USGS dataretrieval Python Package get_samples() Examples
This notebook provides examples of using the Python dataretrieval package to retrieve water quality sample data for United States Geological Survey (USGS) monitoring locations. The dataretrieval package provides a collection of functions to get data from the USGS Samples database and other online sources of hydrology and water quality data, including the United States Environmental Protection Agency (USEPA).
Install the Package
Use the following code to install the package if it doesn’t exist already within your Jupyter Python environment.
[1]:
!pip install dataretrieval
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Load the package so you can use it along with other packages used in this notebook.
[2]:
from IPython.display import display
from dataretrieval import waterdata
Basic Usage
The dataretrieval package has several functions that allow you to retrieve data from different web services. This example uses the get_samples() function to retrieve water quality sample data for USGS monitoring locations from Samples. The following arguments are supported:
ssl_check : boolean, optional Check the SSL certificate.
service : string One of the available Samples services: “results”, “locations”, “activities”, “projects”, or “organizations”. Defaults to “results”.
profile : string One of the available profiles associated with a service. Options for each service are: results - “fullphyschem”, “basicphyschem”, “fullbio”, “basicbio”, “narrow”, “resultdetectionquantitationlimit”, “labsampleprep”, “count” locations - “site”, “count” activities - “sampact”, “actmetric”, “actgroup”, “count” projects - “project”, “projectmonitoringlocationweight” organizations - “organization”, “count”
activityMediaName : string or list of strings, optional Name or code indicating environmental medium in which sample was taken. Check the
activityMediaName_lookup()function in this module for all possible inputs. Example: “Water”.activityStartDateLower : string, optional The start date if using a date range. Takes the format YYYY-MM-DD. The logic is inclusive, i.e. it will also return results that match the date. If left as None, will pull all data on or before activityStartDateUpper, if populated.
activityStartDateUpper : string, optional The end date if using a date range. Takes the format YYYY-MM-DD. The logic is inclusive, i.e. it will also return results that match the date. If left as None, will pull all data after activityStartDateLower up to the most recent available results.
activityTypeCode : string or list of strings, optional Text code that describes type of field activity performed. Example: “Sample-Routine, regular”.
characteristicGroup : string or list of strings, optional Characteristic group is a broad category of characteristics describing one or more results. Check the
characteristicGroup_lookup()function in this module for all possible inputs. Example: “Organics, PFAS”characteristic : string or list of strings, optional Characteristic is a specific category describing one or more results. Check the
characteristic_lookup()function in this module for all possible inputs. Example: “Suspended Sediment Discharge”characteristicUserSupplied : string or list of strings, optional A user supplied characteristic name describing one or more results.
boundingBox: list of four floats, optional Filters on the the associated monitoring location’s point location by checking if it is located within the specified geographic area. The logic is inclusive, i.e. it will include locations that overlap with the edge of the bounding box. Values are separated by commas, expressed in decimal degrees, NAD83, and longitudes west of Greenwich are negative. The format is a string consisting of: - Western-most longitude - Southern-most latitude - Eastern-most longitude - Northern-most longitude Example: [-92.8,44.2,-88.9,46.0]
countryFips : string or list of strings, optional Example: “US” (United States)
stateFips : string or list of strings, optional Check the
stateFips_lookup()function in this module for all possible inputs. Example: “US:15” (United States: Hawaii)countyFips : string or list of strings, optional Check the
countyFips_lookup()function in this module for all possible inputs. Example: “US:15:001” (United States: Hawaii, Hawaii County)siteTypeCode : string or list of strings, optional An abbreviation for a certain site type. Check the
siteType_lookup()function in this module for all possible inputs. Example: “GW” (Groundwater site)siteTypeName : string or list of strings, optional A full name for a certain site type. Check the
siteType_lookup()function in this module for all possible inputs. Example: “Well”usgsPCode : string or list of strings, optional 5-digit number used in the US Geological Survey computerized data system, National Water Information System (NWIS), to uniquely identify a specific constituent. Check the
characteristic_lookup()function in this module for all possible inputs. Example: “00060” (Discharge, cubic feet per second)hydrologicUnit : string or list of strings, optional Max 12-digit number used to describe a hydrologic unit. Example: “070900020502”
monitoringLocationIdentifier : string or list of strings, optional A monitoring location identifier has two parts: the agency code and the location number, separated by a dash (-). Example: “USGS-040851385”
organizationIdentifier : string or list of strings, optional Designator used to uniquely identify a specific organization. Currently only accepting the organization “USGS”.
