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]:

(11282, 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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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]:

(11282, 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 0x7f8e29b8edc0>

[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)

/opt/hostedtoolcache/Python/3.9.23/x64/lib/python3.9/site-packages/dataretrieval/wqp.py:153: DtypeWarning: Columns (8,10,13,15,17,19,20,21,22,23,31,33,34,36,38,60,64,65,66,67,68,69,70,71,72) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv(StringIO(response.text), delimiter=",")

[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	130809111433.0	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
569619	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
569620	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
569621	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
569622	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
569623	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

569624 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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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: 2134
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
317	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
...	...	...	...	...	...	...
569619	11NPSWRD_WQX-CACO_DUCK_W	10.0	m	NaN	m	10.0 meter
569620	11NPSWRD_WQX-CACO_DUCK_W	17.0	m	NaN	m	17.0 meter
569621	11NPSWRD_WQX-CACO_DUCK_W	16.0	m	NaN	m	16.0 meter
569622	11NPSWRD_WQX-CACO_GREAT_W	7.8	m	NaN	m	7.8 meter
569623	11NPSWRD_WQX-CACO_SLOUGH	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN

11095 rows × 6 columns

[26]:

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

[26]:

	MonitoringLocationIdentifier	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	Units	Secchi
25120	NARS_WQX-NLA12_MA-102	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
...	...	...	...	...	...	...
486665	11113300-DARLITD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
494386	11113300-WORSALD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
510410	11113300-DARLITD	=1.1	m	ResultMeasureValue: "=1.1" result cannot be used	m	NaN
510446	11113300-DARLITD	=1.5	m	ResultMeasureValue: "=1.5" result cannot be used	m	NaN
569623	11NPSWRD_WQX-CACO_SLOUGH	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN

2134 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
25120	NARS_WQX-NLA12_MA-102	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
...	...	...	...	...	...	...
486665	11113300-DARLITD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
494386	11113300-WORSALD	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	m	NaN
510410	11113300-DARLITD	=1.1	m	ResultMeasureValue: "=1.1" result cannot be used	m	NaN
510446	11113300-DARLITD	=1.5	m	ResultMeasureValue: "=1.5" result cannot be used	m	NaN
569623	11NPSWRD_WQX-CACO_SLOUGH	NaN	m	ResultMeasureValue: missing (NaN) result	m	NaN

1176 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
...	...	...	...
987	WWMD_VA-SH1	2	1.500000
988	WWMD_VA-SH2	2	1.800000
989	WWMD_VA-SR6A	1	0.500000
990	WWMD_VA-WF2	1	0.600000
991	WWMD_VA-WR5	1	2.000000

992 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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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(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    149003.000000
mean         16.297725
std           7.274194
min          -9.830000
25%          11.200000
50%          17.296809
75%          21.900000
max         910.000000
dtype: float64
Unusable results: 214
Usable results with inferred units: 217
Results outside threshold (0.0 to 59.942891214934136): 467

../_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
...	...	...	...	...	...	...
569604	11NPSWRD_WQX-CACO_GULL	4.8	deg C	NaN	4.8 degree_Celsius	degC
569605	11NPSWRD_WQX-CACO_GULL	9.1	deg C	NaN	9.1 degree_Celsius	degC
569606	11NPSWRD_WQX-CACO_GULL	4.0	deg C	NaN	4.0 degree_Celsius	degC
569607	11NPSWRD_WQX-CACO_GULL	1.5	deg C	NaN	1.5 degree_Celsius	degC
569608	11NPSWRD_WQX-CACO_GULL	3.9	deg C	NaN	3.9 degree_Celsius	degC

149217 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
...	...	...	...	...	...	...
565725	11NPSWRD_WQX-SAIR_DMF11_HS1	56.0	deg F	NaN	13.333333333333371 degree_Celsius	degF
565726	11NPSWRD_WQX-SAIR_DMF11_OS3	62.0	deg F	NaN	16.666666666666686 degree_Celsius	degF
565727	11NPSWRD_WQX-SAIR_DMF11_OS4	61.0	deg F	NaN	16.111111111111143 degree_Celsius	degF
565732	11NPSWRD_WQX-SAIR_DMF11_S2	43.0	deg F	NaN	6.111111111111143 degree_Celsius	degF
565734	11NPSWRD_WQX-SAIR_DMF11_OS1	59.0	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
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
...	...	...	...	...	...	...
227459	AQUINNAH-SQUIB # 33	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
227460	AQUINNAH-SQUIB # 33	0	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	0.0 degree_Celsius	degC
227616	AQUINNAH-SQUIB # 33	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
554090	USGS-414654070002901	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC

218 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
...	...	...	...	...	...	...
385997	11NPSWRD_WQX-SAIR_SWC_SR3	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC
393664	11NPSWRD_WQX-SAIR_SWC_SR6	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC
395152	11NPSWRD_WQX-SAIR_SWC_SR7	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC
400114	11NPSWRD_WQX-SAIR_SWC_SRT9	NaN	deg C	ResultMeasureValue: missing (NaN) result	NaN	degC
554090	USGS-414654070002901	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN	degC

214 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
...	...	...	...
6488	WWMD_VA-WWE1149PI	1	18.800000
6489	WWMD_VA-WWE1150PI	1	21.300000
6490	WWMD_VA-WWE1151PI	2	20.700000
6491	WWMD_VA-WWE1152PI	1	20.000000
6492	WWMD_VA-WWE1153PI	1	20.800000

6493 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/3.9.23/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/3.9.23/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(8.07, 'milligram / liter')>
 <Quantity(9.51, 'milligram / liter')>
 <Quantity(8.3, '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
...	...	...	...	...	...
566477	HORSLEYWITTEN_WQX-B3	10.71	mg/L	NaN	10.71 milligram / liter
566484	HORSLEYWITTEN_WQX-HW-1	8.63	mg/L	NaN	8.63 milligram / liter
566491	HORSLEYWITTEN_WQX-B1	8.07	mg/L	NaN	8.07 milligram / liter
566528	HORSLEYWITTEN_WQX-B2	9.51	mg/L	NaN	9.51 milligram / liter
566529	HORSLEYWITTEN_WQX-MMR3A	8.3	mg/L	NaN	8.3 milligram / liter

93895 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
...	...	...	...	...	...
566477	HORSLEYWITTEN_WQX-B3	10.71	mg/L	NaN	10.71 milligram / liter
566484	HORSLEYWITTEN_WQX-HW-1	8.63	mg/L	NaN	8.63 milligram / liter
566491	HORSLEYWITTEN_WQX-B1	8.07	mg/L	NaN	8.07 milligram / liter
566528	HORSLEYWITTEN_WQX-B2	9.51	mg/L	NaN	9.51 milligram / liter
566529	HORSLEYWITTEN_WQX-MMR3A	8.3	mg/L	NaN	8.3 milligram / liter

92598 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
...	...	...	...
2804	WWMD_VA-SR5	1	10.810000
2805	WWMD_VA-WF2	2	6.800000
2806	WWMD_VA-WI1	1	9.510000
2807	WWMD_VA-WR2X	2	6.000000
2808	WWMD_VA-WR5	1	8.355000

2809 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/3.9.23/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/3.9.23/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/3.9.23/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(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    107468.000000
mean          6.682906
std           4.086999
min          -3.124705
25%           5.860000
50%           6.800000
75%           7.680000
max         788.000000
dtype: float64
Unusable results: 4532
Usable results with inferred units: 85914
Results outside threshold (0.0 to 31.204898362817303): 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
...	...	...	...	...
569516	6.6	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.6 dimensionless
569523	6.9	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.9 dimensionless
569547	6.6	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.6 dimensionless
569562	6.8	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	6.8 dimensionless
569589	7.1	NaN	ResultMeasure/MeasureUnitCode: MISSING UNITS, ...	7.1 dimensionless

112000 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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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    59967.000000
mean        23.325032
std         20.607709
min        -30.000000
25%         13.530000
50%         30.000000
75%         31.500000
max       4003.482834
dtype: float64
Unusable results: 219
Usable results with inferred units: 1
Results outside threshold (0.0 to 146.97128535439936): 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
...	...	...	...	...
568250	0.4	PSU	NaN	0.4 Practical_Salinity_Units
568908	4.6	PSU	NaN	4.6 Practical_Salinity_Units
569008	21.0	PSU	NaN	21.0 Practical_Salinity_Units
569021	18.7	PSU	NaN	18.7 Practical_Salinity_Units
569132	10.1	PSU	NaN	10.1 Practical_Salinity_Units

60186 rows × 4 columns

Nitrogen

[51]:

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

/opt/hostedtoolcache/Python/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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    2459.000000
mean        6.364974
std       105.839795
min         0.000800
25%         0.075500
50%         0.250000
75%         1.013360
max      2800.000000
dtype: float64
Unusable results: 243
Usable results with inferred units: 0
Results outside threshold (0.0 to 641.4037445624855): 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
...	...	...	...	...
569328	0.119	mg/l	NaN	0.119 milligram / liter
569386	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN
569429	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN
569435	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN
569472	NaN	NaN	ResultMeasureValue: missing (NaN) result; Resu...	NaN

2702 rows × 4 columns

Conductivity

[53]:

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

/opt/hostedtoolcache/Python/3.9.23/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/3.9.23/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/3.9.23/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(16323.0, 'microsiemens / centimeter')>
 <Quantity(209.8, 'microsiemens / centimeter')>
 <Quantity(36896.0, '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    13491.000000
mean     27213.534130
std      16253.979994
min          0.000000
25%      16685.000000
50%      30740.000000
75%      41850.000000
max      57700.000000
dtype: float64
Unusable results: 82
Usable results with inferred units: 0
Results outside threshold (0.0 to 124737.41409546307): 0

../_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
52	49.7	mS/cm	NaN	49700.0 microsiemens / centimeter
58	8	count	NaN	NaN
...	...	...	...	...
511276	33.048	mS/cm	NaN	33048.0 microsiemens / centimeter
511282	0.71399999999999997	mS/cm	NaN	713.9999999999999 microsiemens / centimeter
511284	16.323	mS/cm	NaN	16323.0 microsiemens / centimeter
511296	0.20979999999999999	mS/cm	NaN	209.7999999999999 microsiemens / centimeter
511303	36.896	mS/cm	NaN	36896.0 microsiemens / centimeter

13573 rows × 4 columns

Chlorophyll a

[55]:

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

/opt/hostedtoolcache/Python/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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    14012.000000
mean         0.176872
std          1.809121
min         -0.002400
25%          0.001800
50%          0.003500
75%          0.007282
max         92.900000
dtype: float64
Unusable results: 204
Usable results with inferred units: 9
Results outside threshold (0.0 to 11.031595436333584): 63

../_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
...	...	...	...	...
569212	0.5	ug/l	NaN	0.0005 milligram / liter
569219	2.5	ug/l	NaN	0.0025 milligram / liter
569232	0.7	ug/l	NaN	0.0007 milligram / liter
569265	1.1	ug/l	NaN	0.0011 milligram / liter
569320	0.7	ug/l	NaN	0.0007 milligram / liter

14216 rows × 4 columns

Organic Carbon

[57]:

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

/opt/hostedtoolcache/Python/3.9.23/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/3.9.23/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/3.9.23/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      6174.000000
mean       5259.051952
std       35499.155967
min      -90000.000000
25%           1.800000
50%           4.900000
75%           8.900000
max      530000.000000
dtype: float64
Unusable results: 89
Usable results with inferred units: 0
Results outside threshold (0.0 to 218253.98775659996): 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
1145	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
...	...	...	...	...
569316	7.01	mg/l	NaN	7.01 milligram / liter
569385	5.96	mg/l	NaN	5.96 milligram / liter
569428	4.1	mg/l	NaN	4.1 milligram / liter
569434	7.79	mg/l	NaN	7.79 milligram / liter
569471	5.79	mg/l	NaN	5.79 milligram / liter

6263 rows × 4 columns

Turbidity (NTU)

[59]:

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

/opt/hostedtoolcache/Python/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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: 3137
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
...	...	...	...	...
569489	1.6	NTRU	NaN	1.6 Nephelometric_Turbidity_Units
569499	5.8	NTRU	NaN	5.8 Nephelometric_Turbidity_Units
569503	2.1	NTRU	NaN	2.1 Nephelometric_Turbidity_Units
569507	2.9	NTRU	NaN	2.9 Nephelometric_Turbidity_Units
569511	2.7	NTRU	NaN	2.7 Nephelometric_Turbidity_Units

37598 rows × 4 columns

Sediment

[61]:

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

/opt/hostedtoolcache/Python/3.9.23/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/3.9.23/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/3.9.23/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
517638	0.012	g	NaN	NaN
517639	0.0037	g	NaN	NaN
517640	0.0048	g	NaN	NaN
517641	0.001	g	NaN	NaN
517642	0.0088	g	NaN	NaN
...	...	...	...	...
568593	0.0051	g	NaN	NaN
568667	0.0025	g	NaN	NaN
568739	0.002	g	NaN	NaN
568778	0.0023	g	NaN	NaN
568824	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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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
...	...	...	...	...
568800	0.006	mg/l as P	NaN	0.006 milligram / liter
568812	0.016	mg/l as P	NaN	NaN
568813	0.008	mg/l as P	NaN	0.008 milligram / liter
568820	0.047	mg/l as P	NaN	NaN
568821	0.028	mg/l as P	NaN	0.028 milligram / liter

