Interacting with the Mosqlimate datastore¶

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Fetching data from the Mosqlimate API. Below, you can find example code to pull data from both the Infodengue and Climate datasets.

In [1]:

import os
from dotenv import load_dotenv
load_dotenv()

api_key = os.getenv("API_KEY")

import os from dotenv import load_dotenv load_dotenv() api_key = os.getenv("API_KEY")

In [2]:

import mosqlient

import mosqlient

InfoDengue¶

In [3]:

mosqlient.get_infodengue?

mosqlient.get_infodengue?

Signature:
mosqlient.get_infodengue(
    api_key: str,
    disease: Literal['dengue', 'zika', 'chikungunya'],
    start_date: datetime.date | str,
    end_date: datetime.date | str,
    uf: Optional[Annotated[str, AfterValidator(func=<function validate_uf at 0x7eefe72ddb20>)]] = None,
    geocode: Optional[int] = None,
) -> pandas.core.frame.DataFrame
Docstring:
Fetch InfoDengue Data from Mosqlimate API for dengue, zika, or chikungunya.

Parameters
----------
    api_key : str
        API key used to authenticate with the Mosqlimate service.
    disease : {'dengue', 'zika', 'chikungunya'}
        The arbovirus to retrieve data for.
    start_date : date or str
        Start date of the data range (as a `datetime.date` or ISO format string).
    end_date : date or str
        End date of the data range (as a `datetime.date` or ISO format string).
    uf : types.UF, optional
        The Brazilian state abbreviation (e.g., 'SP', 'RJ'). If provided without `geocode`, filters by state.
    geocode : int, optional
        IBGE geocode of a municipality. If provided, overrides `uf`.

Returns
-------
pandas.DataFrame
    DataFrame containing the epidemiological time series data for the specified region and time period.
    Detailed descriptions of each column in the DataFrame can be found in the official API documentation:
    https://api.mosqlimate.org/docs/datastore/GET/infodengue/

Notes
-----
Either `uf` or `geocode` must be provided to specify the target geographic area.

Examples
--------
>>> get_infodengue(
...     api_key="your_api_key",
...     disease="dengue",
...     start_date="2023-01-01",
...     end_date="2023-03-01",
...     uf="RJ"
... )
File:      ~/Documentos/mosqlimate-client/mosqlient/datastore/_infodengue_get_impl.py
Type:      function

In [4]:

mosqlient.get_infodengue(
    api_key = api_key,
    disease =  "dengue",
    start_date = "2022-01-01",
    end_date = "2023-01-01",
    geocode = 4108304
).head()

mosqlient.get_infodengue( api_key = api_key, disease = "dengue", start_date = "2022-01-01", end_date = "2023-01-01", geocode = 4108304 ).head()

Out[4]:

	data_iniSE	SE	casos_est	casos_est_min	casos_est_max	casos	municipio_geocodigo	p_rt1	p_inc100k	Localidade_id	...	nivel_inc	umidmed	umidmin	tempmed	tempmax	casprov	casprov_est	casprov_est_min	casprov_est_max	casconf
0	2022-01-02	202201	138.0	138	138	138	4108304	0.989673	53.437008	0	...	1	70.345655	41.780660	26.753882	32.857143	24	None	None	None	None
1	2022-01-09	202202	87.0	87	87	87	4108304	0.085834	33.688550	0	...	1	68.869453	34.261969	26.857143	33.857143	6	None	None	None	None
2	2022-01-16	202203	108.0	108	108	108	4108304	0.420955	41.820267	0	...	1	54.516546	31.172127	30.606793	36.571429	7	None	None	None	None
3	2022-01-23	202204	76.0	76	76	76	4108304	0.018033	29.429075	0	...	1	77.647770	52.576590	27.251985	32.571429	3	None	None	None	None
4	2022-01-30	202205	57.0	57	57	57	4108304	0.001357	22.071808	0	...	0	75.748420	52.041916	26.686224	31.714286	2	None	None	None	None

