{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The SARS-CoV-2 (Covid-19) epidemic" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data on the SARS-CoV-2 (Covid-19) epidemic are available from the Web site of the [Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)](https://systems.jhu.edu/). We download them as a file in CSV format, in which each line corresponds to a state in the observation period. Only the complete dataset, starting on 22nd Jenuary 2020 and ending with a recent week, is available for download." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the documentation of the data from [the download site](https://github.com/CSSEGISandData/COVID-19):\n", "\n", "| Columns name | Description |\n", "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", "| `Province/State` | |\n", "| `Country/Region` | |\n", "| `Lat` | |\n", "| `Long` | |\n", "| `839 x Dates` | |\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...4/30/225/1/225/2/225/3/225/4/225/5/225/6/225/7/225/8/225/9/22
0NaNAfghanistan33.93911067.709953000000...178879178899178901178901178901178905178919178922178981179010
1NaNAlbania41.15330020.168300000000...275107275167275177275191275211275266275310275341275366275372
2NaNAlgeria28.0339001.659600000000...265780265782265782265782265782265786265791265794265798265800
3NaNAndorra42.5063001.521800000000...41349413494134941349417174171741717417174171741717
4NaNAngola-11.20270017.873900000000...99287992879928799287992879928799287992879928799287
5NaNAntarctica-71.94990023.347000000000...11111111111111111111
6NaNAntigua and Barbuda17.060800-61.796400000000...7626762676267626765476637663766376637663
7NaNArgentina-38.416100-63.616700000000...9072230908367390836739083673908367390836739083673908367391013199101319
8NaNArmenia40.06910045.038200000000...422867422871422874422874422877422877422877422877422877422896
9Australian Capital TerritoryAustralia-35.473500149.012400000000...104941105739106705107769108830109879110846111599112407113368
10New South WalesAustralia-33.868800151.209300000034...2287281229490023044352316269233436023451562356642236548423732352383412
11Northern TerritoryAustralia-12.463400130.845600000000...63289635686415164540649836498365659658816608366427
12QueenslandAustralia-27.469800153.025100000000...997619100225710074591015120102314910301721036786104145010462001052748
13South AustraliaAustralia-34.928500138.600700000000...409717413321416371416371424182427205424182430466436318440223
14TasmaniaAustralia-42.882100147.327200000000...136457137359138395139468140554141647142541143267144107145182
15VictoriaAustralia-37.813600144.963100000011...1561415157694515868761597464160906016192961628531163713516470281659695
16Western AustraliaAustralia-31.950500115.860500000000...392423398270405848415563425704433295442481451192461553473909
17NaNAustria47.51620014.550100000000...4140543414490641490044154244416100441667594172760417792841815454185678
18NaNAzerbaijan40.14310047.576900000000...792562792578792579792585792585792599792607792618792630792632
19NaNBahamas25.025885-78.035889000000...33558335783358233601336263365533672336953369533748
20NaNBahrain26.02750050.550000000000...568706569091569370569727570103570761571441572082572810573757
21NaNBangladesh23.68500090.356300000000...1952691195271619527261952733195274319527471952766195277619527991952829
22NaNBarbados13.193900-59.543200000000...70080704497079371127711277228272743731987359273856
23NaNBelarus53.70980027.953400000000...980345980345980345980868981070981259981838981838982331982331
24NaNBelgium50.8333004.469936000000...4056448405644840712794071279407127940883364088336408833640883364102082
25NaNBelize17.189900-88.497600000000...57497574975749757535575615757857612576125761257675
26NaNBenin9.3077002.315800000000...26952269522695226952269522695226952269522695226952
27NaNBhutan27.51420090.433600000000...59163592195924759274593585942259450594835949759512
28NaNBolivia-16.290200-63.588700000000...904974905074905074905097905150905212905355905428905453905453
29NaNBosnia and Herzegovina43.91590017.679100000000...377087377087377087377145377175377229377229377276377276377345
..................................................................
