Forming Queries through the API!

Forming Queries through the API!#

Get familiar with the tools available for querying the database. The simplest way is to use the api classes

snowexsql.api.PointMeasurements
snowexsql.api.LayerMeasurements
Each class has to very useful functions
1. from_filter
2. from_area

Useful Function - `from_filter`#

Use the from filter function to find density profiles

# Import in our two classes to access the db
from snowexsql.api import LayerMeasurements
from datetime import datetime 

# Find some density pit measurements at the Boise site in december 2019.
df = LayerMeasurements.from_filter(
    type="density",
    site_name="Boise River Basin",
    date_less_equal=datetime(2020, 1, 1),
    date_greater_equal=datetime(2019, 12, 1),
)

# Plot Example!
df.plot()

# Show off the dataframe
df

# Analysis Example - Find the bulk density 
df['value'] = df['value'].astype(float)
print(df[['site_id', 'value']].groupby(by='site_id').mean())

                    value
site_id                  
Banner Open    235.500000
Banner Snotel  216.666667
Bogus Upper    260.625000

../../_images/568409ec60e95d9d7150358c5ec5d0c5801c630bfa3503ab2d7c897dee589759.png

Useful Function - `from_area`#

Find specific surface area within a certain distance of a pit.

# Import our api class
from snowexsql.api import LayerMeasurements
from datetime import datetime
import geopandas as gpd 

# import some gis functionality 
from shapely.geometry import Point 

# Find some SSA measurements within a distance of a known point
pnt = Point(740820.624625,4.327326e+06)
df = LayerMeasurements.from_area(pt=pnt, crs=26912, buffer=500,
    type='specific_surface_area')

# plot up the results
ax = df.plot()

# plot the site so we can see how close everything is.
site = gpd.GeoDataFrame(geometry=[pnt], crs=26912)
site.plot(ax=ax, marker='^', color='magenta')

# show off the dataframe
df

	depth	site_id	pit_id	bottom_depth	comments	sample_a	sample_b	sample_c	value	flags	...	date	time_created	time_updated	id	doi	date_accessed	instrument	type	units	observers
0	84.0	1C5	COGM1C5_20200212	None	None	None	None	None	45.6	None	...	2020-02-12	2024-08-15 20:03:26.508508+00:00	None	2407961	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
1	79.0	1C5	COGM1C5_20200212	None	None	None	None	None	38.2	None	...	2020-02-12	2024-08-15 20:03:26.508508+00:00	None	2407962	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
2	74.0	1C5	COGM1C5_20200212	None	None	None	None	None	24.5	None	...	2020-02-12	2024-08-15 20:03:26.508508+00:00	None	2407963	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
3	69.0	1C5	COGM1C5_20200212	None	None	None	None	None	23.5	None	...	2020-02-12	2024-08-15 20:03:26.508508+00:00	None	2407964	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
4	64.0	1C5	COGM1C5_20200212	None	None	None	None	None	22.4	None	...	2020-02-12	2024-08-15 20:03:26.508508+00:00	None	2407965	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
155	28.0	1C1	COGM1C1_20200131	None	None	None	None	None	13.1	None	...	2020-01-31	2024-08-15 20:03:31.924769+00:00	None	2410266	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Juha Lemmetyinen & Ioanna Merkouriadi
156	23.0	1C1	COGM1C1_20200131	None	None	None	None	None	10.1	None	...	2020-01-31	2024-08-15 20:03:31.924769+00:00	None	2410267	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Juha Lemmetyinen & Ioanna Merkouriadi
157	18.0	1C1	COGM1C1_20200131	None	None	None	None	None	10.6	None	...	2020-01-31	2024-08-15 20:03:31.924769+00:00	None	2410268	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Juha Lemmetyinen & Ioanna Merkouriadi
158	13.0	1C1	COGM1C1_20200131	None	None	None	None	None	10.5	None	...	2020-01-31	2024-08-15 20:03:31.924769+00:00	None	2410269	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Juha Lemmetyinen & Ioanna Merkouriadi
159	8.0	1C1	COGM1C1_20200131	None	None	None	None	None	13.2	None	...	2020-01-31	2024-08-15 20:03:31.924769+00:00	None	2410270	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Juha Lemmetyinen & Ioanna Merkouriadi

160 rows × 29 columns

../../_images/f81d4a47124ddf21e91cf1c8db6d59681dd43e62aa709b7cdf0e2be05f2e7374.png