5 rows × 30 columns

To get the data for all the cities in a state, don't fill the geocode field, as in the example below:

In [5]:

mosqlient.get_infodengue(
    api_key = api_key,
    disease =  "dengue",
    start_date = "2022-01-01",
    end_date = "2023-01-01", 
    uf = 'AL'
).head()

mosqlient.get_infodengue( api_key = api_key, disease = "dengue", start_date = "2022-01-01", end_date = "2023-01-01", uf = 'AL' ).head()

100%|██████████| 18/18 [00:03<00:00,  5.43requests/s]

Out[5]:

	data_iniSE	SE	casos_est	casos_est_min	casos_est_max	casos	municipio_geocodigo	p_rt1	p_inc100k	Localidade_id	...	nivel_inc	umidmed	umidmin	tempmed	tempmax	casprov	casprov_est	casprov_est_min	casprov_est_max	casconf
0	2023-01-01	202301	0.0	0	0	0	2703700	0.0	0.0	0	...	0	83.854557	60.179396	25.375	29.857143	0	None	None	None	None
1	2023-01-01	202301	0.0	0	0	0	2700102	0.0	0.0	0	...	0	83.854557	60.179396	25.375	29.857143	0	None	None	None	None
2	2023-01-01	202301	0.0	0	0	0	2702504	0.0	0.0	0	...	0	83.854557	60.179396	25.375	29.857143	0	None	None	None	None
3	2023-01-01	202301	0.0	0	0	0	2709202	0.0	0.0	0	...	0	83.854557	60.179396	25.375	29.857143	0	None	None	None	None
4	2023-01-01	202301	0.0	0	0	0	2709152	0.0	0.0	0	...	0	83.854557	60.179396	25.375	29.857143	0	None	None	None	None

5 rows × 30 columns

Climate¶

In [6]:

mosqlient.get_climate?

mosqlient.get_climate?

Signature:
mosqlient.get_climate(
    api_key: str,
    start_date: datetime.date | str,
    end_date: datetime.date | str,
    uf: Optional[Annotated[str, AfterValidator(func=<function validate_uf at 0x7eefe72ddb20>)]] = None,
    geocode: Optional[int] = None,
) -> pandas.core.frame.DataFrame
Docstring:
Retrieve historical climate data from the Mosqlimate API for a specific region and date range.

Parameters
----------
    api_key : str
        API key used to authenticate with the Mosqlimate service.
    start_date : date or str
        Start date of the data range (as a `datetime.date` or ISO format string).
    end_date : date or str
        End date of the data range (as a `datetime.date` or ISO format string).
    uf : types.UF, optional
        The Brazilian state abbreviation (e.g., 'SP', 'MG'). If provided and `geocode` is not, filters by state.
    geocode : int, optional
        IBGE geocode of a municipality. If provided, overrides `uf`.

Returns
-------
    pandas.DataFrame
        DataFrame containing daily climate data. Detailed descriptions of each column in the DataFrame can be found in the official API documentation:
        https://api.mosqlimate.org/docs/datastore/GET/climate/#output_items

Notes
-----
- Either `uf` or `geocode` must be provided to define the target location.

Examples
--------
>>> get_climate(
...     api_key="your_api_key",
...     start_date="2023-01-01",
...     end_date="2023-01-31",
...     geocode=3550308
... )
File:      ~/Documentos/mosqlimate-client/mosqlient/datastore/_climate_get_impl.py
Type:      function

In [7]:

mosqlient.get_climate(
    api_key = api_key,
    start_date = "2022-01-01",
    end_date = "2022-01-01", 
    uf = "RJ",
).head()

mosqlient.get_climate( api_key = api_key, start_date = "2022-01-01", end_date = "2022-01-01", uf = "RJ", ).head()

