Note

Click here to download the full example code or to run this example in your browser via Binder

Quadica dataset

# sphinx_gallery_thumbnail_number = 3

import pandas as pd
import matplotlib.pyplot as plt
from easy_mpl import hist, ridge
from ai4water.datasets import Quadica
from easy_mpl.utils import create_subplots
from ai4water.utils.utils import get_version_info

for k,v in get_version_info().items():
    print(k, v)

python 3.7.9 (default, Oct 19 2020, 15:13:17)
[GCC 7.5.0]
os posix
ai4water 1.06
easy_mpl 0.21.2
SeqMetrics 1.3.4
numpy 1.21.6
pandas 1.2.3
matplotlib 3.5.3
joblib 1.2.0

dataset = Quadica()

avg_temp = dataset.avg_temp()
print(avg_temp.shape)

0% of 38.49 MB downloaded
100% of 38.49 MB downloaded
0% of 0.03 MB downloaded
100% of 0.03 MB downloaded
0% of 1.77 MB downloaded
100% of 1.77 MB downloaded
unzipping /home/docs/checkouts/readthedocs.org/user_builds/ai4water-datasets/envs/latest/lib/python3.7/site-packages/ai4water/datasets/data/Quadica/quadica.zip to /home/docs/checkouts/readthedocs.org/user_builds/ai4water-datasets/envs/latest/lib/python3.7/site-packages/ai4water/datasets/data/Quadica/quadica
(828, 1386)

avg_temp.head()