How do I know what to filter on?#

We got tools for that! Each class has a host of functions that start with all_* these function return the unique value in that column.

all_types - all the data types e.g. depth, swe, density…
all_instruments - all instruments available in the table
all_dates - all dates listed in the table
all_site_names - all the site names available in the table. e.g. Grand Mesa

from snowexsql.api import PointMeasurements

# Instantiate the class to use the properties!
measurements = PointMeasurements()

# Get the unique data names/types in the table
results = measurements.all_types
print('Available types = {}'.format(', '.join([str(r) for r in results])))

# Get the unique instrument in the table
results = measurements.all_instruments
print('\nAvailable Instruments = {}'.format(', '.join([str(r) for r in results])))

# Get the unique dates in the table
results = measurements.all_dates
print('\nAvailable Dates = {}'.format(', '.join([str(r) for r in results])))

# Get the unique site names in the table
results = measurements.all_site_names
print('\nAvailable sites = {}'.format(', '.join([str(r) for r in results])))

Available types = two_way_travel, snow_void, density, swe, depth

Available Instruments = mesa, magnaprobe, camera, pulseEkko pro 1 GHz GPR, None, Mala 1600 MHz GPR, Mala 800 MHz GPR, pulse EKKO Pro multi-polarization 1 GHz GPR, pit ruler

