Covid-19 Year End 2021 Analysis

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

url_C = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
confirmed = pd.read_csv(url_C)
url_D = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
death = pd.read_csv(url_D)
url_R = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'
recover = pd.read_csv(url_R)

confirmed.head(2)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	12/21/21	12/22/21	12/23/21	12/24/21	12/25/21	12/26/21	12/27/21	12/28/21	12/29/21	12/30/21
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	157816	157841	157878	157887	157895	157951	157967	157998	158037	158056
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	206273	206616	206935	207221	207542	207709	207709	208352	208899	208899

2 rows × 713 columns

death.head(2)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	12/21/21	12/22/21	12/23/21	12/24/21	12/25/21	12/26/21	12/27/21	12/28/21	12/29/21	12/30/21
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	7335	7341	7346	7348	7349	7354	7354	7355	7356	7356
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	3178	3180	3181	3187	3189	3194	3194	3207	3212	3212

2 rows × 713 columns

recover.head(2)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	12/21/21	12/22/21	12/23/21	12/24/21	12/25/21	12/26/21	12/27/21	12/28/21	12/29/21	12/30/21
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

2 rows × 713 columns

confirmed.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 280 entries, 0 to 279
Columns: 713 entries, Province/State to 12/30/21
dtypes: float64(2), int64(709), object(2)
memory usage: 1.5+ MB

confirmed.describe()

	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	...	12/21/21	12/22/21	12/23/21	12/24/21	12/25/21	12/26/21	12/27/21	12/28/21	12/29/21	12/30/21
count	278.000000	278.000000	280.000000	280.000000	280.000000	280.000000	280.000000	280.000000	280.000000	280.000000	...	2.800000e+02	2.800000e+02	2.800000e+02	2.800000e+02	2.800000e+02	2.800000e+02	2.800000e+02	2.800000e+02	2.800000e+02	2.800000e+02
mean	20.156042	21.788955	1.989286	2.339286	3.360714	5.121429	7.564286	10.453571	19.921429	22.025000	...	9.867638e+05	9.899810e+05	9.934898e+05	9.964687e+05	9.982992e+05	9.999857e+05	1.005308e+06	1.010000e+06	1.016181e+06	1.023357e+06
std	25.283318	76.200169	26.590143	26.687678	33.225879	46.244243	64.627991	87.077220	213.666694	214.980193	...	4.159908e+06	4.173259e+06	4.187721e+06	4.200353e+06	4.204475e+06	4.213582e+06	4.241047e+06	4.261903e+06	4.289039e+06	4.323975e+06
min	-51.796300	-178.116500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	4.643279	-37.713675	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	3.206500e+03	3.221500e+03	3.235750e+03	3.377750e+03	3.517750e+03	3.675250e+03	3.676500e+03	3.700750e+03	3.754000e+03	3.838750e+03
50%	21.517170	20.921188	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	4.612550e+04	4.620600e+04	4.623800e+04	4.623800e+04	4.623800e+04	4.631300e+04	4.673200e+04	4.685700e+04	4.707450e+04	4.707450e+04
75%	40.393350	84.992575	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	4.542255e+05	4.551728e+05	4.560768e+05	4.569805e+05	4.577965e+05	4.582998e+05	4.589572e+05	4.603170e+05	4.617822e+05	4.631392e+05
max	71.706900	178.065000	444.000000	444.000000	549.000000	761.000000	1058.000000	1423.000000	3554.000000	3554.000000	...	5.131243e+07	5.155348e+07	5.181482e+07	5.204195e+07	5.209891e+07	5.228085e+07	5.279341e+07	5.317042e+07	5.365969e+07	5.430676e+07

8 rows × 711 columns

confirmed.shape

(280, 713)

confirmed.columns

Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',
       '1/24/20', '1/25/20', '1/26/20', '1/27/20',
       ...
       '12/21/21', '12/22/21', '12/23/21', '12/24/21', '12/25/21', '12/26/21',
       '12/27/21', '12/28/21', '12/29/21', '12/30/21'],
      dtype='object', length=713)

confirmed.value_counts()

