Portfolio 2.ipynb

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    "# Portfolio 2: Analysing COVID-19 Data\n",
    "\n",
    "For the second portfolio we'll look at the most relevant dataset to our lives at the moment: the global spread of COVID-19.   There is an open dataset avaialable that is updated often showing the number of cases in different regions of the world.  This is the source of the graphs and visualisations that you'll see on the news. \n",
    "\n",
    "Here are some sample stories for reference:\n",
    "\n",
    "* [From the ABC](https://www.abc.net.au/news/2020-03-26/coronavirus-covid19-global-spread-data-explained/12089028)\n",
    "* [From the Guardian](https://www.theguardian.com/australia-news/datablog/ng-interactive/2020/apr/06/coronavirus-cases-in-australia-map-curve-confirmed-numbers-stats-how-many-covid-19-nsw-by-postcode-maps-victoria-live-data-qld-sa-wa-tas-nt-act-latest-statistics)\n",
    "* [Coronavirus in Australia](https://www.covid19data.com.au/)\n",
    "* [ArcGIS Dashboard](https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) Johns Hopkins\n"
   ]
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   "source": [
    "#Importing libraries\n",
    "import pandas as pd\n",
    "import pandas.util.testing\n",
    "import pandas.testing\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from datetime import timedelta\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "import statistics as st\n",
    "import scipy\n",
    "from pylab import rcParams\n",
    "import seaborn as sb\n",
    "from scipy.stats.stats import pearsonr\n",
    "rcParams['figure.figsize'] = 10,9\n",
    "sb.set_style('whitegrid')\n",
    "from scipy.stats import ttest_ind\n",
    "pd.set_option('mode.chained_assignment', None)\n"
   ]
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   "cell_type": "markdown",
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    "## Getting the Data\n",
    "\n",
    "The first step is to get a copy of the raw data.  The data is being made available by Johns Hopkins University in [this GitHub repository](https://github.com/CSSEGISandData/COVID-19). We're interestd in the global confirmed cases dataset but you can also get data on deaths and recovered cases.  \n",
    "\n",
    "You can either download a copy of the data into your project or just read it from the URL. The advantage of reading the URL is that you'll get live updates, but this might make it harder for you to repeat your experiments if the data changes.  Also, you would be making new requests for data every time you ran your worksheet putting load on the server (and your own network connection).  Consider downloading a copy as you are developing your worksheet and then switching to the live version once everything is debugged. (Eg. you could clone the github repository and copy the relevant data file into this project)."
   ]
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   "cell_type": "code",
   "execution_count": 5,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>5/14/20</th>\n",
       "      <th>5/15/20</th>\n",
       "      <th>5/16/20</th>\n",
       "      <th>5/17/20</th>\n",
       "      <th>5/18/20</th>\n",
       "      <th>5/19/20</th>\n",
       "      <th>5/20/20</th>\n",
       "      <th>5/21/20</th>\n",
       "      <th>5/22/20</th>\n",
       "      <th>5/23/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5639</td>\n",
       "      <td>6053</td>\n",
       "      <td>6402</td>\n",
       "      <td>6664</td>\n",
       "      <td>7072</td>\n",
       "      <td>7653</td>\n",
       "      <td>8145</td>\n",
       "      <td>8676</td>\n",
       "      <td>9216</td>\n",
       "      <td>9998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>898</td>\n",
       "      <td>916</td>\n",
       "      <td>933</td>\n",
       "      <td>946</td>\n",
       "      <td>948</td>\n",
       "      <td>949</td>\n",
       "      <td>964</td>\n",
       "      <td>969</td>\n",
       "      <td>981</td>\n",
       "      <td>989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6442</td>\n",
       "      <td>6629</td>\n",
       "      <td>6821</td>\n",
       "      <td>7019</td>\n",
       "      <td>7201</td>\n",
       "      <td>7377</td>\n",
       "      <td>7542</td>\n",
       "      <td>7728</td>\n",
       "      <td>7918</td>\n",
       "      <td>8113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>50</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 127 columns</p>\n",
       "</div>"
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      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  5/14/20  5/15/20  5/16/20  5/17/20  \\\n",
       "0        0        0        0  ...     5639     6053     6402     6664   \n",
       "1        0        0        0  ...      898      916      933      946   \n",
       "2        0        0        0  ...     6442     6629     6821     7019   \n",
       "3        0        0        0  ...      761      761      761      761   \n",
       "4        0        0        0  ...       48       48       48       48   \n",
       "\n",
       "   5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  \n",
       "0     7072     7653     8145     8676     9216     9998  \n",
       "1      948      949      964      969      981      989  \n",
       "2     7201     7377     7542     7728     7918     8113  \n",
       "3      761      761      762      762      762      762  \n",
       "4       50       52       52       58       60       61  \n",
       "\n",
       "[5 rows x 127 columns]"
      ]
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   "source": [
    "# loading the live dataset, assumes a working network connection\n",
    "covid_data_url = 'https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
    "covid = pd.read_csv(covid_data_url)\n",
    "covid.head()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data\n",
    "\n",
    "The format of this data is one row per geographical region with columns for Lat/Long and then one column for each day's data.  Most regions are countries but in some cases, as in Australia, they are states.  \n",
    "\n",
    "For the analysis we want to do we'll look at data for countries only, so the first operation on the data frame is to combine all of the rows for countries like Australia that are split into states.  We then want to drop the non-numeric columns to leave us with just the numbers on each day for each country.  This can be done using the [`groupby`](https://pandas.pydata.org/docs/getting_started/10min.html#grouping) method and the [`drop`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html) method.  We can use these to make a new dataframe containing just the numerical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country/Region', '1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20',\n",
       "       '1/27/20', '1/28/20', '1/29/20', '1/30/20',\n",
       "       ...\n",
       "       '5/14/20', '5/15/20', '5/16/20', '5/17/20', '5/18/20', '5/19/20',\n",
       "       '5/20/20', '5/21/20', '5/22/20', '5/23/20'],\n",
       "      dtype='object', length=124)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping on the basis of Country \n",
    "CovidGroupedData = covid.groupby('Country/Region').sum()\n",
    "\n",
    "#Dropping Lat and long\n",
    "GroupedData_Drop = CovidGroupedData.drop(['Lat', 'Long'],axis = 1)\n",
    "\n",
    "# Dropping columns that have complete 0 records in a column\n",
    "NumNumericGroup = GroupedData_Drop.loc[:, (GroupedData_Drop != 0).any(axis=0)]\n",
    "\n",
    "#Reindex to see new grouped data\n",
    "GroupedData_Clean = NumNumericGroup.reset_index()\n",
    "\n",
    "#Checking Columns of grouped data\n",
    "GroupedData_Clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now select just the data for one country [using the `loc` method](https://pandas.pydata.org/docs/getting_started/10min.html#selection) since the new dataframe will have the grouped values (Countries) as the row labels. Select the data for one country (you choose which) and plot it.  You should see the exponential rise in case numbers over time."
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of Cases</th>\n",
       "      <th>Parsed Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>400</td>\n",
       "      <td>2/15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11327</td>\n",
       "      <td>2/29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>888589</td>\n",
       "      <td>3/15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5990057</td>\n",
       "      <td>3/31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12652195</td>\n",
       "      <td>4/15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   No of Cases Parsed Date\n",
       "2          400        2/15\n",
       "3        11327        2/29\n",
       "4       888589        3/15\n",
       "5      5990057        3/31\n",
       "6     12652195        4/15"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using loc to look for country 'US'\n",
    "CovidUS = GroupedData_Clean.loc[GroupedData_Clean['Country/Region'] == 'US']\n",
    "\n",
    "# Stacking table to retrive year and month part from date field\n",
    "CovidStack = CovidUS.stack()\n",
    "\n",
    "# Reseting the index to view stacked data\n",
    "Coviddata1 = CovidStack.reset_index()\n",
    "\n",
    "# Cleaning the column name\n",
    "Coviddata1['Date'] =  Coviddata1['level_1']\n",
    "CovidClean1 = Coviddata1[['Date',0]]\n",
    "CovidClean1.columns = ['Date', 'No of Cases']\n",
    "DropFirstrow = CovidClean1[ CovidClean1['Date'] == 'Country/Region' ].index\n",
    "DropFirstrow\n",
    "\n",
    "# Delete these row indexes from dataFrame\n",
    "CovidClean1.drop(DropFirstrow , inplace=True)\n",
    "\n",
    "# Converting Parsed Date to date time\n",
    "CovidClean1['ParsedDate'] = pd.to_datetime(CovidClean1.Date)\n",
    "\n",
    "# Selecting Specific columns\n",
    "CovidClean2 = CovidClean1[['No of Cases','ParsedDate']]\n",
    "\n",
    "# Grouped data based on semi-monthly\n",
    "dt = CovidClean2.groupby([pd.Grouper(key='ParsedDate', freq='SM')])['No of Cases'].sum().reset_index().sort_values('ParsedDate')\n",
    "\n",
    "# Coverting to string and taking left 6 \n",
    "dt['ParsedDate'] = dt.astype(str)\n",
    "dt['FinalParsedDate'] =  dt['ParsedDate'].str[6:]\n",
    "\n",
    "# Cleaning the string\n",
    "dt['Parsed Date'] = dt['FinalParsedDate'].replace({'-':'/'}, regex=True)\n",
    "\n",
    "# Final Parsed data set\n",
    "dt_Final = dt[['No of Cases', 'Parsed Date']]\n",
    "\n",
    "# Filtering to remove data from 4/30\n",
    "dt_FinalCOVID = dt_Final[dt_Final['Parsed Date'] >= '4/30' ].index\n",
    "\n",
    "# Dropping data from 4/30\n",
    "dt_Final.drop(dt_FinalCOVID , inplace=True)\n",
    "\n",
    "# Viewing tail records\n",
    "dt_Final.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/15\n"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Storing the result in a X and Y cordinate\n",
    "x = dt_Final['Parsed Date']\n",
    "y = dt_Final['No of Cases']\n",
    "\n",
    "# Creating a plot \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "line, = ax.plot(x, y)\n",
    "\n",
    "# Taking maximum point to annotate the country name\n",
    "ymax = max(y)         \n",
    "xpos = x.index.max()\n",
    "xmax = x[xpos]\n",
    "print(xmax)\n",
    "ax.annotate('US', xy=(xmax, ymax+200000), xytext=(xmax, ymax+200000)\n",
    "            )\n",
    "ax.plot(xmax,ymax,'bo')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Known Cases')\n",
    "plt.title(\"Covid-19 for US\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ For US, the COVID cases remain negligible starting from 1/15 to 2/29. We see an increment in cases from 2/19 to 3/15 and from 3/15 onwards, we can see an exponential increase of COVID cases. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Countries\n",
    "\n",
    "One of the classic displays in the news is the comparison between countries.  Select a number of countries and plot their data on the same graph to reproduce this visualisation. Suitable countries would be those who have significant outbreaks - China, USA, Italy, UK, Australia. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Country/Region</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>...</th>\n",
       "      <th>5/14/20</th>\n",
       "      <th>5/15/20</th>\n",
       "      <th>5/16/20</th>\n",
       "      <th>5/17/20</th>\n",
       "      <th>5/18/20</th>\n",
       "      <th>5/19/20</th>\n",
       "      <th>5/20/20</th>\n",
       "      <th>5/21/20</th>\n",
       "      <th>5/22/20</th>\n",
       "      <th>5/23/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>7019</td>\n",
       "      <td>7035</td>\n",
       "      <td>7044</td>\n",
       "      <td>7054</td>\n",
       "      <td>7068</td>\n",
       "      <td>7072</td>\n",
       "      <td>7081</td>\n",
       "      <td>7095</td>\n",
       "      <td>7099</td>\n",
       "      <td>7114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>China</td>\n",
       "      <td>548</td>\n",
       "      <td>643</td>\n",
       "      <td>920</td>\n",
       "      <td>1406</td>\n",
       "      <td>2075</td>\n",
       "      <td>2877</td>\n",
       "      <td>5509</td>\n",
       "      <td>6087</td>\n",
       "      <td>8141</td>\n",
       "      <td>...</td>\n",
       "      <td>84029</td>\n",
       "      <td>84038</td>\n",
       "      <td>84044</td>\n",
       "      <td>84054</td>\n",
       "      <td>84063</td>\n",
       "      <td>84063</td>\n",
       "      <td>84063</td>\n",
       "      <td>84063</td>\n",
       "      <td>84081</td>\n",
       "      <td>84084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Italy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>223096</td>\n",
       "      <td>223885</td>\n",
       "      <td>224760</td>\n",
       "      <td>225435</td>\n",
       "      <td>225886</td>\n",
       "      <td>226699</td>\n",
       "      <td>227364</td>\n",
       "      <td>228006</td>\n",
       "      <td>228658</td>\n",
       "      <td>229327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1417774</td>\n",
       "      <td>1442824</td>\n",
       "      <td>1467820</td>\n",
       "      <td>1486757</td>\n",
       "      <td>1508308</td>\n",
       "      <td>1528568</td>\n",
       "      <td>1551853</td>\n",
       "      <td>1577147</td>\n",
       "      <td>1600937</td>\n",
       "      <td>1622612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "8        Australia        0        0        0        0        4        5   \n",
       "36           China      548      643      920     1406     2075     2877   \n",
       "85           Italy        0        0        0        0        0        0   \n",
       "174             US        1        1        2        2        5        5   \n",
       "\n",
       "     1/28/20  1/29/20  1/30/20  ...  5/14/20  5/15/20  5/16/20  5/17/20  \\\n",
       "8          5        6        9  ...     7019     7035     7044     7054   \n",
       "36      5509     6087     8141  ...    84029    84038    84044    84054   \n",
       "85         0        0        0  ...   223096   223885   224760   225435   \n",
       "174        5        5        5  ...  1417774  1442824  1467820  1486757   \n",
       "\n",
       "     5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  \n",
       "8       7068     7072     7081     7095     7099     7114  \n",
       "36     84063    84063    84063    84063    84081    84084  \n",
       "85    225886   226699   227364   228006   228658   229327  \n",
       "174  1508308  1528568  1551853  1577147  1600937  1622612  \n",
       "\n",
       "[4 rows x 124 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using loc to look for countries like 'US', 'China', 'Australia' and 'Italy'\n",
    "CovidSpecificCountries = GroupedData_Clean.loc[GroupedData_Clean['Country/Region'].isin(['US','China','Australia','Italy'])]\n",
    "CovidSpecificCountries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Italy</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1/23/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>US</td>\n",
       "      <td>5/22/20</td>\n",
       "      <td>1600937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Australia</td>\n",
       "      <td>5/23/20</td>\n",
       "      <td>7114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>China</td>\n",
       "      <td>5/23/20</td>\n",
       "      <td>84084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Italy</td>\n",
       "      <td>5/23/20</td>\n",
       "      <td>229327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>US</td>\n",
       "      <td>5/23/20</td>\n",
       "      <td>1622612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country/Region variable    value\n",
       "0        Australia  1/22/20        0\n",
       "1            China  1/22/20      548\n",
       "2            Italy  1/22/20        0\n",
       "3               US  1/22/20        1\n",
       "4        Australia  1/23/20        0\n",
       "..             ...      ...      ...\n",
       "487             US  5/22/20  1600937\n",
       "488      Australia  5/23/20     7114\n",
       "489          China  5/23/20    84084\n",
       "490          Italy  5/23/20   229327\n",
       "491             US  5/23/20  1622612\n",
       "\n",
       "[492 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Melt to group data based on countries and perform pivort\n",
    "CovidSpecificCountries_Melt = CovidSpecificCountries.melt(id_vars = ['Country/Region'])\n",
    "CovidSpecificCountries_Melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Known Cases</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Parsed Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>86513</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3/31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12652195</td>\n",
       "      <td>US</td>\n",
       "      <td>4/15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2794442</td>\n",
       "      <td>Italy</td>\n",
       "      <td>4/15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99486</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4/15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1257055</td>\n",
       "      <td>China</td>\n",
       "      <td>4/15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Known Cases Country/Region Parsed Date\n",
       "5         86513      Australia        3/31\n",
       "33     12652195             US        4/15\n",
       "24      2794442          Italy        4/15\n",
       "6         99486      Australia        4/15\n",
       "15      1257055          China        4/15"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting to date and renaming columns\n",
    "CovidSpecificCountries_Melt['Date'] = pd.to_datetime(CovidSpecificCountries_Melt.variable)\n",
    "CovidSpecificCountries_Cleaned = CovidSpecificCountries_Melt[['Country/Region','Date', 'value']]\n",
    "\n",
    "# Grouping data by taking semi-monthly dates and parsing date field\n",
    "dt1 = CovidSpecificCountries_Cleaned.groupby(['Country/Region', pd.Grouper(key='Date', freq='SM')])[['value','Country/Region']].sum().reset_index().sort_values('Date')\n",
    "dt1['Parsed Dates'] = dt1['Date'].astype(str)\n",
    "dt1['Parsed Date'] =  dt1['Parsed Dates'].str[6:]\n",
    "dt1['Parsed Date'] = dt1['Parsed Date'].replace({'-':'/'}, regex=True)\n",
    "\n",
    "# Choosing specific fields and dropping data from 4/30 so that we have consistent result\n",
    "CovidSpecificCountries_Final = dt1[['value', 'Country/Region','Parsed Date']]\n",
    "CovidSpecificCountries_Final.columns = ['Known Cases', 'Country/Region','Parsed Date']\n",
    "CovidSpecificCountries_Final_Cleaned = CovidSpecificCountries_Final[CovidSpecificCountries_Final['Parsed Date'] >= '4/30' ].index\n",
    "CovidSpecificCountries_Final.drop(CovidSpecificCountries_Final_Cleaned , inplace=True)\n",
    "CovidSpecificCountries_Final.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking maximum value to show the countries in the plot\n",
    "CovidSpecificCountries_Max = CovidSpecificCountries_Final.groupby(['Country/Region'], sort=False)[['Known Cases','Parsed Date']].max()\n",
    "CovidSpecificCountries_Max_Reset = CovidSpecificCountries_Max.reset_index()\n",
    "CovidSpecificCountries_Max_Reset\n",
    "\n",
    "#Choosing colors of specific countries\n",
    "colors = {'Australia':'red', 'China':'green', 'Italy':'olive', 'US':'blue'}\n",
    "\n",
    "# Using subplots\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "line, = ax.plot(x, y)\n",
    "for label, df in CovidSpecificCountries_Final.groupby('Country/Region'):\n",
    "    df.plot(kind='line', x = \"Parsed Date\", y = \"Known Cases\", ax=ax,label=label, color=colors[label])\n",
    "colors1 = {'Australia':'ro', 'China':'go', 'Italy':'yo', 'US':'bo'}\n",
    "\n",
    "# To add country details in the graph\n",
    "for Country,value,date in  zip(CovidSpecificCountries_Max_Reset['Country/Region'], CovidSpecificCountries_Max_Reset['Known Cases'], CovidSpecificCountries_Max_Reset['Parsed Date']):\n",
    "        ymax = value         \n",
    "        xmax = date\n",
    "        ax.annotate(Country, xy=(xmax, ymax+200000), xytext=(xmax, ymax+200000))\n",
    "        ax.plot(xmax,ymax,colors1[Country])\n",
    "        \n",
    "# Adding labels to the graph        \n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Known Cases')\n",
    "plt.title('COVID-19 trend')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation__:- \n",
    "1. For US, the COVID cases remain negligible starting from 1/15 to 2/29. We see an increment in cases from 2/19 to 3/15 and from 3/15 onwards, we can see an exponential increase of COVID cases. \n",
    "2. For China, we see increment in COVID cases starting from 1/15 to 2/15. From 2/15 onwards, the cases remain stable.\n",
    "3. For Italy, we see negligible cases starting from 1/12 to 2/19. However, we see increment in the number of cases starting 2/29 to 4/15.\n",
    "4. For Australia, we see negligible cases starting from 1/15 to 4/15.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__A very useful visualisation shows the data for different countries aligned from the time that they have 100 confirmed cases.  To create this figure, you need to take only the part of each time series after the value is greater than or equal to 100 and then plot this starting at 0 on the x-axis.  This is a bit more involved but will allow you to explore Pandas a bit more.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to calculate time series based on country name\n",
    "\n",
    "def CaltimeSeries(countryname):\n",
    "    li = []\n",
    "    GroupedData_Melt =  GroupedData_Clean.melt(id_vars = ['Country/Region'])\n",
    "    GroupedData_CountryName = GroupedData_Melt[GroupedData_Melt[\"Country/Region\"] == countryname]\n",
    "    GroupedData_CountryName_gtr100 = GroupedData_CountryName[GroupedData_CountryName[\"value\"]>100]\n",