Available Dates = 2020-05-28, 2020-01-09, 2021-03-19, 2020-05-23, 2019-11-29, 2020-01-04, 2019-10-20, 2019-11-30, 2021-01-28, 2020-04-17, 2021-02-19, 2020-02-19, 2020-02-26, 2020-02-03, 2020-05-05, 2019-10-05, 2019-12-29, 2020-06-02, 2019-10-28, 2020-01-30, 2020-05-22, 2020-03-09, 2019-12-09, 2019-12-28, 2020-02-24, 2020-03-17, 2021-03-18, 2020-04-01, 2020-05-14, 2019-10-14, 2019-10-29, 2019-10-02, 2020-01-31, 2020-04-18, 2020-04-26, 2019-10-12, 2020-04-29, 2021-03-03, 2020-02-23, 2021-01-15, 2020-01-22, 2020-01-01, 2019-11-21, 2020-05-10, 2023-03-13, 2020-02-12, 2019-11-19, 2020-05-06, 2019-10-25, 2019-11-02, 2020-02-08, 2020-04-14, 2020-04-02, 2019-11-16, 2020-04-07, 2021-03-21, 2021-04-21, 2023-03-15, 2020-11-25, 2019-12-27, 2019-10-01, 2021-01-27, 2020-04-16, 2020-06-08, 2019-12-13, 2019-10-17, 2019-10-22, 2021-01-22, 2020-04-21, 2020-01-03, 2019-12-12, 2019-12-08, 2021-03-05, 2020-01-25, 2020-02-29, 2019-11-24, 2019-10-18, 2021-03-04, 2021-03-24, 2021-03-16, 2020-05-09, 2020-03-22, 2019-11-06, 2019-12-16, 2020-01-15, 2019-11-22, 2019-10-13, 2019-11-10, 2019-12-06, 2020-02-04, 2019-10-31, 2020-03-07, 2020-04-06, 2020-05-03, 2019-12-10, 2020-05-26, 2019-12-02, 2021-02-09, 2020-02-14, 2020-02-13, 2020-05-11, 2019-12-01, 2020-01-19, 2019-11-28, 2020-01-17, 2019-12-17, 2021-02-17, 2021-01-07, 2021-03-31, 2019-12-25, 2019-12-14, 2019-10-24, 2020-03-11, 2020-02-01, 2021-03-23, 2020-02-09, 2020-05-12, 2020-05-25, 2020-03-29, 2020-04-24, 2019-12-11, 2020-01-10, 2020-06-05, 2019-10-10, 2020-11-20, 2020-04-13, 2020-03-23, 2020-04-23, 2020-05-24, 2019-11-08, 2021-05-05, 2019-12-26, 2019-12-15, 2021-04-06, 2020-05-07, 2021-01-20, 2020-02-28, 2019-11-03, 2020-04-04, 2019-11-27, 2021-01-14, 2020-03-15, 2019-11-23, 2020-01-16, 2019-10-08, 2023-03-14, 2019-11-14, 2020-02-15, 2020-02-11, 2023-03-12, 2019-11-13, 2020-04-30, 2019-10-26, 2020-03-06, 2021-03-17, 2020-05-31, 2020-03-04, 2021-02-24, 2019-10-04, 2020-05-16, 2020-04-03, 2019-10-06, 2019-10-09, 2021-02-25, 2020-03-12, 2019-11-12, 2019-11-01, 2020-03-10, 2019-10-30, 2020-02-21, 2020-12-17, 2020-06-01, 2020-03-20, 2023-03-07, 2020-03-03, 2019-11-07, 2020-01-06, 2019-12-22, 2021-02-11, 2020-01-11, 2019-11-11, 2019-11-05, 2020-01-13, 2023-03-16, 2019-12-18, 2019-12-30, 2020-05-04, 2020-04-20, 2021-04-14, 2023-03-09, 2023-03-08, 2020-02-22, 2020-05-08, 2019-12-24, 2020-12-18, 2020-01-24, 2020-04-22, 2019-11-04, 2020-03-31, 2020-01-08, 2020-02-06, 2021-02-18, 2020-03-05, 2021-05-27, 2020-03-14, 2021-02-04, 2020-06-09, 2021-01-21, 2020-02-20, 2020-11-23, 2020-04-05, 2020-06-03, 2019-10-16, 2021-05-07, 2020-04-15, 2021-01-26, 2019-12-03, 2020-05-30, 2019-11-09, 2021-02-16, 2020-04-28, 2020-01-12, 2020-05-20, 2023-03-10, 2020-05-02, 2020-02-05, 2020-01-28, 2020-01-21, 2019-12-19, 2019-10-07, 2020-03-28, 2020-02-10, 2021-04-28, 2020-03-02, 2019-09-29, 2019-11-15, 2020-01-02, 2020-05-27, 2020-02-18, 2019-10-11, 2019-12-21, 2019-09-30, 2021-03-10, 2020-04-09, 2020-01-05, 2019-10-27, 2020-04-10, 2021-04-23, 2020-03-16, 2020-03-21, 2020-02-02, 2020-02-25, 2020-04-08, 2020-01-29, 2019-12-04, 2021-03-22, 2021-02-10, 2021-02-03, 2019-11-26, 2020-03-19, 2020-01-20, 2019-12-31, 2020-02-27, 2020-03-30, 2020-04-25, 2020-01-26, 2020-01-14, 2020-12-08, 2020-03-01, 2020-02-17, 2020-05-21, 2019-10-23, 2021-03-02, 2020-04-11, 2019-10-21, 2020-12-16, 2019-11-25, 2020-04-12, 2020-03-13, 2020-05-01, 2021-05-20, 2020-03-08, 2021-01-13, 2020-05-19, 2020-03-27, 2019-11-17, 2020-04-19, 2020-01-23, 2020-05-15, 2021-02-23, 2020-02-16, 2019-10-19, 2020-05-29, 2020-03-24, 2019-12-07, 2020-02-07, 2020-03-18, 2020-05-17, 2020-05-13, 2019-12-20, 2019-12-23, 2020-06-07, 2020-01-07, 2020-05-18, 2021-05-17, 2021-04-07, 2019-12-05, 2019-11-20, 2020-06-06, 2020-12-09, 2023-03-11, 2021-02-02, 2019-11-18, 2020-06-10, 2020-01-27, 2020-11-16, 2020-01-18, 2020-06-04, 2020-04-27, 2019-10-15, 2020-12-01, 2020-03-25, 2020-03-26, 2019-10-03, 2021-03-09

Available sites = American River Basin, Central Ag Research Center, Senator Beck, farmers-creamers, Fairbanks, None, Fraser Experimental Forest, Boise River Basin, Little Cottonwood Canyon, East River, North Slope, Jemez River, Grand Mesa, Cameron Pass, Sagehen Creek, Mammoth Lakes, Niwot Ridge

More specific filtering options#

Sometimes we need a bit more filtering to know more about what I can filter on. Questions like “What dates was the SMP used?” are a bit more complicated than “Give me all the dates for snowex”

The good news is, we have tool for that! from_unique_entries is your friend!