19171 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
...	...	...	...	...
568774	0.036	mg/l as P	NaN	NaN
568785	0.021	mg/l as P	NaN	NaN
568799	0.011	mg/l as P	NaN	NaN
568812	0.016	mg/l as P	NaN	NaN
568820	0.047	mg/l as P	NaN	NaN

13898 rows × 4 columns

[66]:

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

[66]:

	ResultMeasureValue	ResultMeasure/MeasureUnitCode	QA_flag	TDP_Phosphorus
38508	0.023	ppm	NaN	0.023000000000000003 milligram / liter
43730	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
...	...	...	...	...
568775	0.015	mg/l as P	NaN	0.015 milligram / liter
568786	0.005	mg/l as P	NaN	0.005 milligram / liter
568800	0.006	mg/l as P	NaN	0.006 milligram / liter
568813	0.008	mg/l as P	NaN	0.008 milligram / liter
568821	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
70310	0.03525375	mg/L	NaN	NaN
70378	0.107028125	mg/L	NaN	NaN
70887	0.0697675	mg/L	NaN	NaN
71152	0.04356	mg/L	NaN	NaN
71281	0.03654875	mg/L	NaN	NaN
...	...	...	...	...
553328	2600.0	mg/kg	NaN	NaN
553366	2900.0	mg/kg	NaN	NaN
553433	2500.0	mg/kg	NaN	NaN
553511	2600.0	mg/kg	NaN	NaN
553520	2200.0	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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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: 5762
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
22181	40	#/100mL	NaN	NaN
22236	9	#/100mL	NaN	NaN
22255	20	#/100mL	NaN	NaN
22474	200	#/100mL	NaN	NaN
22699	NO DATA	#/100mL	ResultMeasureValue: "NO DATA" result cannot be...	NaN
...	...	...	...	...
565328	450.0	cfu/100ml	NaN	450.0 Colony_Forming_Units / milliliter
565349	140.0	cfu/100ml	NaN	140.0 Colony_Forming_Units / milliliter
565935	180.0	#/100mL	NaN	NaN
565992	1.0	cfu/100ml	NaN	1.0 Colony_Forming_Units / milliliter
569514	NaN	#/100mL	ResultMeasureValue: missing (NaN) result	NaN

8224 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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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/3.9.23/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: 26846
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
...	...	...	...	...
569485	7.0	MPN/100 ml	NaN	7.0 Colony_Forming_Units / milliliter
569498	870.0	MPN/100 ml	NaN	870.0 Colony_Forming_Units / milliliter
569502	44.0	MPN/100 ml	NaN	44.0 Colony_Forming_Units / milliliter
569506	14.0	MPN/100 ml	NaN	14.0 Colony_Forming_Units / milliliter
569510	390.0	MPN/100 ml	NaN	390.0 Colony_Forming_Units / milliliter

32036 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: 59967
Mean: 23.325032012123625 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
30987	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
559591	4980.0	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
25878	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
...	...	...	...	...	...	...	...	...
478685	0.0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
478730	0.0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
478742	0.0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
510999	0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN
511213	0	ppth	NaN	0.0 Practical_Salinity_Units	NaN	NaN	NaN	NaN

806 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
52                  49700.0 microsiemens / centimeter
85                    443.0 microsiemens / centimeter
                             ...
511276              33048.0 microsiemens / centimeter
511282    713.9999999999999 microsiemens / centimeter
511284              16323.0 microsiemens / centimeter
511296    209.7999999999999 microsiemens / centimeter
511303              36896.0 microsiemens / centimeter
Name: Conductivity, Length: 13491, 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
279987	57700	uS/cm	NaN	NaN	57700.0 microsiemens / centimeter
318613	52.418	mS/cm	NaN	NaN	52418.0 microsiemens / centimeter
300869	52.418	mS/cm	NaN	NaN	52418.0 microsiemens / centimeter
308898	51.618	mS/cm	NaN	NaN	51618.0 microsiemens / centimeter
315665	51.618	mS/cm	NaN	NaN	51618.0 microsiemens / centimeter
...	...	...	...	...	...
6563	0.02	uS/cm	NaN	NaN	0.02 microsiemens / centimeter
97206	0	mS/cm	NaN	NaN	0.0 microsiemens / centimeter
97054	0	mS/cm	NaN	NaN	0.0 microsiemens / centimeter
3992	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter
8440	0	uS/cm	NaN	NaN	0.0 microsiemens / centimeter