Province/State    Country/Region  Lat       Long       1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  1/29/20  1/30/20  1/31/20  2/1/20  2/2/20  2/3/20  2/4/20  2/5/20  2/6/20  2/7/20  2/8/20  2/9/20  2/10/20  2/11/20  2/12/20  2/13/20  2/14/20  2/15/20  2/16/20  2/17/20  2/18/20  2/19/20  2/20/20  2/21/20  2/22/20  2/23/20  2/24/20  2/25/20  2/26/20  2/27/20  2/28/20  2/29/20  3/1/20  3/2/20  3/3/20  3/4/20  3/5/20  3/6/20  3/7/20  3/8/20  3/9/20  3/10/20  3/11/20  3/12/20  3/13/20  3/14/20  3/15/20  3/16/20  3/17/20  3/18/20  3/19/20  3/20/20  3/21/20  3/22/20  3/23/20  3/24/20  3/25/20  3/26/20  3/27/20  3/28/20  3/29/20  3/30/20  3/31/20  4/1/20  4/2/20  4/3/20  4/4/20  4/5/20  4/6/20  4/7/20  4/8/20  4/9/20  4/10/20  4/11/20  4/12/20  4/13/20  4/14/20  4/15/20  4/16/20  4/17/20  4/18/20  4/19/20  4/20/20  4/21/20  4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  5/1/20  5/2/20  5/3/20  5/4/20  5/5/20  5/6/20  5/7/20  5/8/20  5/9/20  5/10/20  5/11/20  5/12/20  5/13/20  5/14/20  5/15/20  5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  5/25/20  5/26/20  5/27/20  5/28/20  5/29/20  5/30/20  5/31/20  6/1/20  6/2/20  6/3/20  6/4/20  6/5/20  6/6/20  6/7/20  6/8/20  6/9/20  6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  6/16/20  6/17/20  6/18/20  6/19/20  6/20/20  6/21/20  6/22/20  6/23/20  6/24/20  6/25/20  6/26/20  6/27/20  6/28/20  6/29/20  6/30/20  7/1/20  7/2/20  7/3/20  7/4/20  7/5/20  7/6/20  7/7/20  7/8/20  7/9/20  7/10/20  7/11/20  7/12/20  7/13/20  7/14/20  7/15/20  7/16/20  7/17/20  7/18/20  7/19/20  7/20/20  7/21/20  7/22/20  7/23/20  7/24/20  7/25/20  7/26/20  7/27/20  7/28/20  7/29/20  7/30/20  7/31/20  8/1/20  8/2/20  8/3/20  8/4/20  8/5/20  8/6/20  8/7/20  8/8/20  8/9/20  8/10/20  8/11/20  8/12/20  8/13/20  8/14/20  8/15/20  8/16/20  8/17/20  8/18/20  8/19/20  8/20/20  8/21/20  8/22/20  8/23/20  8/24/20  8/25/20  8/26/20  8/27/20  8/28/20  8/29/20  8/30/20  8/31/20  9/1/20  9/2/20  9/3/20  9/4/20  9/5/20  9/6/20  9/7/20  9/8/20  9/9/20  9/10/20  9/11/20  9/12/20  9/13/20  9/14/20  9/15/20  9/16/20  9/17/20  9/18/20  9/19/20  9/20/20  9/21/20  9/22/20  9/23/20  9/24/20  9/25/20  9/26/20  9/27/20  9/28/20  9/29/20  9/30/20  10/1/20  10/2/20  10/3/20  10/4/20  10/5/20  10/6/20  10/7/20  10/8/20  10/9/20  10/10/20  10/11/20  10/12/20  10/13/20  10/14/20  10/15/20  10/16/20  10/17/20  10/18/20  10/19/20  10/20/20  10/21/20  10/22/20  10/23/20  10/24/20  10/25/20  10/26/20  10/27/20  10/28/20  10/29/20  10/30/20  10/31/20  11/1/20  11/2/20  11/3/20  11/4/20  11/5/20  11/6/20  11/7/20  11/8/20  11/9/20  11/10/20  11/11/20  11/12/20  11/13/20  11/14/20  11/15/20  11/16/20  11/17/20  11/18/20  11/19/20  11/20/20  11/21/20  11/22/20  11/23/20  11/24/20  11/25/20  11/26/20  11/27/20  11/28/20  11/29/20  11/30/20  12/1/20  12/2/20  12/3/20  12/4/20  12/5/20  12/6/20  12/7/20  12/8/20  12/9/20  12/10/20  12/11/20  12/12/20  12/13/20  12/14/20  12/15/20  12/16/20  12/17/20  12/18/20  12/19/20  12/20/20  12/21/20  12/22/20  12/23/20  12/24/20  12/25/20  12/26/20  12/27/20  12/28/20  12/29/20  12/30/20  12/31/20  1/1/21  1/2/21  1/3/21  1/4/21  1/5/21  1/6/21  1/7/21  1/8/21  1/9/21  1/10/21  1/11/21  1/12/21  1/13/21  1/14/21  1/15/21  1/16/21  1/17/21  1/18/21  1/19/21  1/20/21  1/21/21  1/22/21  1/23/21  1/24/21  1/25/21  1/26/21  1/27/21  1/28/21  1/29/21  1/30/21  1/31/21  2/1/21  2/2/21  2/3/21  2/4/21  2/5/21  2/6/21  2/7/21  2/8/21  2/9/21  2/10/21  2/11/21  2/12/21  2/13/21  2/14/21  2/15/21  2/16/21  2/17/21  2/18/21  2/19/21  2/20/21  2/21/21  2/22/21  2/23/21  2/24/21  2/25/21  2/26/21  2/27/21  2/28/21  3/1/21  3/2/21  3/3/21  3/4/21  3/5/21  3/6/21  3/7/21  3/8/21  3/9/21  3/10/21  3/11/21  3/12/21  3/13/21  3/14/21  3/15/21  3/16/21  3/17/21  3/18/21  3/19/21  3/20/21  3/21/21  3/22/21  3/23/21  3/24/21  3/25/21  3/26/21  3/27/21  3/28/21  3/29/21  3/30/21  3/31/21  4/1/21  4/2/21  4/3/21  4/4/21  4/5/21  4/6/21  4/7/21  4/8/21  4/9/21  4/10/21  4/11/21  4/12/21  4/13/21  4/14/21  4/15/21  4/16/21  4/17/21  4/18/21  4/19/21  4/20/21  4/21/21  4/22/21  4/23/21  4/24/21  4/25/21  4/26/21  4/27/21  4/28/21  4/29/21  4/30/21  5/1/21  5/2/21  5/3/21  5/4/21  5/5/21  5/6/21  5/7/21  5/8/21  5/9/21  5/10/21  5/11/21  5/12/21  5/13/21  5/14/21  5/15/21  5/16/21  5/17/21  5/18/21  5/19/21  5/20/21  5/21/21  5/22/21  5/23/21  5/24/21  5/25/21  5/26/21  5/27/21  5/28/21  5/29/21  5/30/21  5/31/21  6/1/21  6/2/21  6/3/21  6/4/21  6/5/21  6/6/21  6/7/21  6/8/21  6/9/21  6/10/21  6/11/21  6/12/21  6/13/21  6/14/21  6/15/21  6/16/21  6/17/21  6/18/21  6/19/21  6/20/21  6/21/21  6/22/21  6/23/21  6/24/21  6/25/21  6/26/21  6/27/21  6/28/21  6/29/21  6/30/21  7/1/21  7/2/21  7/3/21  7/4/21  7/5/21  7/6/21  7/7/21  7/8/21  7/9/21  7/10/21  7/11/21  7/12/21  7/13/21  7/14/21  7/15/21  7/16/21  7/17/21  7/18/21  7/19/21  7/20/21  7/21/21  7/22/21  7/23/21  7/24/21  7/25/21  7/26/21  7/27/21  7/28/21  7/29/21  7/30/21  7/31/21  8/1/21  8/2/21  8/3/21  8/4/21  8/5/21  8/6/21  8/7/21  8/8/21  8/9/21  8/10/21  8/11/21  8/12/21  8/13/21  8/14/21  8/15/21  8/16/21  8/17/21  8/18/21  8/19/21  8/20/21  8/21/21  8/22/21  8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  8/30/21  8/31/21  9/1/21  9/2/21  9/3/21  9/4/21  9/5/21  9/6/21  9/7/21  9/8/21  9/9/21  9/10/21  9/11/21  9/12/21  9/13/21  9/14/21  9/15/21  9/16/21  9/17/21  9/18/21  9/19/21  9/20/21  9/21/21  9/22/21  9/23/21  9/24/21  9/25/21  9/26/21  9/27/21  9/28/21  9/29/21  9/30/21  10/1/21  10/2/21  10/3/21  10/4/21  10/5/21  10/6/21  10/7/21  10/8/21  10/9/21  10/10/21  10/11/21  10/12/21  10/13/21  10/14/21  10/15/21  10/16/21  10/17/21  10/18/21  10/19/21  10/20/21  10/21/21  10/22/21  10/23/21  10/24/21  10/25/21  10/26/21  10/27/21  10/28/21  10/29/21  10/30/21  10/31/21  11/1/21  11/2/21  11/3/21  11/4/21  11/5/21  11/6/21  11/7/21  11/8/21  11/9/21  11/10/21  11/11/21  11/12/21  11/13/21  11/14/21  11/15/21  11/16/21  11/17/21  11/18/21  11/19/21  11/20/21  11/21/21  11/22/21  11/23/21  11/24/21  11/25/21  11/26/21  11/27/21  11/28/21  11/29/21  11/30/21  12/1/21  12/2/21  12/3/21  12/4/21  12/5/21  12/6/21  12/7/21  12/8/21  12/9/21  12/10/21  12/11/21  12/12/21  12/13/21  12/14/21  12/15/21  12/16/21  12/17/21  12/18/21  12/19/21  12/20/21  12/21/21  12/22/21  12/23/21  12/24/21  12/25/21  12/26/21  12/27/21  12/28/21  12/29/21  12/30/21
Alberta           Canada           53.9333  -116.5765  0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        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       4       7       7        19       19       29       29       39       56       74       97       119      146      195      259      301      359      358      486      542      542      621      661      690      754     969     969     1075    1181    1250    1373    1373    1423    1451     1567     1567     1732     1870     1870     1996     2397     2562     2803     2908     3095     3401     3720     4017     4233     4480     4696     4850     5165     5355     5573    5670    5766    5836    5893    5963    6017    6098    6157    6253     6300     6345     6407     6457     6515     6587     6644     6683     6716     6735     6768     6800     6818     6860     6879     6901     6926     6955     6979     6992     7010     7044    7057    7076    7091    7098    7138    7138    7202    7229    7276     7316     7346     7383     7433     7453     7482     7530     7579     7625     7673     7704     7736     7781     7825     7851     7888     7957     7996     8067     8108     8108    8202    8259    8259    8259    8389    8436    8482    8519    8596     8596     8596     8826     8912     8994     9114     9219     9219     9219     9587     9728     9728     9975     10086    10086    10086    10390    10470    10603    10716    10843    10843   10843   10843   11146   11240   11296   11430   11430   11430   11687    11772    11893    11969    12053    12053    12053    12053    12419    12501    12501    12748    12748    12748    13006    13083    13210    13318    13476    13476    13476    13902    14066   14180   14310   14474   14474   14474   14474   15093   15093   15304    15415    15415    15415    15833    15957    16128    16274    16381    16381    16381    16739    16889    17032    17190    17343    17343    17343    17749    17909    18062    18235    18357    18357    18357    18935    19211    19354    19718    19995    19995     19995     19995     20956     21199     21443     21775     21775     21775     22673     22996     23402     23829     24261     24261     24261     25733     26155     26565     27042     27664     27664     27664    27664    29932    29932    30447    31858    32777    33504    34160    34873     35545     36405     37312     38338     39329     40189     40962     41692     42797     43952     45288     46872     48421     49536     50801     51878     53105     54836     56444     58177     59484    61169    63023    64851    66730    68566    70301    72028    73488    75054     76792     78382     80099     81986     83327     84597     86168     87581     88933     90219     91459     92480     93781     94788     95979     96893     97352     98269     99141     100428    100428    100428  100428  104228  105535  106378  107501  108469  109652  110641  111452   112091   112743   113618   114585   115370   116087   116837   117311   117767   118436   119114   119757   120330   120793   121535   121901   122360   122821   123364   123747   124208   124563  124831  125090  125672  126068  126416  126767  127036  127231  127570   127921   128235   128540   128824   129075   129338   129615   130030   130355   130735   131063   131336   131603   132033   132432   132788   133203   133504   133795  134052  134454  134785  135196  135537  135837  136119  136374  136773   137137   137562   138036   138424   138788   139143   139622   140127   140823   141379   141934   142390   142855   143547   144311   145028   145696   146340   146885   147461   148332   149207  149207  150307  150307  153194  154125  155476  156905  158426  159719   160902   162038   163119   164531   166177   167793   169279   170795   172186   173531   175230   177087   178777   180369   181806   183301   184840   186679   188727   190734   193167  194898  196910  198653  200924  203135  205115  207157  208790  210387   211836   213635   215193   216626   217821   218961   219682   220559   221467   222279   223011   223632   224195   224647   225034   225424   225937   226449   226855   227246   227509   227718  228128  228424  228668  228961  229192  229319  229458  229771  229949   230119   230298   230463   230578   230705   230858   231008   231132   231259   231359   231419   231476   231568   231641   231641   231641   231641   231850   231911   231987   231987  232097  232097  232097  232236  232269  232336  232359  232411  232411   232411   232501   232536   232582   232635   232676   232676   232676   232806   232875   232956   233062   233160   233160   233160   233547   233681   233875   234108   234295   234295   234295  234295  235038  235244  235641  236010  236010  236010  237027  237306   237807   238357   238939   238939   238939   240346   240753   241431   242248   242997   242997   242997   244969   245598   246674   247786   248954   248954   248954   252010   252930   254245  255584  256985  256985  256985  256985  261888  263054  264564  266037   266037   266037   270777   272211   273820   275538   277558   277558   277558   282191   283710   285046   286706   288357   288357   288357   293538   294784   296466   298172   299802   299802   299802   303839   304502   305765   307019   308275   308275   308275    308275    311633    312285    313201    314252    314252    314252    316433    316964    317750    318520    319176    319176    319176    320768    321210    321855    322386    322989    322989    322989    324199   324514   325001   325517   325983   325983   325983   327283   327705   328189    328189    329030    329030    329030    330098    330419    330831    331214    331626    331626    331626    332751    333004    333468    333847    334203    334203    334203    335009    335247    335677   336043   336392   336392   336392   337180   337420   337808   338141   338428    338428    338428    339291    339541    339997    340470    341023    341023    341023    342948    343734    345080    346705    346705    346705    346705    346705    346705    357623    361623      1
Nova Scotia       Canada           44.6820  -63.7443   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        5        7        12       14       15       21       28       41       51       68       73       90       110      122      127      147      173     193     207     236     262     293     310     310     342     407      428      445      474      517      549      579      606      649      675      721      737      772      827      850      865      873      900      915      935      947      959     963     971     985     991     998     1007    1008    1011    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  8012      8012      8012      8072      8100      8119      8141      8169      8169      8169      8227      8288      8322     8362     8381     8381     8381     8427     8427     8481     8532     8591      8591      8591      8790      8856      8968      9060      9202      9202      9202      9464      9607      9607      9795      9988      9988      9988      9988      9988      10094     10124       1
Saint Barthelemy  France           17.9000  -62.8333   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       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       1       1        1        1        1        1        1        3        3        3        3        3        3        3        3        3        3        3        3        5        5        5        6        6       6       6       6       6       6       6       6       6       6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6       6       6       6       6       6       6       6       6       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Reunion           France          -21.1151   55.5364   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        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        5        6        7        9        9        12       14       28       45       64       71       94       111      135      145      183      183      224      247      281     308     321     334     344     349     358     358     362     382      388      389      391      391      391      394      402      407      408      408      410      410      412      412      417      417      418      418      420      420      422     423     423     424     424     425     427     426     431     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Queensland        Australia       -27.4698   153.0251  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     943     953     965      974      983      987      998      999      1001     1007     1015     1019     1019     1024     1024     1026     1026     1026     1030     1033     1034     1033     1033     1034    1035    1038    1043    1043    1045    1045    1045    1045    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Guangxi           China            23.8298   108.7881  2        5        23       23       36       46       51       58       78       87       100     111     127     139     150     168     172     183     195     210      215      222      222      226      235      237      238      242      244      245      246      249      249      251      252      252      252      252      252      252     252     252     252     252     252     252     252     252     252      252      252      252      252      252      252      253      253      253      254      254      254      254      254      254      254      254      254      254      254      254      254     254     254     254     254     254     254     254     254     254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254     254     254     254     254     254     254     254     254     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  372       373       379       381       386       390       391       393       395       396       400       401       409      413      416      417      426      439      447      456      463      478       486       490       497       499       499       506       516       523       537       540       547       554       558       568       575       593       599       605       610       613         1
Guangdong         China            23.3417   113.4244  26       32       53       78       111      151      207      277      354      436      535     632     725     813     895     970     1034    1095    1131    1159     1177     1219     1241     1261     1294     1316     1322     1328     1331     1332     1333     1339     1342     1345     1347     1347     1347     1348     1349     1349    1350    1350    1350    1351    1352    1352    1352    1352    1353     1356     1356     1356     1356     1360     1361     1364     1370     1378     1395     1400     1413     1415     1428     1433     1448     1456     1467     1475     1484     1494     1501    1507    1514    1516    1524    1532    1533    1536    1539    1544     1548     1552     1555     1564     1566     1571     1577     1579     1580     1581     1582     1582     1585     1585     1586     1587     1587     1588     1588     1588     1588    1588    1588    1588    1588    1589    1589    1589    1589    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Guadeloupe        France           16.2650  -61.5510   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        0        0        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        3        6        18       27       33       45       53       58       62       62       73       73       73       102      106      106      114      125     128     130     134     135     135     139     141     141     143      143      143      143      145      145      145      145      148      148      148      148      148      148      149      149      149      149      149      149      151      152     152     152     152     152     152     153     154     154     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18313   21125   21125   21125   21125   21125   21125   21125   21125   26771    26771    26771    26771    26771    26771    29760    35283    35283    35283    35283    35283    35283    37955    37955    37955    37955    37955    37955    37955    45393    45393    45393   45393   45393   45393   45393   45471   45471   49515   49515   49515    49515    49515    49515    51485    51485    51485    51485    51485    51485    52463    52463    52463    52463    53140    53140    53140    53140    53140    53106    53106    53106    53106    53106    53106    53544    53544    53544    53544    53544    53544     53544     53544     53836     53836     53836     53836     53836     53836     54095     54095     54095     54095     54095     54095     54095     54288     54288     54288     54288     54288     54288     54288    54288    54288    54474    54474    54474    54474    54474    54672    54672     54672     54672     54672     54672     54854     54854     54854     54854     54854     54854     54854     55080     55080     55080     55080     55080     55080     55080     55080     55080     55080    55080    55080    55080    55080    55080    55080    55080    55080    55080     55080     55080     55375     55375     55375     55375     55375     55375     55375     55375     55564     55564     55564     55564     55564     55564     55625     55795     55795     55795       1
Greenland         Denmark          71.7069  -42.6043   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        0        0        0        0        0        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        2        4        4        5        6        6        10       10       10       10       10       10      10      10      11      11      11      11      11      11      11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11      11      11      11      11      11      11      11      11      11       11       11       11       11       11       11       11       11       11       11       11       11       11       12       12       12       13       13       13       13       13       13      13      13      13      13      13      13      13      13      13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13      13      13      13      13      13      13      13      13      13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       13       14       14       14       14       14       14      14      14      14      14      14      14      14      14      14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14      14      14      14      14      14      14      14      14      14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       14       15       15       16       16        16        16        16        16        16        16        16        16        16        16        16        17        17        17        17        17        17        17        17        17        17        17       17       17       17       17       17       17       17       17       17        17        18        18        18        18        18        18        18        18        18        18        18        18        18        18        18        18        18        18        18        18       18       18       18       18       18       18       19       19       19        19        19        19        19        19        19     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31      31       31       31       31       31       31       31       31       31       31       31       31       31       31       31       31       31       31       31       31       31       31      31      31      31      31      31      31      31      31      31       31       31       31       31       31       31       31       31       34       34       34       34       34       34       34       34       36       36       37       40       40       40      40      40      40      43      43      43      43      43      43       43       44       44       44       49       49       49       49       49       49       49       50       50       50       50       50       50       50       50       50       50      50      50      50      51      51      51      51      51      52       55       55       64       65       65       69       72       78       81       83       83       84       85       89       92       92       92       108      117      118      119      122     122     130     135     151     160     168     175     182     187      201      221      235      245      245      249      257      273      285      296      298      298      305      310      313      318      329      329      333      333      334      343     347     347     349     353     353     360     377     377     382      390      395      395      409      433      441      454      464      464      469      504      528      541      553      553      565      565      571      573      576      584      584      591      591      595      600      601      620      623      623       623       630       638       645       654       654       654       654       688       700       716       731       731       731       746       772       779       787       799       799       799       810      823      835      846      859      859      859      882      922      963       982       990       990       990       1039      1070      1095      1116      1129      1129      1129      1188      1292      1311      1311      1332      1332      1332      1456      1456      1456     1542     1582     1582     1582     1621     1663     1746     1774     1774      1813      1831      1876      1876      1917      1937      1969      1969      1969      1969      1969      2182      2249      2249      2249      2249      2306      2437      2437      2610        1
Zhejiang          China            29.1832   120.0934  10       27       43       62       104      128      173      296      428      538      599     661     724     829     895     954     1006    1048    1075    1092     1117     1131     1145     1155     1162     1167     1171     1172     1174     1175     1203     1205     1205     1205     1205     1205     1205     1205     1205     1205    1206    1213    1213    1215    1215    1215    1215    1215    1215     1215     1215     1215     1227     1231     1231     1232     1232     1233     1234     1236     1238     1238     1240     1241     1243     1247     1251     1254     1255     1257     1257    1258    1260    1262    1263    1264    1265    1266    1267    1267     1267     1267     1267     1267     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268    1268    1268    1268    1268    1268    1268    1268    1268    1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268    1268    1268    1268    1268    1268    1268    1268    1268    1268     1268     1268     1268     1268     1268     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269    1269    1269    1269    1269    1269    1269    1269    1269    1269     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270    1270    1270    1270    1270    1270    1271    1272    1273    1274     1275     1275     1275     1275     1275     1275     1275     1277     1277     1277     1277     1277     1277     1277     1277     1277     1277     1277     1277     1278     1278     1278    1278    1278    1278    1278    1278    1278    1278    1278    1278     1278     1280     1280     1281     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1283     1283     1283     1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1286      1286      1286     1287     1287     1287     1288     1288     1290     1290     1291     1291      1291      1291      1291      1291      1291      1291      1291      1292      1292      1292      1293      1293      1293      1293      1293      1293      1294      1294      1294      1294      1294     1294     1294     1294     1294     1295     1295     1295     1295     1295      1296      1296      1297      1297      1297      1297   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1325    1325     1325     1327     1327     1327     1327     1328     1328     1328     1329     1329     1329     1331     1331     1331     1331     1332     1332     1343     1343     1344     1344    1344    1344    1344    1344    1344    1345    1345    1346    1346     1347     1347     1347     1347     1347     1355     1356     1356     1361     1361     1361     1362     1362     1362     1363     1363     1363     1364     1364     1364     1364     1364    1364    1365    1365    1366    1368    1368    1368    1369    1370     1370     1372     1372     1373     1373     1373     1376     1377     1379     1379     1383     1383     1383     1384     1385     1385     1385     1386     1386     1386     1386    1386    1386    1386    1386    1386    1386    1386    1386    1386     1386     1386     1386     1388     1390     1392     1392     1393     1393     1393     1393     1393     1393     1393     1393     1393     1393     1393     1393     1395     1396     1396    1396    1398    1398    1398    1399    1399    1400    1400    1412     1412     1412     1412     1412     1417     1417     1417     1418     1418     1420     1420     1421     1428     1428     1429     1429     1429     1429     1430     1430     1431     1432    1433    1437    1437    1437    1438    1438    1439    1439    1439     1439     1439     1439     1440     1441     1442     1442     1442     1444     1446     1446     1446     1446     1446     1446     1447     1447     1447     1448     1448     1449     1449     1450     1450     1450     1451     1451     1451     1452     1452      1452      1453      1454      1454      1456      1457      1457      1457      1457      1457      1457      1457      1465      1465      1465      1465      1473      1475      1479      1483      1492      1495     1496     1496     1496     1496     1496     1497     1497     1497     1497      1498      1499      1499      1499      1500      1500      1500      1500      1500      1500      1501      1501      1501      1501      1501      1501      1501      1501      1501      1501      1501     1501     1501     1501     1501     1502     1510     1522     1528     1563      1601      1676      1721      1767      1823      1867      1944      1975      1987      1998      1999      2001      2002      2003      2004      2006      2008      2012      2015      2016        1
Length: 85, dtype: int64

confirmed.value_counts(normalize=True)