Out[7]:

	date	geocodigo	epiweek	temp_min	temp_med	temp_max	precip_min	precip_med	precip_max	precip_tot	pressao_min	pressao_med	pressao_max	umid_min	umid_med	umid_max
0	2022-01-01	3304904	202152	22.6257	24.3860	27.0321	3.6204	11.1975	13.9251	89.5801	0.9792	0.9809	0.9822	73.2068	88.1539	96.0204
1	2022-01-01	3300456	202152	22.0534	24.4950	28.0489	2.9373	10.3552	16.4463	82.8420	0.9841	0.9856	0.9871	71.4547	86.2787	95.7525
2	2022-01-01	3306305	202152	19.7466	22.2258	25.2781	0.7202	6.7313	19.1833	53.8505	0.9383	0.9399	0.9413	66.3477	80.9101	91.8641
3	2022-01-01	3302502	202152	21.2800	23.3756	26.6641	2.5879	9.6577	14.7341	77.2612	0.9632	0.9648	0.9660	72.5174	88.8297	96.9553
4	2022-01-01	3303203	202152	21.8972	24.2891	27.5884	3.5619	11.4151	18.2473	91.3207	0.9773	0.9789	0.9804	72.5641	85.7219	95.6470

In [8]:

mosqlient.get_climate(
    api_key = api_key,
    start_date = "2022-01-01",
    end_date = "2023-01-01", 
    geocode = 4108304,
)

mosqlient.get_climate( api_key = api_key, start_date = "2022-01-01", end_date = "2023-01-01", geocode = 4108304, )

100%|██████████| 1/1 [00:00<00:00,  1.50requests/s]

Out[8]:

	date	geocodigo	epiweek	temp_min	temp_med	temp_max	precip_min	precip_med	precip_max	precip_tot	pressao_min	pressao_med	pressao_max	umid_min	umid_med	umid_max
0	2022-01-01	4108304	202152	23.6794	29.6633	35.3228	0.0000	0.0132	0.0978	0.1058	0.9624	0.9652	0.9681	30.1244	52.8195	80.1019
1	2022-01-02	4108304	202201	25.9288	31.0351	36.3261	0.0000	0.0043	0.0220	0.0345	0.9641	0.9662	0.9685	31.7948	48.0208	67.4433
2	2022-01-03	4108304	202201	26.2168	30.1859	34.8287	0.0000	0.1443	0.6431	1.1544	0.9657	0.9673	0.9696	40.3521	56.3396	75.7326
3	2022-01-04	4108304	202201	24.9282	27.3683	29.5687	0.0028	5.3999	15.2487	43.1995	0.9622	0.9646	0.9664	61.2906	76.2920	86.0319
4	2022-01-05	4108304	202201	24.5059	27.6275	31.2310	0.0000	2.2207	15.3052	17.7653	0.9639	0.9648	0.9663	51.0381	74.0777	93.2987
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
361	2022-12-28	4108304	202252	21.7809	25.4237	30.3080	0.0990	0.9195	1.7668	7.3559	0.9679	0.9696	0.9714	42.2631	74.1699	94.4107
362	2022-12-29	4108304	202252	18.9820	25.8130	32.4540	0.0000	0.2209	1.7668	1.7668	0.9705	0.9725	0.9747	29.4484	53.9913	81.7848
363	2022-12-30	4108304	202252	20.5351	27.1068	33.5379	0.0000	0.0001	0.0004	0.0004	0.9689	0.9721	0.9741	29.4692	53.4310	80.9801
364	2022-12-31	4108304	202252	23.5194	28.8650	34.5015	0.0000	0.0182	0.0493	0.1457	0.9685	0.9714	0.9741	35.0052	53.6517	73.5915
365	2023-01-01	4108304	202301	25.5737	30.1169	35.1959	0.0002	0.0817	0.3001	0.6539	0.9664	0.9701	0.9725	38.9653	57.2024	75.5044

366 rows × 16 columns

Climate Weekly¶

In [9]:

mosqlient.get_climate_weekly?

mosqlient.get_climate_weekly?