254NaNTunisia33.8869179.537499000000...1040193104019310401931040193104071210407121040712104071210407121040712
255NaNTurkey38.96370035.243300000000...15032093150335731503491715036110150372421503849515040238150418991504337915044921
256NaNUS40.000000-100.000000112255...81348442813647188145802381506402816205038170760181831608818584988186347981973661
257NaNUganda1.37333332.290275000000...164084164084164104164104164104164104164118164118164118164149
258NaNUkraine48.37940031.165600000000...5040518504051850405185040518504051850405185040518504051850405185040518
259NaNUnited Arab Emirates23.42407653.847818000000...898571898811899033899242899441899637899835900026900251900484
260AnguillaUnited Kingdom18.220600-63.068600000000...2788278827882788278827882788278827882788
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Province/StateLatLong1/22/201/23/201/24/201/25/201/26/201/27/201/28/20...5/1/225/2/225/3/225/4/225/5/225/6/225/7/225/8/225/9/22country
Country/Region
AfghanistanNaN33.93911067.7099530000000...178899178901178901178901178905178919178922178981179010Afghanistan
AlbaniaNaN41.15330020.1683000000000...275167275177275191275211275266275310275341275366275372Albania
AlgeriaNaN28.0339001.6596000000000...265782265782265782265782265786265791265794265798265800Algeria
AndorraNaN42.5063001.5218000000000...413494134941349417174171741717417174171741717Andorra
AngolaNaN-11.20270017.8739000000000...992879928799287992879928799287992879928799287Angola
AntarcticaNaN-71.94990023.3470000000000...111111111111111111Antarctica
Antigua and BarbudaNaN17.060800-61.7964000000000...762676267626765476637663766376637663Antigua and Barbuda
ArgentinaNaN-38.416100-63.6167000000000...908367390836739083673908367390836739083673908367391013199101319Argentina
ArmeniaNaN40.06910045.0382000000000...422871422874422874422877422877422877422877422877422896Armenia
AustraliaAustralian Capital Territory-35.473500149.0124000000000...105739106705107769108830109879110846111599112407113368Australia
AustraliaNew South Wales-33.868800151.2093000000344...229490023044352316269233436023451562356642236548423732352383412Australia
AustraliaNorthern Territory-12.463400130.8456000000000...635686415164540649836498365659658816608366427Australia
AustraliaQueensland-27.469800153.0251000000000...100225710074591015120102314910301721036786104145010462001052748Australia
AustraliaSouth Australia-34.928500138.6007000000000...413321416371416371424182427205424182430466436318440223Australia
AustraliaTasmania-42.882100147.3272000000000...137359138395139468140554141647142541143267144107145182Australia
AustraliaVictoria-37.813600144.9631000000111...157694515868761597464160906016192961628531163713516470281659695Australia
AustraliaWestern Australia-31.950500115.8605000000000...398270405848415563425704433295442481451192461553473909Australia
AustriaNaN47.51620014.5501000000000...414490641490044154244416100441667594172760417792841815454185678Austria
AzerbaijanNaN40.14310047.5769000000000...792578792579792585792585792599792607792618792630792632Azerbaijan
BahamasNaN25.025885-78.0358890000000...335783358233601336263365533672336953369533748Bahamas
BahrainNaN26.02750050.5500000000000...569091569370569727570103570761571441572082572810573757Bahrain
BangladeshNaN23.68500090.3563000000000...195271619527261952733195274319527471952766195277619527991952829Bangladesh
BarbadosNaN13.193900-59.5432000000000...704497079371127711277228272743731987359273856Barbados
BelarusNaN53.70980027.9534000000000...980345980345980868981070981259981838981838982331982331Belarus
BelgiumNaN50.8333004.4699360000000...405644840712794071279407127940883364088336408833640883364102082Belgium
BelizeNaN17.189900-88.4976000000000...574975749757535575615757857612576125761257675Belize
BeninNaN9.3077002.3158000000000...269522695226952269522695226952269522695226952Benin
BhutanNaN27.51420090.4336000000000...592195924759274593585942259450594835949759512Bhutan
BoliviaNaN-16.290200-63.5887000000000...905074905074905097905150905212905355905428905453905453Bolivia
Bosnia and HerzegovinaNaN43.91590017.6791000000000...377087377087377145377175377229377229377276377276377345Bosnia and Herzegovina
..................................................................