# import layer measurements
from snowexsql.api import LayerMeasurements

# Query dates where SMP was used
LayerMeasurements.from_unique_entries(['date'], instrument='snowmicropen')

[datetime.date(2020, 2, 4),
 datetime.date(2020, 2, 3),
 datetime.date(2020, 1, 30),
 datetime.date(2020, 2, 1),
 datetime.date(2020, 2, 6),
 datetime.date(2020, 1, 31),
 datetime.date(2020, 2, 12),
 datetime.date(2020, 2, 8),
 datetime.date(2020, 2, 5),
 datetime.date(2020, 1, 28),
 datetime.date(2020, 2, 11),
 datetime.date(2020, 2, 10),
 datetime.date(2020, 1, 29)]

Query Nuances#

Limit size#

To avoid accidental large queries, we have added some bumper rails. By default if you ask for more than 1000 records then an error will pop up unless you explicitly say you want more.

Try This: Do a large query. Run the code block below without the limit keyword argument (“kwarg”):

# Import PointMeasurements
from snowexsql.api import PointMeasurements

# Query db using a vague filter or on a huge dataset like GPR but remove the limit kwarg
df = PointMeasurements.from_filter(type='two_way_travel', limit=100)

# Show the dataframe
df

	version_number	equipment	value	latitude	longitude	northing	easting	elevation	utm_zone	geom	...	date	time_created	time_updated	id	doi	date_accessed	instrument	type	units	observers
0	None	None	10.625499	39.020194	-108.163545	4.322846e+06	745564.789805	None	12	POINT (745564.790 4322846.170)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005062	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
1	None	None	10.548781	39.020193	-108.163545	4.322846e+06	745564.742731	None	12	POINT (745564.743 4322846.082)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005063	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
2	None	None	10.510421	39.020192	-108.163546	4.322846e+06	745564.695657	None	12	POINT (745564.696 4322845.994)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005064	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
3	None	None	10.433703	39.020191	-108.163547	4.322846e+06	745564.648545	None	12	POINT (745564.649 4322845.906)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005065	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
4	None	None	10.433703	39.020191	-108.163547	4.322846e+06	745564.601508	None	12	POINT (745564.602 4322845.817)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005066	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	None	None	11.968071	39.020163	-108.163472	4.322843e+06	745571.189082	None	12	POINT (745571.189 4322843.014)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005157	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
96	None	None	12.044790	39.020164	-108.163471	4.322843e+06	745571.285304	None	12	POINT (745571.285 4322843.041)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005158	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
97	None	None	12.083149	39.020164	-108.163470	4.322843e+06	745571.381588	None	12	POINT (745571.382 4322843.068)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005159	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
98	None	None	12.159867	39.020164	-108.163469	4.322843e+06	745571.477956	None	12	POINT (745571.478 4322843.095)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005160	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell
99	None	None	12.198226	39.020164	-108.163468	4.322843e+06	745571.574288	None	12	POINT (745571.574 4322843.122)	...	2020-02-09	2022-07-07 19:56:46.884180+00:00	None	4005161	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	two_way_travel	ns	Randall Bonnell

100 rows × 23 columns

We have added this on the db to allow you to explore without accidentally pulling the entire SnowEx universe down. If you know you want a large query (defined as > 1000) then use the limit = #### option in the from_filter or from_area function.

Warning - It is better to filter using other things besides the limit because the limit is not intelligent. It will simply limit the query by the order of entries that were submitted AND fits your filter. So if you encounter this then consider how to tighten up the filter.

List of Criteria#

You can use lists in your requests too!

# Import layer measurements
from snowexsql.api import LayerMeasurements

# Grab all the data that used the one of these instruments (hint hint SSA)
ssa_instruments = ["IS3-SP-15-01US", "IRIS",  "IS3-SP-11-01F"]