13491 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]:

279987    38.468 dimensionless
318613    34.521 dimensionless
300869    34.521 dimensionless
308898    33.929 dimensionless
315665    33.929 dimensionless
                  ...
6563       0.012 dimensionless
97206      0.012 dimensionless
97054      0.012 dimensionless
3992       0.012 dimensionless
8440       0.012 dimensionless
Name: Salinity, Length: 13491, 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
279987	2021-06-21	NaN	NaN
318613	2022-08-09	12:58:00	EST
300869	2022-08-09	12:58:00	EST
308898	2022-08-09	13:25:00	EST
315665	2022-08-09	13:25:00	EST
...	...	...	...
569619	1956-08-16	NaN	NaN
569620	1956-06-16	NaN	NaN
569621	1956-07-01	NaN	NaN
569622	1955-08-16	NaN	NaN
569623	1952-08-16	NaN	NaN

569624 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
279987	2021-06-21	NaT
318613	2022-08-09	2022-08-09 17:58:00+00:00
300869	2022-08-09	2022-08-09 17:58:00+00:00
308898	2022-08-09	2022-08-09 18:25:00+00:00
315665	2022-08-09	2022-08-09 18:25:00+00:00
...	...	...
569619	1956-08-16	NaT
569620	1956-06-16	NaT
569621	1956-07-01	NaT
569622	1955-08-16	NaT
569623	1952-08-16	NaT

569624 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]:

169594    0.15
171730    0.15
104590    0.15
109799    0.15
170263    0.15
          ...
446854    0.15
446856    0.15
446857    0.15
446858    1.23
446859    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_DO	QA_Nitrogen	QA_Fecal_Coliform	QA_Sediment	QA_Turbidity	QA_Conductivity	QA_Carbon	QA_E_coli	QA_Secchi	QA_Salinity
279987	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
318613	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
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
308898	AQUINNAH_WQX	Wampanoag Tribe of Gay Head (Aquinnah) (Tribal)	AQUINNAH_WQX-P4:202208091325:FM	2022-08-09	13:25:00	EST	AQUINNAH_WQX-P4	STORET-1057585502	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
315665	WTGHA	Wompanoag Tribe of Gay Head Aquinnah (Tribal)	WTGHA-P4:20220809132500:FM:B	2022-08-09	13:25:00	EST	WTGHA-P4	STORET-1041330098	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
569618	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
569619	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
569620	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
569621	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
569622	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

516191 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)))

53433 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_TP_Phosphorus', 'QA_TDP_Phosphorus',
       'QA_Other_Phosphorus', 'QA_Temperature', 'QA_Chlorophyll', 'QA_pH',
       'QA_DO', 'QA_Nitrogen', 'QA_Fecal_Coliform', 'QA_Sediment',
       'QA_Turbidity', 'QA_Conductivity', 'QA_Carbon', 'QA_E_coli',
       'QA_Secchi', 'QA_Salinity'],
      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_DO	QA_Nitrogen	QA_Fecal_Coliform	QA_Sediment	QA_Turbidity	QA_Conductivity	QA_Carbon	QA_E_coli	QA_Secchi	QA_Salinity
279987	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
318613	AQUINNAH_WQX	Wampanoag Tribe of Gay Head (Aquinnah) (Tribal)	AQUINNAH_WQX-P3:202208091258:FM	AQUINNAH_WQX-P3	STORET	NaN	NaN	NaN	NaN	34.521 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
308898	AQUINNAH_WQX	Wampanoag Tribe of Gay Head (Aquinnah) (Tribal)	AQUINNAH_WQX-P4:202208091325:FM	AQUINNAH_WQX-P4	STORET	NaN	NaN	NaN	NaN	33.929 dimensionless	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
315665	WTGHA	Wompanoag Tribe of Gay Head Aquinnah (Tribal)	WTGHA-P4:20220809132500:FM:B	WTGHA-P4	STORET	NaN	NaN	NaN	NaN	33.929 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_TDP_Phosphorus',
 'QA_DO',
 'QA_Sediment',
 'QA_Conductivity',
 'QA_Carbon']

[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