COVID19 PAKISTAN .ipynb

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   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "from itertools import compress\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "from IPython.display import HTML\n",
    "import plotly\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA SOURCE: WIKIPEDIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Extract all tables from the wikipage\n",
    "dfs = pd.read_html(\"https://en.m.wikipedia.org/wiki/Template:2019%E2%80%9320_coronavirus_pandemic_data/Pakistan_medical_cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = dfs[0]\n",
    "df = df.iloc[:-3]\n",
    "df = df.drop(df.columns[-6:], axis = 1) \n",
    "\n",
    "df['Province or Territory'].columns\n",
    "for i in df['Province or Territory'].columns:\n",
    "    df.loc[:, ('Province or Territory', i)]= df['Province or Territory'][i].astype(float)\n",
    "    df.loc[0:12, ('Province or Territory', i)] = df['Province or Territory'][i][0:13].cumsum()\n",
    "\n",
    "p = []\n",
    "for i in df['Province or Territory'].columns:\n",
    "    t = df['Province or Territory'][i].replace(np.nan, \"null\")\n",
    "    \n",
    "    p.append({\n",
    "            i: [j for j in list(t.index) if t[j] != \"null\"]\n",
    "        })\n",
    "\n",
    "pair = []\n",
    "for i in p:\n",
    "    for key, value in i.items():\n",
    "        pair.append({\n",
    "            key:list(zip(value, value[1:] + value[:1]))[:-1]\n",
    "        })\n",
    "        \n",
    "\n",
    "for i in pair:\n",
    "    for key,value in i.items():\n",
    "        col = key+' Daily Cases'\n",
    "        df[col] = 0\n",
    "        df.loc[df.index[value[0][0]], col] =df['Province or Territory'][key][value[0][0]]\n",
    "        for j in value:\n",
    "            index1 = j[0]\n",
    "            index2 = j[1]\n",
    "            dif = df['Province or Territory'][key][index2] - df['Province or Territory'][key][index1]\n",
    "            if dif != 0 and dif > 0: \n",
    "                df.loc[df.index[index2], col] = dif\n",
    "            elif dif == 0:\n",
    "                df.loc[df.index[index2], col] = dif\n",
    "            elif dif < 0:\n",
    "                df.loc[df.index[index2], col] = 0\n",
    "\n",
    "def DailyCases(city):\n",
    "    date = [ i[0] for i in df['Date'].values.tolist()[:-1]]\n",
    "    \n",
    "    cases = [i for i in df[city].values.tolist()[:-1]]\n",
    "    fig = px.bar(x=date, y=cases)\n",
    "    fig.update_layout(\n",
    "        title=\"{} COVID-19 \".format(city),\n",
    "        xaxis_title=\"Date\",\n",
    "        yaxis_title=\"Daily Cases\",\n",
    "        )\n",
    "    fig.write_html(\"DailyCases.html\")\n",
    "    fig.show()\n",
    "\n",
    "    \n",
    "DailyCases('Punjab Daily Cases')\n",
    "\n",
    "\n",
    "# CITY KEYWORDS:\n",
    "# Punjab Daily Cases\n",
    "# Sindh Daily Cases\n",
    "# Khyber Pakhtunkhwa Daily Cases\n",
    "# Balochistan Daily Cases\n",
    "# Gilgit-Baltistan Daily Cases\n",
    "# Azad Kashmir Daily Cases\n",
    "# Islamabad Daily Cases\n",
    "# ''' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA SOURCE: Ministry of National Health Services, Regulations and Coordination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('COVID_FINAL_DATA_PAKISTAN - TimeSeries_KeyIndicators.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recover(city):\n",
    "    y = []\n",
    "    for i in data['Date'].unique():\n",
    "        new = data[(data['Date'] == i) & (data['Region'] == city)]\n",
    "        y.append(new['Discharged'].values[0])\n",
    "    res = list(zip(y, y[1:] + y[:1])) \n",
    "    re = [y[0]]\n",
    "    for i in res[:-1]:\n",
    "        re.append(i[1]-i[0])\n",
    "    \n",
    "    fig = px.bar(x=data['Date'].unique(), y=re)\n",
    "    fig.update_layout(\n",
    "    title=\"Daily New COVID-19 Recoveries in {}\".format(city),\n",
    "    xaxis_title=\"Date\",\n",
    "    yaxis_title=\"Recovery Cases\",\n",
    "    )\n",
    "    fig.write_html(\"Recover.html\")\n",
    "    fig.show()"
   ]
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   "source": [
    "recover('Sindh')\n",
    "# recover('Punjab')\n",
    "# recover('KP')\n",
    "# recover('ICT')\n",
    "# recover('Balochistan')\n",
    "# recover('AJK')\n",
    "# recover('GB')\n"
   ]
  },
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    "def death(city):\n",
    "    y = []\n",
    "    for i in data['Date'].unique():\n",
    "        new = data[(data['Date'] == i) & (data['Region'] == city)]\n",
    "        y.append(new['Expired'].values[0])\n",
    "    res = list(zip(y, y[1:] + y[:1])) \n",
    "    deaths = [y[0]]\n",
    "    for i in res[:-1]:\n",
    "        deaths.append(i[1]-i[0])\n",
    "    fig = px.bar(x=data['Date'].unique(), y=deaths)\n",
    "    fig.update_layout(\n",
    "    title=\"Daily New COVID-19 Deaths in {}\".format(city),\n",
    "    xaxis_title=\"Date\",\n",
    "    yaxis_title=\"Death Cases\",\n",
    "    )\n",
    "    fig.write_html(\"Death.html\")\n",
    "    fig.show()\n"
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   "source": [
    "death('Sindh')\n",
    "# death('Punjab')\n",
    "# death('KP')\n",
    "# death('ICT')\n",
    "# death('Balochistan')\n",
    "# death('AJK')\n",
    "# death('GB')"
   ]
  },
  {
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   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def still_admit(city):\n",
    "    y = []\n",
    "    for i in data['Date'].unique():\n",
    "        new = data[(data['Date'] == i) & (data['Region'] == city)]\n",
    "        a = new['Still admitted'].values[0]\n",
    "        y.append(a)\n",
    "    fig = px.bar(x=data['Date'].unique(), y=y)\n",
    "    fig.update_layout(\n",
    "    title=\"Admitted Cases of COVID-19 in Hospitals - {}\".format(city),\n",
    "    xaxis_title=\"Date\",\n",
    "    yaxis_title=\"Admitted cases\",\n",
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    "    fig.write_html(\"Admitted.html\")\n",
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "still_admit('Sindh')\n",
    "# still_admit('Punjab')\n",
    "# still_admit('KP')\n",
    "# still_admit('ICT')\n",
    "# still_admit('Balochistan')\n",
    "# still_admit('AJK')\n",
    "# still_admit('GB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url2 = \"http://covid.gov.pk/\"\n",
    "html_content2 = requests.get(url2).text\n",
    "soup2 = BeautifulSoup(html_content2, \"lxml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp_table = soup2.find(\"div\", attrs={\"class\": \"row provinc-stat\"})\n",
    "gdp_table.findAll(\"div\", attrs={\"class\": \"col\"})\n",
    "dd = {}\n",
    "for i in gdp_table.findAll(\"div\", attrs={\"class\": \"col\"}):\n",
    "    dd[i.find('a').text] = i.find('h4').text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today Cases Mar-28-2020\n",
      "TOTAL CASES IN PAKISTAN: 1,408\n",
      "Sindh 0.0\n",
      "ICT 0.0\n",
      "Punjab 0.0\n",
      "KP 0.0\n",
      "Balochistan 0.0\n",
      "AJK nan\n",
      "GB 0.0\n"
     ]
    }
   ],
   "source": [
    "from datetime import date\n",
    "today = date.today()\n",
    "\n",
    "print(\"Today Cases\",today.strftime(\"%b-%d-%Y\"))\n",
    "print(\"TOTAL CASES IN PAKISTAN:\",soup2.find('h1', attrs={'class':'text-muted numbers-main'}).text)\n",
    "print(\"Sindh\",int(dd['Sindh']) - df.loc[len(df)-2]['Province or Territory']['Sindh'])\n",
    "print(\"ICT\",int(dd['ICT']) - df.loc[len(df)-2]['Province or Territory']['Islamabad'])\n",
    "print(\"Punjab\",int(dd['Punjab']) - df.loc[len(df)-2]['Province or Territory']['Punjab'])\n",
    "print(\"KP\",int(dd['KP']) - df.loc[len(df)-2]['Province or Territory']['Khyber Pakhtunkhwa'])\n",
    "print(\"Balochistan\",int(dd['Balochistan']) - df.loc[len(df)-2]['Province or Territory']['Balochistan'])\n",
    "print(\"AJK\",int(dd['AJK']) - df.loc[len(df)-2]['Province or Territory']['Azad Kashmir'])\n",
    "print(\"GB\",int(dd['GB']) - df.loc[len(df)-2]['Province or Territory']['Gilgit-Baltistan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## - sign indicates 0 new cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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