    "    GroupedData_CountryName_gtr100['Date'] = pd.to_datetime(GroupedData_CountryName_gtr100.variable)\n",
    "    a_df=np.unique(GroupedData_CountryName_gtr100['Date'], axis=0)\n",
    "    UniqDate= pd.DataFrame(a_df)\n",
    "    UniqDate['New_ID'] = UniqDate.index\n",
    "    UniqDate20days = UniqDate.groupby([pd.Grouper(key=0, freq='20D')])['New_ID'].min().reset_index().sort_values(0)\n",
    "    CountryGrp = GroupedData_CountryName_gtr100.groupby(['Country/Region', pd.Grouper(key='Date', freq='20D')])[['value','Country/Region']].sum().reset_index().sort_values('Date')\n",
    "    MergeFinal= pd.merge(CountryGrp, UniqDate20days, how='left', left_on='Date', right_on=0)\n",
    "    dt_FinalCOVID = MergeFinal[MergeFinal[0]> '2020-04-12' ].index\n",
    "    MergeFinal.drop(dt_FinalCOVID , inplace=True)\n",
    "    FinalMerge = MergeFinal[[\"Country/Region\",\"Date\",\"value\",\"New_ID\"]]\n",
    "    FinalMerge.columns = [\"Country/Region\",\"Date\",\"Know Cases\",\"Days since 100th case\"]\n",
    "    li.append(FinalMerge)\n",
    "    frame = pd.concat(li, axis=0, ignore_index=True)\n",
    "    return frame\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating time series of specific countries\n",
    "US = CaltimeSeries('US')\n",
    "Australia = CaltimeSeries('Australia')\n",
    "Italy = CaltimeSeries('Italy')\n",
    "China = CaltimeSeries('China')\n",
    "UK = CaltimeSeries('United Kingdom')\n",
    "Taiwan = CaltimeSeries('Taiwan*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = US.append([Australia,Italy,China,UK,Taiwan])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Country/Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Know Cases</th>\n",
       "      <th>Days since 100th case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>125404</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>5020722</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-12</td>\n",
       "      <td>16568687</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>26266</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>116866</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>99459</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>1339959</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>3116742</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>321086</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>1430215</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1617032</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>1644927</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>1673678</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>44061</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>879692</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>5421</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>8118</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country/Region       Date  Know Cases  Days since 100th case\n",
       "0              US 2020-03-03      125404                      0\n",
       "1              US 2020-03-23     5020722                     20\n",
       "2              US 2020-04-12    16568687                     40\n",
       "0       Australia 2020-03-10       26266                      0\n",
       "1       Australia 2020-03-30      116866                     20\n",
       "0           Italy 2020-02-23       99459                      0\n",
       "1           Italy 2020-03-14     1339959                     20\n",
       "2           Italy 2020-04-03     3116742                     40\n",
       "0           China 2020-01-22      321086                      0\n",
       "1           China 2020-02-11     1430215                     20\n",
       "2           China 2020-03-02     1617032                     40\n",
       "3           China 2020-03-22     1644927                     60\n",
       "4           China 2020-04-11     1673678                     80\n",
       "0  United Kingdom 2020-03-05       44061                      0\n",
       "1  United Kingdom 2020-03-25      879692                     20\n",
       "0         Taiwan* 2020-03-19        5421                      0\n",
       "1         Taiwan* 2020-04-08        8118                     20"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking maximum value to add annotation based on each countries\n",
    "SpecificCountiriesMax = result.groupby(['Country/Region'], sort=False)[['Know Cases','Days since 100th case']].max()\n",
    "SpecificCountiriesMaxResult = SpecificCountiriesMax.reset_index()\n",
    "SpecificCountiriesMaxResult\n",
    "\n",
    "#Using subplots \n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "ax.plot( 'Days since 100th case', 'Know Cases', data=US, marker='.', markerfacecolor='blue', markersize=8, color='blue', linewidth=2,label=\"US\")\n",
    "ax.plot( 'Days since 100th case', 'Know Cases', data=Australia, marker='.', color='red', markersize=8, linewidth=2,label=\"Australia\")\n",
    "ax.plot( 'Days since 100th case', 'Know Cases', data=Italy, marker='.', color='#ffb3ff', markersize=8,linewidth=2,label=\"Italy\")\n",
    "ax.plot( 'Days since 100th case', 'Know Cases', data=China, marker='.', color='green',markersize=8, linewidth=2,label=\"China\")\n",
    "ax.plot( 'Days since 100th case', 'Know Cases', data=UK, marker='.', color='#eafb50',markersize=8, linewidth=2,label=\"United Kingdom\")\n",
    "ax.plot( 'Days since 100th case', 'Know Cases', data=Taiwan, marker='.', color='#f7bb5f', markersize=8,linewidth=2,label=\"Taiwan\")\n",
    "\n",
    "# Using colors to distinguish countries\n",
    "colors = {'Australia':'ro', 'China':'go', 'Italy':'yo', 'US':'bo','United Kingdom':'yo','Taiwan*':'yo'}\n",
    "\n",
    "# Using loop to show different countries \n",
    "for Country,Cases,Days in  zip(SpecificCountiriesMaxResult['Country/Region'], SpecificCountiriesMaxResult['Know Cases'], SpecificCountiriesMaxResult['Days since 100th case']):\n",
    "        ymax = Cases         \n",
    "        xmax = Days\n",
    "        ax.annotate(Country, xy=(xmax, ymax+200000), xytext=(xmax, ymax+200000))\n",
    "        ax.plot(xmax,ymax,colors[Country])\n",
    "\n",
    "plt.xlabel('Days since 100th case')\n",
    "plt.ylabel('Cumulative Known Cases since 100th case')\n",
    "plt.title('COVID-19 trend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ The plot shows data for different countries aligned from the time they have 100 confirmed cases. For US, we can how rapidly the virus was spread in next 40 days. However, for china, we see increase in number of cases for first 20 days, but later the cases remain stable. For Italy, initially for first 20 days, the number of cases were in line with china. However, after 20 days, we see Italy overtaking china. The COVID cases in United Kingdom is slightly below the china. It seems difficult to compare early days of each outbreak as they are compressed down the bottom of the chart. To aid with this, we need to use a logarithmic scale with is shown towards the end of this portfolio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalisation by Population\n",
    "\n",
    "The raw data includes the overall count of cases in each country. Clearly small countries will have smaller overall values than large countries.  It would be interesting to see whether the number of cases in China and the US was more per-capita than those in other countries and find which country has the highest number as a proportion of the population. (A good way to measure this is the number of cases per million people in the population. If a country has 25 cases and a population of 2.5 million they would have 10 cases per million).\n",