Signature:
mosqlient.get_climate_weekly(
    api_key: str,
    start: str,
    end: str,
    uf: Optional[Annotated[str, AfterValidator(func=<function validate_uf at 0x7eefe72ddb20>)]] = None,
    geocode: Optional[int] = None,
    macro_health_code: Optional[int] = None,
) -> pandas.core.frame.DataFrame
Docstring:
Retrieve historical climate data weekly aggregated

Parameters
----------
    api_key : str
        API key used to authenticate with the Mosqlimate service.
    start : str
        Start epiweek in YYYYWW format (example: 202501).
    end : str
        End epiweek in YYYYWW format (example: 202501).
    uf : str, optional
        The Brazilian state abbreviation (e.g., 'SP', 'MG').
    geocode : int, optional
        IBGE geocode of a municipality.
    macro_health_code : int, optional
        Macro Health IBGE geocode. Example: 1101

Returns
-------
    pandas.DataFrame
        DataFrame containing daily climate data. Detailed descriptions of
        each column in the DataFrame can be found in the official API
        documentation:
        https://api.mosqlimate.org/docs/datastore/GET/climate-weekly/

Notes
-----
- Either `uf` or `geocode` or `macro_health_code` must be provided.

Examples
--------
>>> get_climate_weekly(
...     api_key="your_api_key",
...     start="202401",
...     end="202402",
...     geocode=3550308
... )
File:      ~/Documentos/mosqlimate-client/mosqlient/datastore/_climate_get_impl.py
Type:      function

In [10]:

mosqlient.get_climate_weekly(
    api_key = api_key,
    start = "202201",
    end = "202301", 
    geocode = 4108304,
)

mosqlient.get_climate_weekly( api_key = api_key, start = "202201", end = "202301", geocode = 4108304, )

Out[10]:

	epiweek	geocodigo	temp_min_avg	temp_med_avg	temp_max_avg	temp_amplit_avg	precip_tot_sum	umid_min_avg	umid_med_avg	umid_max_avg
0	202201	4108304	24.2693	28.0864	32.2336	7.9643	73.6119	48.2413	65.0009	80.8675
1	202202	4108304	23.2775	27.7596	32.7722	9.4947	12.3791	40.3529	61.9382	82.8912
2	202203	4108304	26.5513	31.5175	36.6646	10.1133	3.5952	31.6914	51.4059	73.1761
3	202204	4108304	25.6391	28.9419	32.6696	7.0305	107.7333	51.6893	66.7820	79.7656
4	202205	4108304	23.2069	26.4899	30.0356	6.8286	379.5296	58.2638	74.6899	88.3973
5	202206	4108304	20.4277	25.2807	30.4224	9.9948	38.2804	39.5132	60.9358	80.2294
6	202207	4108304	22.6134	28.3236	34.4828	11.8694	1.2217	31.0025	52.0062	73.5311
7	202208	4108304	24.2454	28.9652	34.7633	10.5179	17.9363	39.6146	64.2397	82.2612
8	202209	4108304	25.0768	28.4139	32.6476	7.5708	95.9880	54.8607	72.7155	86.3444
9	202210	4108304	24.1314	27.3415	31.2148	7.0834	153.9231	56.8073	72.5290	84.6526
10	202211	4108304	21.3372	24.7396	28.8178	7.4806	30.2727	57.8031	75.6584	90.6286
11	202212	4108304	19.7560	23.5228	27.6772	7.9213	422.6312	56.2705	73.7866	86.6719
12	202213	4108304	17.3800	21.5469	26.2013	8.8213	14.7462	57.7195	73.0022	85.2373
13	202214	4108304	20.9972	23.4041	26.8100	5.8128	190.0897	71.5951	85.7991	94.4932
14	202215	4108304	18.4571	21.4106	25.1715	6.7144	573.4511	63.5038	79.5803	90.2552
15	202216	4108304	17.2569	20.8463	25.3899	8.1330	184.7128	60.1331	80.1188	91.8362
16	202217	4108304	21.8869	24.8824	28.9868	7.0999	17.1170	61.6010	75.4950	83.8907
17	202218	4108304	16.3898	19.2897	22.7472	6.3573	236.7260	74.1734	87.6770	96.7032
18	202219	4108304	15.4910	18.5293	22.0499	6.5588	98.5714	67.1117	83.4311	94.2703
19	202220	4108304	11.6351	13.8234	16.5648	4.9298	63.2339	58.9717	74.8305	85.7494
20	202221	4108304	15.5323	18.4400	22.7846	7.2523	156.9500	62.5740	77.7694	87.7332
21	202222	4108304	13.0001	15.0447	17.5892	4.5890	385.9908	76.0159	86.2809	93.5513
22	202223	4108304	14.8368	17.3320	20.3325	5.4957	151.9685	68.9078	83.8573	93.6670
23	202224	4108304	10.3534	14.2385	19.2203	8.8670	128.1389	58.8954	77.3282	89.7154
24	202225	4108304	15.5134	18.6317	23.1847	7.6713	26.1895	70.9959	85.2621	95.3533
25	202226	4108304	14.4944	17.8652	22.7446	8.2502	3.6815	64.5179	79.8649	89.8262
26	202227	4108304	18.7058	21.4994	25.6933	6.9875	0.0590	53.3317	63.9078	70.5480
27	202228	4108304	16.3582	20.1024	25.1023	8.7441	156.5804	53.1867	66.3308	78.9570
28	202229	4108304	15.2610	18.5497	23.5356	8.2746	22.4484	61.4076	77.5086	88.4148
29	202230	4108304	15.3607	19.6843	25.5888	10.2282	17.5597	47.7834	69.1146	81.6339
30	202231	4108304	15.7657	19.1177	23.9893	8.2236	56.6368	53.8345	71.2351	85.4401
31	202232	4108304	13.2316	16.1198	20.0585	6.8269	197.0766	64.4530	80.9308	91.9683
32	202233	4108304	13.8443	17.2642	21.6443	7.8000	393.9289	52.1527	69.6702	81.7272
33	202234	4108304	14.3736	19.3000	25.7743	11.4007	0.0150	52.2872	72.8005	85.6528
34	202235	4108304	14.6189	18.2129	22.8961	8.2773	31.3380	50.6867	68.2177	80.9691
35	202236	4108304	15.4411	18.9193	23.6033	8.1622	113.7425	59.1491	74.2494	85.7853
36	202237	4108304	14.1578	18.4527	23.2701	9.1123	89.7901	56.2110	74.7256	87.5815
37	202238	4108304	13.8362	17.4820	21.8021	7.9659	189.5922	58.1364	75.0785	87.6932
38	202239	4108304	16.7805	18.9067	21.6213	4.8408	306.1936	74.0554	86.4893	94.3622
39	202240	4108304	15.3462	20.1158	25.2276	9.8815	195.0713	54.0466	74.4443	89.6146
40	202241	4108304	16.3979	19.0968	22.3537	5.9559	581.5409	74.1726	85.9418	94.9288
41	202242	4108304	18.5514	21.9085	25.5600	7.0087	377.9014	66.1247	82.8756	95.0863
42	202243	4108304	19.1568	24.0412	29.6198	10.4631	27.5611	48.3676	69.0319	84.9735
43	202244	4108304	13.6581	18.5943	23.7490	10.0909	203.2810	50.9235	69.6229	84.9016
44	202245	4108304	17.3162	22.4091	27.9459	10.6297	10.5248	43.8071	62.1495	76.0782
45	202246	4108304	18.4738	23.2314	29.0222	10.5484	81.7213	41.5326	63.5969	81.0047
46	202247	4108304	19.8099	24.2382	29.2912	9.4814	59.5623	48.6269	66.7669	83.7793
47	202248	4108304	21.7150	26.1369	30.9838	9.2688	26.2677	48.2079	66.8091	82.8010
48	202249	4108304	24.2743	28.1523	32.9083	8.6340	44.1106	54.9971	76.0358	92.8916
49	202250	4108304	22.1361	26.4932	31.0494	8.9133	64.0720	45.4950	66.9451	85.0606
50	202251	4108304	21.1739	25.3331	29.4418	8.2678	65.3105	48.8330	66.9984	82.3829
51	202252	4108304	21.7558	26.9329	32.3541	10.5983	12.4078	37.6928	60.9106	83.2672
52	202301	4108304	21.2979	25.4136	30.2249	8.9270	410.8377	46.8968	68.3932	85.2647

Episcanner¶

In [11]:

mosqlient.get_episcanner?

mosqlient.get_episcanner?