# Query the DB (throw a limit for safety)
LayerMeasurements.from_filter(instrument=ssa_instruments, limit=100)

	depth	site_id	pit_id	bottom_depth	comments	sample_a	sample_b	sample_c	value	flags	...	date	time_created	time_updated	id	doi	date_accessed	instrument	type	units	observers
0	91.0	2C12	COGM2C12_20200212	None	None	None	None	None	45.02	None	...	2020-02-12	2024-08-15 20:03:26.019334+00:00	None	2407735	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	reflectance	None	Kate Hale
1	86.0	2C12	COGM2C12_20200212	None	None	None	None	None	39.82	None	...	2020-02-12	2024-08-15 20:03:26.019334+00:00	None	2407736	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	reflectance	None	Kate Hale
2	81.0	2C12	COGM2C12_20200212	None	None	None	None	None	37.85	None	...	2020-02-12	2024-08-15 20:03:26.019334+00:00	None	2407737	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	reflectance	None	Kate Hale
3	76.0	2C12	COGM2C12_20200212	None	None	None	None	None	35.11	None	...	2020-02-12	2024-08-15 20:03:26.019334+00:00	None	2407738	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	reflectance	None	Kate Hale
4	71.0	2C12	COGM2C12_20200212	None	None	None	None	None	34.86	None	...	2020-02-12	2024-08-15 20:03:26.019334+00:00	None	2407739	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	reflectance	None	Kate Hale
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	72.0	2C13	COGM2C13_20200212	None	None	None	None	None	40.5	None	...	2020-02-12	2024-08-15 20:03:26.225144+00:00	None	2407830	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
96	67.0	2C13	COGM2C13_20200212	None	None	None	None	None	22.6	None	...	2020-02-12	2024-08-15 20:03:26.225144+00:00	None	2407831	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
97	62.0	2C13	COGM2C13_20200212	None	None	None	None	None	26.6	None	...	2020-02-12	2024-08-15 20:03:26.225144+00:00	None	2407832	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
98	57.0	2C13	COGM2C13_20200212	None	None	None	None	None	24.3	None	...	2020-02-12	2024-08-15 20:03:26.225144+00:00	None	2407833	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale
99	52.0	2C13	COGM2C13_20200212	None	None	None	None	None	26.0	None	...	2020-02-12	2024-08-15 20:03:26.225144+00:00	None	2407834	https://doi.org/10.5067/SNMM6NGGKWIT	2022-06-30	IS3-SP-11-01F	specific_surface_area	None	Kate Hale

100 rows × 29 columns

Greater than or Less than#

Sometimes we want to isolate certain ranges of value or even dates. The greater_equal and less_equal terms can be added on to value or dates.

date_greater_equal
date_less_equal
value_greater_equal
value_less_equal

# Import the point measurements class
from snowexsql.api import PointMeasurements

# Filter values > 100 cm from the pulse ecko GPR
df = PointMeasurements.from_filter(value_greater_equal=100, type='depth', instrument='pulse EKKO Pro multi-polarization 1 GHz GPR', limit=100)

# Show off the dataframe
df

	version_number	equipment	value	latitude	longitude	northing	easting	elevation	utm_zone	geom	...	date	time_created	time_updated	id	doi	date_accessed	instrument	type	units	observers
0	None	None	100.075450	39.018715	-108.177205	4.322645e+06	744387.060768	None	12	POINT (744387.061 4322645.255)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4007772	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
1	None	None	100.575830	39.018713	-108.177204	4.322645e+06	744387.159823	None	12	POINT (744387.160 4322644.972)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4007773	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
2	None	None	100.575830	39.018710	-108.177203	4.322645e+06	744387.259312	None	12	POINT (744387.259 4322644.689)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4007774	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
3	None	None	100.575830	39.018708	-108.177202	4.322644e+06	744387.359537	None	12	POINT (744387.360 4322644.406)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4007775	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
4	None	None	100.075450	39.018705	-108.177201	4.322644e+06	744387.459761	None	12	POINT (744387.460 4322644.124)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4007776	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	None	None	103.578097	39.018577	-108.176503	4.322632e+06	744448.372728	None	12	POINT (744448.373 4322631.820)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4008003	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
96	None	None	103.578097	39.018578	-108.176499	4.322632e+06	744448.664264	None	12	POINT (744448.664 4322631.891)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4008004	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
97	None	None	103.077717	39.018578	-108.176496	4.322632e+06	744448.955799	None	12	POINT (744448.956 4322631.962)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4008005	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
98	None	None	100.075450	39.018581	-108.176482	4.322632e+06	744450.096586	None	12	POINT (744450.097 4322632.327)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4008009	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell
99	None	None	100.575830	39.018606	-108.176377	4.322635e+06	744459.151034	None	12	POINT (744459.151 4322635.383)	...	2020-02-06	2022-07-07 19:57:21.141883+00:00	None	4008041	https://doi.org/10.5067/S5EGFLCIAB18	2022-07-07	pulse EKKO Pro multi-polarization 1 GHz GPR	depth	cm	Randall Bonnell

100 rows × 23 columns

Recap#

You just came in contact with the new API tools. We can use each API class to pull from specific tables and filter the data. You should know:

How to build queries using from_filter, from_area, from_unique_entries
Determine what values to filter on
Manage the limit error
Filtering on greater and less than

If you don’t feel comfortable with these, you are probably not alone, let’s discuss it!