	1	2	3	4	8	9	10	11	12	16	17	19	21	22	23	24	25	27	32	34	35	36	39	44	45	46	49	52	53	55	56	57	58	59	61	63	66	67	69	70	...	1671	1672	1673	1674	1675	1676	1677	1678	1679	1680	1682	1683	1684	1685	1686	1687	1688	1689	1690	1691	1692	213	230	239	628	632	633	634	639	642	651	655	660	1002	1007	1012	1013	1277	1279	1281
Year_Month
1950-01-01	-2.297333	-2.459127	-2.342236	-2.014634	-1.537954	-1.470925	-2.055828	-2.097191	-2.068334	-1.822387	-1.772743	-2.047889	-2.504809	-1.869479	-1.871972	-1.857247	-2.376290	-2.206003	-2.411030	-2.425919	-1.935651	-2.480944	-2.086975	-1.875532	-1.977438	-1.882829	-2.470911	-2.004062	-1.767894	-1.564868	-1.378942	-2.492037	-2.462426	-1.955984	-1.805639	-2.188958	-2.325543	-2.455672	-1.398771	-2.404221	...	-1.895954	-1.548110	-1.427348	-1.577656	-1.618221	-1.677317	-2.844258	-2.876431	-2.506312	-3.327056	-2.180930	-3.769703	-2.180257	-2.509932	-2.469956	-1.842600	-2.137318	-2.189340	-2.599620	-2.970233	-1.542587	-2.032665	-0.790419	-1.742529	-0.933036	-1.253562	-1.507427	-0.544249	-0.988151	-0.969828	-1.627405	-2.270952	-2.310768	0.328745	0.124732	-0.292483	0.567522	-4.408611	-4.568310	-4.675208
1950-10-01	7.961588	8.154172	8.073226	8.199285	7.843346	7.887170	8.030769	8.013243	8.092491	7.503624	7.523192	8.241731	8.200541	7.599001	7.607003	7.639354	7.992703	8.046181	7.881928	8.134697	8.279377	8.219387	8.248683	8.203867	8.173280	8.285077	8.257347	8.137908	7.625259	7.644325	7.663720	8.153169	8.191144	8.208229	8.268900	8.219151	7.968035	8.169020	7.604195	7.936869	...	7.261101	7.238174	7.060747	7.356757	7.326744	7.219848	6.707846	6.597306	6.943840	6.322590	7.253147	5.870874	6.435931	6.323321	6.586710	6.821360	6.354657	6.396986	6.043407	6.176832	7.273913	6.529052	8.000924	6.838108	8.224440	8.551949	8.572838	8.271382	8.647158	8.308331	8.462639	8.232144	8.088379	8.616307	8.907722	8.254993	8.664533	4.981876	4.807424	4.693177
1950-11-01	4.387634	4.434822	4.156425	4.526889	4.255621	4.334019	4.359056	4.304337	4.374317	3.918750	3.960344	4.550589	4.428353	3.997693	4.013709	4.074886	4.105527	4.492467	4.309518	4.482456	4.489067	4.461328	4.541105	4.630757	4.368642	4.692349	4.495065	4.416324	4.072423	4.136957	4.237387	4.377676	4.421263	4.598390	4.855722	4.513628	4.235945	4.439093	4.139834	4.345180	...	3.370226	4.095329	4.082694	3.939229	3.788431	4.016925	3.117489	3.130568	3.422641	2.614886	3.867744	2.281671	2.805051	2.319286	2.527699	3.229656	3.043285	2.938956	2.075951	2.081059	3.744436	2.797345	4.299353	3.175289	4.305898	4.399458	4.393638	4.375525	4.515997	4.392313	4.312323	4.267094	4.137416	4.930104	5.048923	4.665080	4.964220	1.274203	1.065288	0.925376
1950-12-01	-1.291138	-1.312995	-0.986580	-1.682717	-1.870502	-1.807383	-1.876309	-1.883287	-1.772402	-1.421752	-1.429276	-1.668503	-1.484473	-1.291764	-1.292251	-1.306577	-1.057599	-1.110716	-1.354559	-1.207020	-1.470050	-1.496449	-1.669649	-1.708167	-1.727293	-1.632738	-1.411235	-1.406521	-1.339405	-1.423382	-1.399356	-1.401333	-1.357817	-1.705975	-1.600559	-1.719196	-1.054414	-1.312360	-1.302687	-1.410167	...	-3.238444	-2.777927	-2.768358	-2.837895	-2.933643	-2.860122	-3.369706	-3.059771	-2.805074	-3.726380	-2.846201	-3.887763	-3.477513	-3.814405	-3.568534	-3.229875	-3.419026	-3.424014	-4.064105	-4.030361	-2.920314	-3.938506	-2.612163	-3.664287	-0.351332	-0.176288	-0.146767	-0.329221	-0.053480	-0.285266	-0.261073	-0.820888	-0.987980	-0.022088	0.577974	-0.173298	0.067284	-4.234557	-4.388704	-4.488470
1950-02-01	2.526310	2.694384	2.198610	2.699495	2.684751	2.752606	2.562898	2.520741	2.582700	2.225678	2.298676	2.750591	2.654111	2.153683	2.170797	2.250322	2.163911	2.625240	2.464343	2.684688	2.675851	2.660001	2.772022	2.693103	2.616079	2.816674	2.715209	2.597191	2.338520	2.575147	2.729227	2.681391	2.722991	2.738317	2.921200	2.820852	2.346801	2.705734	2.591320	2.509214	...	2.883144	2.868927	2.574379	2.959392	2.914690	2.771423	1.839515	1.723641	2.223401	1.294461	2.654499	0.705181	1.800954	1.700351	1.758643	2.350362	1.624708	1.545567	1.647964	1.411653	2.828581	1.904826	3.394405	2.247791	2.290921	1.952942	2.008026	2.633178	2.231733	2.350517	2.099005	2.235437	2.112160	2.358829	2.370221	2.440856	2.475693	0.020008	-0.113062	-0.189216

5 rows × 1386 columns

pet

pet = dataset.pet()
print(pet.shape)

(828, 1386)

precipitation

pcp = dataset.precipitation()
print(pcp.shape)

(828, 1386)

monthly median values

mon_medians = dataset.monthly_medians()
print(mon_medians.shape)

(16629, 18)

mon_medians.head()

