Use Python CodeYour task is : Visualization of confirmed cases of COVID-19 by country . (Use pandas to read the data table csv. Example df = pd.read_csv('COVID19.csv' ) then make a visualization using that csv table)Province/StateCountryLatLong################################################################################2/1/20202/2/20202/3/20202/4/20202/5/20202/6/20202/7/20202/8/20202/9/2020################################################################################################################################################################3/1/20203/2/20203/3/20203/4/20203/5/20203/6/20203/7/20203/8/20203/9/2020################################################################################################################################################################################4/1/20204/2/20204/3/20204/4/20204/5/20204/6/20204/7/2020 Afghanistan3365000000000000000000000000000000000111111111111144577711162122222224244040748494110110120170174237273281299349367423 Albania41.153320.168300000000000000000000000000000000000000000000000210122333384251555964707689104123146174186197212223243259277304333361377383 Algeria28.03391.6596000000000000000000000000000000000011111135121217171920202024263748546074879013920123026430236740945451158471684798611711251132014231468 Andorra42.50631.521800000000000000000000000000000000000000001111111111111123939537588113133164188224267308334370376390428439466501525545 Angola-11.202717.8739000000000000000000000000000000000000000000000000000000000012233344577788810141617 Antigua and Barbuda17.0608-61.79640000000000000000000000000000000000000000000000000001111111111333777777791515151519 Argentina-38.4161-63.6167000000000000000000000000000000000000000001112812121719193134455668799712815826630138738750258969074582010541054113312651451145115541628 Armenia40.069145.038200000000000000000000000000000000000000011111111111481826527884115136160194235249265290329407424482532571663736770822833853Australian Capital TerritoryAustralia-35.4735149.01240000000000000000000000000000000000000000000000000001112234691932393953627177788084879193969696New South WalesAustralia-33.8688151.209300003444444444444444444444444444444444466132222262838485565659211213417121026730735343666966981810291219140516171791203220322182229823892493258026372686Northern TerritoryAustralia-12.4634130.8456000000000000000000000000000000000000000000110000111111111133556612121515151719212226272828QueenslandAustralia-28.0167153.40000000132322334555555555555555555555599911111313131515182020354661687894144184221259319397443493555625656689743781835873900907921934South AustraliaAustralia-34.9285138.6007000000000012222222222222222222222222223333557777799161920292937425067100134170170235257287299305337367367396407407411411TasmaniaAustralia-41.4545145.970700000000000000000000000000000000000000001111112223355677101010162228283647476266666969727480828689VictoriaAustralia-37.8136144.96310000111123444444444444444444444444444477991010101111151821213649577194121121121229355355411466520574685769821917968103610851115113511581191Western AustraliaAustralia-31.9505115.8605000000000000000000000000000000000000002222233334699141717283135526490120140175175231231278311355364392400400436453460460 Austria47.516214.5501000000000000000000000000000000000022339141821294155791041311822463025046558601018133216462013238828143582447452835588690976578271878896181018010711111291152411781120511229712639 Azerbaijan40.143147.576900000000000000000000000000000000000000033336699911111115152328282844445365728793122165182209273298359400443521584641717 Bahamas25.0343-77.396300000000000000000000000000000000000000000000000000000011133444559101011141421242428282933 Bahrain26.027550.5500000000000000000000000000000000012333333641474949525560858595110195195195210214214228256278285305334377392419458466476499515567569643672688700756811 Bangladesh23.68590.356300000000000000000000000000000000000000000000003333333581014172025273339394448484849515456617088123164 Barbados13.1939-59.54320000000000000000000000000000000000000000000000000000000225561417181818242633333434465152566063 Belarus53.709827.953400000000000000000000000000000000000001111166666699122727273636515169767681818686949494152152163304351440562700861 Belgium50.8333400000000000001111111111111111111111111128132350109169200239267314314559689886105812431486179522572815340137434269493762357284913410836118991277513964153481677018431196912081422194

base_url <- # it's long, so I'm pasting it together to avoid ugly long lines…

Answered: Your task is : Visualization of…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Use Python Code

Your task is : Visualization of confirmed cases of COVID-19 by country . (Use pandas to read the data table csv. Example df = pd.read_csv('COVID19.csv' ) then make a visualization using that csv table)