Province/State    Country/Region  Lat       Long       1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  1/29/20  1/30/20  1/31/20  2/1/20  2/2/20  2/3/20  2/4/20  2/5/20  2/6/20  2/7/20  2/8/20  2/9/20  2/10/20  2/11/20  2/12/20  2/13/20  2/14/20  2/15/20  2/16/20  2/17/20  2/18/20  2/19/20  2/20/20  2/21/20  2/22/20  2/23/20  2/24/20  2/25/20  2/26/20  2/27/20  2/28/20  2/29/20  3/1/20  3/2/20  3/3/20  3/4/20  3/5/20  3/6/20  3/7/20  3/8/20  3/9/20  3/10/20  3/11/20  3/12/20  3/13/20  3/14/20  3/15/20  3/16/20  3/17/20  3/18/20  3/19/20  3/20/20  3/21/20  3/22/20  3/23/20  3/24/20  3/25/20  3/26/20  3/27/20  3/28/20  3/29/20  3/30/20  3/31/20  4/1/20  4/2/20  4/3/20  4/4/20  4/5/20  4/6/20  4/7/20  4/8/20  4/9/20  4/10/20  4/11/20  4/12/20  4/13/20  4/14/20  4/15/20  4/16/20  4/17/20  4/18/20  4/19/20  4/20/20  4/21/20  4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  5/1/20  5/2/20  5/3/20  5/4/20  5/5/20  5/6/20  5/7/20  5/8/20  5/9/20  5/10/20  5/11/20  5/12/20  5/13/20  5/14/20  5/15/20  5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  5/25/20  5/26/20  5/27/20  5/28/20  5/29/20  5/30/20  5/31/20  6/1/20  6/2/20  6/3/20  6/4/20  6/5/20  6/6/20  6/7/20  6/8/20  6/9/20  6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  6/16/20  6/17/20  6/18/20  6/19/20  6/20/20  6/21/20  6/22/20  6/23/20  6/24/20  6/25/20  6/26/20  6/27/20  6/28/20  6/29/20  6/30/20  7/1/20  7/2/20  7/3/20  7/4/20  7/5/20  7/6/20  7/7/20  7/8/20  7/9/20  7/10/20  7/11/20  7/12/20  7/13/20  7/14/20  7/15/20  7/16/20  7/17/20  7/18/20  7/19/20  7/20/20  7/21/20  7/22/20  7/23/20  7/24/20  7/25/20  7/26/20  7/27/20  7/28/20  7/29/20  7/30/20  7/31/20  8/1/20  8/2/20  8/3/20  8/4/20  8/5/20  8/6/20  8/7/20  8/8/20  8/9/20  8/10/20  8/11/20  8/12/20  8/13/20  8/14/20  8/15/20  8/16/20  8/17/20  8/18/20  8/19/20  8/20/20  8/21/20  8/22/20  8/23/20  8/24/20  8/25/20  8/26/20  8/27/20  8/28/20  8/29/20  8/30/20  8/31/20  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7/31/21  8/1/21  8/2/21  8/3/21  8/4/21  8/5/21  8/6/21  8/7/21  8/8/21  8/9/21  8/10/21  8/11/21  8/12/21  8/13/21  8/14/21  8/15/21  8/16/21  8/17/21  8/18/21  8/19/21  8/20/21  8/21/21  8/22/21  8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  8/30/21  8/31/21  9/1/21  9/2/21  9/3/21  9/4/21  9/5/21  9/6/21  9/7/21  9/8/21  9/9/21  9/10/21  9/11/21  9/12/21  9/13/21  9/14/21  9/15/21  9/16/21  9/17/21  9/18/21  9/19/21  9/20/21  9/21/21  9/22/21  9/23/21  9/24/21  9/25/21  9/26/21  9/27/21  9/28/21  9/29/21  9/30/21  10/1/21  10/2/21  10/3/21  10/4/21  10/5/21  10/6/21  10/7/21  10/8/21  10/9/21  10/10/21  10/11/21  10/12/21  10/13/21  10/14/21  10/15/21  10/16/21  10/17/21  10/18/21  10/19/21  10/20/21  10/21/21  10/22/21  10/23/21  10/24/21  10/25/21  10/26/21  10/27/21  10/28/21  10/29/21  10/30/21  10/31/21  11/1/21  11/2/21  11/3/21  11/4/21  11/5/21  11/6/21  11/7/21  11/8/21  11/9/21  11/10/21  11/11/21  11/12/21  11/13/21  11/14/21  11/15/21  11/16/21  11/17/21  11/18/21  11/19/21  11/20/21  11/21/21  11/22/21  11/23/21  11/24/21  11/25/21  11/26/21  11/27/21  11/28/21  11/29/21  11/30/21  12/1/21  12/2/21  12/3/21  12/4/21  12/5/21  12/6/21  12/7/21  12/8/21  12/9/21  12/10/21  12/11/21  12/12/21  12/13/21  12/14/21  12/15/21  12/16/21  12/17/21  12/18/21  12/19/21  12/20/21  12/21/21  12/22/21  12/23/21  12/24/21  12/25/21  12/26/21  12/27/21  12/28/21  12/29/21  12/30/21
Alberta           Canada           53.9333  -116.5765  0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        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       4       7       7        19       19       29       29       39       56       74       97       119      146      195      259      301      359      358      486      542      542      621      661      690      754     969     969     1075    1181    1250    1373    1373    1423    1451     1567     1567     1732     1870     1870     1996     2397     2562     2803     2908     3095     3401     3720     4017     4233     4480     4696     4850     5165     5355     5573    5670    5766    5836    5893    5963    6017    6098    6157    6253     6300     6345     6407     6457     6515     6587     6644     6683     6716     6735     6768     6800     6818     6860     6879     6901     6926     6955     6979     6992     7010     7044    7057    7076    7091    7098    7138    7138    7202    7229    7276     7316     7346     7383     7433     7453     7482     7530     7579     7625     7673     7704     7736     7781     7825     7851     7888     7957     7996     8067     8108     8108    8202    8259    8259    8259    8389    8436    8482    8519    8596     8596     8596     8826     8912     8994     9114     9219     9219     9219     9587     9728     9728     9975     10086    10086    10086    10390    10470    10603    10716    10843    10843   10843   10843   11146   11240   11296   11430   11430   11430   11687    11772    11893    11969    12053    12053    12053    12053    12419    12501    12501    12748    12748    12748    13006    13083    13210    13318    13476    13476    13476    13902    14066   14180   14310   14474   14474   14474   14474   15093   15093   15304    15415    15415    15415    15833    15957    16128    16274    16381    16381    16381    16739    16889    17032    17190    17343    17343    17343    17749    17909    18062    18235    18357    18357    18357    18935    19211    19354    19718    19995    19995     19995     19995     20956     21199     21443     21775     21775     21775     22673     22996     23402     23829     24261     24261     24261     25733     26155     26565     27042     27664     27664     27664    27664    29932    29932    30447    31858    32777    33504    34160    34873     35545     36405     37312     38338     39329     40189     40962     41692     42797     43952     45288     46872     48421     49536     50801     51878     53105     54836     56444     58177     59484    61169    63023    64851    66730    68566    70301    72028    73488    75054     76792     78382     80099     81986     83327     84597     86168     87581     88933     90219     91459     92480     93781     94788     95979     96893     97352     98269     99141     100428    100428    100428  100428  104228  105535  106378  107501  108469  109652  110641  111452   112091   112743   113618   114585   115370   116087   116837   117311   117767   118436   119114   119757   120330   120793   121535   121901   122360   122821   123364   123747   124208   124563  124831  125090  125672  126068  126416  126767  127036  127231  127570   127921   128235   128540   128824   129075   129338   129615   130030   130355   130735   131063   131336   131603   132033   132432   132788   133203   133504   133795  134052  134454  134785  135196  135537  135837  136119  136374  136773   137137   137562   138036   138424   138788   139143   139622   140127   140823   141379   141934   142390   142855   143547   144311   145028   145696   146340   146885   147461   148332   149207  149207  150307  150307  153194  154125  155476  156905  158426  159719   160902   162038   163119   164531   166177   167793   169279   170795   172186   173531   175230   177087   178777   180369   181806   183301   184840   186679   188727   190734   193167  194898  196910  198653  200924  203135  205115  207157  208790  210387   211836   213635   215193   216626   217821   218961   219682   220559   221467   222279   223011   223632   224195   224647   225034   225424   225937   226449   226855   227246   227509   227718  228128  228424  228668  228961  229192  229319  229458  229771  229949   230119   230298   230463   230578   230705   230858   231008   231132   231259   231359   231419   231476   231568   231641   231641   231641   231641   231850   231911   231987   231987  232097  232097  232097  232236  232269  232336  232359  232411  232411   232411   232501   232536   232582   232635   232676   232676   232676   232806   232875   232956   233062   233160   233160   233160   233547   233681   233875   234108   234295   234295   234295  234295  235038  235244  235641  236010  236010  236010  237027  237306   237807   238357   238939   238939   238939   240346   240753   241431   242248   242997   242997   242997   244969   245598   246674   247786   248954   248954   248954   252010   252930   254245  255584  256985  256985  256985  256985  261888  263054  264564  266037   266037   266037   270777   272211   273820   275538   277558   277558   277558   282191   283710   285046   286706   288357   288357   288357   293538   294784   296466   298172   299802   299802   299802   303839   304502   305765   307019   308275   308275   308275    308275    311633    312285    313201    314252    314252    314252    316433    316964    317750    318520    319176    319176    319176    320768    321210    321855    322386    322989    322989    322989    324199   324514   325001   325517   325983   325983   325983   327283   327705   328189    328189    329030    329030    329030    330098    330419    330831    331214    331626    331626    331626    332751    333004    333468    333847    334203    334203    334203    335009    335247    335677   336043   336392   336392   336392   337180   337420   337808   338141   338428    338428    338428    339291    339541    339997    340470    341023    341023    341023    342948    343734    345080    346705    346705    346705    346705    346705    346705    357623    361623      0.011765
Nova Scotia       Canada           44.6820  -63.7443   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        5        7        12       14       15       21       28       41       51       68       73       90       110      122      127      147      173     193     207     236     262     293     310     310     342     407      428      445      474      517      549      579      606      649      675      721      737      772      827      850      865      873      900      915      935      947      959     963     971     985     991     998     1007    1008    1011    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5887    5887    5893    5895    5899    5900    5900    5900    5907    5908     5911     5918     5920     5920     5920     5928     5929     5938     5946     5956     5956     5956     5956     5982     5989     5990     5999     5999     5999     6030     6031     6038    6042    6047    6047    6047    6047    6076    6090    6107    6117     6117     6117     6188     6254     6260     6294     6312     6312     6312     6367     6392     6411     6452     6486     6486     6486     6569     6598     6638     6638     6715     6715     6715     6800     6839     6864     6893     6918     6918     6918      6918      7011      7033      7059      7077      7077      7077      7077      7161      7166      7185      7208      7208      7208      7265      7272      7298      7328      7354      7354      7354      7413     7424     7462     7512     7550     7550     7550     7661     7717     7746      7746      7815      7815      7815      7913      7944      7963      7985      8012      8012      8012      8072      8100      8119      8141      8169      8169      8169      8227      8288      8322     8362     8381     8381     8381     8427     8427     8481     8532     8591      8591      8591      8790      8856      8968      9060      9202      9202      9202      9464      9607      9607      9795      9988      9988      9988      9988      9988      10094     10124       0.011765
Saint Barthelemy  France           17.9000  -62.8333   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       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       1       1        1        1        1        1        1        3        3        3        3        3        3        3        3        3        3        3        3        5        5        5        6        6       6       6       6       6       6       6       6       6       6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6        6       6       6       6       6       6       6       6       6       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  1661      1661      1661      1666      1666      1666      1666      1666      1666      1666      1666      1672      1672     1672     1672     1672     1672     1674     1674     1674     1674     1674      1674      1674      1683      1683      1683      1683      1683      1683      1683      1683      1692      1692      1692      1692      1692      1692      1725      1726      1726      1895        0.011765
Reunion           France          -21.1151   55.5364   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        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        5        6        7        9        9        12       14       28       45       64       71       94       111      135      145      183      183      224      247      281     308     321     334     344     349     358     358     362     382      388      389      391      391      391      394      402      407      408      408      410      410      412      412      417      417      418      418      420      420      422     423     423     424     424     425     427     426     431     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17508   17508    17508    17508    18425    18425    18425    18425    18425    18425    18425    18425    19343    19343    19343    19343    19343    19343    20381    20381    20381    20381    20381   20381   20381   20381   21451   21451   21451   21451   21451   21451    21451    22644    22644    22644    22644    22644    22644    22644    23566    23566    23566    23566    23566    23566    23566    24901    24901    24901    24901    24901    24901    24901   26075   26075   26075   26075   26075   26075   26075   27235   27235    27235    27235    27235    27235    27235    28441    28441    28441    28441    28441    28441    28441    29502    29502    29502    29502    29502    29502    30583    30583    30583   30583   30583   30583   30583   31845   31845   31845   31845   31845    31845    31845    31845    33295    33295    33295    33295    33295    33295    33295    34615    34615    34615    34615    34615    34615    34615    37231    37231    37231    37231    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  57173     57173     57173     57173     57173     59048     59048     59048     59048     59048     59048     61188     61188    61188    61188    61188    61188    61188    63863    63863    63863    63863     63863     63863     63863     67237     67237     67237     67237     67237     67237     67237     67237     67237     71795     71795     71795     71795     71795     71795     76602     76602       0.011765
Queensland        Australia       -27.4698   153.0251  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     943     953     965      974      983      987      998      999      1001     1007     1015     1019     1019     1024     1024     1026     1026     1026     1030     1033     1034     1033     1033     1034    1035    1038    1043    1043    1045    1045    1045    1045    1045     1051     1052     1051     1054     1055     1055     1057     1057     1058     1058     1058     1060     1061     1056     1057     1058     1058     1058     1058     1058     1058     1059    1059    1060    1060    1061    1061    1062    1062    1062    1063     1064     1065     1065     1065     1065     1066     1066     1066     1066     1066     1066     1066     1066     1066     1067     1067     1067     1067     1067     1067     1067    1067    1067    1067    1067    1068    1068    1068    1068    1070     1070     1071     1071     1071     1071     1071     1071     1071     1072     1072     1073     1074     1076     1076     1076     1076     1076     1078     1082     1083     1084     1085    1085    1085    1088    1088    1087    1088    1088    1089    1089     1089     1089     1091     1091     1091     1091     1091     1092     1093     1094     1103     1105     1106     1106     1107     1110     1113     1117     1121     1122     1124     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1824    1840    1859    1886    1896    1909    1918    1923    1926    1929     1940     1948     1955     1956     1955     1957     1961     1961     1962     1964     1964     1966     1972     1972     1972     1973     1977     1977     1979     1979     1980     1982    1982    1984    1985    1991    1991    1991    1992    1995    2002     2003     2007     2009     2010     2013     2014     2015     2015     2017     2018     2019     2021     2021     2022     2022     2022     2028     2029     2035     2039     2042     2043     2046     2048     2051     2056     2056     2059     2062     2063      2067      2067      2067      2068      2071      2071      2071      2071      2072      2077      2082      2082      2082      2082      2085      2086      2087      2089      2089      2089      2090      2090     2089     2092     2094     2095     2098     2098     2099     2102     2105      2109      2106      2106      2109      2110      2110      2111      2112      2112      2112      2112      2113      2115      2117      2116      2117      2120      2125      2127      2130      2133     2139     2146     2152     2155     2155     2157     2157     2165     2167      2168      2176      2180      2188      2210      2227      2258      2297      2356      2442      2613      2977      2977      3563      4322      5033      6961      8534      10752     10752       0.011765