    "\n",
    "To answer these questions we need population data. I'll present two possible sources of data (there are more of course) [datahub.io](https://datahub.io/JohnSnowLabs/population-figures-by-country) has data per country up to 2016 in a handy CSV format. The [United Nations Population Dynamics page](https://population.un.org/wpp/Download/Standard/CSV/) has data that predicts populations up to 2100 in CSV format.  Use one of these data sets to compute the number of confirmed cases for each country per million population.   Create a new plot with this data. Create a plot to compare the most recent day's data - which country has the most cases per million right now? \n",
    "\n",
    "_Note, to use the population data you'll have to make sure that the country names match up in the different data sets. There's no magic way to do this, either you edit one of the datasets or write code to modify the data once you read it in.  The second of these is better because it makes it easy for someone else to repeat your analysis._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Country/Region</th>\n",
       "      <th>Cases Per Million</th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>67.37</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>2335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>71.23</td>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>2469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>78.02</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>2704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>83.50</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>2894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>93.02</td>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>3224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>97.86</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>102.80</td>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>3563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>109.00</td>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>3778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>116.36</td>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>4033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>127.01</td>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>4402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>135.23</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>4687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>143.19</td>\n",
       "      <td>2020-05-12</td>\n",
       "      <td>4963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>150.78</td>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>5226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>162.69</td>\n",
       "      <td>2020-05-14</td>\n",
       "      <td>5639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>174.64</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>6053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>184.71</td>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>6402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>192.27</td>\n",
       "      <td>2020-05-17</td>\n",
       "      <td>6664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>204.04</td>\n",
       "      <td>2020-05-18</td>\n",
       "      <td>7072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>220.80</td>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>7653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>235.00</td>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>8145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country/Region  Cases Per Million       Date  Cases\n",
       "100    Afghanistan              67.37 2020-05-01   2335\n",
       "101    Afghanistan              71.23 2020-05-02   2469\n",
       "102    Afghanistan              78.02 2020-05-03   2704\n",
       "103    Afghanistan              83.50 2020-05-04   2894\n",
       "104    Afghanistan              93.02 2020-05-05   3224\n",
       "105    Afghanistan              97.86 2020-05-06   3392\n",
       "106    Afghanistan             102.80 2020-05-07   3563\n",
       "107    Afghanistan             109.00 2020-05-08   3778\n",
       "108    Afghanistan             116.36 2020-05-09   4033\n",
       "109    Afghanistan             127.01 2020-05-10   4402\n",
       "110    Afghanistan             135.23 2020-05-11   4687\n",
       "111    Afghanistan             143.19 2020-05-12   4963\n",
       "112    Afghanistan             150.78 2020-05-13   5226\n",
       "113    Afghanistan             162.69 2020-05-14   5639\n",
       "114    Afghanistan             174.64 2020-05-15   6053\n",
       "115    Afghanistan             184.71 2020-05-16   6402\n",
       "116    Afghanistan             192.27 2020-05-17   6664\n",
       "117    Afghanistan             204.04 2020-05-18   7072\n",
       "118    Afghanistan             220.80 2020-05-19   7653\n",
       "119    Afghanistan             235.00 2020-05-20   8145"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying melt to group and pivot the table\n",
    "CovidNor = GroupedData_Clean.melt(id_vars = ['Country/Region'])\n",
    "\n",
    "# Using lookup table to match country code\n",
    "Lookuptable = pd.read_csv('data/Portfolio2/Country-Code.csv')\n",
    "\n",
    "# Downloading data from datahub.io\n",
    "TotalPop = pd.read_csv('data/Portfolio2/population-figures-by-country.csv')\n",
    "\n",
    "#Merging Covid data with total population\n",
    "CovMergeFinal= pd.merge(CovidNor, Lookuptable, how='left', left_on='Country/Region', right_on='Country/Region')\n",
    "CovMergePopulation = pd.merge(CovMergeFinal, TotalPop, how='left', left_on='Country', right_on='Country_Code')\n",
    "CovMergePopulationClean = CovMergePopulation[['Country/Region','variable','value','Year_2016']]\n",
    "CovMergePopulationClean[\"Population in Million\"] = round(CovMergePopulationClean['Year_2016']/1000000,2)\n",
    "CovMergePopulationClean = CovMergePopulationClean.reset_index()\n",
    "\n",
    "# Removing cases where we have less the 1 million population in order to calculate of cases per million.\n",
    "CovMergePopulationCleangtr1 = CovMergePopulationClean[CovMergePopulationClean[\"Population in Million\"] >=1]\n",
    "CovMergePopulationCleangtr1\n",
    "CovMergePopulationgtr1Clean = CovMergePopulationCleangtr1[['Country/Region','variable','value', 'Population in Million']]\n",
    "CovMergePopulationgtr1Clean.sort_values('Country/Region')\n",
    "CovMergePopulationgtr1Clean.columns = ['Country/Region', 'Date','Cases','Population in Million']\n",
    "CovMergePopulationgtr1Clean['Cases Per Million'] = round(CovMergePopulationgtr1Clean['Cases']/CovMergePopulationgtr1Clean['Population in Million'],2)\n",
    "CovMergePopulationgtr1CleanCases =  CovMergePopulationgtr1Clean.groupby(['Country/Region','Cases Per Million','Date'])['Cases'].sum().to_frame()\n",
    "CovMergePopulationFinal = CovMergePopulationgtr1CleanCases.dropna()\n",
    "CovMergePopulationFinal\n",
    "CovMergePopulationFinalClean =  CovMergePopulationFinal.groupby(['Country/Region','Cases Per Million','Date'])['Cases'].sum().to_frame()\n",
    "CovPopulationFinalClean = CovMergePopulationFinalClean.reset_index()\n",
    "CovPopulationFinalClean['Date'] = pd.to_datetime(CovPopulationFinalClean.Date)\n",
    "CovPopulationFinalClean[100:120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x12b135bb5c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a running Bar graph that shows top 10 countries with most cases per million\n",
    "from time import sleep\n",
    "from matplotlib import animation\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# We will be running Bar Graph starting 20200401\n",
    "CovDate = CovPopulationFinalClean[CovPopulationFinalClean['Date'] > '2020-04-01']\n",
    "CovDateFinal = CovDate['Date']\n",
    "CovDateFinalDrop = CovDateFinal.drop_duplicates()\n",
    "CovDatFinal  =CovDateFinalDrop.reset_index()\n",
    "CovDatFinals = CovDatFinal['Date']\n",
    "CovDatFinals \n",
    "\n",
    "\n",
    "#top 10 country having Cases per million population\n",
    "list_countries = ['Qatar', 'Bahrain', 'Singapore', 'Ireland', 'Spain','Belgium','US','Kuwait','United Kingdom','Italy']\n",
    "list_colors = ['#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff','#f7bb5f','#eafb50','olive','cyan','gray']\n",
    "#Subplots\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "def plot_bar(some_date):\n",
    "    df2 = CovPopulationFinalClean[CovPopulationFinalClean['Date'].eq(some_date)]\n",
    "    ax.clear()\n",
    "    # Only take Confirmed column in descending order\n",
    "    df3 = df2.sort_values(by = 'Cases Per Million', ascending = False)\n",
    "    # Select the top 10  countries having cases per million population \n",
    "    df4 = df3[['Country/Region','Cases Per Million']].head(10)\n",
    "    # To slow down the animation\n",
    "    sleep(4)  \n",
    "    # ax.barh() makes a horizontal bar plot.\n",
    "    for i, (value, name) in enumerate(zip(df4['Cases Per Million'], df4['Country/Region'])):\n",
    "        ax.text(value, i,     name,            ha='right')  # Tokyo: name\n",
    "        ax.text(value, i,     value,           ha='left')   # 38194.2: value\n",
    "    # Coloring and transforming data    \n",
    "    ax.text(1.2, 0.6, some_date, transform=ax.transAxes, color='#777777', size=25, ha='right', weight=800)\n",
    "    ax.text(0, 1.06, 'Population Per Million', transform=ax.transAxes, size=16, color='#777777')\n",
    "    ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "    ax.xaxis.set_ticks_position('top')\n",
    "    ax.tick_params(axis='x', colors='#777777', labelsize=12)\n",
    "    ax.set_yticks([])\n",
    "    ax.margins(0, 0.01)\n",
    "    ax.grid(which='major', axis='x', linestyle='-')\n",
    "    ax.set_axisbelow(True)\n",
    "    # Adding header to bar chart\n",
    "    ax.text(0, 1.12, 'Top 10 Counties With COVID-19 Cases Per Million',\n",
    "            transform=ax.transAxes, size=24, weight=600, ha='left')\n",
    "    plt.box(False)\n",
    "    return ax.barh(df4['Country/Region'], df4['Cases Per Million'], color= list_colors)\n",
    "\n",
    "# using FuncAnimation to show data till most recent days\n",
    "my_anim = animation.FuncAnimation(fig = fig, func = plot_bar,\n",
    "                    frames= CovDatFinals,interval = 1000)\n",
    "my_anim\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#path_mp4 = r'data\\CovidCases.mp4' \n",
    "path_mp4 = r'data\\CovidCases.mp4' \n",
    "my_anim.save(filename = path_mp4, writer = 'ffmpeg',\n",
    "             fps=10,\n",
    "             extra_args= ['-vcodec', 'libx264', '-pix_fmt', 'yuv420p'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"500\"\n",
       "            src=\"https://www.youtube.com/embed/2BAj7PpHFgU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1b983964cc8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Audio, Image, YouTubeVideo\n",
    "# Added the video in youtube\n",
    "YouTubeVideo(id='2BAj7PpHFgU',width =600,height =500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ From the mentioned video, we can see that Qatar has most cases per million."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Predictive Model\n",
    "\n",
    "It is well understood that the spread of the virus follows an exponential pattern. This is because each infected person infects more than one new person on average, leading to exponential growth.  This is why the key to stopping the growth of the pandemic is to reduce the number of people infected by stopping contact and isolating infected poeple. \n",
    "\n",
    "An exponential curve has the equation $y = e^{mx}$. It can be converted to a linear relationship by taking the logarithm of each side: $log(y) = mx$.  This means that we can fit a linear regression model to the data as long as we take the log of the number of cases. \n",
    "\n",
    "Select a country with a clear exponential curve (for example, the US) and build a linear regression model to predict the log of the number of case.  Test how well the model fits the data. \n",
    "\n",
    "Now, select the data from China. This should show some divergence from the model since they have managed to slow the growth of cases.  Can you show from the model that China's data does not show exponential growth?   If China had not acted to stop the virus, how many cases would there be now according to this simple model? \n",
    "\n",
    "Can you use this metric to identify countries where the virus is under control and those where it is not? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using libraries to predict the model\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import scale\n",
    "linear_model = LinearRegression(normalize = True,fit_intercept=True)\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'COVID-19 trend')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting cases for US\n",
    "US = CaltimeSeries('US')\n",
    "plt.plot(US['Days since 100th case'],US['Know Cases'],label = 'US')\n",
    "plt.xlabel(\"Days since 2020-01-22\")\n",
    "plt.ylabel(\"No of Cases\")\n",
    "plt.legend()\n",
    "plt.title('COVID-19 trend')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For US, the intercept is: 12.155270414729243 and the coefficient is: [0.12209323]\n"
     ]
    }
   ],
   "source": [
    "# Defining X and Y coordinates\n",
    "X = US[['Days since 100th case']]\n",
    "y = US['Know Cases']\n",
    "\n",
    "# taking logarithmic value\n",
    "log_y =  np.log(US['Know Cases'])\n",
    "log_y\n",
    "\n",
    "# Applying linear regression\n",
    "LinReg = LinearRegression()\n",
    "LinReg.fit(X,log_y)\n",
    "print(\"For US, the intercept is:\",LinReg.intercept_,'and the coefficient is:',LinReg.coef_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ We see a linear co-efficient of 0.122 and intercept of 12.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.15527041, 14.59713509, 17.03899976])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting values\n",
    "y_pred = LinReg.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'COVID-19 trend for US')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log_y, label = \"Actual Value\")\n",
    "plt.plot(y_pred, label = \"Predicted Value\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Days since 2020-01-22\")\n",
    "plt.ylabel(\"No of Cases\")\n",
    "plt.title('COVID-19 trend for US')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r squared of the Prediction:(US) 0.9199136023765467\n"
     ]
    }
   ],
   "source": [
    "print('r squared of the Prediction:(US)',LinReg.score(X,log_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ We can see that actual and predicted value for US matches closely with each other. The linear regression model is performing well and r square value is close to 1. The model fits well in predicting the data for US."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "select the data from China. This should show some divergence from the model since they have managed to slow the growth of cases.  Can you show from the model that China's data does not show exponential growth?   If China had not acted to stop the virus, how many cases would there be now according to this simple model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12b11105588>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "China = CaltimeSeries('China')\n",
    "China\n",
    "#dfUS1 = dfUS[dfUS['Days']>50]\n",
    "plt.plot(China['Days since 100th case'],China['Know Cases'],label = 'China')\n",
    "plt.xlabel(\"Days since 2020-01-22\")\n",
    "plt.ylabel(\"No of Cases\")\n",
    "plt.title('COVID-19 trend for China')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For China Intercept is: 753408.4  and Coefficient is: [14599.48]\n"
     ]
    }
   ],
   "source": [
    "# Defining X and Y Coordinate\n",
    "X = China[['Days since 100th case']]\n",
    "y = China['Know Cases']\n",
    "LinReg = LinearRegression()\n",
    "LinReg.fit(X,y)\n",
    "print('For China Intercept is:',LinReg.intercept_,' and Coefficient is:',LinReg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 753408.4, 1045398. , 1337387.6, 1629377.2, 1921366.8])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1 = LinReg.predict(X)\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'COVID-19 trend for China')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y, label = \"Actual Value\")\n",
    "plt.plot(y_pred1, label = \"Predicted Value\")\n",
    "plt.legend()\n",
    "plt.title('COVID-19 trend for China')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r squared of the Prediction(China) 0.6423126964088759\n"
     ]
    }
   ],
   "source": [
    "print('r squared of the Prediction(China)',LinReg.score(X,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ The linear regression model is not performing well and r square value is 64%. The model does not fit well in predicting the data for China."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'COVID-19 trend')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "China = CaltimeSeries('China')\n",
    "\n",
    "# If China had not acted to stop the virus, how many cases would there be now according to this simple model?\n",
    "dfChina1 = China[China['Days since 100th case']<=40]\n",
    "dfChina1\n",
    "plt.plot(dfChina1['Days since 100th case'],dfChina1['Know Cases'],label = 'China')\n",
    "plt.xlabel(\"Days since 2020-01-22\")\n",
    "plt.ylabel(\"No of Cases\")\n",
    "plt.legend()\n",
    "plt.title('COVID-19 trend')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is: 474804.66666666674 and Coefficient is: [32398.65]\n"
     ]
    }
   ],
   "source": [
    "X = dfChina1[['Days since 100th case']]\n",
    "y = dfChina1['Know Cases']\n",
    "LinReg = LinearRegression()\n",
    "LinReg.fit(X,y)\n",
    "print('Intercept is:',LinReg.intercept_,'and Coefficient is:',LinReg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 474804.66666667, 1122777.66666667, 1770750.66666667])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1 = LinReg.predict(X)\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If China had not acted to stop the virus,there would be above mentioned cases till now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r squared of the Prediction: 0.8555532718783158\n"
     ]
    }
   ],
   "source": [
    "print('r squared of the Prediction:',LinReg.score(X,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation__:- The r squared value show 85% for the inital 40 days of virus spread in china. The model perform well in predicting the cases if china had not acted to stop the spread of the virus. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating logarithmic time series \n",
    "def Callogrithmictimeseries(countryname):\n",
    "    li = []\n",
    "    GroupedData_Clean1 =  GroupedData_Clean.melt(id_vars = ['Country/Region'])\n",
    "    GroupedData_Clean2 = GroupedData_Clean1[GroupedData_Clean1[\"Country/Region\"] == countryname]\n",
    "    GroupedData_Clean3 = GroupedData_Clean2[GroupedData_Clean2[\"value\"]>100]\n",
    "    GroupedData_Clean3['variable'] = pd.to_datetime(GroupedData_Clean3.variable)\n",
    "    a_df=np.unique(GroupedData_Clean3['variable'], axis=0)\n",
    "    UniqVariable= pd.DataFrame(a_df)\n",
    "    UniqVariable.index += 1\n",
    "    UniqVariable['New_ID'] = UniqVariable.index\n",
    "    UniqVariable\n",
    "    UniqVariable20days = UniqVariable.groupby([pd.Grouper(key=0, freq='20D')])['New_ID'].min().reset_index().sort_values(0)\n",
    "    UniqVariable20days\n",
    "    CountryGrp = GroupedData_Clean3.groupby(['Country/Region', pd.Grouper(key='variable', freq='20D')])[['value','Country/Region']].sum().reset_index().sort_values('variable')\n",
    "    CountryGrp\n",
    "    MergeFinal= pd.merge(CountryGrp, UniqVariable20days, how='left', left_on='variable', right_on=0)\n",
    "    MergeFinal1 = MergeFinal.groupby(['Country/Region'])['variable'].min().to_frame()\n",
    "    MergeFinal2 = MergeFinal1.reset_index()\n",
    "    MergeFinal\n",
    "    MergeFinal['Log Of Known Cases'] =  np.log(MergeFinal['value'])\n",
    "    MergeFinal\n",
    "    dt_FinalCOVID = MergeFinal[MergeFinal[0] > '2020-04-12' ].index\n",
    "    MergeFinal.drop(dt_FinalCOVID , inplace=True)\n",
    "    MergeFinal\n",
    "    p1 = []\n",
    "    for Country,variable in zip(MergeFinal2['Country/Region'],MergeFinal2['variable']):\n",
    "        p1.append(Country)\n",
    "        p1.append(variable)\n",
    "        p1.append(0)\n",
    "        p1.append(0)\n",
    "        p1.append(0)\n",
    "        p1.append(0)\n",
    "    p2 = pd.DataFrame(p1)\n",
    "    p3 = p2.T\n",
    "    p4 =p3.reset_index()\n",
    "    p5 =pd.DataFrame(p4)\n",
    "    p6  =p5[[0,1,2,3,4,5]]\n",
    "    p6.columns = ['Country/Region','variable','value',0,'New_ID','Log Of Known Cases']\n",
    "    p7 = p6\n",
    "    p7\n",
    "    append = MergeFinal.append(p7)\n",
    "    append\n",
    "    FinalMerge1 = append[[\"Country/Region\",\"variable\",\"value\",\"New_ID\",'Log Of Known Cases']]\n",
    "    FinalMerge1.columns = [\"Country/Region\",\"Date\",\"Known Cases\",\"Days since 100th case\",'Log Of Known Cases']\n",
    "    dffinalcases = FinalMerge1.sort_values('Known Cases')\n",
    "    dffinalcases1 = dffinalcases.reset_index()\n",
    "    dffinalcases1\n",
    "    li.append(dffinalcases1)\n",
    "    frame = pd.concat(li, axis=0, ignore_index=True)\n",
    "    return frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Known Cases</th>\n",
       "      <th>Days since 100th case</th>\n",
       "      <th>Log Of Known Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>125404</td>\n",
       "      <td>1</td>\n",
       "      <td>11.7393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>5020722</td>\n",
       "      <td>21</td>\n",
       "      <td>15.4291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-12</td>\n",
       "      <td>16568687</td>\n",
       "      <td>41</td>\n",
       "      <td>16.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>26266</td>\n",
       "      <td>1</td>\n",
       "      <td>10.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>116866</td>\n",
       "      <td>21</td>\n",
       "      <td>11.6688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>321086</td>\n",
       "      <td>1</td>\n",
       "      <td>12.6795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>1430215</td>\n",
       "      <td>21</td>\n",
       "      <td>14.1733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1617032</td>\n",
       "      <td>41</td>\n",
       "      <td>14.2961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>1644927</td>\n",
       "      <td>61</td>\n",
       "      <td>14.3132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>1673678</td>\n",
       "      <td>81</td>\n",
       "      <td>14.3305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>99459</td>\n",
       "      <td>1</td>\n",
       "      <td>11.5075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>1339959</td>\n",
       "      <td>21</td>\n",
       "      <td>14.1081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>3116742</td>\n",
       "      <td>41</td>\n",
       "      <td>14.9523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>44061</td>\n",
       "      <td>1</td>\n",
       "      <td>10.6933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>879692</td>\n",
       "      <td>21</td>\n",
       "      <td>13.6873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>5421</td>\n",
       "      <td>1</td>\n",
       "      <td>8.59804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>8118</td>\n",
       "      <td>21</td>\n",
       "      <td>9.00184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    level_0  index  Country/Region       Date Known Cases  \\\n",
       "0         0      0              US 2020-03-03           0   \n",
       "1         1      0              US 2020-03-03      125404   \n",
       "2         2      1              US 2020-03-23     5020722   \n",
       "3         3      2              US 2020-04-12    16568687   \n",
       "4         0      0       Australia 2020-03-10           0   \n",
       "5         1      0       Australia 2020-03-10       26266   \n",
       "6         2      1       Australia 2020-03-30      116866   \n",
       "7         0      0           China 2020-01-22           0   \n",
       "8         1      0           China 2020-01-22      321086   \n",
       "9         2      1           China 2020-02-11     1430215   \n",
       "10        3      2           China 2020-03-02     1617032   \n",
       "11        4      3           China 2020-03-22     1644927   \n",
       "12        5      4           China 2020-04-11     1673678   \n",
       "13        0      0           Italy 2020-02-23           0   \n",
       "14        1      0           Italy 2020-02-23       99459   \n",
       "15        2      1           Italy 2020-03-14     1339959   \n",
       "16        3      2           Italy 2020-04-03     3116742   \n",
       "17        0      0  United Kingdom 2020-03-05           0   \n",
       "18        1      0  United Kingdom 2020-03-05       44061   \n",
       "19        2      1  United Kingdom 2020-03-25      879692   \n",
       "20        0      0         Taiwan* 2020-03-19           0   \n",
       "21        1      0         Taiwan* 2020-03-19        5421   \n",
       "22        2      1         Taiwan* 2020-04-08        8118   \n",
       "\n",
       "   Days since 100th case Log Of Known Cases  \n",
       "0                      0                  0  \n",
       "1                      1            11.7393  \n",
       "2                     21            15.4291  \n",
       "3                     41             16.623  \n",
       "4                      0                  0  \n",
       "5                      1             10.176  \n",
       "6                     21            11.6688  \n",
       "7                      0                  0  \n",
       "8                      1            12.6795  \n",
       "9                     21            14.1733  \n",
       "10                    41            14.2961  \n",
       "11                    61            14.3132  \n",
       "12                    81            14.3305  \n",
       "13                     0                  0  \n",
       "14                     1            11.5075  \n",
       "15                    21            14.1081  \n",
       "16                    41            14.9523  \n",
       "17                     0                  0  \n",
       "18                     1            10.6933  \n",
       "19                    21            13.6873  \n",
       "20                     0                  0  \n",
       "21                     1            8.59804  \n",
       "22                    21            9.00184  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USdf1 = Callogrithmictimeseries('US')\n",
    "USdf1\n",
    "AUdf2 = Callogrithmictimeseries('Australia')\n",
    "AUdf2\n",
    "ITdf3 = Callogrithmictimeseries('Italy')\n",
    "ITdf3\n",
    "CHdf4 = Callogrithmictimeseries('China')\n",
    "UKdf5 = Callogrithmictimeseries('United Kingdom')\n",
    "TAdf6 = Callogrithmictimeseries('Taiwan*')\n",
    "resultFI = USdf1.append([AUdf2,CHdf4,ITdf3,UKdf5,TAdf6])\n",
    "resultFI1 = resultFI.reset_index()\n",
    "resultFI1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking maximum value to add label in the plot\n",
    "dfResult1 = resultFI.groupby(['Country/Region'], sort=False)[['Log Of Known Cases','Days since 100th case']].max()\n",
    "dfResult2 = dfResult1.reset_index()\n",
    "dfResult2\n",
    "\n",
    "# Using subplots\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "ax.clear()\n",
    "#line, = ax.plot(x, y)\n",
    "ax.plot( 'Days since 100th case', 'Log Of Known Cases', data=USdf1, marker='.', markerfacecolor='blue', markersize=8, color='blue', linewidth=2,label=\"US\")\n",
    "ax.plot( 'Days since 100th case', 'Log Of Known Cases', data=AUdf2, marker='.', color='red', markersize=8, linewidth=2,label=\"Australia\")\n",
    "ax.plot( 'Days since 100th case', 'Log Of Known Cases', data=ITdf3, marker='.', color='#ffb3ff', markersize=8,linewidth=2,label=\"Italy\")\n",
    "ax.plot( 'Days since 100th case', 'Log Of Known Cases', data=CHdf4, marker='.', color='green',markersize=8, linewidth=2,label=\"China\")\n",
    "ax.plot( 'Days since 100th case', 'Log Of Known Cases', data=UKdf5, marker='.', color='#eafb50',markersize=8, linewidth=2,label=\"United Kingdom\")\n",
    "ax.plot( 'Days since 100th case', 'Log Of Known Cases', data=TAdf6, marker='.', color='#f7bb5f', markersize=8,linewidth=2,label=\"Taiwan\")\n",
    "\n",
    "colors = {'Australia':'ro', 'China':'go', 'Italy':'o', 'US':'bo','United Kingdom':'yo','Taiwan*':'yo'}\n",
    "\n",
    "for Country,Cases,Days in  zip(dfResult2['Country/Region'], dfResult2['Log Of Known Cases'], dfResult2['Days since 100th case']):\n",
    "        ymax = Cases         \n",
    "        xmax = Days\n",
    "        ax.annotate(Country, xy=(xmax, ymax+0.2), xytext=(xmax, ymax+0.2))\n",
    "        ax.plot(xmax,ymax,colors[Country])\n",
    "plt.xlabel('Days since 100th case')\n",
    "plt.ylabel('Logarithmic Scale of  Known Cases since 100th case')\n",
    "plt.title('COVID-19 trend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation__:-By using the logarithmic plot, we can identify that for China, Australia, Taiwan, the virus is under control and for US and Italy, we see an exponential increase in the number of confirmed cases."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}