Tampa Bay, FL - Detailed step-by-step
Standardize, clean and wrangle Water Quality Portal data in Tampa Bay, FL into more analytic-ready formats using the harmonize_wq package
US EPA’s Water Quality Portal (WQP) aggregates water quality, biological, and physical data provided by many organizations and has become an essential resource with tools to query and retrieval data using python or R. Given the variety of data and variety of data originators, using the data in analysis often requires data cleaning to ensure it meets the required quality standards and data wrangling to get it in a more analytic-ready format. Recognizing the definition of analysis-ready varies depending on the analysis, the harmonixe_wq package is intended to be a flexible water quality specific framework to help:
Identify differences in data units (including speciation and basis)
Identify differences in sampling or analytic methods
Resolve data errors using transparent assumptions
Reduce data to the columns that are most commonly needed
Transform data from long to wide format
Domain experts must decide what data meets their quality standards for data comparability and any thresholds for acceptance or rejection.
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 Tampa Bay, FL
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@new_release_0-3-8
[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
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/dataretrieval/nadp.py:44: UserWarning: GDAL not installed. Some functions will not work.
warnings.warn('GDAL not installed. Some functions will not work.')
Download location data using dataretrieval
[3]:
# Read geometry for Area of Interest from geojson file url and plot
aoi_url = r'https://github.com/USEPA/Coastal_Ecological_Indicators/raw/master/DGGS_Coastal/temperature_data/TampaBay.geojson'
# geoJSON should be WGS1984 standard, but this one isn't
aoi_gdf = wrangle.as_gdf(aoi_url).to_crs(epsg=4326)
aoi_gdf.plot()
[3]:
<Axes: >
[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]:
(16226, 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-FL | USGS Florida Water Science Center | USGS-02300009 | MANATEE RIVER AT DEVILS ELBOW NEAR FT HAMER FL | Estuary | NaN | 3100202.0 | 139.0 | sq mi | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 1 | USGS-FL | USGS Florida Water Science Center | USGS-02300018 | GAMBLE CREEK NEAR PARRISH FL | Stream | NaN | 3100202.0 | 50.6 | sq mi | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 2 | USGS-FL | USGS Florida Water Science Center | USGS-02300021 | MANATEE RIVER AT FORT HAMER FL | Estuary | NaN | 3100202.0 | 216.0 | sq mi | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 3 | USGS-FL | USGS Florida Water Science Center | USGS-02300062 | GLEN CREEK NEAR BRADENTON FL | Stream | NaN | 3100202.0 | 2.5 | sq mi | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 4 | USGS-FL | USGS Florida Water Science Center | USGS-02300064 | BRADEN RIVER AT BRADENTON FL | Stream | NaN | 3100202.0 | 83.0 | sq mi | NaN | ... | 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 27.520872
LongitudeMeasure -82.40176
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)
[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]:
(16226, 42)
[12]:
# Example results for the new columns
stations_gdf.iloc[0][['geom_orig', 'EPSG', 'QA_flag', 'geom', 'geometry']]
[12]:
geom_orig (-82.4017604, 27.5208719)
EPSG 4269.0
QA_flag NaN
geom POINT (-82.4017604 27.5208719)
geometry POINT (-82.4017604 27.5208719)
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 0x7f7efa582210>
[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; HorizontalCoordinateReferenceSystemDatumName: Bad datum UNKWN, EPSG:4326 assumed',
'LatitudeMeasure: Imprecise: lessthan3decimaldigits; LongitudeMeasure: Imprecise: lessthan3decimaldigits',
'LongitudeMeasure: Imprecise: lessthan3decimaldigits',
'LongitudeMeasure: Imprecise: lessthan3decimaldigits; HorizontalCoordinateReferenceSystemDatumName: Bad datum OTHER, EPSG:4326 assumed'}
[15]:
# Map it
stations_gdf.plot()
[15]:
<Axes: >
[16]:
# Clip it to area of interest
stations_clipped = wrangle.clip_stations(stations_gdf, aoi_gdf)
[17]:
# Map it
stations_clipped.plot()
[17]:
<Axes: >
[18]:
# How many stations now?
len(stations_clipped)
[18]:
10720
[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, 'Tampa_stations.shp'))
Retrieve Characteristic Data
[20]:
# Now query for results
query['dataProfile'] = 'narrowResult'
res_narrow, md_narrow = wqp.get_results(**query)
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/dataretrieval/wqp.py:83: DtypeWarning: Columns (9,10,13,15,17,19,22,23,28,31,32,33,36,38,58,60,61,63,64,65,70,71,73) have mixed types. Specify dtype option on import or set low_memory=False.
df = pd.read_csv(StringIO(response.text), delimiter=',')
[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 | 21FLHILL_WQX | Environmental Protection Commission of Hillsbo... | 21FLHILL_WQX-130612585-W | 2013-06-12 | 11:01:00 | EST | 21FLHILL_WQX-585 | STORET-301235413 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | STORET |
| 1 | 21FLSEAS_WQX | Florida Department of Environmental Protection | 21FLSEAS_WQX-481901119134 | 2013-11-19 | 14:01:00 | EST | 21FLSEAS_WQX-48SEAS190 | STORET-310535134 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | STORET |
| 2 | 21FLHILL_WQX | Environmental Protection Commission of Hillsbo... | 21FLHILL_WQX-130702047-M | 2013-07-02 | 11:01:00 | EST | 21FLHILL_WQX-047 | STORET-300620295 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | STORET |
| 3 | 21FLHILL_WQX | Environmental Protection Commission of Hillsbo... | 21FLHILL_WQX-130716021 | 2013-07-16 | 11:01:00 | EST | 21FLHILL_WQX-021 | STORET-300666279 | NaN | NaN | ... | NaN | NaN | NaN | https://www.waterqualitydata.us/data/providers... | NaN | NaN | NaN | NaN | NaN | STORET |
| 4 | 21FLHILL_WQX | Environmental Protection Commission of Hillsbo... | 21FLHILL_WQX-131216112-M | 2013-12-16 | 12:01:00 | EST | 21FLHILL_WQX-112 | STORET-301229196 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | STORET |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1475274 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500762 | 1955-04-08 | NaN | NaN | USGS-273926082304501 | NWIS-6842541 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 1475275 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500762 | 1955-04-08 | NaN | NaN | USGS-273926082304501 | NWIS-6842544 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 1475276 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95800924 | 1957-10-21 | 14:05:00 | EST | USGS-02306001 | NWIS-6894410 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 1475277 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500782 | 1955-04-08 | NaN | NaN | USGS-274455082253601 | NWIS-6842941 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
| 1475278 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500782 | 1955-04-08 | NaN | NaN | USGS-274455082253601 | NWIS-6842944 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NWIS |
1475279 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: >
Harmonize Characteristic Results
Two options for functions to harmonize characteristics: harmonize_all() or harmonize(). harmonize_all runs functions on all characteristics and lets you specify how to handle errors harmonize 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.harmonize_all?