Signature:
mosqlient.get_episcanner(
    api_key: str,
    uf: Annotated[str, AfterValidator(func=<function validate_uf at 0x7eefe72ddb20>)],
    disease: Literal['dengue', 'zika', 'chikungunya'] = 'dengue',
    year: Optional[int] = 2026,
) -> pandas.core.frame.DataFrame
Docstring:
Parameters
----------
    api_key : str
        API key used to authenticate with the Mosqlimate service.
    disease : types.Disease
        Default: "dengue". Options: dengue, chikungunya or zika
    uf : types.UF
        The Brazilian state abbreviation (e.g., 'SP', 'MG').
    year : int, optional
        Default value is the current year.

Returns
-------
    pandas.DataFrame
        DataFrame containing Episcanner data. Detailed
        descriptions of each column in the DataFrame can be found in the
        official API documentation:
        https://api.mosqlimate.org/docs/datastore/GET/episcanner/

Examples
--------
>>> get_episcanner(
...     api_key="your_api_key",
...     uf="SP",
... )
File:      ~/Documentos/mosqlimate-client/mosqlient/datastore/_episcanner_get_impl.py
Type:      function

In [12]:

mosqlient.get_episcanner(
    api_key = api_key,
    uf = "SP"
)

mosqlient.get_episcanner( api_key = api_key, uf = "SP" )

Out[12]:

	disease	CID10	year	geocode	muni_name	peak_week	beta	gamma	R0	total_cases	alpha	sum_res	ep_ini	ep_end	ep_dur
0	dengue	A90	2026	3550506	São Pedro do Turvo	13.877793	0.458385	0.301359	1.521060	94.639347	0.342564	0.793885	202546	202619	26
1	dengue	A90	2026	3533007	Nova Granada	18.880888	0.577349	0.300000	1.924498	623.670356	0.480384	0.604165	202550	202619	22
2	dengue	A90	2026	3551702	Sertãozinho	16.061778	0.463552	0.300000	1.545173	1900.458789	0.352823	0.389143	202546	202619	26
3	dengue	A90	2026	3541703	Quatá	18.912783	0.448055	0.300569	1.490688	108.872426	0.329169	1.019257	202546	202619	26
4	dengue	A90	2026	3540606	Porto Feliz	18.877950	0.457426	0.300049	1.524504	513.686635	0.344049	0.371010	202546	202619	26
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
151	dengue	A90	2026	3513405	Cruzeiro	17.417791	0.445615	0.300139	1.484696	420.871787	0.326462	0.460958	202546	202619	26
152	dengue	A90	2026	3520004	Igaraçu do Tietê	17.847540	0.495304	0.300255	1.649610	126.841052	0.393796	0.370874	202546	202619	26
153	dengue	A90	2026	3539400	Piratininga	21.427819	0.655899	0.330000	1.987574	70.147139	0.496874	1.643955	202602	202619	17
154	dengue	A90	2026	3554805	Tremembé	22.311137	0.517386	0.330000	1.567838	2026.800000	0.362179	0.499657	202546	202619	26
155	dengue	A90	2026	3530508	Mococa	19.606789	0.469730	0.330000	1.423425	112.800000	0.297469	1.106596	202546	202619	26

156 rows × 15 columns

Mosquito¶

In [13]:

mosqlient.get_mosquito?

mosqlient.get_mosquito?