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Guangxi           China            23.8298   108.7881  2        5        23       23       36       46       51       58       78       87       100     111     127     139     150     168     172     183     195     210      215      222      222      226      235      237      238      242      244      245      246      249      249      251      252      252      252      252      252      252     252     252     252     252     252     252     252     252     252      252      252      252      252      252      252      253      253      253      254      254      254      254      254      254      254      254      254      254      254      254      254     254     254     254     254     254     254     254     254     254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254     254     254     254     254     254     254     254     254     254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254     254     254     254     254     254     254     254     254     254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254      254     254     254     254     254     254     254     254     254     254      254      254      254      254      254      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255     255     255     255     255     255     255     255     255     255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      255      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267     270      270      270      270      270      270      270      270      270      270      270      270      270      270      270      270      270      270      270      270      271      274     274     274     275     275     275     275     275     275     275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275     275     275     275     275     275     275     275     275     275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275      275     275     276     276     276     276     276     276     276     276      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277      277     277     277     277     277     277     277     277     277     277      280      287      287      287      287      289      289      289      289      289      289      289      289      289      289      289      289      289      289      289      290      290     290     290     290     291     291     291     291     292     292      292      292      292      292      292      292      292      295      297      297      297      297      297      298      298      298      298      298      298      298      298      298      299      299      301      301      301      304      305      306       306       314       316       317       317       320       320       320       320       320       323       325       326       327       329       330       331       334       335       338       339       340      341      346      349      349      350      351      355      358      359       360       361       362       362       362       362       366       367       372       373       379       381       386       390       391       393       395       396       400       401       409      413      416      417      426      439      447      456      463      478       486       490       497       499       499       506       516       523       537       540       547       554       558       568       575       593       599       605       610       613         0.011765
Guangdong         China            23.3417   113.4244  26       32       53       78       111      151      207      277      354      436      535     632     725     813     895     970     1034    1095    1131    1159     1177     1219     1241     1261     1294     1316     1322     1328     1331     1332     1333     1339     1342     1345     1347     1347     1347     1348     1349     1349    1350    1350    1350    1351    1352    1352    1352    1352    1353     1356     1356     1356     1356     1360     1361     1364     1370     1378     1395     1400     1413     1415     1428     1433     1448     1456     1467     1475     1484     1494     1501    1507    1514    1516    1524    1532    1533    1536    1539    1544     1548     1552     1555     1564     1566     1571     1577     1579     1580     1581     1582     1582     1585     1585     1586     1587     1587     1588     1588     1588     1588    1588    1588    1588    1588    1589    1589    1589    1589    1589     1589     1589     1589     1589     1589     1590     1590     1590     1590     1590     1590     1591     1592     1592     1592     1592     1592     1592     1593     1593     1595     1596    1597    1598    1598    1601    1602    1602    1604    1604    1607     1607     1608     1625     1625     1628     1628     1628     1631     1631     1634     1634     1634     1634     1635     1635     1637     1637     1637     1641     1641     1642    1642    1643    1643    1643    1643    1645    1647    1647    1647     1648     1650     1650     1650     1650     1654     1657     1659     1659     1659     1661     1662     1667     1669     1672     1672     1672     1674     1675     1678     1680     1682    1683    1687    1687    1687    1687    1688    1693    1696    1699     1699     1707     1707     1709     1712     1720     1721     1725     1725     1725     1725     1727     1727     1730     1734     1734     1735     1737     1738     1739     1740     1742    1745    1758    1760    1763    1767    1769    1769    1770    1774     1776     1777     1778     1782     1783     1784     1787     1793     1797     1800     1803     1807     1807     1809     1812     1814     1819     1819     1827     1827     1831     1834     1840     1841     1846     1848     1848     1851     1852     1858      1861      1863      1869      1873      1875      1877      1881      1884      1889      1892      1895      1895      1904      1907      1908      1909      1911      1914      1916      1919      1922      1927     1935     1938     1938     1941     1943     1945     1949     1955     1955      1956      1956      1963      1966      1968      1971      1972      1973      1975      1975      1975      1979      1983      1984      1988      1988      1988      1988      1989      1989      1992     1996     1997     2000     2002     2004     2007     2009     2010     2013      2015      2016      2017      2018      2021      2022   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2295    2296     2299     2301     2304     2308     2310     2313     2317     2319     2319     2320     2322     2327     2328     2328     2329     2331     2334     2335     2337     2344     2350    2354    2358    2359    2360    2365    2365    2370    2372    2373     2381     2383     2386     2387     2392     2396     2397     2398     2399     2400     2406     2409     2412     2412     2413     2427     2428     2431     2432     2455     2468     2481    2498    2509    2525    2534    2542    2564    2573    2582    2593     2605     2618     2625     2635     2650     2657     2666     2680     2692     2699     2706     2709     2717     2723     2727     2728     2733     2736     2737     2737     2745    2748    2751    2756    2759    2764    2766    2769    2770    2770     2774     2777     2779     2791     2795     2796     2800     2808     2812     2812     2820     2834     2839     2845     2853     2866     2869     2872     2881     2882     2884     2886    2892    2894    2896    2909    2912    2915    2923    2931    2933     2938     2947     2950     2963     2968     2977     2978     2988     2997     3001     3007     3012     3020     3023     3032     3040     3043     3046     3055     3059     3065     3074    3079    3083    3087    3094    3096    3100    3104    3109    3111     3117     3120     3128     3132     3135     3137     3140     3142     3142     3145     3147     3149     3150     3159     3163     3164     3167     3168     3171     3177     3180     3183     3187     3191     3191     3191     3193     3193     3193     3195      3195      3197      3199      3199      3199      3199      3201      3204      3207      3208      3209      3210      3211      3211      3215      3217      3217      3221      3223      3224      3224      3226     3230     3233     3234     3237     3240     3243     3247     3248     3249      3251      3252      3253      3257      3259      3260      3264      3267      3269      3272      3274      3279      3281      3285      3291      3294      3297      3301      3310      3314      3315     3318     3322     3323     3324     3327     3330     3333     3333     3333      3334      3339      3343      3347      3353      3359      3366      3374      3384      3394      3399      3408      3413      3419      3421      3427      3429      3433      3443      3446        0.011765
Guadeloupe        France           16.2650  -61.5510   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        0        0        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        3        6        18       27       33       45       53       58       62       62       73       73       73       102      106      106      114      125     128     130     134     135     135     139     141     141     143      143      143      143      145      145      145      145      148      148      148      148      148      148      149      149      149      149      149      149      151      152     152     152     152     152     152     153     154     154     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   8498      8498      8498      8557      8557      8557      8557      8557      8557      8557      8620      8620      8620      8620      8620      8620    8620    8620    8620    8702    8702    8702    8702    8702    8702     8702     8834     8834     8834     8834     8834     8834     8834     8948     8948     8948     8948     9056     9056     9056     9056     9056     9056     9056     9056     9156     9156    9156    9156    9156    9156    9156    9156    9156    9302    9302     9302     9302     9302     9302     9302     9351     9455     9455     9455     9455     9455     9455     9610     9610     9610     9610     9968     9968     9968    9968    9968    9968    9968    10458   10458   10458   10458   10458    10458    10458    10458    10725    10725    10725    10725    10725    10725    10725    11095    11095    11095    11095    11095    11095    11095    11512    11512    11512    11512    11512   11512   11890   11890   11890   11890   11890   11890   11890   12304    12304    12304    12304    12304    12304    12304    12304    12927    12927    12927    12927    12927    12927    12927    12927    13770    13770    13770    13770    13770    13770   14634   14634   14634   14634   14634   14634   15429   15429   15429    15429    15429    15429    15429    15429    15429    15429    16079    16079    16079    16079    16079    16517    16517    16517    16517    16517    16517    16517    16874    16874    16874   16874   16874   16874   16874   17108   17108   17108   17108   17108    17108    17108    17288    17288    17288    17288    17288    17288    17288    17288    17427    17427    17427    17427    17427    17427    17427    17427    17539    17539    17539   17539   17539   17539   17684   17684   17684   17684   17684   17684    17684    17809    17809    17809    17809    17809    17809    17809    17982    17982    17982    17982    17982    17982    17982    18313    18313    18313    18313    18313    18313    18313   21125   21125   21125   21125   21125   21125   21125   21125   26771    26771    26771    26771    26771    26771    29760    35283    35283    35283    35283    35283    35283    37955    37955    37955    37955    37955    37955    37955    45393    45393    45393   45393   45393   45393   45393   45471   45471   49515   49515   49515    49515    49515    49515    51485    51485    51485    51485    51485    51485    52463    52463    52463    52463    53140    53140    53140    53140    53140    53106    53106    53106    53106    53106    53106    53544    53544    53544    53544    53544    53544     53544     53544     53836     53836     53836     53836     53836     53836     54095     54095     54095     54095     54095     54095     54095     54288     54288     54288     54288     54288     54288     54288    54288    54288    54474    54474    54474    54474    54474    54672    54672     54672     54672     54672     54672     54854     54854     54854     54854     54854     54854     54854     55080     55080     55080     55080     55080     55080     55080     55080     55080     55080    55080    55080    55080    55080    55080    55080    55080    55080    55080     55080     55080     55375     55375     55375     55375     55375     55375     55375     55375     55564     55564     55564     55564     55564     55564     55625     55795     55795     55795       0.011765
Greenland         Denmark          71.7069  -42.6043   0        0        0        0        0        0        0        0        0        0        0       0       0       0       0       0       0       0       0       0        0        0        0        0        0        0        0        0        0        0        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        2        4        4        5        6        6        10       10       10       10       10       10      10      10      11      11      11      11      11      11      11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11       11      11      11      11      11      11      11      11      11      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122     122     130     135     151     160     168     175     182     187      201      221      235      245      245      249      257      273      285      296      298      298      305      310      313      318      329      329      333      333      334      343     347     347     349     353     353     360     377     377     382      390      395      395      409      433      441      454      464      464      469      504      528      541      553      553      565      565      571      573      576      584      584      591      591      595      600      601      620      623      623       623       630       638       645       654       654       654       654       688       700       716       731       731       731       746       772       779       787       799       799       799       810      823      835      846      859      859      859      882      922      963       982       990       990       990       1039      1070      1095      1116      1129      1129      1129      1188      1292      1311      1311      1332      1332      1332      1456      1456      1456     1542     1582     1582     1582     1621     1663     1746     1774     1774      1813      1831      1876      1876      1917      1937      1969      1969      1969      1969      1969      2182      2249      2249      2249      2249      2306      2437      2437      2610        0.011765
Zhejiang          China            29.1832   120.0934  10       27       43       62       104      128      173      296      428      538      599     661     724     829     895     954     1006    1048    1075    1092     1117     1131     1145     1155     1162     1167     1171     1172     1174     1175     1203     1205     1205     1205     1205     1205     1205     1205     1205     1205    1206    1213    1213    1215    1215    1215    1215    1215    1215     1215     1215     1215     1227     1231     1231     1232     1232     1233     1234     1236     1238     1238     1240     1241     1243     1247     1251     1254     1255     1257     1257    1258    1260    1262    1263    1264    1265    1266    1267    1267     1267     1267     1267     1267     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268    1268    1268    1268    1268    1268    1268    1268    1268    1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268     1268    1268    1268    1268    1268    1268    1268    1268    1268    1268     1268     1268     1268     1268     1268     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269     1269    1269    1269    1269    1269    1269    1269    1269    1269    1269     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270     1270    1270    1270    1270    1270    1270    1271    1272    1273    1274     1275     1275     1275     1275     1275     1275     1275     1277     1277     1277     1277     1277     1277     1277     1277     1277     1277     1277     1277     1278     1278     1278    1278    1278    1278    1278    1278    1278    1278    1278    1278     1278     1280     1280     1281     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1282     1283     1283     1283     1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1283      1286      1286      1286     1287     1287     1287     1288     1288     1290     1290     1291     1291      1291      1291      1291      1291      1291      1291      1291      1292      1292      1292      1293      1293      1293      1293      1293      1293      1294      1294      1294      1294      1294     1294     1294     1294     1294     1295     1295     1295     1295     1295      1296      1296      1297      1297      1297      1297      1297      1298      1299      1299      1299      1300      1300      1300      1300      1302      1305      1305      1306      1306      1306      1306    1306    1306    1306    1306    1306    1306    1306    1308    1308     1308     1308     1308     1309     1309     1309     1311     1314     1316     1316     1316     1316     1316     1316     1316     1316     1316     1316     1316     1316     1316     1316    1316    1316    1316    1316    1316    1316    1317    1320    1320     1320     1320     1320     1320     1320     1320     1320     1320     1320     1320     1320     1320     1321     1321     1321     1321     1321     1321     1321    1321    1322    1322    1322    1322    1322    1322    1322    1322     1322     1322     1322     1322     1322     1322     1322     1322     1323     1323     1323     1323     1323     1323     1323     1323     1323     1323     1323     1323     1323     1323    1323    1323    1324    1324    1324    1324    1324    1325    1325     1325     1327     1327     1327     1327     1328     1328     1328     1329     1329     1329     1331     1331     1331     1331     1332     1332     1343     1343     1344     1344    1344    1344    1344    1344    1344    1345    1345    1346    1346     1347     1347     1347     1347     1347     1355     1356     1356     1361     1361     1361     1362     1362     1362     1363     1363     1363     1364     1364     1364     1364     1364    1364    1365    1365    1366    1368    1368    1368    1369    1370     1370     1372     1372     1373     1373     1373     1376     1377     1379     1379     1383     1383     1383     1384     1385     1385     1385     1386     1386     1386     1386    1386    1386    1386    1386    1386    1386    1386    1386    1386     1386     1386     1386     1388     1390     1392     1392     1393     1393     1393     1393     1393     1393     1393     1393     1393     1393     1393     1393     1395     1396     1396    1396    1398    1398    1398    1399    1399    1400    1400    1412     1412     1412     1412     1412     1417     1417     1417     1418     1418     1420     1420     1421     1428     1428     1429     1429     1429     1429     1430     1430     1431     1432    1433    1437    1437    1437    1438    1438    1439    1439    1439     1439     1439     1439     1440     1441     1442     1442     1442     1444     1446     1446     1446     1446     1446     1446     1447     1447     1447     1448     1448     1449     1449     1450     1450     1450     1451     1451     1451     1452     1452      1452      1453      1454      1454      1456      1457      1457      1457      1457      1457      1457      1457      1465      1465      1465      1465      1473      1475      1479      1483      1492      1495     1496     1496     1496     1496     1496     1497     1497     1497     1497      1498      1499      1499      1499      1500      1500      1500      1500      1500      1500      1501      1501      1501      1501      1501      1501      1501      1501      1501      1501      1501     1501     1501     1501     1501     1502     1510     1522     1528     1563      1601      1676      1721      1767      1823      1867      1944      1975      1987      1998      1999      2001      2002      2003      2004      2006      2008      2012      2015      2016        0.011765
Length: 85, dtype: float64