#harmonize.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', intermediate_columns=True, report=True)
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/harmonize_wq/wq_data.py:395: UserWarning: WARNING: 'None' UNDEFINED UNIT for Secchi
warn("WARNING: " + problem)
-Usable results-
count 87241.000000
mean 1.469177
std 0.902037
min -9.000000
25% 0.900000
50% 1.300000
75% 1.900000
max 32.004000
dtype: float64
Unusable results: 219
Usable results with inferred units: 0
Results outside threshold (0.0 to 6.881397411106354): 48
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 | |
|---|---|---|---|---|---|---|
| 36 | 21FLHILL_WQX-1510 | 0.80 | m | NaN | m | 0.8 meter |
| 68 | 21FLMANA_WQX-428 | 2.2 | m | NaN | m | 2.2 meter |
| 96 | 21FLHILL_WQX-096 | 2.70 | m | NaN | m | 2.7 meter |
| 106 | 21FLHILL_WQX-064 | 0.80 | m | NaN | m | 0.8 meter |
| 127 | 21FLCOSP_WQX-COSPE6-2 | 1.7 | m | NaN | m | 1.7 meter |
| ... | ... | ... | ... | ... | ... | ... |
| 1474925 | 21FLPDEM_WQX-W8-B-24-01 | 3.1 | m | NaN | m | 3.1 meter |
| 1474932 | 21FLPDEM_WQX-W7-B-24-02 | 1.6 | m | NaN | m | 1.6 meter |
| 1474935 | 21FLPDEM_WQX-W7-B-24-01 | 3.128 | m | NaN | m | 3.128 meter |
| 1474976 | 21FLTPA_WQX-G1SW0136 | 0.5 | m | NaN | m | 0.5 meter |
| 1474980 | 21FLTPA_WQX-G1SW0129 | 0.4 | m | NaN | m | 0.4 meter |
87460 rows × 6 columns
[26]:
# Look at unusable(NAN) results
sechi_results.loc[df['Secchi'].isna()]
[26]:
| MonitoringLocationIdentifier | ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Units | Secchi | |
|---|---|---|---|---|---|---|
| 734900 | 21FLPDEM_WQX-14-02 | Not Reported | m | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| 739786 | 21FLKWAT_WQX-HIL-RAINBOW-1 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| 743105 | 21FLKWAT_WQX-PIN-COFFEEPOBAYOU-8 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| 745032 | 21FLKWAT_WQX-PIN-COFFEEPOBAYOU-6 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| 746511 | 21FLPDEM_WQX-E2-D-19-02 | Not Reported | m | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| ... | ... | ... | ... | ... | ... | ... |
| 1460090 | USGS-273631082452600 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460418 | USGS-275100082280500 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460423 | USGS-275100082280500 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460626 | USGS-275530082383300 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460866 | USGS-275530082383300 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
219 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: "Not Reported" result cannot be used'
[28]:
# All cases where there was a QA flag
sechi_results.loc[df['QA_flag'].notna()]
[28]:
| MonitoringLocationIdentifier | ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Units | Secchi | |
|---|---|---|---|---|---|---|
| 244340 | NARS_WQX-NCCA10-1674 | -9 | None | ResultMeasure/MeasureUnitCode: 'None' UNDEFINE... | m | -9.0 meter |
| 734900 | 21FLPDEM_WQX-14-02 | Not Reported | m | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| 739786 | 21FLKWAT_WQX-HIL-RAINBOW-1 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| 743105 | 21FLKWAT_WQX-PIN-COFFEEPOBAYOU-8 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| 745032 | 21FLKWAT_WQX-PIN-COFFEEPOBAYOU-6 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | m | NaN |
| ... | ... | ... | ... | ... | ... | ... |
| 1460090 | USGS-273631082452600 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460418 | USGS-275100082280500 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460423 | USGS-275100082280500 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460626 | USGS-275530082383300 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
| 1460866 | USGS-275530082383300 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | m | NaN |
220 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 | 21FLBRA-1530-A | 2 | 0.375000 |
| 1 | 21FLBRA-1541B-A | 3 | 1.166667 |
| 2 | 21FLBRA-1574-A | 1 | 0.250000 |
| 3 | 21FLBRA-1574A-A | 2 | 0.250000 |
| 4 | 21FLBRA-1574A-B | 1 | 1.250000 |
| ... | ... | ... | ... |
| 11832 | USGS-280630082350900 | 3 | 1.966667 |
| 11833 | USGS-280635082322100 | 2 | 2.100000 |
| 11834 | USGS-280640082434700 | 3 | 2.302933 |
| 11835 | USGS-280719082291400 | 2 | 1.000000 |
| 11836 | USGS-280730082431800 | 3 | 1.947333 |
11837 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/3.11.11/x64/lib/python3.11/site-packages/mapclassify/classifiers.py:1653: UserWarning: Not enough unique values in array to form 5 classes. Setting k to 2.