	OBJECTID	Month	median_Q	n_NO3	median_NO3N	n_NMin	median_NMin	n_TN	median_TN	n_PO4	median_PO4P	n_TP	median_TP	median_DOC	n_TOC	median_TOC
0	1	1	NaN	11	1.700	11	1.960	11	3.60	11	0.0250	11	0.1180	NaN	11	6.60
1	1	2	NaN	12	1.740	12	1.975	12	4.30	12	0.0285	12	0.1375	NaN	12	6.85
2	1	3	NaN	11	1.900	11	2.100	11	4.70	11	0.0220	11	0.0880	NaN	11	7.50
3	1	4	NaN	10	1.405	10	1.580	10	2.95	10	0.0150	10	0.1115	NaN	10	7.00
4	1	5	NaN	11	1.000	11	1.260	11	2.60	11	0.0280	11	0.1550	NaN	11	9.00

wrtds_mon = dataset.wrtds_monthly()
print(wrtds_mon.shape)

(50186, 47)

catchment attributes

cat_attrs = dataset.catchment_attributes()
print(cat_attrs.shape)

(1386, 113)

print(cat_attrs.columns)

Index(['OBJECTID', 'Station', 'Area_km2', 'f_AreaGer', 'dem.mean',
       'dem.median', 'slo.mean', 'slo.median', 'twi.mean', 'twi.med',
       ...
       'flashi', 'BFI', 'P_mm', 'P_SIsw', 'P_SI', 'P_lambda', 'P_alpha',
       'PET_mm', 'AI', 'T_mean'],
      dtype='object', length=113)

dataset.catchment_attributes(stations=[1,2,3])

	OBJECTID	Station	Area_km2	f_AreaGer	dem.mean	dem.median	slo.mean	slo.median	twi.mean	twi.med	twi.90p	ddhad	DrainDens	f_artif	f_agric	f_forest	f_water	f_urban	f_arable	f_agri_perm	f_pastures	f_agri_hetero	f_fores	f_scrub	pdens	Nsurp00_15	Nsurp91_15	Nsurp80_15	Nsurp71_90	dNsurp71_91	N_WW	P_WW	N_T_YKM2	P_T_YKM2	BOD_T_YKM2	...	f_sand	f_silt	f_clay	f_clay_agri	thetaS	WaterRoots	soilP.mean	soilN.mean	soilCN.mean	StartQobs	EndQobs	meanQobs	medQobs	specQobs	CVQobs	medSuQobs	medWiQobs	seasRQobs	flashQobs	BFIQobs	RCQobs	Q_StartDate	Q_EndDate	Q_mean	Q_median	Q_spec	Q_CVQ	Q_medSum	Q_medWin	Q_Sum2Win	flashi	BFI	P_mm	P_SIsw	P_SI	P_lambda	P_alpha	PET_mm	AI	T_mean
0	1	BB_AMFL_0010	21.65	1.0	74.683632	72.135452	0.750141	0.678210	15.002993	14.357248	17.938291	0.435433	0.274781	0.035104	0.927021	0.037875	0.000000	0.034642	0.798614	0.058199	0.032333	0.03649	0.039261	0.000462	80.208466	39.388125	37.3812	59.134444	107.3095	69.9283	0.00000	0.000000	0.072245	0.006198	0.096626	...	0.672402	0.191968	0.135630	0.134284	383.555458	47.368818	46.673761	1.482709	9.603404	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	589.535167	1.338849	2.044286	0.322334	5.007660	760.654789	1.290294	9.425497
1	2	BB_AZMFL_0010	50.47	1.0	61.898052	56.878677	1.157724	0.823584	14.753934	14.246800	17.612621	0.305067	0.254349	0.041014	0.770160	0.163463	0.025362	0.041213	0.762433	0.000000	0.010105	0.00000	0.160888	0.000198	40.778515	37.623125	35.8580	54.071944	94.3255	58.4675	0.00000	0.000000	0.232419	0.021061	0.356625	...	0.695927	0.175527	0.128546	0.132143	379.241735	47.590416	40.921448	1.720343	11.328608	1999-11-01	2001-10-31	0.028119	0.017	17.582101	1.071292	0.001	0.052	0.019231	0.0	0.907575	0.032277	1986-01-01	2001-10-31	0.052133	0.039	32.597524	1.017324	0.006	0.075	0.08	0.0	0.878186	544.733603	1.661279	2.726427	0.306447	4.866778	774.804494	1.422743	9.381932
2	3	BB_BAFL_0010	56.19	1.0	48.056680	50.443848	0.973699	0.846759	14.805566	14.195425	17.810382	0.311593	NaN	0.036305	0.935220	0.014771	0.013704	0.035949	0.921872	0.000000	0.013881	0.00000	0.014771	0.000000	20.113589	38.851875	36.2972	61.200833	116.3780	80.0808	0.25129	0.048585	0.000000	0.000000	0.000000	...	0.499989	0.266901	0.233110	0.233052	411.091884	47.102315	43.596680	1.452292	9.799369	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	535.680048	1.813461	3.034012	0.308551	4.753558	719.133840	1.342496	8.983454