pointLocationLatitude : float, optional Latitude for a point/radius query (decimal degrees). Must be used with pointLocationLongitude and pointLocationWithinMiles.
pointLocationLongitude : float, optional Longitude for a point/radius query (decimal degrees). Must be used with pointLocationLatitude and pointLocationWithinMiles.
pointLocationWithinMiles : float, optional Radius for a point/radius query. Must be used with pointLocationLatitude and pointLocationLongitude
projectIdentifier : string or list of strings, optional Designator used to uniquely identify a data collection project. Project identifiers are specific to an organization (e.g. USGS). Example: “ZH003QW03”
recordIdentifierUserSupplied : string or list of strings, optional Internal AQS record identifier that returns 1 entry. Only available for the “results” service.
Example 1: Get all water quality sample data for a single monitoring site
[3]:
siteID = "USGS-10109000"
wq_data = waterdata.get_samples(monitoringLocationIdentifier=siteID)
print("Retrieved data for " + str(len(wq_data[0])) + " samples.")
Retrieved data for 2394 samples.
Interpreting the Result
The result of calling the get_samples() function is an object that contains a Pandas data frame object and an associated metadata object. The Pandas data frame contains the water quality sample data for the requested monitoring location, observed variables, and time frame.
Once you’ve got the data frame, there are several useful things you can do to explore the data.
Display the data frame as a table. The default data frame for this function is a long, flat table, with a row for each observed variable at a given monitoring location and date/time.
[4]:
display(wq_data[0])
| Org_Identifier | Org_FormalName | Project_Identifier | Project_Name | Project_QAPPApproved | Project_QAPPApprovalAgency | ProjectAttachment_FileName | ProjectAttachment_FileType | Location_Identifier | Location_Name | ... | Org_Type | LastChangeDate | USGSpcode | USGSSampleAquifer | Activity_StartDateTime | Activity_EndDateTime | LabInfo_AnalysisStartDateTime | LabInfo_AnalysisEndDateTime | LabSamplePrepMethod_StartDateTime | LabSamplePrepMethod_EndDateTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-27 | 70300 | NaN | 1967-09-13 13:35:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 1 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-28 | 30208 | NaN | 1967-09-13 13:35:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 2 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-27 | 410 | NaN | 1967-09-13 13:35:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 3 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-27 | 933 | NaN | 1967-09-13 13:35:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 4 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-28 | 70303 | NaN | 1967-09-13 13:35:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2389 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-28 | 30209 | NaN | NaT | NaT | NaT | NaT | NaT | NaT |
| 2390 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-28 | 20 | NaN | NaT | NaT | NaT | NaT | NaT | NaT |
| 2391 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-27 | 28 | NaN | NaT | NaT | NaT | NaT | NaT | NaT |
| 2392 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-27 | 10 | NaN | NaT | NaT | NaT | NaT | NaT | NaT |
| 2393 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-10109000 | LOGAN RIVER ABOVE STATE DAM, NEAR LOGAN, UT | ... | Federal/US Government | 2026-04-27 | 30209 | NaN | NaT | NaT | NaT | NaT | NaT | NaT |
2394 rows × 187 columns
Show the data types of the columns in the resulting data frame.
[5]:
print(wq_data[0].dtypes)
Org_Identifier str
Org_FormalName str
Project_Identifier str
Project_Name float64
Project_QAPPApproved float64
...
Activity_EndDateTime datetime64[s, UTC]
LabInfo_AnalysisStartDateTime datetime64[s, UTC]
LabInfo_AnalysisEndDateTime datetime64[s, UTC]
LabSamplePrepMethod_StartDateTime datetime64[s, UTC]
LabSamplePrepMethod_EndDateTime datetime64[s, UTC]
Length: 187, dtype: object
The other part of the result returned from the get_samples() function is a metadata object that contains information about the query that was executed to return the data. For example, you can access the URL that was assembled to retrieve the requested data from the USGS Water Data API.
