Kernel: Python 3 (system-wide)
SARS-CoV-19 Corona Virus Data
Working with some datasets from Kaggle on CoCalc
https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset
Download covid_19_data.csv
Better: adjust all below to work with https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_daily_reports/
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SNo | time | state | country | lastupdate | Confirmed | Deaths | Recovered | |
---|---|---|---|---|---|---|---|---|
4242 | 4243 | 2020-03-08 | Northern Territory | Australia | 2020-03-06 04:33:03 | 0.0 | 0.0 | 0.0 |
4243 | 4244 | 2020-03-08 | Lackland, TX (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 |
4244 | 4245 | 2020-03-08 | Montgomery County, TX | US | 2020-03-07 19:53:02 | 0.0 | 0.0 | 0.0 |
4245 | 4246 | 2020-03-08 | Omaha, NE (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 |
4246 | 4247 | 2020-03-08 | Travis, CA (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 |
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SNo int64
time datetime64[ns]
state category
country category
lastupdate datetime64[ns]
Confirmed float64
Deaths float64
Recovered float64
dtype: object
"sick" are those, who are neither dead nor are recovered
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"deathrate" based on confirmed cases
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filter latest data only!
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SNo | time | state | country | lastupdate | Confirmed | Deaths | Recovered | sick | deathrate | recrate | |
---|---|---|---|---|---|---|---|---|---|---|---|
3992 | 3993 | 2020-03-08 | Hubei | Mainland China | 2020-03-08 14:43:03 | 67707.0 | 2986.0 | 45235.0 | 19486.0 | 4.410179 | 66.809931 |
3993 | 3994 | 2020-03-08 | NaN | Italy | 2020-03-08 18:03:04 | 7375.0 | 366.0 | 622.0 | 6387.0 | 4.962712 | 8.433898 |
3994 | 3995 | 2020-03-08 | NaN | South Korea | 2020-03-08 12:53:03 | 7314.0 | 50.0 | 118.0 | 7146.0 | 0.683620 | 1.613344 |
3995 | 3996 | 2020-03-08 | NaN | Iran | 2020-03-08 11:03:30 | 6566.0 | 194.0 | 2134.0 | 4238.0 | 2.954615 | 32.500761 |
3996 | 3997 | 2020-03-08 | Guangdong | Mainland China | 2020-03-08 14:43:03 | 1352.0 | 7.0 | 1256.0 | 89.0 | 0.517751 | 92.899408 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
4242 | 4243 | 2020-03-08 | Northern Territory | Australia | 2020-03-06 04:33:03 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4243 | 4244 | 2020-03-08 | Lackland, TX (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4244 | 4245 | 2020-03-08 | Montgomery County, TX | US | 2020-03-07 19:53:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4245 | 4246 | 2020-03-08 | Omaha, NE (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4246 | 4247 | 2020-03-08 | Travis, CA (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
255 rows × 11 columns
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SNo | Confirmed | Deaths | Recovered | sick | deathrate | recrate | |
---|---|---|---|---|---|---|---|
count | 255.000000 | 255.00000 | 255.000000 | 255.000000 | 255.000000 | 249.000000 | 249.000000 |
mean | 4120.000000 | 430.72549 | 14.913725 | 238.019608 | 177.792157 | 1.320539 | 16.819022 |
std | 73.756356 | 4302.71234 | 188.609605 | 2838.177099 | 1382.094730 | 7.645967 | 33.098950 |
min | 3993.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
25% | 4056.500000 | 1.00000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 |
50% | 4120.000000 | 4.00000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 |
75% | 4183.500000 | 35.50000 | 0.000000 | 1.000000 | 16.500000 | 0.000000 | 10.000000 |
max | 4247.000000 | 67707.00000 | 2986.000000 | 45235.000000 | 19486.000000 | 100.000000 | 100.000000 |
death rate stats, where at least a significant amount of patients are known …
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3.0
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count 20.000000
mean 3.243667
std 4.624665
min 0.392927
25% 0.844441
50% 1.799359
75% 3.108818
max 20.481928
Name: deathrate, dtype: float64
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[<matplotlib.lines.Line2D at 0x7f09f9525240>]
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SNo | time | state | country | lastupdate | Confirmed | Deaths | Recovered | sick | deathrate | recrate | |
---|---|---|---|---|---|---|---|---|---|---|---|
3992 | 3993 | 2020-03-08 | Hubei | Mainland China | 2020-03-08 14:43:03 | 67707.0 | 2986.0 | 45235.0 | 19486.0 | 4.410179 | 66.809931 |
3993 | 3994 | 2020-03-08 | NaN | Italy | 2020-03-08 18:03:04 | 7375.0 | 366.0 | 622.0 | 6387.0 | 4.962712 | 8.433898 |
3995 | 3996 | 2020-03-08 | NaN | Iran | 2020-03-08 11:03:30 | 6566.0 | 194.0 | 2134.0 | 4238.0 | 2.954615 | 32.500761 |
3994 | 3995 | 2020-03-08 | NaN | South Korea | 2020-03-08 12:53:03 | 7314.0 | 50.0 | 118.0 | 7146.0 | 0.683620 | 1.613344 |
3997 | 3998 | 2020-03-08 | Henan | Mainland China | 2020-03-08 05:03:02 | 1272.0 | 22.0 | 1247.0 | 3.0 | 1.729560 | 98.034591 |
3999 | 4000 | 2020-03-08 | NaN | France | 2020-03-08 18:03:04 | 1126.0 | 19.0 | 12.0 | 1095.0 | 1.687389 | 1.065719 |
4037 | 4038 | 2020-03-08 | King County, WA | US | 2020-03-08 20:23:09 | 83.0 | 17.0 | 1.0 | 65.0 | 20.481928 | 1.204819 |
4006 | 4007 | 2020-03-08 | NaN | Spain | 2020-03-08 20:33:02 | 673.0 | 17.0 | 30.0 | 626.0 | 2.526003 | 4.457652 |
4011 | 4012 | 2020-03-08 | Heilongjiang | Mainland China | 2020-03-08 14:43:03 | 481.0 | 13.0 | 412.0 | 56.0 | 2.702703 | 85.654886 |
4012 | 4013 | 2020-03-08 | Beijing | Mainland China | 2020-03-08 01:23:07 | 428.0 | 8.0 | 308.0 | 112.0 | 1.869159 | 71.962617 |
3996 | 3997 | 2020-03-08 | Guangdong | Mainland China | 2020-03-08 14:43:03 | 1352.0 | 7.0 | 1256.0 | 89.0 | 0.517751 | 92.899408 |
4008 | 4009 | 2020-03-08 | Chongqing | Mainland China | 2020-03-08 23:23:03 | 576.0 | 6.0 | 527.0 | 43.0 | 1.041667 | 91.493056 |
4015 | 4016 | 2020-03-08 | Hebei | Mainland China | 2020-03-07 13:03:05 | 318.0 | 6.0 | 307.0 | 5.0 | 1.886792 | 96.540881 |
4025 | 4026 | 2020-03-08 | Hainan | Mainland China | 2020-03-08 04:33:02 | 168.0 | 6.0 | 159.0 | 3.0 | 3.571429 | 94.642857 |
4010 | 4011 | 2020-03-08 | NaN | Japan | 2020-03-08 14:53:11 | 502.0 | 6.0 | 76.0 | 420.0 | 1.195219 | 15.139442 |
4004 | 4005 | 2020-03-08 | Shandong | Mainland China | 2020-03-08 10:03:11 | 758.0 | 6.0 | 642.0 | 110.0 | 0.791557 | 84.696570 |
4005 | 4006 | 2020-03-08 | Diamond Princess cruise ship | Others | 2020-03-06 01:29:39 | 696.0 | 6.0 | 40.0 | 650.0 | 0.862069 | 5.747126 |
4044 | 4045 | 2020-03-08 | NaN | Iraq | 2020-03-08 21:03:03 | 60.0 | 6.0 | 0.0 | 54.0 | 10.000000 | 0.000000 |
4002 | 4003 | 2020-03-08 | Anhui | Mainland China | 2020-03-08 05:13:06 | 990.0 | 6.0 | 984.0 | 0.0 | 0.606061 | 99.393939 |
4001 | 4002 | 2020-03-08 | Hunan | Mainland China | 2020-03-08 12:53:03 | 1018.0 | 4.0 | 968.0 | 46.0 | 0.392927 | 95.088409 |
Situation in Italy
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<matplotlib.axes._subplots.AxesSubplot at 0x7f09f90971d0>