3 rows × 113 columns

monthly data

dyn, cat = dataset.fetch_monthly(max_nan_tol=None)
print(dyn.shape)

(29484, 33)

dyn['OBJECTID'].unique()

array([ 333,  334,  335,  336,  337,  340,  341,  342,  345,  346,  347,
        348,  349,  350,  352,  355,  358,  359,  360,  362,  363,  364,
        365,  368,  370,  373,  374,  376,  380,  381,  391,  393,  637,
        663,  667,  673,  678,  686,  687,  688,  690,  692,  696,  701,
        705,  711,  716,  718,  722,  723,  728,  730,  734,  735,  736,
        737,  739,  740,  742,  744,  745,  746,  750,  752,  754,  769,
        773,  774,  775,  776,  778,  782,  783,  785,  786,  787,  789,
        796,  797,  874,  885,  899,  985,  986,  991, 1011, 1016, 1017,
       1019, 1082, 1113, 1186, 1237, 1238, 1255, 1270, 1271, 1275, 1287,
       1303, 1332, 1467, 1473, 1482, 1495, 1570, 1571, 1573, 1672, 1677,
       1678, 1679, 1680, 1683, 1688, 1690, 1691])

print(dyn.columns)

Index(['mean_Flux_NMin', 'median_C_NO3', 'median_FNC_NMin', 'median_FNC_PO4',
       'mean_Flux_PO4', 'median_C_NMin', 'mean_Flux_TOC', 'mean_FNFlux_TN',
       'mean_Flux_NO3', 'median_Q', 'mean_Flux_TN', 'mean_FNFlux_TOC',
       'mean_FNFlux_TP', 'median_FNC_TOC', 'median_FNC_TN', 'median_FNC_TP',
       'mean_FNFlux_DOC', 'median_C_TN', 'mean_FNFlux_NO3', 'median_C_TP',
       'median_FNC_DOC', 'mean_FNFlux_PO4', 'median_C_DOC', 'mean_Flux_DOC',
       'mean_FNFlux_NMin', 'median_C_TOC', 'median_C_PO4', 'mean_Flux_TP',
       'median_FNC_NO3', 'OBJECTID', 'avg_temp', 'precip', 'pet'],
      dtype='object')

print(dyn.isna().sum())