[6]:
print(
"The query URL used to retrieve the data from USGS Samples was: " + wq_data[1].url
)
The query URL used to retrieve the data from USGS Samples was: https://api.waterdata.usgs.gov/samples-data/results/fullphyschem?monitoringLocationIdentifier=USGS-10109000&mimeType=text%2Fcsv
Additional Examples
Example 2: Get water quality sample data for multiple sites for a single parameter
[7]:
site_ids = ["USGS-04024430", "USGS-04024000"]
parameter_code = "00065"
wq_multi_site = waterdata.get_samples(
monitoringLocationIdentifier=site_ids, usgsPCode=parameter_code
)
print("Retrieved data for " + str(len(wq_multi_site[0])) + " samples.")
display(wq_multi_site[0])
Retrieved data for 309 samples.
| Org_Identifier | Org_FormalName | Project_Identifier | Project_Name | Project_QAPPApproved | Project_QAPPApprovalAgency | ProjectAttachment_FileName | ProjectAttachment_FileType | Location_Identifier | Location_Name | ... | Org_Type | LastChangeDate | USGSpcode | USGSSampleAquifer | Activity_StartDateTime | Activity_EndDateTime | LabInfo_AnalysisStartDateTime | LabInfo_AnalysisEndDateTime | LabSamplePrepMethod_StartDateTime | LabSamplePrepMethod_EndDateTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-04024430 | NEMADJI RIVER NEAR SOUTH SUPERIOR, WI | ... | Federal/US Government | 2026-04-28 | 65 | NaN | 1982-11-02 20:10:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 1 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-04024430 | NEMADJI RIVER NEAR SOUTH SUPERIOR, WI | ... | Federal/US Government | 2026-04-28 | 65 | NaN | 1983-01-18 18:30:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 2 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-04024430 | NEMADJI RIVER NEAR SOUTH SUPERIOR, WI | ... | Federal/US Government | 2026-04-27 | 65 | NaN | 1983-04-19 16:00:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 3 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-04024430 | NEMADJI RIVER NEAR SOUTH SUPERIOR, WI | ... | Federal/US Government | 2026-04-28 | 65 | NaN | 1983-05-24 21:10:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 4 | USGS | U.S. Geological Survey | ["USGS"] | NaN | NaN | NaN | NaN | NaN | USGS-04024430 | NEMADJI RIVER NEAR SOUTH SUPERIOR, WI | ... | Federal/US Government | 2026-04-27 | 65 | NaN | 1983-07-06 17:00:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 304 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GLRITRI... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 65 | NaN | 2025-12-02 17:00:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 305 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GLRITRI... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-28 | 65 | NaN | 2026-01-12 16:35:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 306 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GLRITRI... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-05-14 | 65 | NaN | 2026-03-26 16:00:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 307 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative"] | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-05-21 | 65 | NaN | 2026-04-20 15:50:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 308 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative"] | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-05-21 | 65 | NaN | 2026-04-28 18:45:00+00:00 | NaT | NaT | NaT | NaT | NaT |
309 rows × 187 columns
Example 3: Retrieve water quality sample data for multiple sites, including a list of parameters, within a time period defined by start date until present
[8]:
site_ids = ["USGS-04024430", "USGS-04024000"]
parameterCd = ["34247", "30234", "32104", "34220"]
startDate = "2012-01-01"
wq_data2 = waterdata.get_samples(
monitoringLocationIdentifier=site_ids,
usgsPCode=parameterCd,
activityStartDateLower=startDate,
)
print("Retrieved data for " + str(len(wq_data2[0])) + " samples.")
display(wq_data2[0])
Retrieved data for 152 samples.
| Org_Identifier | Org_FormalName | Project_Identifier | Project_Name | Project_QAPPApproved | Project_QAPPApprovalAgency | ProjectAttachment_FileName | ProjectAttachment_FileType | Location_Identifier | Location_Name | ... | Org_Type | LastChangeDate | USGSpcode | USGSSampleAquifer | Activity_StartDateTime | Activity_EndDateTime | LabInfo_AnalysisStartDateTime | LabInfo_AnalysisEndDateTime | LabSamplePrepMethod_StartDateTime | LabSamplePrepMethod_EndDateTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GR12NK0... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-28 | 32104 | NaN | 2012-01-25 14:40:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 1 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GR12NK0... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 30234 | NaN | 2012-01-25 14:40:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 2 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GR12NK0... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 34247 | NaN | 2012-01-25 14:40:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 3 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GR12NK0... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 34220 | NaN | 2012-01-25 14:40:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 4 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","GR12NK0... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 32104 | NaN | 2012-02-22 14:45:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 147 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","00GQ414... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-28 | 34220 | NaN | 2018-05-01 16:05:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 148 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","00GQ414... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 34220 | NaN | 2018-07-09 16:25:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 149 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","00GQ414... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-28 | 32104 | NaN | 2018-07-09 16:25:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 150 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","00GQ414... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 30234 | NaN | 2018-07-09 16:25:00+00:00 | NaT | NaT | NaT | NaT | NaT |
| 151 | USGS | U.S. Geological Survey | ["Great Lakes Restoration Initiative","00GQ414... | NaN | NaN | NaN | NaN | NaN | USGS-04024000 | ST. LOUIS RIVER AT SCANLON, MN | ... | Federal/US Government | 2026-04-27 | 34247 | NaN | 2018-07-09 16:25:00+00:00 | NaT | NaT | NaT | NaT | NaT |
152 rows × 187 columns
Example 4: Retrieve water quality sample data for one site and convert to a wide format
Note that the USGS Samples database returns multiple parameters in a “long” format: each row in the resulting table represents a single observation of a single parameter. Furthermore, every observation has 181 fields of metadata. However, if you wanted to place your water quality data into a “wide” format, where each column represents a water quality parameter code, the code below details one solution.
[9]:
siteID = "USGS-10109000"
wq_data, _ = waterdata.get_samples(monitoringLocationIdentifier=siteID)
print("Retrieved data for " + str(len(wq_data)) + " sample results.")
wq_data["characteristic_unit"] = (
wq_data["Result_Characteristic"] + ", " + wq_data["Result_MeasureUnit"]
)
wq_data_wide = wq_data.pivot_table(
index=["Location_Identifier", "Activity_StartDate", "Activity_StartTime"],
columns="characteristic_unit",
values="Result_Measure",
aggfunc="first",
)
display(wq_data_wide)
Retrieved data for 2394 sample results.
/tmp/ipykernel_6562/1863275440.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
wq_data["characteristic_unit"] = (
| characteristic_unit | Acidity, (H+), mg/L | Alkalinity, mg/L | Bicarbonate, mg/L | Calcium, mg/L | Carbon dioxide, mg/L | Carbonate, mg/L | Chloride, mg/L | Depth of water column, ft | Hardness, Ca, Mg, mg/L | Hardness, non-carbonate, mg/L | ... | Stream flow, instantaneous, m3/sec | Stream flow, m3/sec | Stream width measure, ft | Sulfate, mg/L | Temperature, air, deg C | Temperature, water, deg C | Total dissolved solids, mg/L | Total dissolved solids, tons/ac ft | Total dissolved solids, tons/day | pH, standard units | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Location_Identifier | Activity_StartDate | Activity_StartTime | |||||||||||||||||||||
| USGS-10109000 | 1967-09-13 | 07:35:00 | 0.00001 | 187 | 228 | 44.0 | 2.9 | 0.0 | 3.5 | NaN | 190 | 0.0 | ... | NaN | 5.8 | NaN | 6.5 | NaN | 7.0 | 196 | 0.27 | 108 | 8.1 |
| 1968-01-18 | 12:20:00 | 0.00002 | 207 | 252 | 52.0 | 6.5 | 0.0 | 3.9 | NaN | 220 | 17 | ... | NaN | 3.2 | NaN | 17.0 | NaN | 4.0 | 210 | 0.29 | 63.5 | 7.8 | |
| 1968-05-15 | 12:30:00 | 0.00004 | 149 | 182 | 38.0 | 12 | 0.0 | 1.7 | NaN | 150 | 0.0 | ... | NaN | 10 | NaN | 5.5 | NaN | 7.0 | 156 | 0.21 | 151 | 7.4 | |
| 1968-07-26 | 14:40:00 | 0.00002 | 179 | 218 | 53.0 | 7.0 | 0.0 | 2.2 | NaN | 180 | 3 | ... | NaN | 7.5 | NaN | 6.2 | NaN | 12.0 | 188 | 0.26 | 135 | 7.7 | |
| 1972-12-08 | 16:15:00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 4.0 | NaN | NaN | NaN | NaN | 3.0 | NaN | NaN | NaN | NaN | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | |
| 2025-11-18 | 08:55:00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 3.0 | NaN | 48.0 | NaN | NaN | 6.8 | NaN | NaN | NaN | NaN | |
| 2025-12-29 | 16:07:00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 2.8 | NaN | 48.0 | NaN | NaN | 3.6 | NaN | NaN | NaN | NaN | |
| 2026-03-04 | 14:53:00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 4.0 | NaN | 46.9 | NaN | NaN | 6.7 | NaN | NaN | NaN | NaN | |
| 2026-04-20 | 10:08:00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 7.0 | NaN | 50.6 | NaN | NaN | 7.7 | NaN | NaN | NaN | NaN | |
| 2026-05-12 | 13:58:00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 10 | NaN | 51.9 | NaN | NaN | 10.4 | NaN | NaN | NaN | NaN |
356 rows × 34 columns