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<matplotlib.axes._subplots.AxesSubplot at 0x7f09f8fc2828>
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<matplotlib.axes._subplots.AxesSubplot at 0x7f09f8f066a0>
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outside china
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SNo | time | state | country | lastupdate | Confirmed | Deaths | Recovered | sick | deathrate | recrate | |
---|---|---|---|---|---|---|---|---|---|---|---|
3993 | 3994 | 2020-03-08 | NaN | Italy | 2020-03-08 18:03:04 | 7375.0 | 366.0 | 622.0 | 6387.0 | 4.962712 | 8.433898 |
3994 | 3995 | 2020-03-08 | NaN | South Korea | 2020-03-08 12:53:03 | 7314.0 | 50.0 | 118.0 | 7146.0 | 0.683620 | 1.613344 |
3995 | 3996 | 2020-03-08 | NaN | Iran | 2020-03-08 11:03:30 | 6566.0 | 194.0 | 2134.0 | 4238.0 | 2.954615 | 32.500761 |
3999 | 4000 | 2020-03-08 | NaN | France | 2020-03-08 18:03:04 | 1126.0 | 19.0 | 12.0 | 1095.0 | 1.687389 | 1.065719 |
4000 | 4001 | 2020-03-08 | NaN | Germany | 2020-03-08 21:03:03 | 1040.0 | 0.0 | 18.0 | 1022.0 | 0.000000 | 1.730769 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
4242 | 4243 | 2020-03-08 | Northern Territory | Australia | 2020-03-06 04:33:03 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4243 | 4244 | 2020-03-08 | Lackland, TX (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4244 | 4245 | 2020-03-08 | Montgomery County, TX | US | 2020-03-07 19:53:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4245 | 4246 | 2020-03-08 | Omaha, NE (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
4246 | 4247 | 2020-03-08 | Travis, CA (From Diamond Princess) | US | 2020-02-24 23:33:02 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN |
224 rows × 11 columns
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Confirmed | Deaths | Recovered | sick | |
---|---|---|---|---|
country | ||||
Italy | 7375.0 | 366.0 | 622.0 | 6387.0 |
Iran | 6566.0 | 194.0 | 2134.0 | 4238.0 |
South Korea | 7314.0 | 50.0 | 118.0 | 7146.0 |
US | 537.0 | 21.0 | 8.0 | 508.0 |
France | 1126.0 | 19.0 | 12.0 | 1095.0 |
Spain | 673.0 | 17.0 | 30.0 | 626.0 |
Iraq | 60.0 | 6.0 | 0.0 | 54.0 |
Japan | 502.0 | 6.0 | 76.0 | 420.0 |
Others | 696.0 | 6.0 | 40.0 | 650.0 |
Australia | 76.0 | 4.0 | 21.0 | 51.0 |
Hong Kong | 114.0 | 3.0 | 58.0 | 53.0 |
UK | 273.0 | 3.0 | 18.0 | 252.0 |
Netherlands | 265.0 | 3.0 | 0.0 | 262.0 |
Switzerland | 337.0 | 2.0 | 3.0 | 332.0 |
Philippines | 10.0 | 1.0 | 1.0 | 8.0 |
Egypt | 49.0 | 1.0 | 1.0 | 47.0 |
Taiwan | 45.0 | 1.0 | 13.0 | 31.0 |
Thailand | 50.0 | 1.0 | 31.0 | 18.0 |
Argentina | 12.0 | 1.0 | 0.0 | 11.0 |
San Marino | 36.0 | 1.0 | 0.0 | 35.0 |
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Confirmed | Deaths | Recovered | sick | |
---|---|---|---|---|
lastupdate | ||||
2020-01-22 17:00:00 | 3.0 | 0.0 | 0.0 | 3.0 |
2020-01-23 17:00:00 | 6.0 | 0.0 | 0.0 | 6.0 |
2020-01-24 17:00:00 | 9.0 | 0.0 | 0.0 | 9.0 |
2020-01-25 17:00:00 | 12.0 | 0.0 | 0.0 | 12.0 |
2020-01-26 16:00:00 | 23.0 | 0.0 | 0.0 | 23.0 |
... | ... | ... | ... | ... |
2020-03-08 21:23:03 | 44.0 | 0.0 | 1.0 | 43.0 |
2020-03-08 21:33:02 | 30.0 | 0.0 | 3.0 | 27.0 |
2020-03-08 21:43:03 | 55.0 | 1.0 | 0.0 | 54.0 |
2020-03-08 21:53:03 | 1.0 | 0.0 | 0.0 | 1.0 |
2020-03-08 21:53:04 | 5.0 | 0.0 | 0.0 | 5.0 |
294 rows × 4 columns
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<matplotlib.axes._subplots.AxesSubplot at 0x7f09f8da3208>
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