mean_Flux_NMin       9161
median_C_NO3         2691
median_FNC_NMin      9161
median_FNC_PO4       1988
mean_Flux_PO4        1988
median_C_NMin        9161
mean_Flux_TOC       15456
mean_FNFlux_TN      18880
mean_Flux_NO3        2691
median_Q               13
mean_Flux_TN        18880
mean_FNFlux_TOC     15469
mean_FNFlux_TP       1819
median_FNC_TOC      15469
median_FNC_TN       18880
median_FNC_TP        1819
mean_FNFlux_DOC     16361
median_C_TN         18880
mean_FNFlux_NO3      2709
median_C_TP          1819
median_FNC_DOC      16361
mean_FNFlux_PO4      1988
median_C_DOC        16361
mean_Flux_DOC       16361
mean_FNFlux_NMin     9161
median_C_TOC        15456
median_C_PO4         1988
mean_Flux_TP         1819
median_FNC_NO3       2709
OBJECTID                0
avg_temp                0
precip                  0
pet                     0
dtype: int64

print(cat.shape)

(29484, 113)

monthly TN

dyn, cat = dataset.fetch_monthly(features="TN", max_nan_tol=0)
print(dyn.shape)

(6300, 9)

dyn.head()

	median_Q	mean_Flux_TN	median_FNC_TN	mean_FNFlux_TN	median_C_TN	OBJECTID	avg_temp	precip	pet
1993-01-01	6.70	4854.350816	8.143008	3785.002788	7.973254	663	3.807984	121.793169	11.415899
1993-02-01	5.29	3698.383160	8.062551	3531.640525	7.955991	663	8.473467	116.131558	28.869268
1993-03-01	3.17	2249.559645	7.878655	3076.825302	8.138089	663	1.430167	35.333157	9.847851
1993-04-01	3.28	2272.942794	7.780824	2298.055504	7.665461	663	4.333394	180.090165	8.050768
1993-05-01	2.28	1551.660935	7.807650	1607.637873	7.843202	663	0.830066	30.062856	13.271998

dyn.tail()

	median_Q	mean_Flux_TN	median_FNC_TN	mean_FNFlux_TN	median_C_TN	OBJECTID	avg_temp	precip	pet
2013-08-01	21.62	6315.751380	3.065682	7280.583175	3.035308	1019	11.212706	148.730218	90.947478
2013-09-01	38.94	12811.035546	3.194456	8107.321967	3.288561	1019	16.430328	74.409189	130.848008
2013-10-01	84.51	30742.345242	3.452440	12261.944444	3.444130	1019	20.729773	43.103508	154.377919
2013-11-01	136.87	45608.256567	3.742268	22491.901904	3.732225	1019	18.462523	54.366963	122.983270
2013-12-01	61.38	30066.266276	3.963254	37932.025054	4.176698	1019	14.342434	96.153852	70.408549

print(dyn.isna().sum())

median_Q          0
mean_Flux_TN      0
median_FNC_TN     0
mean_FNFlux_TN    0
median_C_TN       0
OBJECTID          0
avg_temp          0
precip            0
pet               0
dtype: int64

dyn['OBJECTID'].unique()

array([ 663,  673,  678,  686,  687,  688,  690,  728,  730,  734,  744,
        745,  746,  750,  754,  782,  783,  785,  786,  985,  986,  991,
       1016, 1017, 1019])

print(len(dyn['OBJECTID'].unique()))

print(cat.shape)

(6300, 113)

df = pd.concat([grp['median_C_TN'] for idx,grp in dyn.groupby('OBJECTID')], axis=1)
df.columns = dyn['OBJECTID'].unique()
ridge(df, figsize=(10, 10), color="GnBu", title="median_C_TN")

[<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]

monthly TP

dyn, cat = dataset.fetch_monthly(features="TP", max_nan_tol=0)
print(dyn.shape)

