{"cells":[{"metadata":{},"cell_type":"markdown","source":"## COVID-19 Daily Analysis Table  \n  - This-document: https://github.com/infchg/COVID-19/JH-calculate-daily.ipynb\n  - HOW-TO-USE: see results or re-calculate on your own Notebook (Jupyter, Mybinder, Azure, ... )\n  - RESULTS: WARNING ON COUNTRIES BY COVID DAILY DEATH RATES\n      - rankings and trends on the worst daily deaths by country evaluating last 7 days\n  - References:\n      - Web charts:  https://dasn.herokuapp.com/covidzoom and https://dasn.herokuapp.com/covid19\n      - wolfram & azure notebooks \n      - Data-source: John Hopkins, Stats by Country,  https://github.com/CSSEGISandData/\n  - LOOKING FOR REGIONAL SOURCES (Estado, Staat, CCAA, Canton, ...) IN ORDER TO CALCULATE BY AREA"},{"metadata":{"trusted":true},"cell_type":"code","source":"try:  ## This table sumarizes the daily deaths by country (calculations below)   %store df9  \n    display(df9.transpose().tail(3) )\n    print(\"worst daily deaths by country evaluating last 7 days:\")\n    display(df9.transpose().tail(9).head(9).mean(axis=0).sort_values(ascending=False).head(7) )\nexcept NameError:\n    print(\"Click Run on this cell only after the RUN ALL completes all calculations\")","execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"Country/Region    US  Brazil  United Kingdom  Mexico  Italy  Peru  Russia  \\\n5/14/20         1779     759             428     257    262    98      93   \n5/15/20         1632     963             384     290    242   125     113   \n5/16/20         1224     700             468     278    153   131     119   \n\nCountry/Region  India  Spain  Ecuador  Canada  Belgium  Germany  Turkey  \\\n5/14/20            98    217        4     131       60       23      55   \n5/15/20           104    138      256      50       56       13      48   \n5/16/20           118    104       94      82       46       41      41   \n\nCountry/Region  Canada  Iran  \n5/14/20             32    71  \n5/15/20             24    48  \n5/16/20             37    35  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Country/Region</th>\n      <th>US</th>\n      <th>Brazil</th>\n      <th>United Kingdom</th>\n      <th>Mexico</th>\n      <th>Italy</th>\n      <th>Peru</th>\n      <th>Russia</th>\n      <th>India</th>\n      <th>Spain</th>\n      <th>Ecuador</th>\n      <th>Canada</th>\n      <th>Belgium</th>\n      <th>Germany</th>\n      <th>Turkey</th>\n      <th>Canada</th>\n      <th>Iran</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5/14/20</th>\n      <td>1779</td>\n      <td>759</td>\n      <td>428</td>\n      <td>257</td>\n      <td>262</td>\n      <td>98</td>\n      <td>93</td>\n      <td>98</td>\n      <td>217</td>\n      <td>4</td>\n      <td>131</td>\n      <td>60</td>\n      <td>23</td>\n      <td>55</td>\n      <td>32</td>\n      <td>71</td>\n    </tr>\n    <tr>\n      <th>5/15/20</th>\n      <td>1632</td>\n      <td>963</td>\n      <td>384</td>\n      <td>290</td>\n      <td>242</td>\n      <td>125</td>\n      <td>113</td>\n      <td>104</td>\n      <td>138</td>\n      <td>256</td>\n      <td>50</td>\n      <td>56</td>\n      <td>13</td>\n      <td>48</td>\n      <td>24</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>5/16/20</th>\n      <td>1224</td>\n      <td>700</td>\n      <td>468</td>\n      <td>278</td>\n      <td>153</td>\n      <td>131</td>\n      <td>119</td>\n      <td>118</td>\n      <td>104</td>\n      <td>94</td>\n      <td>82</td>\n      <td>46</td>\n      <td>41</td>\n      <td>41</td>\n      <td>37</td>\n      <td>35</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"stream","text":"worst daily deaths by country evaluating last 7 days:\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"Country/Region\nUS                1454.666667\nBrazil             719.111111\nUnited Kingdom     427.888889\nMexico             231.555556\nItaly              200.555556\nSpain              165.888889\nEcuador            114.888889\ndtype: float64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### How is this daily Calculus performed :   https://github.com/infchg/COVID-19\n  - calculations