self.bins = quantile(y, k=k)
[30]:
<Axes: >
[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: >
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'
# Note: Default errors='raise'
df = harmonize.harmonize(df, 'Temperature, water', intermediate_columns=True, report=True)
-Usable results-
count 301964.000000
mean 25.302932
std 79.645591
min -2.900000
25% 21.210000
50% 25.900000
75% 29.200000
max 43696.000000
dtype: float64
Unusable results: 166
Usable results with inferred units: 0
Results outside threshold (0.0 to 503.176478477458): 2
[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 | |
|---|---|---|---|---|---|---|
| 2 | 21FLHILL_WQX-047 | 28.19 | deg C | NaN | 28.19 degree_Celsius | degC |
| 9 | 21FLTBW_WQX-M23 | 29.52 | deg C | NaN | 29.52 degree_Celsius | degC |
| 14 | 21FLMANA_WQX-GA1 | 21 | deg C | NaN | 21.0 degree_Celsius | degC |
| 34 | 21FLHILL_WQX-1509 | 27.67 | deg C | NaN | 27.67 degree_Celsius | degC |
| 37 | 21FLTBW_WQX-PR103026 | 17.73 | deg C | NaN | 17.73 degree_Celsius | degC |
| ... | ... | ... | ... | ... | ... | ... |
| 1475263 | USGS-273217082335701 | 28.9 | deg C | NaN | 28.9 degree_Celsius | degC |
| 1475266 | USGS-274322082245501 | 24.4 | deg C | NaN | 24.4 degree_Celsius | degC |
| 1475271 | USGS-274302082280801 | 25.0 | deg C | NaN | 25.0 degree_Celsius | degC |
| 1475274 | USGS-273926082304501 | 25.7 | deg C | NaN | 25.7 degree_Celsius | degC |
| 1475277 | USGS-274455082253601 | 26.1 | deg C | NaN | 26.1 degree_Celsius | degC |
302130 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
[34]:
# Examine missing units
temperature_results.loc[df['ResultMeasure/MeasureUnitCode'].isna()]
[34]:
| MonitoringLocationIdentifier | ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Temperature | Units | |
|---|---|---|---|---|---|---|
| 739028 | 21FLPDEM_WQX-19-13 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 740113 | 21FLPDEM_WQX-24-07 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 741546 | 21FLPDEM_WQX-12-04 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 746594 | 21FLPDEM_WQX-23-08 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 752680 | 21FLPDEM_WQX-04-04 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| ... | ... | ... | ... | ... | ... | ... |
| 893028 | 21FLPDEM_WQX-35-01 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 896706 | 21FLPDEM_WQX-23-08 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 1362248 | USGS-280228082343000 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | NaN | degC |
| 1444305 | USGS-02306028 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | NaN | degC |
| 1444588 | USGS-02306028 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | NaN | degC |
87 rows × 6 columns
We can see where the units were missing, the results were assumed to be in degree_Celsius already
[35]:
# This is also noted in the QA_flag field
list(temperature_results.loc[df['ResultMeasure/MeasureUnitCode'].isna(), 'QA_flag'])[0]
[35]:
'ResultMeasureValue: "Not Reported" result cannot be used; ResultMeasure/MeasureUnitCode: MISSING UNITS, degC assumed'
[36]:
# Look for any without usable results
temperature_results.loc[df['Temperature'].isna()]
[36]:
| MonitoringLocationIdentifier | ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Temperature | Units | |
|---|---|---|---|---|---|---|
| 739028 | 21FLPDEM_WQX-19-13 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 740113 | 21FLPDEM_WQX-24-07 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 741546 | 21FLPDEM_WQX-12-04 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 746594 | 21FLPDEM_WQX-23-08 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 752680 | 21FLPDEM_WQX-04-04 | Not Reported | NaN | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| ... | ... | ... | ... | ... | ... | ... |
| 1418840 | 21FLPDEM_WQX-12-02 | Not Reported | deg C | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 1444305 | USGS-02306028 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | NaN | degC |
| 1444588 | USGS-02306028 | NaN | NaN | ResultMeasureValue: missing (NaN) result; Resu... | NaN | degC |
| 1474642 | 21FLPDEM_WQX-24-01 | Not Reported | deg C | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
| 1474729 | 21FLPDEM_WQX-04-04 | Not Reported | deg C | ResultMeasureValue: "Not Reported" result cann... | NaN | degC |
166 rows × 6 columns
[37]:
# Aggregate temperature data by station
visualize.station_summary(temperature_results, 'Temperature')
[37]:
| MonitoringLocationIdentifier | cnt | mean | |
|---|---|---|---|
| 0 | 21FLBRA-1530-A | 12 | 27.593333 |
| 1 | 21FLBRA-1530-B | 7 | 26.290000 |
| 2 | 21FLBRA-1541A-A | 6 | 26.016667 |
| 3 | 21FLBRA-1541B-A | 6 | 26.743333 |
| 4 | 21FLBRA-1574-A | 5 | 27.890000 |
| ... | ... | ... | ... |
| 15037 | USGS-280726082313300 | 4 | 28.025000 |
| 15038 | USGS-280728082301101 | 54 | 25.083333 |
| 15039 | USGS-280729082313501 | 1 | 27.400000 |
| 15040 | USGS-280730082313201 | 1 | 24.700000 |
| 15041 | USGS-280730082431800 | 11 | 22.018182 |
15042 rows × 3 columns
[38]:
# 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)
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/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)
[38]:
<Axes: >
[39]:
# 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)
[39]:
<Axes: >
Dissolved oxygen
[40]:
# look at Dissolved oxygen (DO), but this time without intermediate fields
df = harmonize.harmonize(df, 'Dissolved oxygen (DO)')
Note: Imediately when we run a harmonization function without the intermediate fields they’re deleted.
[41]:
# 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
[41]:
| MonitoringLocationIdentifier | ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | DO | |
|---|---|---|---|---|---|
| 0 | 21FLHILL_WQX-585 | 9.32 | mg/L | NaN | 9.32 milligram / liter |
| 10 | 21FLHILL_WQX-1606 | 8.08 | mg/L | NaN | 8.08 milligram / liter |
| 12 | 21FLHILL_WQX-1611 | 4.90 | mg/L | NaN | 4.9 milligram / liter |
| 16 | 21FLHILL_WQX-1606 | 2.56 | mg/L | NaN | 2.56 milligram / liter |
| 19 | 21FLPDEM_WQX-24-01 | 68.7 | % | NaN | 0.05676222371166 milligram / liter |
| ... | ... | ... | ... | ... | ... |
| 1474946 | 21FLPDEM_WQX-14-02 | 8.16 | mg/L | NaN | 8.16 milligram / liter |
| 1474956 | 21FLTPA_WQX-G2SW0138 | 0.49 | mg/L | NaN | 0.49 milligram / liter |
| 1474961 | 21FLTPA_WQX-G1SW0126 | 10.15 | mg/L | NaN | 10.15 milligram / liter |
| 1474977 | 21FLHESD_WQX-BEAUDETTE POND OUTLET | 8.40 | mg/L | NaN | 8.4 milligram / liter |
| 1474979 | 21FLTPA_WQX-G1SW0071 | 8.48 | mg/L | NaN | 8.48 milligram / liter |
271522 rows × 5 columns
[42]:
do_res.loc[do_res['ResultMeasure/MeasureUnitCode']!='mg/l']
[42]:
| MonitoringLocationIdentifier | ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | DO | |
|---|---|---|---|---|---|
| 0 | 21FLHILL_WQX-585 | 9.32 | mg/L | NaN | 9.32 milligram / liter |
| 10 | 21FLHILL_WQX-1606 | 8.08 | mg/L | NaN | 8.08 milligram / liter |
| 12 | 21FLHILL_WQX-1611 | 4.90 | mg/L | NaN | 4.9 milligram / liter |
| 16 | 21FLHILL_WQX-1606 | 2.56 | mg/L | NaN | 2.56 milligram / liter |
| 19 | 21FLPDEM_WQX-24-01 | 68.7 | % | NaN | 0.05676222371166 milligram / liter |
| ... | ... | ... | ... | ... | ... |
| 1474946 | 21FLPDEM_WQX-14-02 | 8.16 | mg/L | NaN | 8.16 milligram / liter |
| 1474956 | 21FLTPA_WQX-G2SW0138 | 0.49 | mg/L | NaN | 0.49 milligram / liter |
| 1474961 | 21FLTPA_WQX-G1SW0126 | 10.15 | mg/L | NaN | 10.15 milligram / liter |
| 1474977 | 21FLHESD_WQX-BEAUDETTE POND OUTLET | 8.40 | mg/L | NaN | 8.4 milligram / liter |
| 1474979 | 21FLTPA_WQX-G1SW0071 | 8.48 | mg/L | NaN | 8.48 milligram / liter |
174533 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())
[43]:
# Aggregate data by station
visualize.station_summary(do_res, 'DO')
[43]:
| MonitoringLocationIdentifier | cnt | mean | |
|---|---|---|---|
| 0 | 21FLBRA-1530-A | 12 | 2.785000 |
| 1 | 21FLBRA-1530-B | 7 | 4.042857 |
| 2 | 21FLBRA-1541A-A | 6 | 4.721667 |
| 3 | 21FLBRA-1541B-A | 6 | 6.600000 |
| 4 | 21FLBRA-1574-A | 5 | 4.378000 |
| ... | ... | ... | ... |
| 13610 | NARS_WQX-NCCA10-1672 | 8 | 5.437500 |
| 13611 | NARS_WQX-NCCA10-1673 | 20 | 4.115000 |
| 13612 | NARS_WQX-NCCA10-1674 | 6 | 2.466667 |
| 13613 | NARS_WQX-NLA06608-0161 | 5 | 6.500000 |
| 13614 | NARS_WQX-NLA_FL-10127 | 3 | 9.033333 |
13615 rows × 3 columns
[44]:
# 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)
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/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)
[44]:
<Axes: >
[45]:
# 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)
[45]:
<Axes: >
pH
[46]:
# pH, this time looking at a report
df = harmonize.harmonize(df, 'pH', report=True)
-Usable results-
count 278289.000000
mean 7.762995
std 0.472536
min 0.370000
25% 7.520000
50% 7.898646
75% 8.070000
max 12.970000
dtype: float64
Unusable results: 186
Usable results with inferred units: 0
Results outside threshold (0.0 to 10.598211492474329): 7
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.