confirmed['Country/Region'].value_counts(normalize=True)

China             0.121429
Canada            0.057143
United Kingdom    0.042857
France            0.042857
Australia         0.028571
                    ...   
Guinea            0.003571
Guinea-Bissau     0.003571
Guyana            0.003571
Haiti             0.003571
Zimbabwe          0.003571
Name: Country/Region, Length: 196, dtype: float64

confirmed.sort_values(by="Country/Region", ascending=True)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	12/21/21	12/22/21	12/23/21	12/24/21	12/25/21	12/26/21	12/27/21	12/28/21	12/29/21	12/30/21
0	NaN	Afghanistan	33.939110	67.709953	0	0	0	0	0	0	...	157816	157841	157878	157887	157895	157951	157967	157998	158037	158056
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	206273	206616	206935	207221	207542	207709	207709	208352	208899	208899
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	215145	215430	215723	216098	216376	216637	216930	217265	217647	218037
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	21062	21372	21571	21730	21730	21730	22332	22540	22823	23122
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	66566	67199	68362	70221	71142	71752	71752	76787	78475	79871
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
275	NaN	Vietnam	14.058324	108.277199	0	2	2	2	2	2	...	1571780	1588335	1604712	1620869	1636455	1651673	1666545	1680985	1694874	1714742
276	NaN	West Bank and Gaza	31.952200	35.233200	0	0	0	0	0	0	...	467058	467391	467682	467682	467682	467682	468619	469452	469748	469748
277	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	10103	10103	10105	10109	10111	10115	10118	10123	10125	10126
278	NaN	Zambia	-13.133897	27.849332	0	0	0	0	0	0	...	219023	221880	225260	228932	231581	233120	234476	238383	243638	249193
279	NaN	Zimbabwe	-19.015438	29.154857	0	0	0	0	0	0	...	197768	199404	201344	202736	203746	204351	205449	207548	207548	211728

280 rows × 713 columns

confirmed['12/30/21'].mean()

1023357.3035714285

death['12/30/21'].mean()

19391.22857142857

recover[recover['Country/Region']== 'Bangladesh'].mean()

C:\Users\User\AppData\Local\Temp/ipykernel_3132/4110245.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  recover[recover['Country/Region']== 'Bangladesh'].mean()

Province/State        NaN
Lat               23.6850
Long              90.3563
1/22/20            0.0000
1/23/20            0.0000
                   ...   
12/26/21           0.0000
12/27/21           0.0000
12/28/21           0.0000
12/29/21           0.0000
12/30/21           0.0000
Length: 712, dtype: float64

death[death['Country/Region']== 'Bangladesh'].mean()

C:\Users\User\AppData\Local\Temp/ipykernel_3132/1511692858.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  death[death['Country/Region']== 'Bangladesh'].mean()

Province/State           NaN
Lat                  23.6850
Long                 90.3563
1/22/20               0.0000
1/23/20               0.0000
                     ...    
12/26/21          28060.0000
12/27/21          28061.0000
12/28/21          28062.0000
12/29/21          28063.0000
12/30/21          28070.0000
Length: 712, dtype: float64

confirmed[confirmed['Country/Region']== 'Bangladesh']

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	12/21/21	12/22/21	12/23/21	12/24/21	12/25/21	12/26/21	12/27/21	12/28/21	12/29/21	12/30/21
20	NaN	Bangladesh	23.685	90.3563	0	0	0	0	0	0	...	1581634	1581986	1582368	1582710	1582985	1583253	1583626	1584023	1584518	1585027

1 rows × 713 columns

confirmed[confirmed['Country/Region']== 'Bangladesh'].max()

Province/State          None
Country/Region    Bangladesh
Lat                   23.685
Long                 90.3563
1/22/20                    0
                     ...    
12/26/21             1583253
12/27/21             1583626
12/28/21             1584023
12/29/21             1584518
12/30/21             1585027
Length: 713, dtype: object

confirmed.iloc[1:5,700:713]

	12/18/21	12/19/21	12/20/21	12/21/21	12/22/21	12/23/21	12/24/21	12/25/21	12/26/21	12/27/21	12/28/21	12/29/21	12/30/21
1	205549	205777	205897	206273	206616	206935	207221	207542	207709	207709	208352	208899	208899
2	214330	214592	214835	215145	215430	215723	216098	216376	216637	216930	217265	217647	218037
3	20549	20549	21062	21062	21372	21571	21730	21730	21730	22332	22540	22823	23122
4	65868	65938	66086	66566	67199	68362	70221	71142	71752	71752	76787	78475	79871

confirmed.pivot_table(["12/30/20"],["12/30/21", "Country/Region"], margins = True)

		12/30/20
12/30/21	Country/Region
0	Canada	0.000000e+00
	China	0.000000e+00
1	China	1.000000e+00
	Micronesia	0.000000e+00
	Tonga	0.000000e+00
...	...	...
12748050	United Kingdom	2.432888e+06
22281649	Brazil	7.626563e+06
34838804	India	1.026667e+07
54306755	US	1.990594e+07
All		2.962861e+05

280 rows × 1 columns

sns.set()

%config InlineBackend.figure_fromat = 'retina'

for (country,now_days) ,sb_conf in confirmed.groupby(['Country/Region','12/30/21']):
    print("country:{}, now_days:{}".format(country,now_days))

country:Afghanistan, now_days:158056
country:Albania, now_days:208899
country:Algeria, now_days:218037
country:Andorra, now_days:23122
country:Angola, now_days:79871
country:Antigua and Barbuda, now_days:4283
country:Argentina, now_days:5606745
country:Armenia, now_days:344826
country:Australia, now_days:510
country:Australia, now_days:637
country:Australia, now_days:1153
country:Australia, now_days:3564
country:Australia, now_days:8993
country:Australia, now_days:10752
country:Australia, now_days:166373
country:Australia, now_days:176614
country:Austria, now_days:1274995
country:Azerbaijan, now_days:616352
country:Bahamas, now_days:24269
country:Bahrain, now_days:281406
country:Bangladesh, now_days:1585027
country:Barbados, now_days:28310
country:Belarus, now_days:697600
country:Belgium, now_days:2089657
country:Belize, now_days:32488
country:Benin, now_days:24935
country:Bhutan, now_days:2660
country:Bolivia, now_days:591773
country:Bosnia and Herzegovina, now_days:290471
country:Botswana, now_days:219509
country:Brazil, now_days:22281649
country:Brunei, now_days:15470
country:Bulgaria, now_days:744298
country:Burkina Faso, now_days:17632
country:Burma, now_days:530645
country:Burundi, now_days:27366
country:Cabo Verde, now_days:40738
country:Cambodia, now_days:120487
country:Cameroon, now_days:109367
country:Canada, now_days:0
country:Canada, now_days:13
country:Canada, now_days:765
country:Canada, now_days:1022
country:Canada, now_days:1729
country:Canada, now_days:2187
country:Canada, now_days:3724
country:Canada, now_days:10124
country:Canada, now_days:13590
country:Canada, now_days:78606
country:Canada, now_days:84446
country:Canada, now_days:251054
country:Canada, now_days:361623
country:Canada, now_days:586606
country:Canada, now_days:750128
country:Central African Republic, now_days:12163
country:Chad, now_days:5703
country:Chile, now_days:1804682
country:China, now_days:0
country:China, now_days:1
country:China, now_days:30
country:China, now_days:79
country:China, now_days:122
country:China, now_days:159
country:China, now_days:190
country:China, now_days:266
country:China, now_days:356
country:China, now_days:580
country:China, now_days:589
country:China, now_days:611
country:China, now_days:613
country:China, now_days:794
country:China, now_days:959
country:China, now_days:981
country:China, now_days:1009
country:China, now_days:1042
country:China, now_days:1186
country:China, now_days:1209
country:China, now_days:1218
country:China, now_days:1319
country:China, now_days:1359
country:China, now_days:1458
country:China, now_days:1625
country:China, now_days:1641
country:China, now_days:1815
country:China, now_days:2016
country:China, now_days:2035
country:China, now_days:2046
country:China, now_days:3093
country:China, now_days:3446
country:China, now_days:12630
country:China, now_days:68317
country:Colombia, now_days:5147039
country:Comoros, now_days:6284
country:Congo (Brazzaville), now_days:20089
country:Congo (Kinshasa), now_days:74793
country:Costa Rica, now_days:570556
country:Cote d'Ivoire, now_days:68146
country:Croatia, now_days:709678
country:Cuba, now_days:965571
country:Cyprus, now_days:161779
country:Czechia, now_days:2469951
country:Denmark, now_days:2610
country:Denmark, now_days:5764
country:Denmark, now_days:783702
country:Diamond Princess, now_days:712
country:Djibouti, now_days:13632
country:Dominica, now_days:6814
country:Dominican Republic, now_days:417268
country:Ecuador, now_days:547186
country:Egypt, now_days:384728
country:El Salvador, now_days:121945
country:Equatorial Guinea, now_days:13710
country:Eritrea, now_days:7982
country:Estonia, now_days:240334
country:Eswatini, now_days:65832
country:Ethiopia, now_days:415443
country:Fiji, now_days:53332
country:Finland, now_days:248958
country:France, now_days:100
country:France, now_days:454
country:France, now_days:1895
country:France, now_days:4250
country:France, now_days:12826
country:France, now_days:21911
country:France, now_days:46382
country:France, now_days:47631
country:France, now_days:48123
country:France, now_days:55795
country:France, now_days:76602
country:France, now_days:9529614
country:Gabon, now_days:41073
country:Gambia, now_days:10136
country:Georgia, now_days:932641
country:Germany, now_days:7171422
country:Ghana, now_days:141295
country:Greece, now_days:1170293
country:Grenada, now_days:6181
country:Guatemala, now_days:627562
country:Guinea, now_days:31641
country:Guinea-Bissau, now_days:6476
country:Guyana, now_days:39395
country:Haiti, now_days:25985
country:Holy See, now_days:27
country:Honduras, now_days:379402
country:Hungary, now_days:1253055
country:Iceland, now_days:27059
country:India, now_days:34838804
country:Indonesia, now_days:4262540
country:Iran, now_days:6192698
country:Iraq, now_days:2093436
country:Ireland, now_days:768449
country:Israel, now_days:1380046
country:Italy, now_days:5981428
country:Jamaica, now_days:93591
country:Japan, now_days:1731878
country:Jordan, now_days:1047953
country:Kazakhstan, now_days:1072037
country:Kenya, now_days:292237
country:Kiribati, now_days:2
country:Korea, South, now_days:630838
country:Kosovo, now_days:161442
country:Kuwait, now_days:416631
country:Kyrgyzstan, now_days:184633
country:Laos, now_days:110054
country:Latvia, now_days:275517
country:Lebanon, now_days:723640
country:Lesotho, now_days:28408
country:Liberia, now_days:6278
country:Libya, now_days:388183
country:Liechtenstein, now_days:6131
country:Lithuania, now_days:517655
country:Luxembourg, now_days:102486
country:MS Zaandam, now_days:9
country:Madagascar, now_days:50279
country:Malawi, now_days:74201
country:Malaysia, now_days:2755130
country:Maldives, now_days:95540
country:Mali, now_days:20670
country:Malta, now_days:51070
country:Marshall Islands, now_days:4
country:Mauritania, now_days:41154
country:Mauritius, now_days:23372
country:Mexico, now_days:3961662
country:Micronesia, now_days:1
country:Moldova, now_days:375780
country:Monaco, now_days:4985
country:Mongolia, now_days:389361
country:Montenegro, now_days:168328
country:Morocco, now_days:961058
country:Mozambique, now_days:184219
country:Namibia, now_days:146720
country:Nepal, now_days:828207
country:Netherlands, now_days:3368
country:Netherlands, now_days:5054
country:Netherlands, now_days:19719
country:Netherlands, now_days:20710
country:Netherlands, now_days:3116039
country:New Zealand, now_days:2
country:New Zealand, now_days:14118
country:Nicaragua, now_days:17487
country:Niger, now_days:7331
country:Nigeria, now_days:241513
country:North Macedonia, now_days:224507
country:Norway, now_days:391214
country:Oman, now_days:305489
country:Pakistan, now_days:1295376
country:Palau, now_days:8
country:Panama, now_days:491043
country:Papua New Guinea, now_days:36158
country:Paraguay, now_days:466101
country:Peru, now_days:2292254
country:Philippines, now_days:2841260
country:Poland, now_days:4094608
country:Portugal, now_days:1358817
country:Qatar, now_days:249787
country:Romania, now_days:1807223
country:Russia, now_days:10299923
country:Rwanda, now_days:110558
country:Saint Kitts and Nevis, now_days:2918
country:Saint Lucia, now_days:13473
country:Saint Vincent and the Grenadines, now_days:5850
country:Samoa, now_days:3
country:San Marino, now_days:7808
country:Sao Tome and Principe, now_days:3877
country:Saudi Arabia, now_days:555417
country:Senegal, now_days:74870
country:Serbia, now_days:1297147
country:Seychelles, now_days:24788
country:Sierra Leone, now_days:6983
country:Singapore, now_days:279061
country:Slovakia, now_days:1367361
country:Slovenia, now_days:462152
country:Solomon Islands, now_days:24
country:Somalia, now_days:23532
country:South Africa, now_days:3446532
country:South Sudan, now_days:15242
country:Spain, now_days:6294745
country:Sri Lanka, now_days:586746
country:Sudan, now_days:46518
country:Summer Olympics 2020, now_days:865
country:Suriname, now_days:52269
country:Sweden, now_days:1314784
country:Switzerland, now_days:1313628
country:Syria, now_days:50243
country:Taiwan*, now_days:16988
country:Tajikistan, now_days:17493
country:Tanzania, now_days:29306
country:Thailand, now_days:2220324
country:Timor-Leste, now_days:19833
country:Togo, now_days:29416
country:Tonga, now_days:1
country:Trinidad and Tobago, now_days:91320
country:Tunisia, now_days:724680
country:Turkey, now_days:9443734
country:US, now_days:54306755
country:Uganda, now_days:139079
country:Ukraine, now_days:3840041
country:United Arab Emirates, now_days:759511
country:United Kingdom, now_days:4
country:United Kingdom, now_days:46
country:United Kingdom, now_days:83
country:United Kingdom, now_days:1674
country:United Kingdom, now_days:3071
country:United Kingdom, now_days:3283
country:United Kingdom, now_days:6420
country:United Kingdom, now_days:8533
country:United Kingdom, now_days:8818
country:United Kingdom, now_days:13578
country:United Kingdom, now_days:26799
country:United Kingdom, now_days:12748050
country:Uruguay, now_days:411658
country:Uzbekistan, now_days:198769
country:Vanuatu, now_days:7
country:Venezuela, now_days:444411
country:Vietnam, now_days:1714742
country:West Bank and Gaza, now_days:469748
country:Yemen, now_days:10126
country:Zambia, now_days:249193
country:Zimbabwe, now_days:211728