TunisiaNaN33.8869179.5374990000000...104019310401931040193104071210407121040712104071210407121040712Tunisia
TurkeyNaN38.96370035.2433000000000...150335731503491715036110150372421503849515040238150418991504337915044921Turkey
USNaN40.000000-100.0000001122555...813647188145802381506402816205038170760181831608818584988186347981973661US
UgandaNaN1.37333332.2902750000000...164084164104164104164104164104164118164118164118164149Uganda
UkraineNaN48.37940031.1656000000000...504051850405185040518504051850405185040518504051850405185040518Ukraine
United Arab EmiratesNaN23.42407653.8478180000000...898811899033899242899441899637899835900026900251900484United Arab Emirates
United KingdomAnguilla18.220600-63.0686000000000...278827882788278827882788278827882788United Kingdom
United KingdomBermuda32.307800-64.7505000000000...134881355913559138321383213832138321383213832United Kingdom
United KingdomBritish Virgin Islands18.420700-64.6400000000000...635563556355635563556467646764676467United Kingdom
United KingdomCayman Islands19.313300-81.2546000000000...222232222322223228282282822828228282282822828United Kingdom
United KingdomChannel Islands49.372300-2.3644000000000...000000000United Kingdom
United KingdomFalkland Islands (Malvinas)-51.796300-59.5236000000000...177181181197197392392392392United Kingdom
United KingdomGibraltar36.140800-5.3536000000000...178371783717837178371799617996179961799617996United Kingdom
United KingdomGuernsey49.448196-2.5894900000000...245732457324573248322483224832248322483224832United Kingdom
United KingdomIsle of Man54.236100-4.5481000000000...322053220532205324243242432424324243242432424United Kingdom
United KingdomJersey49.213800-2.1358000000000...497784981049810498104981050033500335003350033United Kingdom
United KingdomMontserrat16.742498-62.1873660000000...273273273371452452493493493United Kingdom
United KingdomSaint Helena, Ascension and Tristan da Cunha-7.946700-14.3559000000000...444444444United Kingdom
United KingdomTurks and Caicos Islands21.694000-71.7979000000000...596359635995599559955995599559955995United Kingdom
United KingdomNaN55.378100-3.4360000000000...220383402203834022073858220905232210298322114034221140342211403422140287United Kingdom
UruguayNaN-32.522800-55.7658000000000...897867897867899723899723899723899723899723899723899723Uruguay
UzbekistanNaN41.37749164.5852620000000...238596238627238628238636238651238660238695238695238736Uzbekistan
VanuatuNaN-15.376700166.9592000000000...750775147533762276997734773477858056Vanuatu
VenezuelaNaN6.423800-66.5897000000000...522434522434522514522564522587522625522672522749522749Venezuela
VietnamNaN14.058324108.2771990222222...106535261065664910659358106624461066675110670570106739151067618410678359Vietnam
West Bank and GazaNaN31.95220035.2332000000000...657060657060657060657060657060657060657060657060657193West Bank and Gaza
Winter Olympics 2022NaN39.904200116.4074000000000...535535535535535535535535535Winter Olympics 2022
YemenNaN15.55272748.5163880000000...118181181911819118191181911819118191181911819Yemen
ZambiaNaN-13.13389727.8493320000000...319733319755319755319755319755319755320099320099320157Zambia
ZimbabweNaN-19.01543829.1548570000000...247911247935247990248050248050248214248214248352248536Zimbabwe
\n", "

284 rows × 843 columns

\n", "