(21420, 9)

dyn['OBJECTID'].unique()

array([ 334,  335,  336,  337,  340,  341,  342,  345,  347,  350,  352,
        355,  358,  359,  360,  362,  363,  364,  365,  368,  370,  374,
        376,  380,  381,  391,  663,  673,  678,  686,  687,  688,  690,
        692,  696,  701,  705,  711,  716,  718,  722,  723,  728,  730,
        734,  735,  736,  737,  739,  740,  742,  744,  745,  746,  750,
        754,  769,  773,  776,  778,  782,  783,  785,  786,  874,  885,
        899,  985,  986,  991, 1016, 1017, 1019, 1082, 1113, 1186, 1271,
       1275, 1570, 1571, 1573, 1677, 1678, 1680, 1683])

print(len(dyn['OBJECTID'].unique()))

dyn.head()

	median_Q	median_C_TP	mean_Flux_TP	mean_FNFlux_TP	median_FNC_TP	OBJECTID	avg_temp	precip	pet
1993-01-01	53.20	0.074464	396.554076	320.694272	0.062897	334	1.593407	80.646032	14.305556
1993-02-01	25.15	0.046901	119.576067	273.244117	0.054044	334	5.956323	105.239667	32.282010
1993-03-01	33.50	0.045366	247.820224	436.670327	0.060441	334	-1.057203	38.820471	10.558618
1993-04-01	52.10	0.054396	245.905231	447.722065	0.064261	334	1.455312	126.850398	12.198744
1993-05-01	47.70	0.051911	213.419345	686.400592	0.069937	334	-2.350673	26.185855	13.697404

dyn.tail()

	median_Q	median_C_TP	mean_Flux_TP	mean_FNFlux_TP	median_FNC_TP	OBJECTID	avg_temp	precip	pet
2013-08-01	4.33	0.171760	68.195512	83.103094	0.174829	1683	10.229236	147.140054	87.170391
2013-09-01	6.11	0.137811	83.325140	90.632953	0.150251	1683	14.173561	99.925348	117.320530
2013-10-01	5.25	0.116680	56.504054	92.819774	0.124092	1683	18.329772	42.926368	146.748663
2013-11-01	11.85	0.095941	111.452591	133.620403	0.105120	1683	16.463807	49.014143	116.420306
2013-12-01	9.61	0.081465	77.797496	149.404412	0.090173	1683	11.541938	78.161588	58.856660

print(dyn.isna().sum())

median_Q          0
median_C_TP       0
mean_Flux_TP      0
mean_FNFlux_TP    0
median_FNC_TP     0
OBJECTID          0
avg_temp          0
precip            0
pet               0
dtype: int64

print(cat.shape)

(21420, 113)

monthly TOC

dyn, cat = dataset.fetch_monthly(features="TOC", max_nan_tol=0)
print(dyn.shape)

(5796, 9)

dyn['OBJECTID'].unique()

array([ 352,  355,  358,  359,  370,  374,  796,  797,  985,  991, 1016,
       1019, 1473, 1482, 1570, 1571, 1573, 1677, 1678, 1680, 1683, 1688,
       1690])

print(len(dyn['OBJECTID'].unique()))

grouper = dyn.groupby("OBJECTID")



fig, axes = create_subplots(grouper.ngroups, figsize=(12, 10))
for (idx, grp), ax in zip(grouper, axes.flat):
    hist(grp['median_C_TOC'], ax=ax, show=False, ax_kws=dict(title=idx))
plt.show()

352, 355, 358, 359, 370, 374, 796, 797, 985, 991, 1016, 1019, 1473, 1482, 1570, 1571, 1573, 1677, 1678, 1680, 1683, 1688, 1690

df = pd.concat([grp['median_C_TOC'] for idx,grp in dyn.groupby('OBJECTID')], axis=1)
df.columns = dyn['OBJECTID'].unique()

ridge(df, figsize=(10, 10), color="GnBu", title="median_C_TOC")