[47]:
df.loc[df['CharacteristicName']=='pH', ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'pH']]
[47]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | pH | |
|---|---|---|---|---|
| 4 | 7.48 | None | NaN | 7.48 dimensionless |
| 5 | 8.18 | None | NaN | 8.18 dimensionless |
| 7 | 7.81 | None | NaN | 7.81 dimensionless |
| 11 | 7.96 | None | NaN | 7.96 dimensionless |
| 17 | 7.92 | None | NaN | 7.92 dimensionless |
| ... | ... | ... | ... | ... |
| 1475272 | 7.9 | std units | NaN | 7.9 dimensionless |
| 1475273 | 7.3 | std units | NaN | 7.3 dimensionless |
| 1475275 | 7.8 | std units | NaN | 7.8 dimensionless |
| 1475276 | 7.7 | std units | NaN | 7.7 dimensionless |
| 1475278 | 7.6 | std units | NaN | 7.6 dimensionless |
278475 rows × 4 columns
‘None’ is uninterpretable and replaced with NaN, which then gets replaced with ‘dimensionless’ since pH is unitless
Salinity
[48]:
# Salinity
df = harmonize.harmonize(df, 'Salinity', report=True, errors='ignore')
-Usable results-
count 269049.000000
mean 21.708380
std 95.024786
min -0.020000
25% 18.060000
50% 24.900000
75% 28.650000
max 48930.000000
dtype: float64
Unusable results: 1265
Usable results with inferred units: 0
Results outside threshold (0.0 to 591.8570961272843): 4
[49]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Salinity']
df.loc[df['CharacteristicName']=='Salinity', cols]
[49]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Salinity | |
|---|---|---|---|---|
| 1 | 40 | ppth | NaN | 40.0 Practical_Salinity_Units |
| 6 | 29 | PSS | NaN | 29.0 Practical_Salinity_Units |
| 8 | 26.04 | PSS | NaN | 26.04 Practical_Salinity_Units |
| 21 | 0.18 | ppth | NaN | 0.18 Practical_Salinity_Units |
| 23 | 5.9 | ppth | NaN | 5.9 Practical_Salinity_Units |
| ... | ... | ... | ... | ... |
| 1474954 | 0.37 | ppth | NaN | 0.37 Practical_Salinity_Units |
| 1474965 | 34.41 | ppth | NaN | 34.41 Practical_Salinity_Units |
| 1474968 | 18.14 | ppth | NaN | 18.14 Practical_Salinity_Units |
| 1474970 | 0.56 | ppth | NaN | 0.56 Practical_Salinity_Units |
| 1474972 | 0.26 | ppth | NaN | 0.26 Practical_Salinity_Units |
270314 rows × 4 columns
Nitrogen
[50]:
# Nitrogen
df = harmonize.harmonize(df, 'Nitrogen', report=True)
-Usable results-
count 163.000000
mean 1.575389
std 4.532429
min 0.024000
25% 0.202720
50% 0.315560
75% 0.500170
max 22.500000
dtype: float64
Unusable results: 2
Usable results with inferred units: 0
Results outside threshold (0.0 to 28.769965070579055): 0
[51]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Nitrogen']
df.loc[df['CharacteristicName']=='Nitrogen', cols]
[51]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Nitrogen | |
|---|---|---|---|---|
| 221551 | 0.39 | mg/L | NaN | 0.39 milligram / liter |
| 237684 | 0.4475 | mg/L | NaN | 0.4475 milligram / liter |
| 240477 | 0.425 | mg/L | NaN | 0.425 milligram / liter |
| 254309 | 0.4625 | mg/L | NaN | 0.4625 milligram / liter |
| 255698 | 0.33625 | mg/L | NaN | 0.33625 milligram / liter |
| ... | ... | ... | ... | ... |
| 1472938 | 0.084 | mg/l | NaN | 0.084 milligram / liter |
| 1472946 | 0.166 | mg/l | NaN | 0.166 milligram / liter |
| 1472956 | 0.091 | mg/l | NaN | 0.091 milligram / liter |
| 1472990 | 0.057 | mg/l | NaN | 0.057 milligram / liter |
| 1473145 | 0.030 | mg/l | NaN | 0.03 milligram / liter |
165 rows × 4 columns
Conductivity
[52]:
# Conductivity
df = harmonize.harmonize(df, 'Conductivity', report=True)
-Usable results-
count 11.000000
mean 2503.363636
std 5969.279978
min 606.000000
25% 629.500000
50% 775.000000
75% 776.000000
max 20500.000000
dtype: float64
Unusable results: 8
Usable results with inferred units: 0
Results outside threshold (0.0 to 38319.04350375742): 0
[53]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Conductivity']
df.loc[df['CharacteristicName']=='Conductivity', cols]
[53]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Conductivity | |
|---|---|---|---|---|
| 436005 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 440696 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 442133 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 444571 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 445435 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 447223 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 451907 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 468050 | NaN | uS/cm | ResultMeasureValue: missing (NaN) result | NaN |
| 479556 | 626 | uS/cm | NaN | 626.0 microsiemens / centimeter |
| 483915 | 688 | uS/cm | NaN | 688.0 microsiemens / centimeter |
| 501316 | 606 | uS/cm | NaN | 606.0 microsiemens / centimeter |
| 502422 | 606 | uS/cm | NaN | 606.0 microsiemens / centimeter |