Province/State	Country	Lat	Long	########	########	########	########	########	########	########	########	########	########	2/1/2020	2/2/2020	2/3/2020	2/4/2020	2/5/2020	2/6/2020	2/7/2020	2/8/2020	2/9/2020	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	3/1/2020	3/2/2020	3/3/2020	3/4/2020	3/5/2020	3/6/2020	3/7/2020	3/8/2020	3/9/2020	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	########	4/1/2020	4/2/2020	4/3/2020	4/4/2020	4/5/2020	4/6/2020	4/7/2020
	Afghanistan	33	65	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	4	4	5	7	7	7	11	16	21	22	22	22	24	24	40	40	74	84	94	110	110	120	170	174	237	273	281	299	349	367	423
	Albania	41.1533	20.1683	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	10	12	23	33	38	42	51	55	59	64	70	76	89	104	123	146	174	186	197	212	223	243	259	277	304	333	361	377	383
	Algeria	28.0339	1.6596	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	3	5	12	12	17	17	19	20	20	20	24	26	37	48	54	60	74	87	90	139	201	230	264	302	367	409	454	511	584	716	847	986	1171	1251	1320	1423	1468
	Andorra	42.5063	1.5218	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	39	39	53	75	88	113	133	164	188	224	267	308	334	370	376	390	428	439	466	501	525	545
	Angola	-11.2027	17.8739	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2	2	3	3	3	4	4	5	7	7	7	8	8	8	10	14	16	17
	Antigua and Barbuda	17.0608	-61.7964	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	3	3	3	7	7	7	7	7	7	7	9	15	15	15	15	19
	Argentina	-38.4161	-63.6167	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	2	8	12	12	17	19	19	31	34	45	56	68	79	97	128	158	266	301	387	387	502	589	690	745	820	1054	1054	1133	1265	1451	1451	1554	1628
	Armenia	40.0691	45.0382	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	4	8	18	26	52	78	84	115	136	160	194	235	249	265	290	329	407	424	482	532	571	663	736	770	822	833	853
Australian Capital Territory	Australia	-35.4735	149.0124	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	2	2	3	4	6	9	19	32	39	39	53	62	71	77	78	80	84	87	91	93	96	96	96
New South Wales	Australia	-33.8688	151.2093	0	0	0	0	3	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	6	6	13	22	22	26	28	38	48	55	65	65	92	112	134	171	210	267	307	353	436	669	669	818	1029	1219	1405	1617	1791	2032	2032	2182	2298	2389	2493	2580	2637	2686
Northern Territory	Australia	-12.4634	130.8456	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	1	1	1	1	1	1	1	1	1	3	3	5	5	6	6	12	12	15	15	15	17	19	21	22	26	27	28	28
Queensland	Australia	-28.0167	153.4	0	0	0	0	0	0	0	1	3	2	3	2	2	3	3	4	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	9	9	9	11	11	13	13	13	15	15	18	20	20	35	46	61	68	78	94	144	184	221	259	319	397	443	493	555	625	656	689	743	781	835	873	900	907	921	934
South Australia	Australia	-34.9285	138.6007	0	0	0	0	0	0	0	0	0	0	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	3	3	3	3	5	5	7	7	7	7	7	9	9	16	19	20	29	29	37	42	50	67	100	134	170	170	235	257	287	299	305	337	367	367	396	407	407	411	411
Tasmania	Australia	-41.4545	145.9707	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	2	2	2	3	3	5	5	6	7	7	10	10	10	16	22	28	28	36	47	47	62	66	66	69	69	72	74	80	82	86	89
Victoria	Australia	-37.8136	144.9631	0	0	0	0	1	1	1	1	2	3	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	7	7	9	9	10	10	10	11	11	15	18	21	21	36	49	57	71	94	121	121	121	229	355	355	411	466	520	574	685	769	821	917	968	1036	1085	1115	1135	1158	1191
Western Australia	Australia	-31.9505	115.8605	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	2	2	2	3	3	3	3	4	6	9	9	14	17	17	28	31	35	52	64	90	120	140	175	175	231	231	278	311	355	364	392	400	400	436	453	460	460
	Austria	47.5162	14.5501	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	3	3	9	14	18	21	29	41	55	79	104	131	182	246	302	504	655	860	1018	1332	1646	2013	2388	2814	3582	4474	5283	5588	6909	7657	8271	8788	9618	10180	10711	11129	11524	11781	12051	12297	12639
	Azerbaijan	40.1431	47.5769	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	3	3	3	6	6	9	9	9	11	11	11	15	15	23	28	28	28	44	44	53	65	72	87	93	122	165	182	209	273	298	359	400	443	521	584	641	717
	Bahamas	25.0343	-77.3963	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	3	3	4	4	4	5	5	9	10	10	11	14	14	21	24	24	28	28	29	33
	Bahrain	26.0275	50.55	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	23	33	33	36	41	47	49	49	52	55	60	85	85	95	110	195	195	195	210	214	214	228	256	278	285	305	334	377	392	419	458	466	476	499	515	567	569	643	672	688	700	756	811
	Bangladesh	23.685	90.3563	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	3	3	3	3	3	3	5	8	10	14	17	20	25	27	33	39	39	44	48	48	48	49	51	54	56	61	70	88	123	164
	Barbados	13.1939	-59.5432	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	5	5	6	14	17	18	18	18	24	26	33	33	34	34	46	51	52	56	60	63
	Belarus	53.7098	27.9534	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	6	6	6	6	6	6	9	9	12	27	27	27	36	36	51	51	69	76	76	81	81	86	86	94	94	94	152	152	163	304	351	440	562	700	861
	Belgium	50.8333	4	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	8	13	23	50	109	169	200	239	267	314	314	559	689	886	1058	1243	1486	1795	2257	2815	3401	3743	4269	4937	6235	7284	9134	10836	11899	12775	13964	15348	16770	18431	19691	20814	22194

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Your task is : Visualization of confirmed cases of COVID-19 by country . (Use pandas to read the data table csv. Example df = pd.read_csv('COVID19.csv' ) then make a visualization using that csv table) Province/State Country Lat