[<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]

dyn.head()

	median_C_TOC	median_Q	mean_FNFlux_TOC	median_FNC_TOC	mean_Flux_TOC	OBJECTID	avg_temp	precip	pet
1993-01-01	3.582176	2.49	1768.257729	4.205242	849.784539	352	1.874273	45.230158	13.236276
1993-02-01	3.076343	1.94	2043.161550	4.473708	553.020333	352	8.082227	70.054926	34.415847
1993-03-01	3.596575	2.30	2314.616099	4.774344	802.060684	352	0.408168	25.903097	10.312989
1993-04-01	3.678589	1.71	1247.634582	4.386409	545.261698	352	2.823309	119.545130	11.483053
1993-05-01	3.825010	1.24	959.253376	4.580450	411.800177	352	-2.816553	20.795173	9.571560

dyn.tail()

	median_C_TOC	median_Q	mean_FNFlux_TOC	median_FNC_TOC	mean_Flux_TOC	OBJECTID	avg_temp	precip	pet
2013-08-01	3.252773	1.560	577.937111	3.352540	439.230872	1690	10.204866	236.756149	85.705574
2013-09-01	3.384114	1.845	840.649765	3.508932	571.347176	1690	14.447392	54.103719	119.770443
2013-10-01	3.476477	2.160	859.957624	3.564205	640.434776	1690	18.357580	52.262876	144.086108
2013-11-01	3.774498	3.695	1534.585561	3.800113	1367.471649	1690	16.688504	39.944479	114.876081
2013-12-01	3.517463	3.950	2066.823176	3.686678	1278.301345	1690	11.801535	101.380651	59.491577

print(dyn.isna().sum())

median_C_TOC       0
median_Q           0
mean_FNFlux_TOC    0
median_FNC_TOC     0
mean_Flux_TOC      0
OBJECTID           0
avg_temp           0
precip             0
pet                0
dtype: int64

print(cat.shape)

(5796, 113)

monthly DOC

dyn, cat = dataset.fetch_monthly(features="DOC", max_nan_tol=0)
print(dyn.shape)

(6804, 9)

dyn['OBJECTID'].unique()

array([ 663,  678,  690,  696,  701,  705,  711,  718,  722,  723,  728,
        734,  744,  745,  746,  750,  754,  776,  782,  783,  785,  786,
       1016, 1017, 1019, 1082, 1271])

print(len(dyn['OBJECTID'].unique()))

dyn.head()

	median_Q	median_FNC_DOC	median_C_DOC	mean_Flux_DOC	mean_FNFlux_DOC	OBJECTID	avg_temp	precip	pet
1993-01-01	6.70	7.570729	8.168849	5290.522451	3880.725444	663	3.807984	121.793169	11.415899
1993-02-01	5.29	7.409652	7.576350	3562.398652	3470.252080	663	8.473467	116.131558	28.869268
1993-03-01	3.17	7.138509	6.624830	1840.949964	3071.222351	663	1.430167	35.333157	9.847851
1993-04-01	3.28	6.763954	6.769762	2064.170897	2187.148516	663	4.333394	180.090165	8.050768
1993-05-01	2.28	6.355921	6.305964	1291.672996	1380.674341	663	0.830066	30.062856	13.271998

dyn.tail()

	median_Q	median_FNC_DOC	median_C_DOC	mean_Flux_DOC	mean_FNFlux_DOC	OBJECTID	avg_temp	precip	pet
2013-08-01	7.952220	4.061765	4.031046	3158.773568	4779.806779	1271	10.167641	163.066607	87.326095
2013-09-01	7.275374	4.048447	3.949723	2794.135418	5032.150952	1271	13.999010	186.180472	116.162897
2013-10-01	5.771638	3.936584	3.826177	2445.844458	4011.788115	1271	17.790892	33.659651	144.276601
2013-11-01	6.491699	4.007867	3.717356	2187.576948	6221.995187	1271	16.185475	72.816926	115.142294
2013-12-01	9.265053	3.923625	3.687420	3324.340569	6167.013957	1271	11.191066	75.990604	55.997746

print(dyn.isna().sum())

median_Q           0
median_FNC_DOC     0
median_C_DOC       0
mean_Flux_DOC      0
mean_FNFlux_DOC    0
OBJECTID           0
avg_temp           0
precip             0
pet                0
dtype: int64

print(cat.shape)

(6804, 113)

Total running time of the script: ( 0 minutes 21.327 seconds)

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