| 505646 | 633 | uS/cm | NaN | 633.0 microsiemens / centimeter |
| 518089 | 775 | uS/cm | NaN | 775.0 microsiemens / centimeter |
| 520731 | 776 | uS/cm | NaN | 776.0 microsiemens / centimeter |
| 521718 | 776 | uS/cm | NaN | 776.0 microsiemens / centimeter |
| 523763 | 776 | uS/cm | NaN | 776.0 microsiemens / centimeter |
| 529321 | 775 | uS/cm | NaN | 775.0 microsiemens / centimeter |
| 874961 | 20500 | uS/cm | NaN | 20500.0 microsiemens / centimeter |
Chlorophyll a
[54]:
# Chlorophyll a
df = harmonize.harmonize(df, 'Chlorophyll a', report=True)
-Usable results-
count 43334.000000
mean 0.014368
std 0.022741
min -0.000506
25% 0.004600
50% 0.008725
75% 0.016360
max 1.552000
dtype: float64
Unusable results: 1115
Usable results with inferred units: 4
Results outside threshold (0.0 to 0.1508140878070869): 197
[55]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Chlorophyll']
df.loc[df['CharacteristicName']=='Chlorophyll a', cols]
[55]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Chlorophyll | |
|---|---|---|---|---|
| 221067 | 5.94 | ug/L | NaN | 0.005940000000000001 milligram / liter |
| 226038 | 1.45 | ug/L | NaN | 0.00145 milligram / liter |
| 226973 | 2.77 | ug/L | NaN | 0.00277 milligram / liter |
| 227824 | 3.87 | ug/L | NaN | 0.00387 milligram / liter |
| 235915 | 8.15 | ug/L | NaN | 0.008150000000000001 milligram / liter |
| ... | ... | ... | ... | ... |
| 1472901 | 18.16 | ug/l | NaN | 0.01816 milligram / liter |
| 1472902 | 41.09 | ug/l | NaN | 0.04109 milligram / liter |
| 1472913 | 23.55 | ug/l | NaN | 0.02355 milligram / liter |
| 1472915 | 37.07 | ug/l | NaN | 0.03707 milligram / liter |
| 1472916 | 61.95 | ug/l | NaN | 0.061950000000000005 milligram / liter |
44449 rows × 4 columns
Organic Carbon
[56]:
# Organic carbon (%)
df = harmonize.harmonize(df, 'Organic carbon', report=True)
-Usable results-
count 2.173000e+04
mean 2.444555e+04
std 1.898197e+06
min 0.000000e+00
25% 4.600000e+00
50% 7.100000e+00
75% 1.210000e+01
max 2.000000e+08
dtype: float64
Unusable results: 1928
Usable results with inferred units: 0
Results outside threshold (0.0 to 11413630.33831222): 8
[57]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Carbon']
df.loc[df['CharacteristicName']=='Organic carbon', cols]
[57]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Carbon | |
|---|---|---|---|---|
| 3 | 3.8 | mg/L | NaN | 3.8 milligram / liter |
| 124 | 19.5 | mg/L | NaN | 19.5 milligram / liter |
| 125 | 11.3 | mg/L | NaN | 11.3 milligram / liter |
| 129 | 6.7 | mg/L | NaN | 6.7 milligram / liter |
| 135 | 28.0 | mg/L | NaN | 28.0 milligram / liter |
| ... | ... | ... | ... | ... |
| 1474780 | 5.3 | mg/L | NaN | 5.3 milligram / liter |
| 1474795 | 16 | mg/L | NaN | 16.0 milligram / liter |
| 1474863 | 11 | mg/L | NaN | 11.0 milligram / liter |
| 1474867 | 20 | mg/L | NaN | 20.0 milligram / liter |
| 1474920 | 12 | mg/L | NaN | 12.0 milligram / liter |
23658 rows × 4 columns
Turbidity (NTU)
[58]:
# Turbidity (NTU)
df = harmonize.harmonize(df, 'Turbidity', report=True, errors='ignore')
-Usable results-
count 93245.000000
mean 16.072727
std 867.733210
min -0.047700
25% 1.500000
50% 2.400000
75% 4.100000
max 200000.000000
dtype: float64
Unusable results: 1101
Usable results with inferred units: 0
Results outside threshold (0.0 to 5222.471985770489): 155
[59]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Turbidity']
df.loc[df['CharacteristicName']=='Turbidity', cols]
[59]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Turbidity | |
|---|---|---|---|---|
| 25 | 4.49 | NTU | NaN | 4.49 Nephelometric_Turbidity_Units |
| 81 | 1.2 | NTU | NaN | 1.2 Nephelometric_Turbidity_Units |
| 108 | 2.9 | NTU | NaN | 2.9 Nephelometric_Turbidity_Units |
| 147 | 6.9 | NTU | NaN | 6.9 Nephelometric_Turbidity_Units |
| 222 | 4.3 | NTU | NaN | 4.3 Nephelometric_Turbidity_Units |
| ... | ... | ... | ... | ... |
| 1474943 | 1.5 | NTU | NaN | 1.5 Nephelometric_Turbidity_Units |
| 1474947 | 3.7 | NTU | NaN | 3.7 Nephelometric_Turbidity_Units |
| 1474948 | 0.8 | NTU | NaN | 0.8 Nephelometric_Turbidity_Units |
| 1474953 | 0.8 | NTU | NaN | 0.8 Nephelometric_Turbidity_Units |
| 1474973 | 3.3 | NTU | NaN | 3.3 Nephelometric_Turbidity_Units |
94346 rows × 4 columns
Sediment
[60]:
# Sediment
df = harmonize.harmonize(df, 'Sediment', report=False)
[61]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Sediment']
df.loc[df['CharacteristicName']=='Sediment', cols]
[61]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Sediment |
|---|
Phosphorus
Note: must be merged w/ activities (package runs query by site if not already merged)
[62]:
# Phosphorus
df = harmonize.harmonize(df, 'Phosphorus')
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.
[63]:
# All Phosphorus
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'TDP_Phosphorus']
df.loc[df['Phosphorus'].notna(), cols]
[63]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | TDP_Phosphorus | |
|---|---|---|---|---|
| 3156 | 0.049 | mg/L | NaN | NaN |
| 7402 | 0.004 | mg/L | NaN | NaN |
| 9350 | 0.049 | mg/L | NaN | NaN |
| 11185 | 0.036 | mg/L | NaN | NaN |
| 19612 | 0.050 | mg/L | NaN | 0.05 milligram / liter |
| ... | ... | ... | ... | ... |
| 1474959 | 0.02 | mg/L | NaN | NaN |
| 1474960 | 0.18 | mg/L | NaN | NaN |
| 1474964 | 0.091 | mg/L | NaN | NaN |
| 1474966 | 0.180 | mg/L | NaN | NaN |
| 1474982 | 0.04 | mg/L | NaN | NaN |
31328 rows × 4 columns
[64]:
# Total phosphorus
df.loc[df['TP_Phosphorus'].notna(), cols]
[64]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | TDP_Phosphorus | |
|---|---|---|---|---|
| 3156 | 0.049 | mg/L | NaN | NaN |
| 7402 | 0.004 | mg/L | NaN | NaN |
| 9350 | 0.049 | mg/L | NaN | NaN |
| 11185 | 0.036 | mg/L | NaN | NaN |
| 23731 | 0.004 | mg/L | NaN | NaN |
| ... | ... | ... | ... | ... |
| 1474959 | 0.02 | mg/L | NaN | NaN |
| 1474960 | 0.18 | mg/L | NaN | NaN |
| 1474964 | 0.091 | mg/L | NaN | NaN |
| 1474966 | 0.180 | mg/L | NaN | NaN |
| 1474982 | 0.04 | mg/L | NaN | NaN |
29365 rows × 4 columns
[65]:
# Total dissolved phosphorus
df.loc[df['TDP_Phosphorus'].notna(), cols]
[65]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | TDP_Phosphorus | |
|---|---|---|---|---|
| 19612 | 0.050 | mg/L | NaN | 0.05 milligram / liter |
| 29925 | 0.009 | mg/L | NaN | 0.009 milligram / liter |
| 56000 | 0.003 | mg/L | NaN | 0.003 milligram / liter |
| 119455 | 0.050 | mg/L | NaN | 0.05 milligram / liter |
| 124393 | 0.002 | mg/L | NaN | 0.002 milligram / liter |
| ... | ... | ... | ... | ... |
| 1460824 | 1.0 | mg/l as P | NaN | 1.0 milligram / liter |
| 1460842 | 0.93 | mg/l as P | NaN | 0.93 milligram / liter |
| 1460860 | 0.58 | mg/l as P | NaN | 0.58 milligram / liter |
| 1460884 | 0.48 | mg/l as P | NaN | 0.48 milligram / liter |
| 1465353 | 0.166 | mg/l as P | NaN | 0.166 milligram / liter |
1099 rows × 4 columns
[66]:
# All other phosphorus sample fractions
df.loc[df['Other_Phosphorus'].notna(), cols]
[66]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | TDP_Phosphorus | |
|---|---|---|---|---|
| 221871 | 0.13118375 | mg/L | NaN | NaN |
| 238638 | 0.1696225 | mg/L | NaN | NaN |
| 239076 | 0.0835825 | mg/L | NaN | NaN |
| 245367 | 0.16950375 | mg/L | NaN | NaN |
| 254999 | 0.03524375 | mg/L | NaN | NaN |
| ... | ... | ... | ... | ... |
| 1450276 | 0.058 | % | NaN | NaN |
| 1451133 | 0.041 | % | NaN | NaN |
| 1451913 | 0.078 | % | NaN | NaN |
| 1471478 | 460 | mg/kg | NaN | NaN |
| 1471480 | 5400 | mg/kg | NaN | NaN |
864 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
[67]:
# Known unit with bad dimensionality ('Colony_Forming_Units * milliliter')
df = harmonize.harmonize(df, 'Fecal Coliform', report=True, errors='ignore')
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'cfu/100mL' converted to NaN
warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'MPN/100mL' converted to NaN
warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'CFU/100mL' converted to NaN
warn(f"WARNING: '{unit}' converted to NaN")
-Usable results-
count 8.647000e+03
mean 4.903257e+03
std 1.318438e+05
min 0.000000e+00
25% 3.000000e+00
50% 1.100000e+01
75% 6.000000e+01
max 1.000000e+07
dtype: float64
Unusable results: 55593
Usable results with inferred units: 5
Results outside threshold (0.0 to 795966.1242988216): 8
[68]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Fecal_Coliform']
df.loc[df['CharacteristicName']=='Fecal Coliform', cols]
[68]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Fecal_Coliform | |
|---|---|---|---|---|
| 13 | 760 | cfu/100mL | NaN | NaN |
| 15 | 2900 | cfu/100mL | NaN | NaN |
| 55 | 300 | #/100mL | NaN | NaN |
| 72 | 280 | #/100mL | NaN | NaN |
| 109 | 52 | cfu/100mL | NaN | NaN |
| ... | ... | ... | ... | ... |
| 1474326 | 50 | cfu/100mL | NaN | NaN |
| 1474441 | 400 | MPN/100mL | NaN | NaN |
| 1474510 | 100 | cfu/100mL | NaN | NaN |
| 1474974 | 860 | MPN/100mL | NaN | NaN |
| 1474986 | 2 | cfu/100ml | NaN | 2.0 Colony_Forming_Units / milliliter |
64240 rows × 4 columns
Excherichia Coli
[69]:
# Known unit with bad dimensionality ('Colony_Forming_Units * milliliter')
df = harmonize.harmonize(df, 'Escherichia coli', report=True, errors='ignore')
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'cfu/100mL' converted to NaN
warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'MPN/100mL' converted to NaN
warn(f"WARNING: '{unit}' converted to NaN")
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/harmonize_wq/convert.py:128: UserWarning: WARNING: 'CFU/100mL' converted to NaN
warn(f"WARNING: '{unit}' converted to NaN")
-Usable results-
count 142.000000
mean 976.669014
std 4473.446618
min 0.000000
25% 21.000000
50% 46.000000
75% 120.000000
max 41000.000000
dtype: float64
Unusable results: 6413
Usable results with inferred units: 0
Results outside threshold (0.0 to 27817.348725062726): 1
[70]:
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'E_coli']
df.loc[df['CharacteristicName']=='Escherichia coli', cols]
[70]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | E_coli | |
|---|---|---|---|---|
| 275760 | 210 | MPN/100mL | NaN | NaN |
| 277312 | 4800 | MPN/100mL | NaN | NaN |
| 277564 | 74.5 | MPN/100mL | NaN | NaN |
| 277832 | 553.9 | MPN/100mL | NaN | NaN |
| 278394 | 87 | MPN/100mL | NaN | NaN |
| ... | ... | ... | ... | ... |
| 1474963 | 62 | MPN/100mL | NaN | NaN |
| 1474967 | 484 | MPN/100mL | NaN | NaN |
| 1474978 | 116 | MPN/100mL | NaN | NaN |
| 1474981 | 1.0 | MPN/100mL | NaN | NaN |
| 1474983 | 517.2 | MPN/100mL | NaN | NaN |
6555 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:
[71]:
from harmonize_wq import convert
[72]:
# Salinity summary statistics
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: -0.02 to 48930.0
Results: 269049
Mean: 21.708380477587173 PSU
[73]:
# Identify extreme outliers
[x for x in lst if x >3200]
[73]:
[48930.0]
Other fields like units and QA_flag may help understand what caused high values and what results might need to be dropped from consideration
[74]:
# Columns to focus on
cols = ['ResultMeasureValue', 'ResultMeasure/MeasureUnitCode', 'QA_flag', 'Salinity']
[75]:
# 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])]
[75]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Salinity | |
|---|---|---|---|---|
| 288102 | 48930 | ppth | NaN | 48930.0 Practical_Salinity_Units |
| 389920 | 76.57 | ppth | NaN | 76.57 Practical_Salinity_Units |
| 497310 | 54.8 | ppth | NaN | 54.8 Practical_Salinity_Units |
| 620734 | 2976 | ppth | NaN | 2976.0 Practical_Salinity_Units |
| 645813 | 68 | ppth | NaN | 68.0 Practical_Salinity_Units |
Detection limits may help understand what caused low values and what results might need to be dropped or updated
[76]:
df = wrangle.add_detection(df, 'Salinity')
cols+=['ResultDetectionConditionText',
'DetectionQuantitationLimitTypeName',
'DetectionQuantitationLimitMeasure/MeasureValue',
'DetectionQuantitationLimitMeasure/MeasureUnitCode']
[77]:
# Look at important fields for min 5 values (often multiple 0.0)
df[cols][df['Salinity'].isin(salinity_series[-5:])]
[77]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Salinity | ResultDetectionConditionText | DetectionQuantitationLimitTypeName | DetectionQuantitationLimitMeasure/MeasureValue | DetectionQuantitationLimitMeasure/MeasureUnitCode | |
|---|---|---|---|---|---|---|---|---|
| 25645 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | Lower Quantitation Limit | 5.0 | ppth |
| 25646 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | Method Detection Level | 1.0 | ppth |
| 46162 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | Lower Quantitation Limit | 5.0 | ppth |
| 46163 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | Method Detection Level | 1.0 | ppth |
| 373948 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 600312 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 609640 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 695266 | -0.02 | ppth | NaN | -0.02 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 968090 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 970921 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 972585 | 0.00 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 973904 | 0.00 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 975024 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 975801 | 0.00 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 976300 | 0.00 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 978098 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 979274 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 981360 | 0.00 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 993649 | -0.01 | ppth | NaN | -0.01 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1181627 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1181632 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1181718 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1181719 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1181720 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1181721 | 0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184287 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184426 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184427 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184428 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184429 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184430 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184431 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1184526 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1189889 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1189890 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1189934 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1190005 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1190062 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1190063 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1190064 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1190065 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1190066 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1235362 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
| 1245164 | 0.0 | ppth | NaN | 0.0 Practical_Salinity_Units | NaN | NaN | NaN | NaN |
Explore Conductivity results:
[78]:
# Create series and inspect Conductivity values
cond_series = df['Conductivity'].dropna()
cond_series
[78]:
481663 626.0 microsiemens / centimeter
486047 688.0 microsiemens / centimeter
503565 606.0 microsiemens / centimeter
504687 606.0 microsiemens / centimeter
507936 633.0 microsiemens / centimeter
520502 775.0 microsiemens / centimeter
523177 776.0 microsiemens / centimeter
524175 776.0 microsiemens / centimeter
526238 776.0 microsiemens / centimeter
531863 775.0 microsiemens / centimeter
878329 20500.0 microsiemens / centimeter
Name: Conductivity, dtype: object
Conductivity thresholds from Freshwater Explorer: 10 > x < 5000 us/cm, use a higher threshold for coastal waters
[79]:
# 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]
[79]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | QA_flag | Salinity | Conductivity | |
|---|---|---|---|---|---|
| 878329 | 20500 | uS/cm | NaN | NaN | 20500.0 microsiemens / centimeter |
| 523177 | 776 | uS/cm | NaN | NaN | 776.0 microsiemens / centimeter |
| 524175 | 776 | uS/cm | NaN | NaN | 776.0 microsiemens / centimeter |
| 526238 | 776 | uS/cm | NaN | NaN | 776.0 microsiemens / centimeter |
| 520502 | 775 | uS/cm | NaN | NaN | 775.0 microsiemens / centimeter |
| 531863 | 775 | uS/cm | NaN | NaN | 775.0 microsiemens / centimeter |
| 486047 | 688 | uS/cm | NaN | NaN | 688.0 microsiemens / centimeter |
| 507936 | 633 | uS/cm | NaN | NaN | 633.0 microsiemens / centimeter |
| 481663 | 626 | uS/cm | NaN | NaN | 626.0 microsiemens / centimeter |
| 503565 | 606 | uS/cm | NaN | NaN | 606.0 microsiemens / centimeter |
| 504687 | 606 | uS/cm | NaN | NaN | 606.0 microsiemens / centimeter |
[80]:
# 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']
[80]:
878329 12.242 dimensionless
523177 0.379 dimensionless
524175 0.379 dimensionless
526238 0.379 dimensionless
520502 0.379 dimensionless
531863 0.379 dimensionless
486047 0.335 dimensionless
507936 0.308 dimensionless
481663 0.304 dimensionless
503565 0.294 dimensionless
504687 0.294 dimensionless
Name: Salinity, dtype: object
Datetime
datetime() formats time using dataretrieval and ActivityStart
[81]:
# First inspect the existing unformated fields
cols = ['ActivityStartDate', 'ActivityStartTime/Time', 'ActivityStartTime/TimeZoneCode']
df[cols]
[81]:
| ActivityStartDate | ActivityStartTime/Time | ActivityStartTime/TimeZoneCode | |
|---|---|---|---|
| 878329 | 2021-09-17 | NaN | NaN |
| 523177 | 2007-08-15 | NaN | NaN |
| 524175 | 2007-08-15 | NaN | NaN |
| 526238 | 2007-08-15 | NaN | NaN |
| 520502 | 2007-08-15 | NaN | NaN |
| ... | ... | ... | ... |
| 1478918 | 1955-04-08 | NaN | NaN |
| 1478919 | 1955-04-08 | NaN | NaN |
| 1478920 | 1957-10-21 | 14:05:00 | EST |
| 1478921 | 1955-04-08 | NaN | NaN |
| 1478922 | 1955-04-08 | NaN | NaN |
1478923 rows × 3 columns
[82]:
# 'ActivityStartDate' presserves date where 'Activity_datetime' is NAT due to no time zone
df = clean.datetime(df)
df[['ActivityStartDate', 'Activity_datetime']]
/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/dataretrieval/utils.py:87: UserWarning: Warning: 100667 incomplete dates found, consider setting datetime_index to False.