in iPython at  https://github.com/infchg/COVID-19/JH-calculate-daily.ipynb\n      - data source: John Hopkins https://github.com/CSSEGISandData/COVID-19/\n      - thanks to resources by http://Jupyter.org  http://MyBinder.org  http://Heroku.com and above\n  - LOOKING FOR REGIONAL SOURCES (Estado, Staat, CCAA, Canton, ...) IN ORDER TO CALCULATE BY AREA"},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install pandas seaborn\n# initial installations ","execution_count":2,"outputs":[{"output_type":"stream","text":"WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\nPlease see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\nTo avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\nCollecting pandas\n  Downloading pandas-1.0.3-cp37-cp37m-manylinux1_x86_64.whl (10.0 MB)\n\u001b[K     |████████████████████████████████| 10.0 MB 3.6 MB/s eta 0:00:01\n\u001b[?25hCollecting seaborn\n  Downloading seaborn-0.10.1-py3-none-any.whl (215 kB)\n\u001b[K     |████████████████████████████████| 215 kB 25.4 MB/s eta 0:00:01\n\u001b[?25hCollecting pytz>=2017.2\n  Downloading pytz-2020.1-py2.py3-none-any.whl (510 kB)\n\u001b[K     |████████████████████████████████| 510 kB 32.1 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: python-dateutil>=2.6.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas) (2.8.1)\nCollecting numpy>=1.13.3\n  Downloading numpy-1.18.4-cp37-cp37m-manylinux1_x86_64.whl (20.2 MB)\n\u001b[K     |████████████████████████████████| 20.2 MB 38 kB/s s eta 0:00:01         | 7.8 MB 22.7 MB/s eta 0:00:01\n\u001b[?25hCollecting matplotlib>=2.1.2\n  Downloading matplotlib-3.2.1-cp37-cp37m-manylinux1_x86_64.whl (12.4 MB)\n\u001b[K     |████████████████████████████████| 12.4 MB 46.9 MB/s eta 0:00:01     |█████████████████████████████▋  | 11.5 MB 46.9 MB/s eta 0:00:01\n\u001b[?25hCollecting scipy>=1.0.1\n  Downloading scipy-1.4.1-cp37-cp37m-manylinux1_x86_64.whl (26.1 MB)\n\u001b[K     |████████████████████████████████| 26.1 MB 121 kB/s  eta 0:00:01\n\u001b[?25hRequirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.14.0)\nCollecting kiwisolver>=1.0.1\n  Downloading kiwisolver-1.2.0-cp37-cp37m-manylinux1_x86_64.whl (88 kB)\n\u001b[K     |████████████████████████████████| 88 kB 11.7 MB/s eta 0:00:01\n\u001b[?25hCollecting cycler>=0.10\n  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\nCollecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1\n  Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n\u001b[K     |████████████████████████████████| 67 kB 8.5 MB/s  eta 0:00:01\n\u001b[?25hInstalling collected packages: pytz, numpy, pandas, kiwisolver, cycler, pyparsing, matplotlib, scipy, seaborn\nSuccessfully installed cycler-0.10.0 kiwisolver-1.2.0 matplotlib-3.2.1 numpy-1.18.4 pandas-1.0.3 pyparsing-2.4.7 pytz-2020.1 scipy-1.4.1 seaborn-0.10.1\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":" ##JH Source THIS IS THE SOURCE OF ALL CALCULATIONS IN THIS PAGE\n! curl -OL https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv","execution_count":3,"outputs":[{"output_type":"stream","text":"  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n100   213  100   213    0     0   1704      0 --:--:-- --:--:-- --:--:--  1704\n100 79863  100 79863    0     0   301k      0 --:--:-- --:--:-- --:--:-- 4306k\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ! egrep -e ',[0-9]{3,}$' -e ^Provi  -e ',[6-9].$' tim*dea*csv > over50.csv\n! egrep -e ',[^,][^,][^,]+$' -e ^Provi   tim*dea*csv > over50.csv\nimport pandas as pd\ndf=pd.read_csv('over50.csv')\ndf.set_index('Country/Region',inplace=True)  # .T\ntype(df)","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"pandas.core.frame.DataFrame"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"cols20 = df.columns[-13:].tolist()\nidx = cols20 # cols21 = df.columns[-14:-1].tolist()\n#display( )idx=['Country/Region']+cols20   #no need with set index\ndisplay(df.sort_values(by=[df.columns[-1]],ascending=False)[cols20] )","execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"                5/4/20  5/5/20  5/6/20  5/7/20  5/8/20  5/9/20  5/10/20  \\\nCountry/Region                                                            \nUS               68922   71064   73455   75662   77180   78795    79526   \nUnited Kingdom   28734   29427   30076   30615   31241   31587    31855   \nItaly            29079   29315   29684   29958   30201   30395    30560   \nSpain            25428   25613   25857   26070   26299   26478    26621   \nFrance           25168   25498   25772   25949   26192   26271    26341   \n...                ...     ...     ...     ...     ...     ...      ...   \nMalaysia           105     106     107     107     107     108      108   \nKuwait              40      40      42      44      47      49       58   \nBulgaria            78      80      84      84      86      90       91   \nLuxembourg          96      96      98     100     100     101      101   \nSlovenia            97      98      99      99     100     101      102   \n\n                5/11/20  5/12/20  5/13/20  5/14/20  5/15/20  5/16/20  \nCountry/Region                                                        \nUS                80682    82356    84119    85898    87530    88754  \nUnited Kingdom    32065    32692    33186    33614    33998    34466  \nItaly             30739    30911    31106    31368    31610    31763  \nSpain             26744    26920    27104    27321    27459    27563  \nFrance            26604    26951    27032    27381    27485    27483  \n...                 ...      ...      ...      ...      ...      ...  \nMalaysia            109      109      111      112      112      113  \nKuwait               65       75       82       88       96      107  \nBulgaria             93       95       96       99      102      105  \nLuxembourg          101      102      103      103      104      104  \nSlovenia            102      102      103      103      103      103  \n\n[68 rows x 13 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>5/4/20</th>\n      <th>5/5/20</th>\n      <th>5/6/20</th>\n      <th>5/7/20</th>\n      <th>5/8/20</th>\n      <th>5/9/20</th>\n      <th>5/10/20</th>\n      <th>5/11/20</th>\n      <th>5/12/20</th>\n      <th>5/13/20</th>\n      <th>5/14/20</th>\n      <th>5/15/20</th>\n      <th>5/16/20</th>\n    </tr>\n    <tr>\n      <th>Country/Region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>US</th>\n      <td>68922</td>\n      <td>71064</td>\n      <td>73455</td>\n      <td>75662</td>\n      <td>77180</td>\n      <td>78795</td>\n      <td>79526</td>\n      <td>80682</td>\n      <td>82356</td>\n      <td>84119</td>\n      <td>85898</td>\n      <td>87530</td>\n      <td>88754</td>\n    </tr>\n    <tr>\n      <th>United Kingdom</th>\n      <td>28734</td>\n      <td>29427</td>\n      <td>30076</td>\n      <td>30615</td>\n      <td>31241</td>\n      <td>31587</td>\n      <td>31855</td>\n      <td>32065</td>\n      <td>32692</td>\n      <td>33186</td>\n      <td>33614</td>\n      <td>33998</td>\n      <td>34466</td>\n    </tr>\n    <tr>\n      <th>Italy</th>\n      <td>29079</td>\n      <td>29315</td>\n      <td>29684</td>\n      <td>29958</td>\n      <td>30201</td>\n      <td>30395</td>\n      <td>30560</td>\n      <td>30739</td>\n      <td>30911</td>\n      <td>31106</td>\n      <td>31368</td>\n      <td>31610</td>\n      <td>31763</td>\n    </tr>\n    <tr>\n      <th>Spain</th>\n      <td>25428</td>\n      <td>25613</td>\n      <td>25857</td>\n      <td>26070</td>\n      <td>26299</td>\n      <td>26478</td>\n      <td>26621</td>\n      <td>26744</td>\n      <td>26920</td>\n      <td>27104</td>\n      <td>27321</td>\n      <td>27459</td>\n      <td>27563</td>\n    </tr>\n    <tr>\n      <th>France</th>\n      <td>25168</td>\n      <td>25498</td>\n      <td>25772</td>\n      <td>25949</td>\n      <td>26192</td>\n      <td>26271</td>\n      <td>26341</td>\n      <td>26604</td>\n      <td>26951</td>\n      <td>27032</td>\n      <td>27381</td>\n      <td>27485</td>\n      <td>27483</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Malaysia</th>\n      <td>105</td>\n      <td>106</td>\n      <td>107</td>\n      <td>107</td>\n      <td>107</td>\n      <td>108</td>\n      <td>108</td>\n      <td>109</td>\n      <td>109</td>\n      <td>111</td>\n      <td>112</td>\n      <td>112</td>\n      <td>113</td>\n    </tr>\n    <tr>\n      <th>Kuwait</th>\n      <td>40</td>\n      <td>40</td>\n      <td>42</td>\n      <td>44</td>\n      <td>47</td>\n      <td>49</td>\n      <td>58</td>\n      <td>65</td>\n      <td>75</td>\n      <td>82</td>\n      <td>88</td>\n      <td>96</td>\n      <td>107</td>\n    </tr>\n    <tr>\n      <th>Bulgaria</th>\n      <td>78</td>\n      <td>80</td>\n      <td>84</td>\n      <td>84</td>\n      <td>86</td>\n      <td>90</td>\n      <td>91</td>\n      <td>93</td>\n      <td>95</td>\n      <td>96</td>\n      <td>99</td>\n      <td>102</td>\n      <td>105</td>\n    </tr>\n    <tr>\n      <th>Luxembourg</th>\n      <td>96</td>\n      <td>96</td>\n      <td>98</td>\n      <td>100</td>\n      <td>100</td>\n      <td>101</td>\n      <td>101</td>\n      <td>101</td>\n      <td>102</td>\n      <td>103</td>\n      <td>103</td>\n      <td>104</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>Slovenia</th>\n      <td>97</td>\n      <td>98</td>\n      <td>99</td>\n      <td>99</td>\n      <td>100</td>\n      <td>101</td>\n      <td>102</td>\n      <td>102</td>\n      <td>102</td>\n      <td>103</td>\n      <td>103</td>\n      <td>103</td>\n      <td>103</td>\n    </tr>\n  </tbody>\n</table>\n<p>68 rows × 13 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#df['LastDay']=df[df.columns[-1]]-df[df.columns[-2]] \ndf0=df[idx].copy()  #()\ntype(df)\n#for (coln, cold) in df.iteritems():\n#    print(coln, '->', cold)\nfor i in range(1,13):\n         df0[df0.columns[-i]] =    (df0[df.columns[-i]]-df0[df0.columns[-i-1]]) # casualties last day","execution_count":6,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## COVID Daily deaths \nMay 9th warning on Brazil Mexico in addition to cited March-30th: US UK Italy Spain France   "},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"df0.pop( df0.columns[0] )   # 0 \ndf9=df0.sort_values(by=[df.columns[-1]],ascending=False).head(16)\ndisplay(df9) ","execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"                5/5/20  5/6/20  5/7/20  5/8/20  5/9/20  5/10/20  5/11/20  \\\nCountry/Region                                                             \nUS                2142    2391    2207    1518    1615      731     1156   \nBrazil             571     650     602     827     639      467      530   \nUnited Kingdom     693     649     539     626     346      268      210   \nMexico             236     197     257     199     193      112      108   \nItaly              236     369     274     243     194      165      179   \nPeru               100      89      94      87     100       75       72   \nRussia              95      86      88      98     104       88       94   \nIndia              127      92     104      96     116      111       82   \nSpain              185     244     213     229     179      143      123   \nEcuador              0      49      36      50      13      410       18   \nCanada             118     112     121      94      61      142       85   \nBelgium             92     323      76     106      60       75       51   \nGermany              0     282     117     118      39       20       92   \nTurkey              59      64      57      48      50       47       55   \nCanada              59      55      47      58      61       25       37   \nIran                63      78      68      55      48       51       45   \n\n                5/12/20  5/13/20  5/14/20  5/15/20  5/16/20  \nCountry/Region                                               \nUS                 1674     1763     1779     1632     1224  \nBrazil              808      779      759      963      700  \nUnited Kingdom      627      494      428      384      468  \nMexico              353      294      257      290      278  \nItaly               172      195      262      242      153  \nPeru                 96      112       98      125      131  \nRussia              107       96       93      113      119  \nIndia               121      136       98      104      118  \nSpain               176      