{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# John Hopkins csse_covid_19_daily_reports data inconsistent\n",
    "\n",
    "https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_daily_reports/02-04-2020.csv\n",
    "\n",
    "versus\n",
    "\n",
    "https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_daily_reports/03-29-2020.csv\n",
    "\n",
    "\n",
    "\n",
    "Lets assume `Country_Region,Last_Update,Lat,Long_,Confirmed,Deaths,Recovered,Active`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sg = pd.read_csv(\"singapore.csv\", index_col=False, parse_dates=[\"Last_Update\"], names=[\"Country_Region\", \"Last_Update\", \"Lat\", \"Long\", \"Confirmed\", \"Deaths\",\"Recovered\",\"Active\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>Last_Update</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-02 01:03:32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-02 01:03:32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-04 15:33:03</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country_Region         Last_Update   Lat  Long  Confirmed  Deaths  \\\n",
       "0      Singapore 2020-02-02 01:03:32  18.0   0.0          0     NaN   \n",
       "1      Singapore 2020-02-02 01:03:32  18.0   0.0          0     NaN   \n",
       "2      Singapore 2020-02-04 15:33:03  24.0   0.0          0     NaN   \n",
       "\n",
       "   Recovered  Active  \n",
       "0        NaN     NaN  \n",
       "1        NaN     NaN  \n",
       "2        NaN     NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       NaN\n",
       "1       NaN\n",
       "2       NaN\n",
       "3       NaN\n",
       "4       NaN\n",
       "       ... \n",
       "190    27.0\n",
       "191    27.0\n",
       "192    27.0\n",
       "193    27.0\n",
       "194    27.0\n",
       "Name: Deaths, Length: 195, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg.Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country_Region            object\n",
       "Last_Update       datetime64[ns]\n",
       "Lat                      float64\n",
       "Long                     float64\n",
       "Confirmed                  int64\n",
       "Deaths                   float64\n",
       "Recovered                float64\n",
       "Active                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'mplcursors'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-ed6bf68722a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmplcursors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mplcursors'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import mplcursors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Howto plot with mathplot lib\n",
    "\n",
    "<https://discourse.matplotlib.org/t/gnuplot-to-matplotlib-time-series/21496/2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06766c9b20>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(\"Last_Update\", \"Active\", data=sg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lag window\n",
    "\n",
    "https://www.reddit.com/r/datascience/comments/ifhpiv/cumulative_to_daily_count_in_a_panda/g2nr9s0/?context=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Last_Update</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-02 01:03:32</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-02 01:03:32</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-04 15:33:03</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-05 16:33:03</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-05 16:33:03</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-18 04:27:56</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>55838</td>\n",
       "      <td>27.0</td>\n",
       "      <td>52350.0</td>\n",
       "      <td>3461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-20 04:27:43</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56031</td>\n",
       "      <td>27.0</td>\n",
       "      <td>52810.0</td>\n",
       "      <td>3194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-21 04:27:41</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56099</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53119.0</td>\n",
       "      <td>2953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-22 04:27:49</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56216</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53651.0</td>\n",
       "      <td>2538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-23 04:27:48</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56266</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53920.0</td>\n",
       "      <td>2319.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country_Region         Last_Update      Lat      Long  Confirmed  Deaths  \\\n",
       "0        Singapore 2020-02-02 01:03:32  18.0000    0.0000          0     NaN   \n",
       "1        Singapore 2020-02-02 01:03:32  18.0000    0.0000          0     NaN   \n",
       "2        Singapore 2020-02-04 15:33:03  24.0000    0.0000          0     NaN   \n",
       "3        Singapore 2020-02-05 16:33:03  28.0000    0.0000          0     NaN   \n",
       "4        Singapore 2020-02-05 16:33:03  28.0000    0.0000          0     NaN   \n",
       "..             ...                 ...      ...       ...        ...     ...   \n",
       "190      Singapore 2020-08-18 04:27:56   1.2833  103.8333      55838    27.0   \n",
       "191      Singapore 2020-08-20 04:27:43   1.2833  103.8333      56031    27.0   \n",
       "192      Singapore 2020-08-21 04:27:41   1.2833  103.8333      56099    27.0   \n",
       "193      Singapore 2020-08-22 04:27:49   1.2833  103.8333      56216    27.0   \n",
       "194      Singapore 2020-08-23 04:27:48   1.2833  103.8333      56266    27.0   \n",
       "\n",
       "     Recovered  Active  \n",
       "0          NaN     NaN  \n",
       "1          NaN     NaN  \n",
       "2          NaN     NaN  \n",
       "3          NaN     NaN  \n",
       "4          NaN     NaN  \n",
       "..         ...     ...  \n",
       "190    52350.0  3461.0  \n",
       "191    52810.0  3194.0  \n",
       "192    53119.0  2953.0  \n",
       "193    53651.0  2538.0  \n",
       "194    53920.0  2319.0  \n",
       "\n",
       "[195 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sg['confirmed_day_before'] =  sg['Confirmed'].shift(1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sg['daily_increase'] = sg['Confirmed'] - sg['confirmed_day_before']    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Last_Update</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
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       "      <td>Singapore</td>\n",
       "      <td>2020-02-02 01:03:32</td>\n",
       "      <td>18.0000</td>\n",
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       "      <td>2020-02-02 01:03:32</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-04 15:33:03</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-05 16:33:03</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-02-05 16:33:03</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-18 04:27:56</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>55838</td>\n",
       "      <td>27.0</td>\n",
       "      <td>52350.0</td>\n",
       "      <td>3461.0</td>\n",
       "      <td>55747.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-20 04:27:43</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56031</td>\n",
       "      <td>27.0</td>\n",
       "      <td>52810.0</td>\n",
       "      <td>3194.0</td>\n",
       "      <td>55838.0</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-21 04:27:41</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56099</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53119.0</td>\n",
       "      <td>2953.0</td>\n",
       "      <td>56031.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-22 04:27:49</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56216</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53651.0</td>\n",
       "      <td>2538.0</td>\n",
       "      <td>56099.0</td>\n",
       "      <td>117.0</td>\n",
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       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-08-23 04:27:48</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>56266</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53920.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>56216.0</td>\n",
       "      <td>50.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country_Region         Last_Update      Lat      Long  Confirmed  Deaths  \\\n",
       "0        Singapore 2020-02-02 01:03:32  18.0000    0.0000          0     NaN   \n",
       "1        Singapore 2020-02-02 01:03:32  18.0000    0.0000          0     NaN   \n",
       "2        Singapore 2020-02-04 15:33:03  24.0000    0.0000          0     NaN   \n",
       "3        Singapore 2020-02-05 16:33:03  28.0000    0.0000          0     NaN   \n",
       "4        Singapore 2020-02-05 16:33:03  28.0000    0.0000          0     NaN   \n",
       "..             ...                 ...      ...       ...        ...     ...   \n",
       "190      Singapore 2020-08-18 04:27:56   1.2833  103.8333      55838    27.0   \n",
       "191      Singapore 2020-08-20 04:27:43   1.2833  103.8333      56031    27.0   \n",
       "192      Singapore 2020-08-21 04:27:41   1.2833  103.8333      56099    27.0   \n",
       "193      Singapore 2020-08-22 04:27:49   1.2833  103.8333      56216    27.0   \n",
       "194      Singapore 2020-08-23 04:27:48   1.2833  103.8333      56266    27.0   \n",
       "\n",
       "     Recovered  Active  confirmed_day_before  daily_increase  \n",
       "0          NaN     NaN                   NaN             NaN  \n",
       "1          NaN     NaN                   0.0             0.0  \n",
       "2          NaN     NaN                   0.0             0.0  \n",
       "3          NaN     NaN                   0.0             0.0  \n",
       "4          NaN     NaN                   0.0             0.0  \n",
       "..         ...     ...                   ...             ...  \n",
       "190    52350.0  3461.0               55747.0            91.0  \n",
       "191    52810.0  3194.0               55838.0           193.0  \n",
       "192    53119.0  2953.0               56031.0            68.0  \n",
       "193    53651.0  2538.0               56099.0           117.0  \n",
       "194    53920.0  2319.0               56216.0            50.0  \n",
       "\n",
       "[195 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f067622dfa0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(\"Last_Update\", \"daily_increase\", data=sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sg.drop_duplicates(subset=sg.columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06761eca90>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(\"Last_Update\", \"daily_increase\", data=sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sg.to_excel(\"output.xlsx\", sheet_name='covidsg')"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}