warnings.warn(
[82]:
| ActivityStartDate | Activity_datetime | |
|---|---|---|
| 878329 | 2021-09-17 | NaT |
| 523177 | 2007-08-15 | NaT |
| 524175 | 2007-08-15 | NaT |
| 526238 | 2007-08-15 | NaT |
| 520502 | 2007-08-15 | NaT |
| ... | ... | ... |
| 1478918 | 1955-04-08 | NaT |
| 1478919 | 1955-04-08 | NaT |
| 1478920 | 1957-10-21 | 1957-10-21 19:05:00+00:00 |
| 1478921 | 1955-04-08 | NaT |
| 1478922 | 1955-04-08 | NaT |
1478923 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
[83]:
# Depth of sample (default units='meter')
df = clean.harmonize_depth(df)
#df.loc[df['ResultDepthHeightMeasure/MeasureValue'].dropna(), "Depth"]
df['ResultDepthHeightMeasure/MeasureValue'].dropna()
[83]:
486047 0.95
507936 0.50
481663 0.00
381280 3.00
384199 3.00
...
1471326 0.33
1477066 0.30
1477747 0.33
1478054 0.33
1478063 0.30
Name: ResultDepthHeightMeasure/MeasureValue, Length: 506, dtype: float64
Characteristic to Column (long to wide format)
[84]:
# Split single QA column into multiple by characteristic (rename the result to preserve these QA_flags)
df2 = wrangle.split_col(df)
df2
[84]:
| OrganizationIdentifier | OrganizationFormalName | ActivityIdentifier | ActivityStartDate | ActivityStartTime/Time | ActivityStartTime/TimeZoneCode | MonitoringLocationIdentifier | ResultIdentifier | DataLoggerLine | ResultDetectionConditionText | ... | QA_Turbidity | QA_TP_Phosphorus | QA_TDP_Phosphorus | QA_Other_Phosphorus | QA_Conductivity | QA_Chlorophyll | QA_Salinity | QA_Carbon | QA_Temperature | QA_Fecal_Coliform | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 878329 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-192970_2021 | 2021-09-17 | NaN | NaN | NARS_WQX-NWC_FL-10535 | STORET-1040690254 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 523177 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:2 | 2007-08-15 | NaN | NaN | NARS_WQX-NLA06608-0161 | STORET-183201126 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 524175 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:3.3 | 2007-08-15 | NaN | NaN | NARS_WQX-NLA06608-0161 | STORET-183201150 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 526238 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:3 | 2007-08-15 | NaN | NaN | NARS_WQX-NLA06608-0161 | STORET-183201135 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 520502 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:1 | 2007-08-15 | NaN | NaN | NARS_WQX-NLA06608-0161 | STORET-183201115 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1478918 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500762 | 1955-04-08 | NaN | NaN | USGS-273926082304501 | NWIS-6842541 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1478919 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500762 | 1955-04-08 | NaN | NaN | USGS-273926082304501 | NWIS-6842544 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1478920 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95800924 | 1957-10-21 | 14:05:00 | -0500 | USGS-02306001 | NWIS-6894410 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1478921 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500782 | 1955-04-08 | NaN | NaN | USGS-274455082253601 | NWIS-6842941 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1478922 | USGS-FL | USGS Florida Water Science Center | nwisfl.01.95500782 | 1955-04-08 | NaN | NaN | USGS-274455082253601 | NWIS-6842944 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
1409844 rows × 117 columns
[85]:
# 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)))
14 new columns
[86]:
# Note: there are fewer rows because NAN results are also dropped in this step
print('{} fewer rows'.format(len(df)-len(df2)))
69079 fewer rows
[87]:
#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]
[87]:
| ResultMeasureValue | ResultMeasure/MeasureUnitCode | Carbon | QA_Carbon |
|---|
Next the table is divided into the columns of interest (main_df) and characteristic specific metadata (chars_df)
[88]:
# split table into main and characteristics tables
main_df, chars_df = wrangle.split_table(df2)
[89]:
# Columns still in main table
main_df.columns
[89]:
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_E_coli', 'QA_DO', 'QA_pH',
'QA_Nitrogen', 'QA_Secchi', 'QA_Turbidity', 'QA_TP_Phosphorus',
'QA_TDP_Phosphorus', 'QA_Other_Phosphorus', 'QA_Conductivity',
'QA_Chlorophyll', 'QA_Salinity', 'QA_Carbon', 'QA_Temperature',
'QA_Fecal_Coliform'],
dtype='object')
[90]:
# look at main table results (first 5)
main_df.head()
[90]:
| OrganizationIdentifier | OrganizationFormalName | ActivityIdentifier | MonitoringLocationIdentifier | ProviderName | Secchi | Temperature | DO | pH | Salinity | ... | QA_Turbidity | QA_TP_Phosphorus | QA_TDP_Phosphorus | QA_Other_Phosphorus | QA_Conductivity | QA_Chlorophyll | QA_Salinity | QA_Carbon | QA_Temperature | QA_Fecal_Coliform | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 878329 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-192970_2021 | NARS_WQX-NWC_FL-10535 | STORET | NaN | NaN | NaN | NaN | 12.242 dimensionless | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 523177 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:2 | NARS_WQX-NLA06608-0161 | STORET | NaN | NaN | NaN | NaN | 0.379 dimensionless | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 524175 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:3.3 | NARS_WQX-NLA06608-0161 | STORET | NaN | NaN | NaN | NaN | 0.379 dimensionless | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 526238 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:3 | NARS_WQX-NLA06608-0161 | STORET | NaN | NaN | NaN | NaN | 0.379 dimensionless | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 520502 | NARS_WQX | EPA National Aquatic Resources Survey (NARS) | NARS_WQX-PRF:0161:1:070815:1 | NARS_WQX-NLA06608-0161 | STORET | NaN | NaN | NaN | NaN | 0.379 dimensionless | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 44 columns
[91]:
# 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]
[91]:
['Sediment',
'QA_E_coli',
'QA_DO',
'QA_pH',
'QA_Nitrogen',
'QA_Turbidity',
'QA_TDP_Phosphorus',
'QA_Other_Phosphorus',
'QA_Conductivity',
'QA_Salinity',
'QA_Carbon',
'QA_Temperature']
[92]:
# Map average temperature at each station
results_gdf = visualize.map_measure(main_df, stations_clipped, 'Temperature')
results_gdf.plot(column='mean', cmap='OrRd', legend=True)
[92]:
<Axes: >