184      217      138      104  \nEcuador             182        7        4      256       94  \nCanada              118       89      131       50       82  \nBelgium              54       82       60       56       46  \nGermany              77      123       23       13       41  \nTurkey               53       58       55       48       41  \nCanada               64       31       32       24       37  \nIran                 48       50       71       48       35  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>5/5/20</th>\n      <th>5/6/20</th>\n      <th>5/7/20</th>\n      <th>5/8/20</th>\n      <th>5/9/20</th>\n      <th>5/10/20</th>\n      <th>5/11/20</th>\n      <th>5/12/20</th>\n      <th>5/13/20</th>\n      <th>5/14/20</th>\n      <th>5/15/20</th>\n      <th>5/16/20</th>\n    </tr>\n    <tr>\n      <th>Country/Region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>US</th>\n      <td>2142</td>\n      <td>2391</td>\n      <td>2207</td>\n      <td>1518</td>\n      <td>1615</td>\n      <td>731</td>\n      <td>1156</td>\n      <td>1674</td>\n      <td>1763</td>\n      <td>1779</td>\n      <td>1632</td>\n      <td>1224</td>\n    </tr>\n    <tr>\n      <th>Brazil</th>\n      <td>571</td>\n      <td>650</td>\n      <td>602</td>\n      <td>827</td>\n      <td>639</td>\n      <td>467</td>\n      <td>530</td>\n      <td>808</td>\n      <td>779</td>\n      <td>759</td>\n      <td>963</td>\n      <td>700</td>\n    </tr>\n    <tr>\n      <th>United Kingdom</th>\n      <td>693</td>\n      <td>649</td>\n      <td>539</td>\n      <td>626</td>\n      <td>346</td>\n      <td>268</td>\n      <td>210</td>\n      <td>627</td>\n      <td>494</td>\n      <td>428</td>\n      <td>384</td>\n      <td>468</td>\n    </tr>\n    <tr>\n      <th>Mexico</th>\n      <td>236</td>\n      <td>197</td>\n      <td>257</td>\n      <td>199</td>\n      <td>193</td>\n      <td>112</td>\n      <td>108</td>\n      <td>353</td>\n      <td>294</td>\n      <td>257</td>\n      <td>290</td>\n      <td>278</td>\n    </tr>\n    <tr>\n      <th>Italy</th>\n      <td>236</td>\n      <td>369</td>\n      <td>274</td>\n      <td>243</td>\n      <td>194</td>\n      <td>165</td>\n      <td>179</td>\n      <td>172</td>\n      <td>195</td>\n      <td>262</td>\n      <td>242</td>\n      <td>153</td>\n    </tr>\n    <tr>\n      <th>Peru</th>\n      <td>100</td>\n      <td>89</td>\n      <td>94</td>\n      <td>87</td>\n      <td>100</td>\n      <td>75</td>\n      <td>72</td>\n      <td>96</td>\n      <td>112</td>\n      <td>98</td>\n      <td>125</td>\n      <td>131</td>\n    </tr>\n    <tr>\n      <th>Russia</th>\n      <td>95</td>\n      <td>86</td>\n      <td>88</td>\n      <td>98</td>\n      <td>104</td>\n      <td>88</td>\n      <td>94</td>\n      <td>107</td>\n      <td>96</td>\n      <td>93</td>\n      <td>113</td>\n      <td>119</td>\n    </tr>\n    <tr>\n      <th>India</th>\n      <td>127</td>\n      <td>92</td>\n      <td>104</td>\n      <td>96</td>\n      <td>116</td>\n      <td>111</td>\n      <td>82</td>\n      <td>121</td>\n      <td>136</td>\n      <td>98</td>\n      <td>104</td>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>Spain</th>\n      <td>185</td>\n      <td>244</td>\n      <td>213</td>\n      <td>229</td>\n      <td>179</td>\n      <td>143</td>\n      <td>123</td>\n      <td>176</td>\n      <td>184</td>\n      <td>217</td>\n      <td>138</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>Ecuador</th>\n      <td>0</td>\n      <td>49</td>\n      <td>36</td>\n      <td>50</td>\n      <td>13</td>\n      <td>410</td>\n      <td>18</td>\n      <td>182</td>\n      <td>7</td>\n      <td>4</td>\n      <td>256</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>Canada</th>\n      <td>118</td>\n      <td>112</td>\n      <td>121</td>\n      <td>94</td>\n      <td>61</td>\n      <td>142</td>\n      <td>85</td>\n      <td>118</td>\n      <td>89</td>\n      <td>131</td>\n      <td>50</td>\n      <td>82</td>\n    </tr>\n    <tr>\n      <th>Belgium</th>\n      <td>92</td>\n      <td>323</td>\n      <td>76</td>\n      <td>106</td>\n      <td>60</td>\n      <td>75</td>\n      <td>51</td>\n      <td>54</td>\n      <td>82</td>\n      <td>60</td>\n      <td>56</td>\n      <td>46</td>\n    </tr>\n    <tr>\n      <th>Germany</th>\n      <td>0</td>\n      <td>282</td>\n      <td>117</td>\n      <td>118</td>\n      <td>39</td>\n      <td>20</td>\n      <td>92</td>\n      <td>77</td>\n      <td>123</td>\n      <td>23</td>\n      <td>13</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>Turkey</th>\n      <td>59</td>\n      <td>64</td>\n      <td>57</td>\n      <td>48</td>\n      <td>50</td>\n      <td>47</td>\n      <td>55</td>\n      <td>53</td>\n      <td>58</td>\n      <td>55</td>\n      <td>48</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>Canada</th>\n      <td>59</td>\n      <td>55</td>\n      <td>47</td>\n      <td>58</td>\n      <td>61</td>\n      <td>25</td>\n      <td>37</td>\n      <td>64</td>\n      <td>31</td>\n      <td>32</td>\n      <td>24</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>Iran</th>\n      <td>63</td>\n      <td>78</td>\n      <td>68</td>\n      <td>55</td>\n      <td>48</td>\n      <td>51</td>\n      <td>45</td>\n      <td>48</td>\n      <td>50</td>\n      <td>71</td>\n      <td>48</td>\n      <td>35</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(','.join('Mr%.0f' %x for x in range(18,30+1))  ,'\\ndata-datasets=\"')\nprint('\"\\ndata-datasets=\"'.join(','.join('%.0f' %x for x in y) for y in df9.values) )","execution_count":8,"outputs":[{"output_type":"stream","text":"Mr18,Mr19,Mr20,Mr21,Mr22,Mr23,Mr24,Mr25,Mr26,Mr27,Mr28,Mr29,Mr30 \ndata-datasets=\"\n2142,2391,2207,1518,1615,731,1156,1674,1763,1779,1632,1224\"\ndata-datasets=\"571,650,602,827,639,467,530,808,779,759,963,700\"\ndata-datasets=\"693,649,539,626,346,268,210,627,494,428,384,468\"\ndata-datasets=\"236,197,257,199,193,112,108,353,294,257,290,278\"\ndata-datasets=\"236,369,274,243,194,165,179,172,195,262,242,153\"\ndata-datasets=\"100,89,94,87,100,75,72,96,112,98,125,131\"\ndata-datasets=\"95,86,88,98,104,88,94,107,96,93,113,119\"\ndata-datasets=\"127,92,104,96,116,111,82,121,136,98,104,118\"\ndata-datasets=\"185,244,213,229,179,143,123,176,184,217,138,104\"\ndata-datasets=\"0,49,36,50,13,410,18,182,7,4,256,94\"\ndata-datasets=\"118,112,121,94,61,142,85,118,89,131,50,82\"\ndata-datasets=\"92,323,76,106,60,75,51,54,82,60,56,46\"\ndata-datasets=\"0,282,117,118,39,20,92,77,123,23,13,41\"\ndata-datasets=\"59,64,57,48,50,47,55,53,58,55,48,41\"\ndata-datasets=\"59,55,47,58,61,25,37,64,31,32,24,37\"\ndata-datasets=\"63,78,68,55,48,51,45,48,50,71,48,35\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"dxy=df0.transpose()\ndisplay(dxy.tail(4)) #pivot('Country/Region')","execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"Country/Region  Afghanistan  Algeria  Argentina  Austria  Bangladesh  Belarus  \\\n5/13/20                   5        7         10        1          19        4   \n5/14/20                   4        7         24        2          14        5   \n5/15/20                  17        7          3        2          15        5   \n5/16/20                  15        6          7        1          16        4   \n\nCountry/Region  Belgium  Bolivia  Bosnia and Herzegovina  Brazil  ...  \\\n5/13/20              82       14                       3     779  ...   \n5/14/20              60       10                       2     759  ...   \n5/15/20              56       12                       6     963  ...   \n5/16/20              46        1                       1     700  ...   \n\nCountry/Region  Slovenia  South Africa  Spain  Sweden  Switzerland  Turkey  \\\n5/13/20                1            13    184     147            3      58   \n5/14/20                0            19    217      69            2      55   \n5/15/20                0             9    138     117            6      48   \n5/16/20                0            14    104      28            1      41   \n\nCountry/Region  Ukraine  United Arab Emirates  United Kingdom    US  \n5/13/20              14                     3             494  1763  \n5/14/20              17                     2             428  1779  \n5/15/20              20                     2             384  1632  \n5/16/20              21                     4             468  1224  \n\n[4 rows x 68 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Country/Region</th>\n      <th>Afghanistan</th>\n      <th>Algeria</th>\n      <th>Argentina</th>\n      <th>Austria</th>\n      <th>Bangladesh</th>\n      <th>Belarus</th>\n      <th>Belgium</th>\n      <th>Bolivia</th>\n      <th>Bosnia and Herzegovina</th>\n      <th>Brazil</th>\n      <th>...