for (country,now_days) ,sb_conf in recover.groupby(['Country/Region','12/20/21']):
    print("country:{}, now_days:{}".format(country,now_days))

country:Afghanistan, now_days:0
country:Albania, now_days:0
country:Algeria, now_days:0
country:Andorra, now_days:0
country:Angola, now_days:0
country:Antigua and Barbuda, now_days:0
country:Argentina, now_days:0
country:Armenia, now_days:0
country:Australia, now_days:0
country:Austria, now_days:0
country:Azerbaijan, now_days:0
country:Bahamas, now_days:0
country:Bahrain, now_days:0
country:Bangladesh, now_days:0
country:Barbados, now_days:0
country:Belarus, now_days:0
country:Belgium, now_days:0
country:Belize, now_days:0
country:Benin, now_days:0
country:Bhutan, now_days:0
country:Bolivia, now_days:0
country:Bosnia and Herzegovina, now_days:0
country:Botswana, now_days:0
country:Brazil, now_days:0
country:Brunei, now_days:0
country:Bulgaria, now_days:0
country:Burkina Faso, now_days:0
country:Burma, now_days:0
country:Burundi, now_days:0
country:Cabo Verde, now_days:0
country:Cambodia, now_days:0
country:Cameroon, now_days:0
country:Canada, now_days:0
country:Central African Republic, now_days:0
country:Chad, now_days:0
country:Chile, now_days:0
country:China, now_days:0
country:Colombia, now_days:0
country:Comoros, now_days:0
country:Congo (Brazzaville), now_days:0
country:Congo (Kinshasa), now_days:0
country:Costa Rica, now_days:0
country:Cote d'Ivoire, now_days:0
country:Croatia, now_days:0
country:Cuba, now_days:0
country:Cyprus, now_days:0
country:Czechia, now_days:0
country:Denmark, now_days:0
country:Diamond Princess, now_days:0
country:Djibouti, now_days:0
country:Dominica, now_days:0
country:Dominican Republic, now_days:0
country:Ecuador, now_days:0
country:Egypt, now_days:0
country:El Salvador, now_days:0
country:Equatorial Guinea, now_days:0
country:Eritrea, now_days:0
country:Estonia, now_days:0
country:Eswatini, now_days:0
country:Ethiopia, now_days:0
country:Fiji, now_days:0
country:Finland, now_days:0
country:France, now_days:0
country:Gabon, now_days:0
country:Gambia, now_days:0
country:Georgia, now_days:0
country:Germany, now_days:0
country:Ghana, now_days:0
country:Greece, now_days:0
country:Grenada, now_days:0
country:Guatemala, now_days:0
country:Guinea, now_days:0
country:Guinea-Bissau, now_days:0
country:Guyana, now_days:0
country:Haiti, now_days:0
country:Holy See, now_days:0
country:Honduras, now_days:0
country:Hungary, now_days:0
country:Iceland, now_days:0
country:India, now_days:0
country:Indonesia, now_days:0
country:Iran, now_days:0
country:Iraq, now_days:0
country:Ireland, now_days:0
country:Israel, now_days:0
country:Italy, now_days:0
country:Jamaica, now_days:0
country:Japan, now_days:0
country:Jordan, now_days:0
country:Kazakhstan, now_days:0
country:Kenya, now_days:0
country:Kiribati, now_days:0
country:Korea, South, now_days:0
country:Kosovo, now_days:0
country:Kuwait, now_days:0
country:Kyrgyzstan, now_days:0
country:Laos, now_days:0
country:Latvia, now_days:0
country:Lebanon, now_days:0
country:Lesotho, now_days:0
country:Liberia, now_days:0
country:Libya, now_days:0
country:Liechtenstein, now_days:0
country:Lithuania, now_days:0
country:Luxembourg, now_days:0
country:MS Zaandam, now_days:0
country:Madagascar, now_days:0
country:Malawi, now_days:0
country:Malaysia, now_days:0
country:Maldives, now_days:0
country:Mali, now_days:0
country:Malta, now_days:0
country:Marshall Islands, now_days:0
country:Mauritania, now_days:0
country:Mauritius, now_days:0
country:Mexico, now_days:0
country:Micronesia, now_days:0
country:Moldova, now_days:0
country:Monaco, now_days:0
country:Mongolia, now_days:0
country:Montenegro, now_days:0
country:Morocco, now_days:0
country:Mozambique, now_days:0
country:Namibia, now_days:0
country:Nepal, now_days:0
country:Netherlands, now_days:0
country:New Zealand, now_days:0
country:Nicaragua, now_days:0
country:Niger, now_days:0
country:Nigeria, now_days:0
country:North Macedonia, now_days:0
country:Norway, now_days:0
country:Oman, now_days:0
country:Pakistan, now_days:0
country:Palau, now_days:0
country:Panama, now_days:0
country:Papua New Guinea, now_days:0
country:Paraguay, now_days:0
country:Peru, now_days:0
country:Philippines, now_days:0
country:Poland, now_days:0
country:Portugal, now_days:0
country:Qatar, now_days:0
country:Romania, now_days:0
country:Russia, now_days:0
country:Rwanda, now_days:0
country:Saint Kitts and Nevis, now_days:0
country:Saint Lucia, now_days:0
country:Saint Vincent and the Grenadines, now_days:0
country:Samoa, now_days:0
country:San Marino, now_days:0
country:Sao Tome and Principe, now_days:0
country:Saudi Arabia, now_days:0
country:Senegal, now_days:0
country:Serbia, now_days:0
country:Seychelles, now_days:0
country:Sierra Leone, now_days:0
country:Singapore, now_days:0
country:Slovakia, now_days:0
country:Slovenia, now_days:0
country:Solomon Islands, now_days:0
country:Somalia, now_days:0
country:South Africa, now_days:0
country:South Sudan, now_days:0
country:Spain, now_days:0
country:Sri Lanka, now_days:0
country:Sudan, now_days:0
country:Summer Olympics 2020, now_days:0
country:Suriname, now_days:0
country:Sweden, now_days:0
country:Switzerland, now_days:0
country:Syria, now_days:0
country:Taiwan*, now_days:0
country:Tajikistan, now_days:0
country:Tanzania, now_days:0
country:Thailand, now_days:0
country:Timor-Leste, now_days:0
country:Togo, now_days:0
country:Tonga, now_days:0
country:Trinidad and Tobago, now_days:0
country:Tunisia, now_days:0
country:Turkey, now_days:0
country:US, now_days:0
country:Uganda, now_days:0
country:Ukraine, now_days:0
country:United Arab Emirates, now_days:0
country:United Kingdom, now_days:0
country:Uruguay, now_days:0
country:Uzbekistan, now_days:0
country:Vanuatu, now_days:0
country:Venezuela, now_days:0
country:Vietnam, now_days:0
country:West Bank and Gaza, now_days:0
country:Yemen, now_days:0
country:Zambia, now_days:0
country:Zimbabwe, now_days:0

for (country,now_days) ,sb_conf in death.groupby(['Country/Region','12/30/21']):
    print("country:{}, now_days:{}".format(country,now_days))

country:Afghanistan, now_days:7356
country:Albania, now_days:3212
country:Algeria, now_days:6271
country:Andorra, now_days:140
country:Angola, now_days:1764
country:Antigua and Barbuda, now_days:119
country:Argentina, now_days:117146
country:Armenia, now_days:7968
country:Australia, now_days:1
country:Australia, now_days:6
country:Australia, now_days:7
country:Australia, now_days:9
country:Australia, now_days:13
country:Australia, now_days:15
country:Australia, now_days:657
country:Australia, now_days:1525
country:Austria, now_days:13701
country:Azerbaijan, now_days:8346
country:Bahamas, now_days:716
country:Bahrain, now_days:1394
country:Bangladesh, now_days:28070
country:Barbados, now_days:260
country:Belarus, now_days:5561
country:Belgium, now_days:28308
country:Belize, now_days:598
country:Benin, now_days:161
country:Bhutan, now_days:3
country:Bolivia, now_days:19650
country:Bosnia and Herzegovina, now_days:13428
country:Botswana, now_days:2444
country:Brazil, now_days:619249
country:Brunei, now_days:98
country:Bulgaria, now_days:30890
country:Burkina Faso, now_days:318
country:Burma, now_days:19265
country:Burundi, now_days:38
country:Cabo Verde, now_days:352
country:Cambodia, now_days:3012
country:Cameroon, now_days:1851
country:Canada, now_days:0
country:Canada, now_days:1
country:Canada, now_days:4
country:Canada, now_days:12
country:Canada, now_days:14
country:Canada, now_days:18
country:Canada, now_days:111
country:Canada, now_days:159
country:Canada, now_days:955
country:Canada, now_days:1387
country:Canada, now_days:2420
country:Canada, now_days:3310
country:Canada, now_days:10226
country:Canada, now_days:11711
country:Central African Republic, now_days:101
country:Chad, now_days:181
country:Chile, now_days:39096
country:China, now_days:0
country:China, now_days:1
country:China, now_days:2
country:China, now_days:3
country:China, now_days:4
country:China, now_days:6
country:China, now_days:7
country:China, now_days:8
country:China, now_days:9
country:China, now_days:13
country:China, now_days:22
country:China, now_days:213
country:China, now_days:4512
country:Colombia, now_days:129901
country:Comoros, now_days:156
country:Congo (Brazzaville), now_days:367
country:Congo (Kinshasa), now_days:1205
country:Costa Rica, now_days:7353
country:Cote d'Ivoire, now_days:712
country:Croatia, now_days:12493
country:Cuba, now_days:8322
country:Cyprus, now_days:636
country:Czechia, now_days:36061
country:Denmark, now_days:1
country:Denmark, now_days:14
country:Denmark, now_days:3256
country:Diamond Princess, now_days:13
country:Djibouti, now_days:189
country:Dominica, now_days:47
country:Dominican Republic, now_days:4246
country:Ecuador, now_days:33672
country:Egypt, now_days:21727
country:El Salvador, now_days:3823
country:Equatorial Guinea, now_days:175
country:Eritrea, now_days:75
country:Estonia, now_days:1928
country:Eswatini, now_days:1299
country:Ethiopia, now_days:6926
country:Fiji, now_days:698
country:Finland, now_days:1554
country:France, now_days:0
country:France, now_days:6
country:France, now_days:7
country:France, now_days:58
country:France, now_days:185
country:France, now_days:281
country:France, now_days:338
country:France, now_days:409
country:France, now_days:636
country:France, now_days:777
country:France, now_days:831
country:France, now_days:121012
country:Gabon, now_days:288
country:Gambia, now_days:342
country:Georgia, now_days:13758
country:Germany, now_days:111929
country:Ghana, now_days:1287
country:Greece, now_days:20708
country:Grenada, now_days:200
country:Guatemala, now_days:16106
country:Guinea, now_days:391
country:Guinea-Bissau, now_days:149
country:Guyana, now_days:1052
country:Haiti, now_days:766
country:Holy See, now_days:0
country:Honduras, now_days:10433
country:Hungary, now_days:39104
country:Iceland, now_days:37
country:India, now_days:481080
country:Indonesia, now_days:144088
country:Iran, now_days:131572
country:Iraq, now_days:24154
country:Ireland, now_days:5912
country:Israel, now_days:8243
country:Italy, now_days:137247
country:Jamaica, now_days:2470
country:Japan, now_days:18389
country:Jordan, now_days:12372
country:Kazakhstan, now_days:18211
country:Kenya, now_days:5376
country:Kiribati, now_days:0
country:Korea, South, now_days:5563
country:Kosovo, now_days:2990
country:Kuwait, now_days:2468
country:Kyrgyzstan, now_days:2800
country:Laos, now_days:360
country:Latvia, now_days:4561
country:Lebanon, now_days:9102
country:Lesotho, now_days:665
country:Liberia, now_days:287
country:Libya, now_days:5696
country:Liechtenstein, now_days:69
country:Lithuania, now_days:7373
country:Luxembourg, now_days:912
country:MS Zaandam, now_days:2
country:Madagascar, now_days:1027
country:Malawi, now_days:2355
country:Malaysia, now_days:31462
country:Maldives, now_days:262
country:Mali, now_days:658
country:Malta, now_days:476
country:Marshall Islands, now_days:0
country:Mauritania, now_days:863
country:Mauritius, now_days:240
country:Mexico, now_days:299132
country:Micronesia, now_days:0
country:Moldova, now_days:9690
country:Monaco, now_days:38
country:Mongolia, now_days:2059
country:Montenegro, now_days:2407
country:Morocco, now_days:14844
country:Mozambique, now_days:1996
country:Namibia, now_days:3616
country:Nepal, now_days:11590
country:Netherlands, now_days:23
country:Netherlands, now_days:75
country:Netherlands, now_days:181
country:Netherlands, now_days:189
country:Netherlands, now_days:20892
country:New Zealand, now_days:0
country:New Zealand, now_days:51
country:Nicaragua, now_days:217
country:Niger, now_days:274
country:Nigeria, now_days:3030
country:North Macedonia, now_days:7953
country:Norway, now_days:1305
country:Oman, now_days:4116
country:Pakistan, now_days:28927
country:Palau, now_days:0
country:Panama, now_days:7425
country:Papua New Guinea, now_days:590
country:Paraguay, now_days:16624
country:Peru, now_days:202653
country:Philippines, now_days:51373
country:Poland, now_days:96415
country:Portugal, now_days:18937
country:Qatar, now_days:617
country:Romania, now_days:58714
country:Russia, now_days:301791
country:Rwanda, now_days:1349
country:Saint Kitts and Nevis, now_days:28
country:Saint Lucia, now_days:295
country:Saint Vincent and the Grenadines, now_days:81
country:Samoa, now_days:0
country:San Marino, now_days:99
country:Sao Tome and Principe, now_days:57
country:Saudi Arabia, now_days:8875
country:Senegal, now_days:1890
country:Serbia, now_days:12688
country:Seychelles, now_days:134
country:Sierra Leone, now_days:123
country:Singapore, now_days:827
country:Slovakia, now_days:16598
country:Slovenia, now_days:5583
country:Solomon Islands, now_days:0
country:Somalia, now_days:1333
country:South Africa, now_days:91061
country:South Sudan, now_days:135
country:Spain, now_days:89405
country:Sri Lanka, now_days:14962
country:Sudan, now_days:3331
country:Summer Olympics 2020, now_days:0
country:Suriname, now_days:1189
country:Sweden, now_days:15310
country:Switzerland, now_days:12206
country:Syria, now_days:2893
country:Taiwan*, now_days:850
country:Tajikistan, now_days:125
country:Tanzania, now_days:737
country:Thailand, now_days:21672
country:Timor-Leste, now_days:122
country:Togo, now_days:246
country:Tonga, now_days:0
country:Trinidad and Tobago, now_days:2850
country:Tunisia, now_days:25556
country:Turkey, now_days:82198
country:US, now_days:824301
country:Uganda, now_days:3291
country:Ukraine, now_days:101847
country:United Arab Emirates, now_days:2162
country:United Kingdom, now_days:0
country:United Kingdom, now_days:1
country:United Kingdom, now_days:5
country:United Kingdom, now_days:11
country:United Kingdom, now_days:26
country:United Kingdom, now_days:39
country:United Kingdom, now_days:67
country:United Kingdom, now_days:100
country:United Kingdom, now_days:110
country:United Kingdom, now_days:113
country:United Kingdom, now_days:148421
country:Uruguay, now_days:6169
country:Uzbekistan, now_days:1485
country:Vanuatu, now_days:1
country:Venezuela, now_days:5324
country:Vietnam, now_days:32168
country:West Bank and Gaza, now_days:4919
country:Yemen, now_days:1984
country:Zambia, now_days:3730
country:Zimbabwe, now_days:4997