" ], "text/plain": [ " Province/State \\\n", "Country/Region \n", "Afghanistan NaN \n", "Albania NaN \n", "Algeria NaN \n", "Andorra NaN \n", "Angola NaN \n", "Antarctica NaN \n", "Antigua and Barbuda NaN \n", "Argentina NaN \n", "Armenia NaN \n", "Australia Australian Capital Territory \n", "Australia New South Wales \n", "Australia Northern Territory \n", "Australia Queensland \n", "Australia South Australia \n", "Australia Tasmania \n", "Australia Victoria \n", "Australia Western Australia \n", "Austria NaN \n", "Azerbaijan NaN \n", "Bahamas NaN \n", "Bahrain NaN \n", "Bangladesh NaN \n", "Barbados NaN \n", "Belarus NaN \n", "Belgium NaN \n", "Belize NaN \n", "Benin NaN \n", "Bhutan NaN \n", "Bolivia NaN \n", "Bosnia and Herzegovina NaN \n", "... ... \n", "Tunisia NaN \n", "Turkey NaN \n", "US NaN \n", "Uganda NaN \n", "Ukraine NaN \n", "United Arab Emirates NaN \n", "United Kingdom Anguilla \n", "United Kingdom Bermuda \n", "United Kingdom British Virgin Islands \n", "United Kingdom Cayman Islands \n", "United Kingdom Channel Islands \n", "United Kingdom Falkland Islands (Malvinas) \n", "United Kingdom Gibraltar \n", "United Kingdom Guernsey \n", "United Kingdom Isle of Man \n", "United Kingdom Jersey \n", "United Kingdom Montserrat \n", "United Kingdom Saint Helena, Ascension and Tristan da Cunha \n", "United Kingdom Turks and Caicos Islands \n", "United Kingdom NaN \n", "Uruguay NaN \n", "Uzbekistan NaN \n", "Vanuatu NaN \n", "Venezuela NaN \n", "Vietnam NaN \n", "West Bank and Gaza NaN \n", "Winter Olympics 2022 NaN \n", "Yemen NaN \n", "Zambia NaN \n", "Zimbabwe NaN \n", "\n", " Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "Country/Region \n", "Afghanistan 33.939110 67.709953 0 0 0 \n", "Albania 41.153300 20.168300 0 0 0 \n", "Algeria 28.033900 1.659600 0 0 0 \n", "Andorra 42.506300 1.521800 0 0 0 \n", "Angola -11.202700 17.873900 0 0 0 \n", "Antarctica -71.949900 23.347000 0 0 0 \n", "Antigua and Barbuda 17.060800 -61.796400 0 0 0 \n", "Argentina -38.416100 -63.616700 0 0 0 \n", "Armenia 40.069100 45.038200 0 0 0 \n", "Australia -35.473500 149.012400 0 0 0 \n", "Australia -33.868800 151.209300 0 0 0 \n", "Australia -12.463400 130.845600 0 0 0 \n", "Australia -27.469800 153.025100 0 0 0 \n", "Australia -34.928500 138.600700 0 0 0 \n", "Australia -42.882100 147.327200 0 0 0 \n", "Australia -37.813600 144.963100 0 0 0 \n", "Australia -31.950500 115.860500 0 0 0 \n", "Austria 47.516200 14.550100 0 0 0 \n", "Azerbaijan 40.143100 47.576900 0 0 0 \n", "Bahamas 25.025885 -78.035889 0 0 0 \n", "Bahrain 26.027500 50.550000 0 0 0 \n", "Bangladesh 23.685000 90.356300 0 0 0 \n", "Barbados 13.193900 -59.543200 0 0 0 \n", "Belarus 53.709800 27.953400 0 0 0 \n", "Belgium 50.833300 4.469936 0 0 0 \n", "Belize 17.189900 -88.497600 0 0 0 \n", "Benin 9.307700 2.315800 0 0 0 \n", "Bhutan 27.514200 90.433600 0 0 0 \n", "Bolivia -16.290200 -63.588700 0 0 0 \n", "Bosnia and Herzegovina 43.915900 17.679100 0 0 0 \n", "... ... ... ... ... ... \n", "Tunisia 33.886917 9.537499 0 0 0 \n", "Turkey 38.963700 35.243300 0 0 0 \n", "US 40.000000 -100.000000 1 1 2 \n", "Uganda 1.373333 32.290275 0 0 0 \n", "Ukraine 48.379400 31.165600 0 0 0 \n", "United Arab Emirates 23.424076 53.847818 0 0 0 \n", "United Kingdom 18.220600 -63.068600 0 0 0 \n", "United Kingdom 32.307800 -64.750500 0 0 0 \n", "United Kingdom 18.420700 -64.640000 0 0 0 \n", "United Kingdom 19.313300 -81.254600 0 0 0 \n", "United Kingdom 49.372300 -2.364400 0 0 0 \n", "United Kingdom -51.796300 -59.523600 0 0 0 \n", "United Kingdom 36.140800 -5.353600 0 0 0 \n", "United Kingdom 49.448196 -2.589490 0 0 0 \n", "United Kingdom 54.236100 -4.548100 0 0 0 \n", "United Kingdom 49.213800 -2.135800 0 0 0 \n", "United Kingdom 16.742498 -62.187366 0 0 0 \n", "United Kingdom -7.946700 -14.355900 0 0 0 \n", "United Kingdom 21.694000 -71.797900 0 0 0 \n", "United Kingdom 55.378100 -3.436000 0 0 0 \n", "Uruguay -32.522800 -55.765800 0 0 0 \n", "Uzbekistan 41.377491 64.585262 0 0 0 \n", "Vanuatu -15.376700 166.959200 0 0 0 \n", "Venezuela 6.423800 -66.589700 0 0 0 \n", "Vietnam 14.058324 108.277199 0 2 2 \n", "West Bank and Gaza 31.952200 35.233200 0 0 0 \n", "Winter Olympics 2022 39.904200 116.407400 0 0 0 \n", "Yemen 15.552727 48.516388 0 0 0 \n", "Zambia -13.133897 27.849332 0 0 0 \n", "Zimbabwe -19.015438 29.154857 0 0 0 \n", "\n", " 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", "Country/Region \n", "Afghanistan 0 0 0 0 \n", "Albania 0 0 0 0 \n", "Algeria 0 0 0 0 \n", "Andorra 0 0 0 0 \n", "Angola 0 0 0 0 \n", "Antarctica 0 0 0 0 \n", "Antigua and Barbuda 0 0 0 0 \n", "Argentina 0 0 0 0 \n", "Armenia 0 0 0 0 \n", "Australia 0 0 0 0 \n", "Australia 0 3 4 4 \n", "Australia 0 0 0 0 \n", "Australia 0 0 0 0 \n", "Australia 0 0 0 0 \n", "Australia 0 0 0 0 \n", "Australia 0 1 1 1 \n", "Australia 0 0 0 0 \n", "Austria 0 0 0 0 \n", "Azerbaijan 0 0 0 0 \n", "Bahamas 0 0 0 0 \n", "Bahrain 0 0 0 0 \n", "Bangladesh 0 0 0 0 \n", "Barbados 0 0 0 0 \n", "Belarus 0 0 0 0 \n", "Belgium 0 0 0 0 \n", "Belize 0 0 0 0 \n", "Benin 0 0 0 0 \n", "Bhutan 0 0 0 0 \n", "Bolivia 0 0 0 0 \n", "Bosnia and Herzegovina 0 0 0 0 \n", "... ... ... ... ... \n", "Tunisia 0 0 0 0 \n", "Turkey 0 0 0 0 \n", "US 2 5 5 5 \n", "Uganda 0 0 0 0 \n", "Ukraine 0 0 0 0 \n", "United Arab Emirates 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "United Kingdom 0 0 0 0 \n", "Uruguay 0 0 0 0 \n", "Uzbekistan 0 0 0 0 \n", "Vanuatu 0 0 0 0 \n", "Venezuela 0 0 0 0 \n", "Vietnam 2 2 2 2 \n", "West Bank and Gaza 0 0 0 0 \n", "Winter Olympics 2022 0 0 0 0 \n", "Yemen 0 0 0 0 \n", "Zambia 0 0 0 0 \n", "Zimbabwe 0 0 0 0 \n", "\n", " ... 5/1/22 5/2/22 5/3/22 \\\n", "Country/Region ... \n", "Afghanistan ... 178899 178901 178901 \n", "Albania ... 275167 275177 275191 \n", "Algeria ... 265782 265782 265782 \n", "Andorra ... 41349 41349 41349 \n", "Angola ... 99287 99287 99287 \n", "Antarctica ... 11 11 11 \n", "Antigua and Barbuda ... 7626 7626 7626 \n", "Argentina ... 9083673 9083673 9083673 \n", "Armenia ... 422871 422874 422874 \n", "Australia ... 105739 106705 107769 \n", "Australia ... 2294900 2304435 2316269 \n", "Australia ... 63568 64151 64540 \n", "Australia ... 1002257 1007459 1015120 \n", "Australia ... 413321 416371 416371 \n", "Australia ... 137359 138395 139468 \n", "Australia ... 1576945 1586876 1597464 \n", "Australia ... 398270 405848 415563 \n", "Austria ... 4144906 4149004 4154244 \n", "Azerbaijan ... 792578 792579 792585 \n", "Bahamas ... 33578 33582 33601 \n", "Bahrain ... 569091 569370 569727 \n", "Bangladesh ... 1952716 1952726 1952733 \n", "Barbados ... 70449 70793 71127 \n", "Belarus ... 980345 980345 980868 \n", "Belgium ... 4056448 4071279 4071279 \n", "Belize ... 57497 57497 57535 \n", "Benin ... 26952 26952 26952 \n", "Bhutan ... 59219 59247 59274 \n", "Bolivia ... 905074 905074 905097 \n", "Bosnia and Herzegovina ... 377087 377087 377145 \n", "... ... ... ... ... \n", "Tunisia ... 1040193 1040193 1040193 \n", "Turkey ... 15033573 15034917 15036110 \n", "US ... 81364718 81458023 81506402 \n", "Uganda ... 164084 164104 164104 \n", "Ukraine ... 5040518 5040518 5040518 \n", "United Arab Emirates ... 