mg/l	NaN	5.79 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(7.6, 'Nephelometric_Turbidity_Units')>
 <Quantity(1.0, 'Nephelometric_Turbidity_Units')>
 <Quantity(4.2, 'Nephelometric_Turbidity_Units')> ...
 <Quantity(2.1, 'Nephelometric_Turbidity_Units')>
 <Quantity(2.9, 'Nephelometric_Turbidity_Units')>
 <Quantity(2.7, '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    34461.000000
mean        15.657555
std         72.069369
min       -999.000000
25%          1.250000
50%          2.500000
75%          5.770000
max       4100.000000
dtype: float64
Unusable results: 3418
Usable results with inferred units: 275
Results outside threshold (0.0 to 448.07376883334996): 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
0	7.6	NTU	NaN	7.6 Nephelometric_Turbidity_Units
4	1	NTU	NaN	1.0 Nephelometric_Turbidity_Units
11	4.2	NTU	NaN	4.2 Nephelometric_Turbidity_Units
16	1.3	NTU	NaN	1.3 Nephelometric_Turbidity_Units
21	5.2	NTU	NaN	5.2 Nephelometric_Turbidity_Units
...	...	...	...	...
575725	1.6	NTRU	NaN	1.6 Nephelometric_Turbidity_Units
575735	5.8	NTRU	NaN	5.8 Nephelometric_Turbidity_Units
575739	2.1	NTRU	NaN	2.1 Nephelometric_Turbidity_Units
575743	2.9	NTRU	NaN	2.9 Nephelometric_Turbidity_Units
575747	2.7	NTRU	NaN	2.7 Nephelometric_Turbidity_Units

37879 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
522648	0.0120	g	NaN	NaN
522649	0.0037	g	NaN	NaN
522650	0.0048	g	NaN	NaN
522651	0.0010	g	NaN	NaN
522652	0.0088	g	NaN	NaN
...	...	...	...	...
574776	0.0051	g	NaN	NaN
574850	0.0025	g	NaN	NaN
574922	0.0020	g	NaN	NaN
574961	0.0023	g	NaN	NaN
575007	0.0014	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(0.049, 'milligram / liter')>
 <Quantity(0.024, 'milligram / liter')>
 <Quantity(0.05, 'milligram / liter')> ...
 <Quantity(0.008, 'milligram / liter')>
 <Quantity(0.047, 'milligram / liter')>
 <Quantity(0.028, '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
65	0.049	mg/L	NaN	NaN
67	0.024	mg/L	NaN	NaN
72	0.05	mg/L	NaN	NaN
89	0.027	mg/L	NaN	NaN
110	0.059712	mg/L	NaN	NaN
...	...	...	...	...
574983	0.006	mg/l as P	NaN	0.006 milligram / liter
574995	0.016	mg/l as P	NaN	NaN
574996	0.008	mg/l as P	NaN	0.008 milligram / liter
575003	0.047	mg/l as P	NaN	NaN
575004	0.028	mg/l as P	NaN	0.028 milligram / liter

19507 rows × 4 columns

[65]:

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

[65]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	TDP_Phosphorus
65	0.049	mg/L	NaN	NaN
67	0.024	mg/L	NaN	NaN
72	0.05	mg/L	NaN	NaN
89	0.027	mg/L	NaN	NaN
110	0.059712	mg/L	NaN	NaN
...	...	...	...	...
574957	0.036	mg/l as P	NaN	NaN
574968	0.021	mg/l as P	NaN	NaN
574982	0.011	mg/l as P	NaN	NaN
574995	0.016	mg/l as P	NaN	NaN
575003	0.047	mg/l as P	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
38509	0.023	ppm	NaN	0.023000000000000003 milligram / liter
43731	0.035	ppm	NaN	0.03500000000000001 milligram / liter
46336	0.017	ppm	NaN	0.017000000000000005 milligram / liter
46492	0.015	ppm	NaN	0.015000000000000003 milligram / liter
48282	0.015	ppm	NaN	0.015000000000000003 milligram / liter
...	...	...	...	...
574958	0.015	mg/l as P	NaN	0.015 milligram / liter
574969	0.005	mg/l as P	NaN	0.005 milligram / liter
574983	0.006	mg/l as P	NaN	0.006 milligram / liter
574996	0.008	mg/l as P	NaN	0.008 milligram / liter
575004	0.028	mg/l as P	NaN	0.028 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
89055	0.03525375	mg/L	NaN	NaN
89122	0.107028125	mg/L	NaN	NaN
89631	0.0697675	mg/L	NaN	NaN
89897	0.04356	mg/L	NaN	NaN
90026	0.03654875	mg/L	NaN	NaN
...	...	...	...	...
558715	2600	mg/kg	NaN	NaN
558753	2900	mg/kg	NaN	NaN
558820	2500	mg/kg	NaN	NaN
558898	2600	mg/kg	NaN	NaN
558907	2200	mg/kg	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: '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/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/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 ... <Quantity(140.0, 'Colony_Forming_Units / milliliter')>
 nan <Quantity(1.0, 'Colony_Forming_Units / milliliter')>]' 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 37530.708061087345): 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
22182	40	#/100mL	NaN	NaN
22238	9	#/100mL	NaN	NaN
22258	20	#/100mL	NaN	NaN
22478	200	#/100mL	NaN	NaN
22706	NO DATA	#/100mL	ResultMeasureValue: "NO DATA" result cannot be...	NaN
...	...	...	...	...
571178	440	cfu/100ml	NaN	440.0 Colony_Forming_Units / milliliter
571192	450	cfu/100ml	NaN	450.0 Colony_Forming_Units / milliliter
571213	140	cfu/100ml	NaN	140.0 Colony_Forming_Units / milliliter
571419	180	#/100mL	NaN	NaN
572256	1	cfu/100ml	NaN	1.0 Colony_Forming_Units / milliliter

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: '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/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: '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/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 ... <Quantity(44.0, 'Colony_Forming_Units / milliliter')>
 <Quantity(14.0, 'Colony_Forming_Units / milliliter')>
 <Quantity(390.0, 'Colony_Forming_Units / milliliter')>]' 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
15	96	MPN/100mL	NaN	NaN
18	58	#/100mL	NaN	NaN
24	NaN	MPN/100mL	ResultMeasureValue: missing (NaN) result	NaN
37	24200	MPN/100mL	NaN	NaN
38	7270	MPN/100mL	NaN	NaN
...	...	...	...	...
575721	7	MPN/100 ml	NaN	7.0 Colony_Forming_Units / milliliter
575734	870	MPN/100 ml	NaN	870.0 Colony_Forming_Units / milliliter
575738	44	MPN/100 ml	NaN	44.0 Colony_Forming_Units / milliliter
575742	14	MPN/100 ml	NaN	14.0 Colony_Forming_Units / milliliter
575746	390	MPN/100 ml	NaN	390.0 Colony_Forming_Units / milliliter

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: 60754
Mean: 23.163723083341885 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
30988	804	ppth	NaN	804.0 Practical_Salinity_Units
245431	70.62	ppth	NaN	70.62 Practical_Salinity_Units
245931	71.49	ppth	NaN	71.49 Practical_Salinity_Units
270683	77.6666666666667	ppt	NaN	77.6666666666667 Practical_Salinity_Units
565358	4980	mg/mL @25C	ResultTemperatureBasisText: updated from 25 de...	4003.4828342857154 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
14084	0	ppm	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
15918	0	ppm	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
17732	0	ppm	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
25882	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
26524	0	PSS	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...
491525	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
491549	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
491557	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
491669	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
491706	0	ppt	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN

876 rows × 8 columns

Explore conductivity results:

[79]:

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

[79]:

2           590.0 microsiemens / centimeter
7         43500.0 microsiemens / centimeter
49          349.0 microsiemens / centimeter
53        49700.0 microsiemens / centimeter
85          443.0 microsiemens / centimeter
                        ...
571919    23150.0 microsiemens / centimeter
571931      534.0 microsiemens / centimeter
571949      465.6 microsiemens / centimeter
572071      694.0 microsiemens / centimeter
572126      534.0 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
489594	992	mS/cm	NaN	NaN	992000.0 microsiemens / centimeter
490115	270.2	mS/cm	NaN	NaN	270200.0 microsiemens / centimeter
491220	241.1	mS/cm	NaN	NaN	241100.0 microsiemens / centimeter
279986	57700	uS/cm	NaN	NaN	57700.0 microsiemens / centimeter
300869	52.418	mS/cm	NaN	NaN	52418.0 microsiemens / centimeter
...	...	...	...	...	...
3992	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
354141	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
356948	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
71202	0	mS/cm	NaN	NaN	0.0 microsiemens / centimeter
71050	0	mS/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]:

489594    3226.305 dimensionless
490115      269.94 dimensionless
491220      227.97 dimensionless
279986      38.468 dimensionless
300869      34.521 dimensionless
                   ...
3992         0.012 dimensionless
354141       0.012 dimensionless
356948       0.012 dimensionless
71202        0.012 dimensionless
71050        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
489594	2025-08-13	10:29:00	EDT
490115	2025-08-13	10:55:00	EDT
491220	2025-09-02	09:36:00	EDT
279986	2021-06-21	NaN	NaN
300869	2022-08-09	12:58:00	EST
...	...	...	...
575758	1956-08-16	NaN	NaN
575759	1956-06-16	NaN	NaN
575760	1956-07-01	NaN	NaN
575761	1955-08-16	NaN	NaN
575762	1952-08-16	NaN	NaN

575763 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
489594	2025-08-13	2025-08-13 14:29:00+00:00
490115	2025-08-13	2025-08-13 14:55:00+00:00
491220	2025-09-02	2025-09-02 13:36:00+00:00
279986	2021-06-21	NaT
300869	2022-08-09	2022-08-09 17:58:00+00:00
...	...	...
575758	1956-08-16	NaT
575759	1956-06-16	NaT
575760	1956-07-01	NaT
575761	1955-08-16	NaT
575762	1952-08-16	NaT

575763 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]:

207897    0.15
210033    0.15
104590    0.15
109799    0.15
208566    0.15
          ...
453135    0.15
453137    0.15
453138    0.15
453139    1.23
453140    1.65
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_Turbidity	QA_Nitrogen	QA_Carbon	QA_Secchi	QA_TP_Phosphorus	QA_TDP_Phosphorus	QA_Other_Phosphorus	QA_E_coli	QA_Conductivity	QA_Fecal_Coliform
489594	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
490115	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
491220	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
279986	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
300869	WTGHA	Wompanoag Tribe of Gay Head Aquinnah (Tribal)	WTGHA-P3:20220809125800:FM:B	2022-08-09	12:58:00	EST	WTGHA-P3	STORET-1041330082	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
575757	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
575758	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
575759	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
575760	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
575761	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

520599 rows × 118 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',
       '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_pH', 'QA_Temperature', 'QA_Sediment',
       'QA_DO', 'QA_Salinity', 'QA_Chlorophyll', 'QA_Turbidity', 'QA_Nitrogen',
       'QA_Carbon', 'QA_Secchi', 'QA_TP_Phosphorus', 'QA_TDP_Phosphorus',
       'QA_Other_Phosphorus', 'QA_E_coli', 'QA_Conductivity',
       'QA_Fecal_Coliform'],
      dtype='object')

[91]:

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

[91]:

	OrganizationIdentifier	OrganizationFormalName	ActivityIdentifier	MonitoringLocationIdentifier	ProviderName	Secchi	Temperature	DO	pH	Salinity	...	QA_Turbidity	QA_Nitrogen	QA_Carbon	QA_Secchi	QA_TP_Phosphorus	QA_TDP_Phosphorus	QA_Other_Phosphorus	QA_E_coli	QA_Conductivity	QA_Fecal_Coliform
489594	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-552 (P3):20250813:1029:FM:WB:0.5	SSCW-552 (P3)	STORET	NaN	NaN	NaN	NaN	3226.305 dimensionless	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
490115	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-P2:20250813:1055:FM:WB:0.5	SSCW-P2	STORET	NaN	NaN	NaN	NaN	269.94 dimensionless	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
491220	SSCW	Salem Sound Coastwatch (Volunteer)	SSCW-P2:20250902:0936:FM:WB:0.5	SSCW-P2	STORET	NaN	NaN	NaN	NaN	227.97 dimensionless	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
279986	NARS_WQX	EPA National Aquatic Resources Survey (NARS)	NARS_WQX-185450_2021	NARS_WQX-NWC_RI-10033	STORET	NaN	NaN	NaN	NaN	38.468 dimensionless	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
300869	WTGHA	Wompanoag Tribe of Gay Head Aquinnah (Tribal)	WTGHA-P3:20220809125800:FM:B	WTGHA-P3	STORET	NaN	NaN	NaN	NaN	34.521 dimensionless	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 45 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]:

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

[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