features  = ['12/30/21','1/1/21']

confirmed[features].plot(
    kind="density", subplots=True, layout=(1, 2), sharex=False, figsize=(10, 4)
);

recover[features].plot(
    kind="density", subplots=True, layout=(1, 2), sharex=False, figsize=(10, 4)
);

---------------------------------------------------------------------------
LinAlgError                               Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3132/1947723435.py in <module>
----> 1 recover[features].plot(
      2     kind="density", subplots=True, layout=(1, 2), sharex=False, figsize=(10, 4)
      3 );

~\anaconda3\lib\site-packages\pandas\plotting\_core.py in __call__(self, *args, **kwargs)
    970                     data.columns = label_name
    971 
--> 972         return plot_backend.plot(data, kind=kind, **kwargs)
    973 
    974     __call__.__doc__ = __doc__

~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\__init__.py in plot(data, kind, **kwargs)
     69             kwargs["ax"] = getattr(ax, "left_ax", ax)
     70     plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 71     plot_obj.generate()
     72     plot_obj.draw()
     73     return plot_obj.result

~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py in generate(self)
    286         self._compute_plot_data()
    287         self._setup_subplots()
--> 288         self._make_plot()
    289         self._add_table()
    290         self._make_legend()

~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\hist.py in _make_plot(self)
    106                 kwds["weights"] = weights[:, i]
    107 
--> 108             artists = self._plot(ax, y, column_num=i, stacking_id=stacking_id, **kwds)
    109             self._append_legend_handles_labels(artists[0], label)
    110 

~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\hist.py in _plot(cls, ax, y, style, bw_method, ind, column_num, stacking_id, **kwds)
    177 
    178         y = remove_na_arraylike(y)
--> 179         gkde = gaussian_kde(y, bw_method=bw_method)
    180 
    181         y = gkde.evaluate(ind)

~\anaconda3\lib\site-packages\scipy\stats\kde.py in __init__(self, dataset, bw_method, weights)
    204             self._neff = 1/sum(self._weights**2)
    205 
--> 206         self.set_bandwidth(bw_method=bw_method)
    207 
    208     def evaluate(self, points):

~\anaconda3\lib\site-packages\scipy\stats\kde.py in set_bandwidth(self, bw_method)
    552             raise ValueError(msg)
    553 
--> 554         self._compute_covariance()
    555 
    556     def _compute_covariance(self):

~\anaconda3\lib\site-packages\scipy\stats\kde.py in _compute_covariance(self)
    564                                                bias=False,
    565                                                aweights=self.weights))
--> 566             self._data_inv_cov = linalg.inv(self._data_covariance)
    567 
    568         self.covariance = self._data_covariance * self.factor**2

~\anaconda3\lib\site-packages\scipy\linalg\basic.py in inv(a, overwrite_a, check_finite)
    966         inv_a, info = getri(lu, piv, lwork=lwork, overwrite_lu=1)
    967     if info > 0:
--> 968         raise LinAlgError("singular matrix")
    969     if info < 0:
    970         raise ValueError('illegal value in %d-th argument of internal '

LinAlgError: singular matrix

death[features].plot(
    kind="density", subplots=True, layout=(1, 2), sharex=False, figsize=(10, 4)
);

recover[features].hist(figsize=(10, 4));

sns.displot(confirmed['Country/Region'])

<seaborn.axisgrid.FacetGrid at 0x22da6f51970>

sns.boxplot(x="12/30/21", data=confirmed)

<AxesSubplot:xlabel='12/30/21'>

sns.violinplot(x="12/30/21", data=confirmed)

<AxesSubplot:xlabel='12/30/21'>

_, axes = plt.subplots(1, 2, sharey=True, figsize=(6, 4))
sns.boxplot(data=confirmed["12/30/21"],ax=axes[0])
sns.violinplot(data=confirmed["12/30/21"], ax=axes[1]);

numerical = list(
    set(confirmed.columns)
    - set(
        [
            "2/3/21"
            "5/7/21",
            "10/9/21",
            "12/30/21",
        ]
    )
)

corr_matrix = confirmed[numerical].corr()
sns.heatmap(corr_matrix);

plt.scatter(confirmed["12/30/20"],confirmed["12/30/21"]);

plt.scatter(recover["12/30/20"],recover["12/30/21"]);

plt.scatter(death["12/30/20"],death["12/30/21"]);

sns.jointplot(x="12/30/21", y="Country/Region", data=confirmed, kind="scatter")

<seaborn.axisgrid.JointGrid at 0x22d82e44bb0>

sns.jointplot("12/30/20", "12/30/21", data=confirmed, kind="kde",color = "g")

C:\Users\User\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<seaborn.axisgrid.JointGrid at 0x22d838b13a0>

# %config InlineBackend.figure_format = 'png'
# sns.pairplot(confirmed[numerical]);

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3132/2197064883.py in <module>
      1 # `pairplot()` may become very slow with the SVG format
      2 get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'png'")
----> 3 sns.pairplot(confirmed[numerical]);

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\axisgrid.py in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)
   2094     # Set up the PairGrid
   2095     grid_kws.setdefault("diag_sharey", diag_kind == "hist")
-> 2096     grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,
   2097                     hue_order=hue_order, palette=palette, corner=corner,
   2098                     height=height, aspect=aspect, dropna=dropna, **grid_kws)

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\axisgrid.py in __init__(self, data, hue, hue_order, palette, hue_kws, vars, x_vars, y_vars, corner, diag_sharey, height, aspect, layout_pad, despine, dropna, size)
   1242             fig = plt.figure(figsize=figsize)
   1243 
-> 1244         axes = fig.subplots(len(y_vars), len(x_vars),
   1245                             sharex="col", sharey="row",
   1246                             squeeze=False)

~\anaconda3\lib\site-packages\matplotlib\_api\deprecation.py in wrapper(*args, **kwargs)
    469                 "parameter will become keyword-only %(removal)s.",
    470                 name=name, obj_type=f"parameter of {func.__name__}()")
--> 471         return func(*args, **kwargs)
    472 
    473     return wrapper

~\anaconda3\lib\site-packages\matplotlib\figure.py in subplots(self, nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw)
    909             gridspec_kw = {}
    910         gs = self.add_gridspec(nrows, ncols, figure=self, **gridspec_kw)
--> 911         axs = gs.subplots(sharex=sharex, sharey=sharey, squeeze=squeeze,
    912                           subplot_kw=subplot_kw)
    913         return axs

~\anaconda3\lib\site-packages\matplotlib\gridspec.py in subplots(self, sharex, sharey, squeeze, subplot_kw)
    305                 subplot_kw["sharex"] = shared_with[sharex]
    306                 subplot_kw["sharey"] = shared_with[sharey]
--> 307                 axarr[row, col] = figure.add_subplot(
    308                     self[row, col], **subplot_kw)
    309 

~\anaconda3\lib\site-packages\matplotlib\figure.py in add_subplot(self, *args, **kwargs)
    784             ax = subplot_class_factory(projection_class)(self, *args, **pkw)
    785             key = (projection_class, pkw)
--> 786         return self._add_axes_internal(ax, key)
    787 
    788     def _add_axes_internal(self, ax, key):

~\anaconda3\lib\site-packages\matplotlib\figure.py in _add_axes_internal(self, ax, key)
    790         self._axstack.add(ax)
    791         self._localaxes.add(ax)
--> 792         self.sca(ax)
    793         ax._remove_method = self.delaxes
    794         # this is to support plt.subplot's re-selection logic

~\anaconda3\lib\site-packages\matplotlib\figure.py in sca(self, a)
   1496     def sca(self, a):
   1497         """Set the current Axes to be *a* and return *a*."""
-> 1498         self._axstack.bubble(a)
   1499         self._axobservers.process("_axes_change_event", self)
   1500         return a

~\anaconda3\lib\site-packages\matplotlib\figure.py in bubble(self, a)
     84         Move the given axes, which must already exist in the stack, to the top.
     85         """
---> 86         return super().bubble(self._entry_from_axes(a))
     87 
     88     def add(self, a):

~\anaconda3\lib\site-packages\matplotlib\cbook\__init__.py in bubble(self, o)
    680                 top_elements.append(elem)
    681             else:
--> 682                 self.push(elem)
    683         for _ in top_elements:
    684             self.push(o)

~\anaconda3\lib\site-packages\matplotlib\cbook\__init__.py in push(self, o)
    639         """
    640         self._elements = self._elements[:self._pos + 1] + [o]
--> 641         self._pos = len(self._elements) - 1
    642         return self()
    643 

KeyboardInterrupt: 

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

~\anaconda3\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    149         FigureCanvasBase(fig)
    150 
--> 151     fig.canvas.print_figure(bytes_io, **kw)
    152     data = bytes_io.getvalue()
    153     if fmt == 'svg':

~\anaconda3\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2219                 # CL works.  "tight" also needs a draw to get the right
   2220                 # locations:
-> 2221                 renderer = _get_renderer(
   2222                     self.figure,
   2223                     functools.partial(

~\anaconda3\lib\site-packages\matplotlib\backend_bases.py in _get_renderer(figure, print_method)
   1574                 figure.canvas._get_output_canvas(None, fmt), f"print_{fmt}")
   1575         try:
-> 1576             print_method(io.BytesIO())
   1577         except Done as exc:
   1578             renderer, = figure._cachedRenderer, = exc.args

~\anaconda3\lib\site-packages\matplotlib\backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 127620x127620 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 127620x127620 with 2551 Axes>

sns.lmplot(
    "12/30/20", "12/30/21", data=confirmed,hue="Country/Region", fit_reg=False
);

C:\Users\User\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

x = confirmed.drop(['Country/Region','Province/State'],axis=1)

from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(x)

%%time
tsne = TSNE(random_state=7)
tsne_repr = tsne.fit_transform(X_scaled)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<timed exec> in <module>

~\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py in fit_transform(self, X, y)
    930             Embedding of the training data in low-dimensional space.
    931         """
--> 932         embedding = self._fit(X)
    933         self.embedding_ = embedding
    934         return self.embedding_