898811 899033 899242 \n", "United Kingdom ... 2788 2788 2788 \n", "United Kingdom ... 13488 13559 13559 \n", "United Kingdom ... 6355 6355 6355 \n", "United Kingdom ... 22223 22223 22223 \n", "United Kingdom ... 0 0 0 \n", "United Kingdom ... 177 181 181 \n", "United Kingdom ... 17837 17837 17837 \n", "United Kingdom ... 24573 24573 24573 \n", "United Kingdom ... 32205 32205 32205 \n", "United Kingdom ... 49778 49810 49810 \n", "United Kingdom ... 273 273 273 \n", "United Kingdom ... 4 4 4 \n", "United Kingdom ... 5963 5963 5995 \n", "United Kingdom ... 22038340 22038340 22073858 \n", "Uruguay ... 897867 897867 899723 \n", "Uzbekistan ... 238596 238627 238628 \n", "Vanuatu ... 7507 7514 7533 \n", "Venezuela ... 522434 522434 522514 \n", "Vietnam ... 10653526 10656649 10659358 \n", "West Bank and Gaza ... 657060 657060 657060 \n", "Winter Olympics 2022 ... 535 535 535 \n", "Yemen ... 11818 11819 11819 \n", "Zambia ... 319733 319755 319755 \n", "Zimbabwe ... 247911 247935 247990 \n", "\n", " 5/4/22 5/5/22 5/6/22 5/7/22 5/8/22 \\\n", "Country/Region \n", "Afghanistan 178901 178905 178919 178922 178981 \n", "Albania 275211 275266 275310 275341 275366 \n", "Algeria 265782 265786 265791 265794 265798 \n", "Andorra 41717 41717 41717 41717 41717 \n", "Angola 99287 99287 99287 99287 99287 \n", "Antarctica 11 11 11 11 11 \n", "Antigua and Barbuda 7654 7663 7663 7663 7663 \n", "Argentina 9083673 9083673 9083673 9083673 9101319 \n", "Armenia 422877 422877 422877 422877 422877 \n", "Australia 108830 109879 110846 111599 112407 \n", "Australia 2334360 2345156 2356642 2365484 2373235 \n", "Australia 64983 64983 65659 65881 66083 \n", "Australia 1023149 1030172 1036786 1041450 1046200 \n", "Australia 424182 427205 424182 430466 436318 \n", "Australia 140554 141647 142541 143267 144107 \n", "Australia 1609060 1619296 1628531 1637135 1647028 \n", "Australia 425704 433295 442481 451192 461553 \n", "Austria 4161004 4166759 4172760 4177928 4181545 \n", "Azerbaijan 792585 792599 792607 792618 792630 \n", "Bahamas 33626 33655 33672 33695 33695 \n", "Bahrain 570103 570761 571441 572082 572810 \n", "Bangladesh 1952743 1952747 1952766 1952776 1952799 \n", "Barbados 71127 72282 72743 73198 73592 \n", "Belarus 981070 981259 981838 981838 982331 \n", "Belgium 4071279 4088336 4088336 4088336 4088336 \n", "Belize 57561 57578 57612 57612 57612 \n", "Benin 26952 26952 26952 26952 26952 \n", "Bhutan 59358 59422 59450 59483 59497 \n", "Bolivia 905150 905212 905355 905428 905453 \n", "Bosnia and Herzegovina 377175 377229 377229 377276 377276 \n", "... ... ... ... ... ... \n", "Tunisia 1040712 1040712 1040712 1040712 1040712 \n", "Turkey 15037242 15038495 15040238 15041899 15043379 \n", "US 81620503 81707601 81831608 81858498 81863479 \n", "Uganda 164104 164104 164118 164118 164118 \n", "Ukraine 5040518 5040518 5040518 5040518 5040518 \n", "United Arab Emirates 899441 899637 899835 900026 900251 \n", "United Kingdom 2788 2788 2788 2788 2788 \n", "United Kingdom 13832 13832 13832 13832 13832 \n", "United Kingdom 6355 6355 6467 6467 6467 \n", "United Kingdom 22828 22828 22828 22828 22828 \n", "United Kingdom 0 0 0 0 0 \n", "United Kingdom 197 197 392 392 392 \n", "United Kingdom 17837 17996 17996 17996 17996 \n", "United Kingdom 24832 24832 24832 24832 24832 \n", "United Kingdom 32424 32424 32424 32424 32424 \n", "United Kingdom 49810 49810 50033 50033 50033 \n", "United Kingdom 371 452 452 493 493 \n", "United Kingdom 4 