Signature:
mosqlient.get_mosquito(
    api_key: str,
    date_start: Optional[str] = None,
    date_end: Optional[str] = None,
    state: Optional[str] = None,
    municipality: Optional[str] = None,
    page: Optional[int] = None,
) -> pandas.core.frame.DataFrame
Docstring:
Parameters
----------
    api_key : str
        API key used to authenticate with the Mosqlimate service.
    date_start : str, optional
        Format: YYYY-MM-dd
    date_end : str, optional
        Format: YYYY-MM-dd
    state : str, optional
        Example: MG
    municipality : str, optional
        Name of the municipality. Example: "Ponta Porã"
    page : int, optional

Returns
-------
    pandas.DataFrame
        DataFrame containing ContaOvos data. Detailed
        descriptions of each column in the DataFrame can be found in the
        official API documentation:
        https://api.mosqlimate.org/docs/datastore/GET/mosquito/

Examples
--------
>>> get_mosquito(
...     api_key="your_api_key",
...     date_start="2024-01-01",
...     date_end="2024-12-31",
...     municipality="Ponta Porã",
... )
File:      ~/Documentos/mosqlimate-client/mosqlient/datastore/_mosquito_get_impl.py
Type:      function

In [14]:

mosqlient.get_mosquito(
    api_key = api_key,
    date_start = "2024-01-01",
    date_end = "2024-12-31",
    state = "MG",
)

mosqlient.get_mosquito( api_key = api_key, date_start = "2024-01-01", date_end = "2024-12-31", state = "MG", )

Out[14]:

	counting_id	date	date_collect	eggs	latitude	longitude	municipality	municipality_code	ovitrap_id	ovitrap_website_id	state_code	state_name	time	week	year
0	625888	2024-12-30	2025-01-06	150	-19.703060	-43.965170	São José da Lapa	3162955	1	50946	MG	Minas Gerais	Mon, 06 Jan 2025 13:47:17 GMT	52	2024
1	625890	2024-12-30	2025-01-06	139	-19.702480	-43.963040	São José da Lapa	3162955	2	50955	MG	Minas Gerais	Mon, 06 Jan 2025 13:47:37 GMT	52	2024
2	625892	2024-12-30	2025-01-06	0	-19.699580	-43.959390	São José da Lapa	3162955	3	50957	MG	Minas Gerais	Mon, 06 Jan 2025 13:47:51 GMT	52	2024
3	625896	2024-12-30	2025-01-06	156	-19.699110	-43.956040	São José da Lapa	3162955	4	50960	MG	Minas Gerais	Mon, 06 Jan 2025 13:48:14 GMT	52	2024
4	625898	2024-12-30	2025-01-06	130	-19.696980	-43.957700	São José da Lapa	3162955	5	50964	MG	Minas Gerais	Mon, 06 Jan 2025 13:48:32 GMT	52	2024
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	627418	2024-12-30	2025-01-06	0	-18.567246	-46.514526	Patos de Minas	3148004	85	28347	MG	Minas Gerais	Tue, 07 Jan 2025 14:42:53 GMT	52	2024
96	627419	2024-12-30	2025-01-06	7	-18.572615	-46.519128	Patos de Minas	3148004	86	28366	MG	Minas Gerais	Tue, 07 Jan 2025 14:42:59 GMT	52	2024
97	627420	2024-12-30	2025-01-06	57	-18.569503	-46.523549	Patos de Minas	3148004	87	28358	MG	Minas Gerais	Tue, 07 Jan 2025 14:43:08 GMT	52	2024
98	627421	2024-12-30	2025-01-06	29	-18.568464	-46.526139	Patos de Minas	3148004	88	28356	MG	Minas Gerais	Tue, 07 Jan 2025 14:43:16 GMT	52	2024
99	627422	2024-12-30	2025-01-06	0	-18.567022	-46.528580	Patos de Minas	3148004	89	28357	MG	Minas Gerais	Tue, 07 Jan 2025 14:43:24 GMT	52	2024

100 rows × 15 columns