~\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py in _fit(self, X, skip_num_points)
    700             )
    701         if self.method == 'barnes_hut':
--> 702             X = self._validate_data(X, accept_sparse=['csr'],
    703                                     ensure_min_samples=2,
    704                                     dtype=[np.float32, np.float64])

~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    419             out = X
    420         elif isinstance(y, str) and y == 'no_validation':
--> 421             X = check_array(X, **check_params)
    422             out = X
    423         else:

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    718 
    719         if force_all_finite:
--> 720             _assert_all_finite(array,
    721                                allow_nan=force_all_finite == 'allow-nan')
    722 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X, allow_nan, msg_dtype)
    101                 not allow_nan and not np.isfinite(X).all()):
    102             type_err = 'infinity' if allow_nan else 'NaN, infinity'
--> 103             raise ValueError(
    104                     msg_err.format
    105                     (type_err,

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

plt.scatter(X_scaled[:, 0], X_scaled[:, 1], alpha=0.5);

confirmed[[x for x in confirmed.columns if "21" in x] + ["Country/Region"]].groupby(
    "Country/Region"
).sum().plot();

confirmed[[x for x in confirmed.columns if "21" in x] + ["Country/Region"]].groupby(
    "Country/Region"
).sum().plot(kind="bar",rot=40)

<AxesSubplot:xlabel='Country/Region'>

Error in callback <function flush_figures at 0x0000022DFCA4B940> (for post_execute):

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~\anaconda3\lib\site-packages\matplotlib_inline\backend_inline.py in flush_figures()
    119         # ignore the tracking, just draw and close all figures
    120         try:
--> 121             return show(True)
    122         except Exception as e:
    123             # safely show traceback if in IPython, else raise

~\anaconda3\lib\site-packages\matplotlib_inline\backend_inline.py in show(close, block)
     39     try:
     40         for figure_manager in Gcf.get_all_fig_managers():
---> 41             display(
     42                 figure_manager.canvas.figure,
     43                 metadata=_fetch_figure_metadata(figure_manager.canvas.figure)

~\anaconda3\lib\site-packages\IPython\core\display.py in display(include, exclude, metadata, transient, display_id, *objs, **kwargs)
    318             publish_display_data(data=obj, metadata=metadata, **kwargs)
    319         else:
--> 320             format_dict, md_dict = format(obj, include=include, exclude=exclude)
    321             if not format_dict:
    322                 # nothing to display (e.g. _ipython_display_ took over)

~\anaconda3\lib\site-packages\IPython\core\formatters.py in format(self, obj, include, exclude)
    178             md = None
    179             try:
--> 180                 data = formatter(obj)
    181             except:
    182                 # FIXME: log the exception

<decorator-gen-2> in __call__(self, obj)

~\anaconda3\lib\site-packages\IPython\core\formatters.py in catch_format_error(method, self, *args, **kwargs)
    222     """show traceback on failed format call"""
    223     try:
--> 224         r = method(self, *args, **kwargs)
    225     except NotImplementedError:
    226         # don't warn on NotImplementedErrors

~\anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

~\anaconda3\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    149         FigureCanvasBase(fig)
    150 
--> 151     fig.canvas.print_figure(bytes_io, **kw)
    152     data = bytes_io.getvalue()
    153     if fmt == 'svg':

~\anaconda3\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2232             if bbox_inches:
   2233                 if bbox_inches == "tight":
-> 2234                     bbox_inches = self.figure.get_tightbbox(
   2235                         renderer, bbox_extra_artists=bbox_extra_artists)
   2236                     if pad_inches is None:

~\anaconda3\lib\site-packages\matplotlib\figure.py in get_tightbbox(self, renderer, bbox_extra_artists)
   1637 
   1638         for a in artists:
-> 1639             bbox = a.get_tightbbox(renderer)
   1640             if bbox is not None and (bbox.width != 0 or bbox.height != 0):
   1641                 bb.append(bbox)

~\anaconda3\lib\site-packages\matplotlib\axes\_base.py in get_tightbbox(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)
   4475                     # this artist
   4476                     continue
-> 4477             bbox = a.get_tightbbox(renderer)
   4478             if (bbox is not None
   4479                     and 0 < bbox.width < np.inf

~\anaconda3\lib\site-packages\matplotlib\legend.py in get_tightbbox(self, renderer)
    909             The bounding box in figure pixel coordinates.
    910         """
--> 911         return self._legend_box.get_window_extent(renderer)
    912 
    913     def get_frame_on(self):

~\anaconda3\lib\site-packages\matplotlib\offsetbox.py in get_window_extent(self, renderer)
    351         """Return the bounding box (`.Bbox`) in display space."""
    352         w, h, xd, yd, offsets = self.get_extent_offsets(renderer)
--> 353         px, py = self.get_offset(w, h, xd, yd, renderer)
    354         return mtransforms.Bbox.from_bounds(px - xd, py - yd, w, h)
    355 

~\anaconda3\lib\site-packages\matplotlib\offsetbox.py in get_offset(self, width, height, xdescent, ydescent, renderer)
    290 
    291         """
--> 292         return (self._offset(width, height, xdescent, ydescent, renderer)
    293                 if callable(self._offset)
    294                 else self._offset)

~\anaconda3\lib\site-packages\matplotlib\legend.py in _findoffset(self, width, height, xdescent, ydescent, renderer)
    576 
    577         if self._loc == 0:  # "best".
--> 578             x, y = self._find_best_position(width, height, renderer)
    579         elif self._loc in Legend.codes.values():  # Fixed location.
    580             bbox = Bbox.from_bounds(0, 0, width, height)

~\anaconda3\lib\site-packages\matplotlib\legend.py in _find_best_position(self, width, height, renderer, consider)
   1046                            for line in lines)
   1047                        + legendBox.count_contains(offsets)
-> 1048                        + legendBox.count_overlaps(bboxes)
   1049                        + sum(line.intersects_bbox(legendBox, filled=False)
   1050                              for line in lines))

~\anaconda3\lib\site-packages\matplotlib\transforms.py in count_overlaps(self, bboxes)
    629         """
    630         return count_bboxes_overlapping_bbox(
--> 631             self, np.atleast_3d([np.array(x) for x in bboxes]))
    632 
    633     def expanded(self, sw, sh):

<__array_function__ internals> in atleast_3d(*args, **kwargs)

~\anaconda3\lib\site-packages\numpy\core\shape_base.py in atleast_3d(*arys)
    190     res = []
    191     for ary in arys:
--> 192         ary = asanyarray(ary)
    193         if ary.ndim == 0:
    194             result = ary.reshape(1, 1, 1)

~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asanyarray(a, dtype, order, like)
    169         return _asanyarray_with_like(a, dtype=dtype, order=order, like=like)
    170 
--> 171     return array(a, dtype, copy=False, order=order, subok=True)
    172 
    173 

KeyboardInterrupt: 

sns.pairplot(confirmed[700:713])

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3132/428816351.py in <module>
----> 1 sns.pairplot(confirmed[700:713])

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\axisgrid.py in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)
   2094     # Set up the PairGrid
   2095     grid_kws.setdefault("diag_sharey", diag_kind == "hist")
-> 2096     grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,
   2097                     hue_order=hue_order, palette=palette, corner=corner,
   2098                     height=height, aspect=aspect, dropna=dropna, **grid_kws)

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\axisgrid.py in __init__(self, data, hue, hue_order, palette, hue_kws, vars, x_vars, y_vars, corner, diag_sharey, height, aspect, layout_pad, despine, dropna, size)
   1242             fig = plt.figure(figsize=figsize)
   1243 
-> 1244         axes = fig.subplots(len(y_vars), len(x_vars),
   1245                             sharex="col", sharey="row",
   1246                             squeeze=False)

~\anaconda3\lib\site-packages\matplotlib\_api\deprecation.py in wrapper(*args, **kwargs)
    469                 "parameter will become keyword-only %(removal)s.",
    470                 name=name, obj_type=f"parameter of {func.__name__}()")
--> 471         return func(*args, **kwargs)
    472 
    473     return wrapper

~\anaconda3\lib\site-packages\matplotlib\figure.py in subplots(self, nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw)
    909             gridspec_kw = {}
    910         gs = self.add_gridspec(nrows, ncols, figure=self, **gridspec_kw)
--> 911         axs = gs.subplots(sharex=sharex, sharey=sharey, squeeze=squeeze,
    912                           subplot_kw=subplot_kw)
    913         return axs

~\anaconda3\lib\site-packages\matplotlib\gridspec.py in subplots(self, sharex, sharey, squeeze, subplot_kw)
    305                 subplot_kw["sharex"] = shared_with[sharex]
    306                 subplot_kw["sharey"] = shared_with[sharey]
--> 307                 axarr[row, col] = figure.add_subplot(
    308                     self[row, col], **subplot_kw)
    309 

~\anaconda3\lib\site-packages\matplotlib\figure.py in add_subplot(self, *args, **kwargs)
    782             projection_class, pkw = self._process_projection_requirements(
    783                 *args, **kwargs)
--> 784             ax = subplot_class_factory(projection_class)(self, *args, **pkw)
    785             key = (projection_class, pkw)
    786         return self._add_axes_internal(ax, key)

~\anaconda3\lib\site-packages\matplotlib\axes\_subplots.py in __init__(self, fig, *args, **kwargs)
     34         """
     35         # _axes_class is set in the subplot_class_factory
---> 36         self._axes_class.__init__(self, fig, [0, 0, 1, 1], **kwargs)
     37         # This will also update the axes position.
     38         self.set_subplotspec(SubplotSpec._from_subplot_args(fig, args))

~\anaconda3\lib\site-packages\matplotlib\_api\deprecation.py in wrapper(*args, **kwargs)
    469                 "parameter will become keyword-only %(removal)s.",
    470                 name=name, obj_type=f"parameter of {func.__name__}()")
--> 471         return func(*args, **kwargs)
    472 
    473     return wrapper

~\anaconda3\lib\site-packages\matplotlib\axes\_base.py in __init__(self, fig, rect, facecolor, frameon, sharex, sharey, label, xscale, yscale, box_aspect, **kwargs)
    632 
    633         self._rasterization_zorder = None
--> 634         self.cla()
    635 
    636         # funcs used to format x and y - fall back on major formatters

~\anaconda3\lib\site-packages\matplotlib\axes\_base.py in cla(self)
   1295 
   1296         self.xaxis.set_clip_path(self.patch)
-> 1297         self.yaxis.set_clip_path(self.patch)
   1298 
   1299         self._shared_x_axes.clean()

~\anaconda3\lib\site-packages\matplotlib\axis.py in set_clip_path(self, clippath, transform)
    918         super().set_clip_path(clippath, transform)
    919         for child in self.majorTicks + self.minorTicks:
--> 920             child.set_clip_path(clippath, transform)
    921         self.stale = True
    922 

~\anaconda3\lib\site-packages\matplotlib\axis.py in set_clip_path(self, clippath, transform)
    239     def set_clip_path(self, clippath, transform=None):
    240         # docstring inherited
--> 241         super().set_clip_path(clippath, transform)
    242         self.gridline.set_clip_path(clippath, transform)
    243         self.stale = True

~\anaconda3\lib\site-packages\matplotlib\artist.py in set_clip_path(self, path, transform)
    778             if isinstance(path, Rectangle):
    779                 self.clipbox = TransformedBbox(Bbox.unit(),
--> 780                                                path.get_transform())
    781                 self._clippath = None
    782                 success = True

~\anaconda3\lib\site-packages\matplotlib\patches.py in get_transform(self)
    271     def get_transform(self):
    272         """Return the `~.transforms.Transform` applied to the `Patch`."""
--> 273         return self.get_patch_transform() + artist.Artist.get_transform(self)
    274 
    275     def get_data_transform(self):

~\anaconda3\lib\site-packages\matplotlib\patches.py in get_patch_transform(self)
    777         bbox = self.get_bbox()
    778         return (transforms.BboxTransformTo(bbox)
--> 779                 + transforms.Affine2D().rotate_deg_around(
    780                     bbox.x0, bbox.y0, self.angle))
    781 

~\anaconda3\lib\site-packages\matplotlib\transforms.py in rotate_deg_around(self, x, y, degrees)
   2000         # Cast to float to avoid wraparound issues with uint8's
   2001         x, y = float(x), float(y)
-> 2002         return self.translate(-x, -y).rotate_deg(degrees).translate(x, y)
   2003 
   2004     def translate(self, tx, ty):

~\anaconda3\lib\site-packages\matplotlib\transforms.py in rotate_deg(self, degrees)
   1978         and :meth:`scale`.
   1979         """
-> 1980         return self.rotate(math.radians(degrees))
   1981 
   1982     def rotate_around(self, x, y, theta):

~\anaconda3\lib\site-packages\matplotlib\transforms.py in rotate(self, theta)
   1966         rotate_mtx = np.array([[a, -b, 0.0], [b, a, 0.0], [0.0, 0.0, 1.0]],
   1967                               float)
-> 1968         self._mtx = np.dot(rotate_mtx, self._mtx)
   1969         self.invalidate()
   1970         return self

<__array_function__ internals> in dot(*args, **kwargs)

KeyboardInterrupt: 

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

~\anaconda3\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    149         FigureCanvasBase(fig)
    150 
--> 151     fig.canvas.print_figure(bytes_io, **kw)
    152     data = bytes_io.getvalue()
    153     if fmt == 'svg':

~\anaconda3\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2219                 # CL works.  "tight" also needs a draw to get the right
   2220                 # locations:
-> 2221                 renderer = _get_renderer(
   2222                     self.figure,
   2223                     functools.partial(

~\anaconda3\lib\site-packages\matplotlib\backend_bases.py in _get_renderer(figure, print_method)
   1574                 figure.canvas._get_output_canvas(None, fmt), f"print_{fmt}")
   1575         try:
-> 1576             print_method(io.BytesIO())
   1577         except Done as exc:
   1578             renderer, = figure._cachedRenderer, = exc.args

~\anaconda3\lib\site-packages\matplotlib\backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

~\anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 128340x128340 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 128340x128340 with 1007 Axes>

sns.displot(confirmed["Country/Region"])

<seaborn.axisgrid.FacetGrid at 0x22dd36cb400>

sns.FacetGrid(confirmed, hue="Country/Region", size=12).map(sns.kdeplot, "12/30/21").add_legend();

C:\Users\User\anaconda3\lib\site-packages\seaborn\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
  warnings.warn(msg, UserWarning)