4 4 4 4 \n", "United Kingdom 5995 5995 5995 5995 5995 \n", "United Kingdom 22090523 22102983 22114034 22114034 22114034 \n", "Uruguay 899723 899723 899723 899723 899723 \n", "Uzbekistan 238636 238651 238660 238695 238695 \n", "Vanuatu 7622 7699 7734 7734 7785 \n", "Venezuela 522564 522587 522625 522672 522749 \n", "Vietnam 10662446 10666751 10670570 10673915 10676184 \n", "West Bank and Gaza 657060 657060 657060 657060 657060 \n", "Winter Olympics 2022 535 535 535 535 535 \n", "Yemen 11819 11819 11819 11819 11819 \n", "Zambia 319755 319755 319755 320099 320099 \n", "Zimbabwe 248050 248050 248214 248214 248352 \n", "\n", " 5/9/22 country \n", "Country/Region \n", "Afghanistan 179010 Afghanistan \n", "Albania 275372 Albania \n", "Algeria 265800 Algeria \n", "Andorra 41717 Andorra \n", "Angola 99287 Angola \n", "Antarctica 11 Antarctica \n", "Antigua and Barbuda 7663 Antigua and Barbuda \n", "Argentina 9101319 Argentina \n", "Armenia 422896 Armenia \n", "Australia 113368 Australia \n", "Australia 2383412 Australia \n", "Australia 66427 Australia \n", "Australia 1052748 Australia \n", "Australia 440223 Australia \n", "Australia 145182 Australia \n", "Australia 1659695 Australia \n", "Australia 473909 Australia \n", "Austria 4185678 Austria \n", "Azerbaijan 792632 Azerbaijan \n", "Bahamas 33748 Bahamas \n", "Bahrain 573757 Bahrain \n", "Bangladesh 1952829 Bangladesh \n", "Barbados 73856 Barbados \n", "Belarus 982331 Belarus \n", "Belgium 4102082 Belgium \n", "Belize 57675 Belize \n", "Benin 26952 Benin \n", "Bhutan 59512 Bhutan \n", "Bolivia 905453 Bolivia \n", "Bosnia and Herzegovina 377345 Bosnia and Herzegovina \n", "... ... ... \n", "Tunisia 1040712 Tunisia \n", "Turkey 15044921 Turkey \n", "US 81973661 US \n", "Uganda 164149 Uganda \n", "Ukraine 5040518 Ukraine \n", "United Arab Emirates 900484 United Arab Emirates \n", "United Kingdom 2788 United Kingdom \n", "United Kingdom 13832 United Kingdom \n", "United Kingdom 6467 United Kingdom \n", "United Kingdom 22828 United Kingdom \n", "United Kingdom 0 United Kingdom \n", "United Kingdom 392 United Kingdom \n", "United Kingdom 17996 United Kingdom \n", "United Kingdom 24832 United Kingdom \n", "United Kingdom 32424 United Kingdom \n", "United Kingdom 50033 United Kingdom \n", "United Kingdom 493 United Kingdom \n", "United Kingdom 4 United Kingdom \n", "United Kingdom 5995 United Kingdom \n", "United Kingdom 22140287 United Kingdom \n", "Uruguay 899723 Uruguay \n", "Uzbekistan 238736 Uzbekistan \n", "Vanuatu 8056 Vanuatu \n", "Venezuela 522749 Venezuela \n", "Vietnam 10678359 Vietnam \n", "West Bank and Gaza 657193 West Bank and Gaza \n", "Winter Olympics 2022 535 Winter Olympics 2022 \n", "Yemen 11819 Yemen \n", "Zambia 320157 Zambia \n", "Zimbabwe 248536 Zimbabwe \n", "\n", "[284 rows x 843 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = raw_data.set_index('Country/Region').sort_index()\n", "sorted_data" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mbelgium\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Belgium'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'1/22/20'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbelgium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "source": [ "belgium = sorted_data.loc['Belgium', '1/22/20':]\n", "belgium.plot()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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..................................................................