</th>\n      <th>Slovenia</th>\n      <th>South Africa</th>\n      <th>Spain</th>\n      <th>Sweden</th>\n      <th>Switzerland</th>\n      <th>Turkey</th>\n      <th>Ukraine</th>\n      <th>United Arab Emirates</th>\n      <th>United Kingdom</th>\n      <th>US</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5/13/20</th>\n      <td>5</td>\n      <td>7</td>\n      <td>10</td>\n      <td>1</td>\n      <td>19</td>\n      <td>4</td>\n      <td>82</td>\n      <td>14</td>\n      <td>3</td>\n      <td>779</td>\n      <td>...</td>\n      <td>1</td>\n      <td>13</td>\n      <td>184</td>\n      <td>147</td>\n      <td>3</td>\n      <td>58</td>\n      <td>14</td>\n      <td>3</td>\n      <td>494</td>\n      <td>1763</td>\n    </tr>\n    <tr>\n      <th>5/14/20</th>\n      <td>4</td>\n      <td>7</td>\n      <td>24</td>\n      <td>2</td>\n      <td>14</td>\n      <td>5</td>\n      <td>60</td>\n      <td>10</td>\n      <td>2</td>\n      <td>759</td>\n      <td>...</td>\n      <td>0</td>\n      <td>19</td>\n      <td>217</td>\n      <td>69</td>\n      <td>2</td>\n      <td>55</td>\n      <td>17</td>\n      <td>2</td>\n      <td>428</td>\n      <td>1779</td>\n    </tr>\n    <tr>\n      <th>5/15/20</th>\n      <td>17</td>\n      <td>7</td>\n      <td>3</td>\n      <td>2</td>\n      <td>15</td>\n      <td>5</td>\n      <td>56</td>\n      <td>12</td>\n      <td>6</td>\n      <td>963</td>\n      <td>...</td>\n      <td>0</td>\n      <td>9</td>\n      <td>138</td>\n      <td>117</td>\n      <td>6</td>\n      <td>48</td>\n      <td>20</td>\n      <td>2</td>\n      <td>384</td>\n      <td>1632</td>\n    </tr>\n    <tr>\n      <th>5/16/20</th>\n      <td>15</td>\n      <td>6</td>\n      <td>7</td>\n      <td>1</td>\n      <td>16</td>\n      <td>4</td>\n      <td>46</td>\n      <td>1</td>\n      <td>1</td>\n      <td>700</td>\n      <td>...</td>\n      <td>0</td>\n      <td>14</td>\n      <td>104</td>\n      <td>28</td>\n      <td>1</td>\n      <td>41</td>\n      <td>21</td>\n      <td>4</td>\n      <td>468</td>\n      <td>1224</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 68 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Worst 7 countries in daily deaths last week \ndf9.transpose().tail(8).head(7).mean(axis=0).sort_values(ascending=False).head(7)    #1 Average for each row :","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"Country/Region\nUS                1478.571429\nBrazil             706.428571\nUnited Kingdom     393.857143\nMexico             229.571429\nItaly              201.285714\nSpain              165.714286\nEcuador            127.142857\ndtype: float64"},"metadata":{}}]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"import seaborn as sns\n%matplotlib inline\nsns.heatmap(df0)#, annot=True","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f3da041b890>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"###  Daily casualties Heat Map \n   in order of daily casualties : Italy Spain US France UK Iran Netherlands "},{"metadata":{"trusted":true},"cell_type":"code","source":"print( len(dxy.columns) ,dxy['Austria'].tail(20).mean() ) #   with ()   sns.heatmap(dxy)\ndrel=dxy.copy()\nfor r in drel.columns:\n    #print (r,    drel[r].tail(5).mean() )\n    drel[r] =  drel[r] / drel[r].tail(11).mean()\n    \ndinc=df0.copy()  #increase \nDays2 = dxy.tail(2).mean() \nDays4 = dxy.tail(5).head(2).mean() \nincreaserate=round(Days2/Days4,1)\n#display( increaserate )\n   \nfor i in range(1,6):\n         dinc[df0.columns[-i]] =    (df0[df0.columns[-i]]+df0[df0.columns[-i-1]])  /  (df0[df0.columns[-i-4]]+df0[df0.columns[-i-3]])  \n        \nsns.heatmap(dinc.transpose().tail(2))    ","execution_count":12,"outputs":[{"output_type":"stream","text":"68 2.4166666666666665\n","name":"stdout"},{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f3d86f1e090>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"By worst aspect due exponential increase: Sweden UK US Belgium Germany \\n Strangely constant : Iran , Recovered: China \")\n#drel.loc[['3/27/20']].transpose().sort_values(by=[df.columns[-1]],ascending=False)\ndrel.iloc[[-1]].transpose().sort_values(by=[df.columns[-1]],ascending=False)","execution_count":13,"outputs":[{"output_type":"stream","text":"By worst aspect due exponential increase: Sweden UK US Belgium Germany \n Strangely constant : Iran , Recovered: China \n","name":"stdout"},{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"                 5/16/20\nCountry/Region          \nIraq            2.315789\nAfghanistan     2.260274\nChile           2.034247\nMorocco         2.000000\nKuwait          1.805970\n...                  ...\nLuxembourg      0.000000\nPakistan        0.000000\nNorway          0.000000\nFrance         -0.011083\nChina                NaN\n\n[68 rows x 1 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>5/16/20</th>\n    </tr>\n    <tr>\n      <th>Country/Region</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Iraq</th>\n      <td>2.315789</td>\n    </tr>\n    <tr>\n      <th>Afghanistan</th>\n      <td>2.260274</td>\n    </tr>\n    <tr>\n      <th>Chile</th>\n      <td>2.034247</td>\n    </tr>\n    <tr>\n      <th>Morocco</th>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>Kuwait</th>\n      <td>1.805970</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Luxembourg</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Pakistan</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Norway</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>France</th>\n      <td>-0.011083</td>\n    </tr>\n    <tr>\n      <th>China</th>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>68 rows × 1 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"%matplotlib inline\n#dxy.plot(xticks=dxy.index,yticks=dxy.Austria)","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install chart-studio urllib3 # plotly","execution_count":15,"outputs":[{"output_type":"stream","text":"WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\nPlease see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\nTo avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\nCollecting chart-studio\n  Downloading chart_studio-1.1.0-py3-none-any.whl (64 kB)\n\u001b[K     |████████████████████████████████| 64 kB 1.4 MB/s eta 0:00:011\n\u001b[?25hRequirement already satisfied: urllib3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (1.25.7)\nCollecting retrying>=1.3.3\n  Downloading retrying-1.3.3.tar.gz (10 kB)\nRequirement already satisfied: requests in /srv/conda/envs/notebook/lib/python3.7/site-packages (from chart-studio) (2.22.0)\nRequirement already satisfied: six in /srv/conda/envs/notebook/lib/python3.7/site-packages (from chart-studio) (1.14.0)\nCollecting plotly\n  Downloading plotly-4.7.1-py2.py3-none-any.whl (11.5 MB)\n\u001b[K     |████████████████████████████████| 11.5 MB 3.6 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: idna<2.9,>=2.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests->chart-studio) (2.8)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests->chart-studio) (3.0.4)\nRequirement already satisfied: certifi>=2017.4.17 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests->chart-studio) (2019.11.28)\nBuilding wheels for collected packages: retrying\n  Building wheel for retrying (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for retrying: filename=retrying-1.3.3-py3-none-any.whl size=11429 sha256=24398a50acb956a3d99a6021d0bc7fed90f4ae43c6f4cf5d7b2439adc564515c\n  Stored in directory: /home/jovyan/.cache/pip/wheels/f9/8d/8d/f6af3f7f9eea3553bc2fe6d53e4b287dad18b06a861ac56ddf\nSuccessfully built retrying\nInstalling collected packages: retrying, plotly, chart-studio\nSuccessfully installed chart-studio-1.1.0 plotly-4.7.1 retrying-1.3.3\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}