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843 rows × 284 columns

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657060 \n", "5/9/22 8056 522749 10678359 657193 \n", "country Vanuatu Venezuela Vietnam West Bank and Gaza \n", "\n", "Country/Region Winter Olympics 2022 Yemen Zambia Zimbabwe \n", "Province/State NaN NaN NaN NaN \n", "Lat 39.9042 15.5527 -13.1339 -19.0154 \n", "Long 116.407 48.5164 27.8493 29.1549 \n", "1/22/20 0 0 0 0 \n", "1/23/20 0 0 0 0 \n", "1/24/20 0 0 0 0 \n", "1/25/20 0 0 0 0 \n", "1/26/20 0 0 0 0 \n", "1/27/20 0 0 0 0 \n", "1/28/20 0 0 0 0 \n", "1/29/20 0 0 0 0 \n", "1/30/20 0 0 0 0 \n", "1/31/20 0 0 0 0 \n", "2/1/20 0 0 0 0 \n", "2/2/20 0 0 0 0 \n", "2/3/20 0 0 0 0 \n", "2/4/20 0 0 0 0 \n", "2/5/20 0 0 0 0 \n", "2/6/20 0 0 0 0 \n", "2/7/20 0 0 0 0 \n", "2/8/20 0 0 0 0 \n", "2/9/20 0 0 0 0 \n", "2/10/20 0 0 0 0 \n", "2/11/20 0 0 0 0 \n", "2/12/20 0 0 0 0 \n", "2/13/20 0 0 0 0 \n", "2/14/20 0 0 0 0 \n", "2/15/20 0 0 0 0 \n", "2/16/20 0 0 0 0 \n", "2/17/20 0 0 0 0 \n", "... ... ... ... ... \n", "4/11/22 535 11814 317804 247010 \n", "4/12/22 535 11814 317804 247094 \n", "4/13/22 535 11815 318113 247160 \n", "4/14/22 535 11815 318229 247208 \n", "4/15/22 535 11817 318404 247237 \n", "4/16/22 535 11817 318467 247237 \n", "4/17/22 535 11817 318515 247278 \n", "4/18/22 535 11818 318515 247297 \n", "4/19/22 535 11818 318640 247297 \n", "4/20/22 535 11818 318640 247336 \n", "4/21/22 535 11818 318984 247383 \n", "4/22/22 535 11818 318984 247488 \n", "4/23/22 535 11818 318984 247524 \n", "4/24/22 535 11818 318984 247524 \n", "4/25/22 535 11818 319316 247598 \n", "4/26/22 535 11818 319431 247667 \n", "4/27/22 535 11818 319431 247729 \n", "4/28/22 535 11818 319594 247784 \n", "4/29/22 535 11818 319594 247842 \n", "4/30/22 535 11818 319594 247875 \n", "5/1/22 535 11818 319733 247911 \n", "5/2/22 535 11819 319755 247935 \n", "5/3/22 535 11819 319755 247990 \n", "5/4/22 535 11819 319755 248050 \n", "5/5/22 535 11819 319755 248050 \n", "5/6/22 535 11819 319755 248214 \n", "5/7/22 535 11819 320099 248214 \n", "5/8/22 535 11819 320099 248352 \n", "5/9/22 535 11819 320157 248536 \n", "country Winter Olympics 2022 Yemen Zambia Zimbabwe \n", "\n", "[843 rows x 284 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prova = sorted_data.T\n", "prova" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprova\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Belgium'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "source": [ "prova['Belgium'][-20:].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }