monkeypox-cases.ipynb

{
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    "tags": []
   },
   "source": [
    "![](https://raw.githubusercontent.com/Larxel/Assets/main/monkeypox_splash.png)"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "\n",
    "!pip install country_converter -q\n",
    "import country_converter as coco\n",
    "\n",
    "today = datetime.today()\n",
    "day   = today.day if today.day > 9 else '0' + str(today.day)\n",
    "month = today.month if today.month > 9 else '0' + str(today.month)\n",
    "today_str = '{}/{}/{}'.format(day, month, today.year)\n",
    "\n",
    "# Fetch data\n",
    "monkeypox_df = pd.read_csv(\"https://raw.githubusercontent.com/globaldothealth/monkeypox/main/latest.csv\")\n",
    "\n",
    "# Preprocess data\n",
    "monkeypox_df['Location'] = monkeypox_df['Location_information'].apply(lambda x: re.sub(r' \\((.+?)$','',x))\n",
    "monkeypox_df['Location'] = monkeypox_df['Location'].apply(lambda x: re.sub(r'(.+?), United States','United States',x))\n",
    "monkeypox_df['County'] = [x.split(', ')[0] if len(x.split(', ')) > 1 else None for x in monkeypox_df['Location_information']]\n",
    "\n",
    "# Export to disk\n",
    "monkeypox_df.to_csv('monkeypox_df.csv', index = False)\n",
    "\n",
    "# Cases by country\n",
    "country_cases_df = monkeypox_df.loc[monkeypox_df['Case_status'] == 'confirmed',['_id','Location']].groupby('Location').agg('count').reset_index().rename({'_id':'Count'}, axis = 1)\n",
    "country_cases_df.to_csv('monkeypox_cases_by_country.csv', index = False)"
   ]
  },
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   "outputs": [
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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_confirmed_cases = monkeypox_df.loc[monkeypox_df['Case_status'] == 'confirmed'].shape[0]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (20,8))\n",
    "fig.patch.set_facecolor('#111111')\n",
    "\n",
    "plt.text(x = 0.32,\n",
    "         y = 0.4,\n",
    "         s = str(num_confirmed_cases),\n",
    "         multialignment = 'center',\n",
    "         fontsize = 150,\n",
    "         color = '#38cae0'\n",
    "        )\n",
    "\n",
    "plt.text(x = 0.37,\n",
    "         y = 0.34,\n",
    "         s = 'Confirmed cases as of {}'.format(today_str),fontsize = 20,\n",
    "         multialignment = 'center',\n",
    "         color = '#dbdbdb'\n",
    "        )\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "501c6ec8",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-11-18T17:08:27.949816Z",
     "iopub.status.busy": "2022-11-18T17:08:27.949411Z",
     "iopub.status.idle": "2022-11-18T17:08:29.426974Z",
     "shell.execute_reply": "2022-11-18T17:08:29.425994Z"
    },
    "papermill": {
     "duration": 1.486422,
     "end_time": "2022-11-18T17:08:29.429267",
     "exception": false,
     "start_time": "2022-11-18T17:08:27.942845",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "metadata": {},
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   ],
   "source": [
    "filtered_df = monkeypox_df.loc[monkeypox_df['Case_status'] == 'confirmed']\n",
    "map_df = filtered_df[['Location','_id']].groupby('Location').agg('count').reset_index()\n",
    "map_df['CountryCode'] = coco.convert(names = map_df['Location'], to = 'ISO3')\n",
    "map_df.rename({'_id':'Count of Confirmed Cases'}, axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "fig = px.choropleth(map_df,\n",
    "                    locations = \"CountryCode\",\n",
    "                    color = \"Count of Confirmed Cases\",\n",
    "                    hover_name = \"Location\",\n",
    "                    color_continuous_scale = [\"#d7e1ee\", \"#cbd6e4\", \"#bfcbdb\", \"#b3bfd1\", \"#a4a2a8\", \"#df8879\", \"#c86558\", \"#b04238\", \"#991f17\"],\n",
    "                    projection = 'natural earth',\n",
    "                    template = 'plotly_dark',\n",
    "                    title = 'Geographical Distribution of Confirmed Cases<br><sub>Natural Projection</sub>',\n",
    "                    height = 800,\n",
    "                    width = 1120,\n",
    "                   )\n",
    "\n",
    "fig.update_geos(lataxis_showgrid = True,\n",
    "                lonaxis_showgrid = True,\n",
    "                showcountries = True,\n",
    "               )\n",
    "\n",
    "fig.update_geos(lataxis = {'gridcolor':'#222222'},\n",
    "                lonaxis = {'gridcolor':'#222222'},\n",
    "               )\n",
    "\n",
    "fig.show()"
   ]
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  {
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   "execution_count": 4,
   "id": "e10cf76e",
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     "iopub.status.idle": "2022-11-18T17:08:29.519160Z",
     "shell.execute_reply": "2022-11-18T17:08:29.518156Z"
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     "status": "completed"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = px.choropleth(map_df,\n",
    "                    locations = \"CountryCode\",\n",
    "                    color = \"Count of Confirmed Cases\",\n",
    "                    hover_name = \"Location\",\n",
    "                    color_continuous_scale = [\"#d7e1ee\", \"#cbd6e4\", \"#bfcbdb\", \"#b3bfd1\", \"#a4a2a8\", \"#df8879\", \"#c86558\", \"#b04238\", \"#991f17\"],\n",
    "                    projection = 'orthographic',\n",
    "                    template = 'plotly_dark',\n",
    "                    title = 'Geographical Distribution of Confirmed Cases<br><sub>Orthographic Projection</sub>',\n",
    "                    height = 800,\n",
    "                    width = 1120,\n",
    "                   )\n",
    "\n",
    "fig.update_geos(lataxis_showgrid = True,\n",
    "                lonaxis_showgrid = True,\n",
    "                showcountries = True,\n",
    "               )\n",
    "\n",
    "fig.update_geos(lataxis = {'gridcolor':'#222222'},\n",
    "                lonaxis = {'gridcolor':'#222222'},\n",
    "               )\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "769f0f7c",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-11-18T17:08:29.535546Z",
     "iopub.status.busy": "2022-11-18T17:08:29.534785Z",
     "iopub.status.idle": "2022-11-18T17:08:29.612497Z",
     "shell.execute_reply": "2022-11-18T17:08:29.611428Z"
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    "papermill": {
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     "start_time": "2022-11-18T17:08:29.527515",
     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
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   "source": [
    "fig = px.choropleth(map_df,\n",
    "                    locations = \"CountryCode\",\n",
    "                    color = \"Count of Confirmed Cases\",\n",
    "                    hover_name = \"Location\",\n",
    "                    scope = 'europe',\n",
    "                    color_continuous_scale = [\"#d7e1ee\", \"#cbd6e4\", \"#bfcbdb\", \"#b3bfd1\", \"#a4a2a8\", \"#df8879\", \"#c86558\", \"#b04238\", \"#991f17\"],\n",
    "                    template = 'plotly_dark',\n",
    "                    title = 'European Distribution of Confirmed Monkeypox Cases<br><sub>Orthographic Projection</sub>',\n",
    "                    height = 800,\n",
    "                    width = 1120,\n",
    "                   )\n",
    "\n",
    "fig.update_geos(lataxis_showgrid = True,\n",
    "                lonaxis_showgrid = True,\n",
    "                showcountries = True,\n",
    "                showsubunits = True,\n",
    "               )\n",
    "\n",
    "fig.update_geos(lataxis = {'gridcolor':'#222222'},\n",
    "                lonaxis = {'gridcolor':'#222222'},\n",
    "               )\n",
    "\n",
    "fig.show()"
   ]
  },
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   "cell_type": "markdown",
   "id": "2490ac83",
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     "end_time": "2022-11-18T17:08:29.628249",
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     "status": "completed"
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    "tags": []
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   "source": [
    "<div style=\"color:white;\n",
    "           display:fill;\n",
    "           border-radius:5px;\n",
    "           background-color:#111111;\n",
    "           font-size:110%;\n",
    "           font-family:Roboto;\n",
    "           letter-spacing:0.5px\">\n",
    "\n",
    "<p style=\"padding: 10px;\n",
    "              color:white;\">       On september 23rd, the source of the dataset changed its means of aquisition, causing profund changes in the available data. The next charts depict data before these change took effect. \n",
    "    \n",
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   "execution_count": 6,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
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     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3552: DtypeWarning:\n",
      "\n",
      "Columns (3,8,10,11,12,14,16,18,19,20,21,22,23,24,25,29) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
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       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monkeypox_deprecated_df = pd.read_csv(\"https://raw.githubusercontent.com/globaldothealth/monkeypox/main/latest_deprecated.csv\")\n",
    "acumulated_df = monkeypox_deprecated_df[['Date_confirmation','ID']].groupby('Date_confirmation').agg('count').reset_index()\n",
    "acumulated_df['Accumulated Cases'] = acumulated_df['ID'].cumsum()\n",
    "acumulated_df.rename({'ID':'Count'}, axis = 1, inplace = True)\n",
    "\n",
    "# Last date of update before being deprecated\n",
    "acumulated_df['Date_confirmation'][-1:] = '2022-09-23'\n",
    "\n",
    "fig = px.line(acumulated_df,\n",
    "              x = 'Date_confirmation',\n",
    "              y = 'Accumulated Cases',\n",
    "              markers = True,\n",
    "              title = 'Global Accumulated Number of Confirmed Monkeypox Cases<br><sub>As of {}</sub>'.format('23/09/2022'),\n",
    "              template = 'plotly_dark',\n",
    "             )\n",
    "\n",
    "fig['data'][0]['line']['color'] = '#38cae0'\n",
    "fig['data'][0]['line']['width'] = 2\n",
    "\n",
    "fig.update_xaxes(title = 'Date of Confirmation')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6e0f609",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-11-18T17:08:30.458153Z",
     "iopub.status.busy": "2022-11-18T17:08:30.457657Z",
     "iopub.status.idle": "2022-11-18T17:08:30.591961Z",
     "shell.execute_reply": "2022-11-18T17:08:30.590904Z"
    },
    "papermill": {
     "duration": 0.145095,
     "end_time": "2022-11-18T17:08:30.594209",
     "exception": false,
     "start_time": "2022-11-18T17:08:30.449114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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   ],
   "source": [
    "fig = px.bar(acumulated_df,\n",
    "             x = 'Date_confirmation',\n",
    "             y = 'Count',\n",
    "             text = 'Count',\n",
    "             color = 'Count',\n",
    "             opacity = 0.9,\n",
    "             title = 'Number of Confirmed Cases by Date<br><sup>Up until 23/09/2022',\n",
    "             color_continuous_scale = ['#bbbbbb','#38cae0'],\n",
    "             template = 'plotly_dark',\n",
    "             hover_data = ['Count'],\n",
    "             color_continuous_midpoint = 0,\n",
    "             height = 600,\n",
    "            )\n",
    "\n",
    "avg_returns = acumulated_df['Count'].mean()\n",
    "avg_returns_color = '#bbbbbb'\n",
    "\n",
    "\n",
    "fig.add_hline(y = avg_returns,\n",
    "              line_width = 1.,\n",
    "              line_dash = \"dot\",\n",
    "              opacity = 0.7,\n",
    "              fillcolor = avg_returns_color,\n",
    "              annotation_text = \"Average: \" + str(round(avg_returns,1)) + ' cases per day', \n",
    "              annotation_position = \"bottom right\",\n",
    "              annotation_font_size = 10,\n",
    "              annotation_font_color = \"white\",\n",
    "              line_color = avg_returns_color,\n",
    "             )\n",
    "\n",
    "fig.update_xaxes(showticklabels = True,\n",
    "                 dtick = \"M1\",\n",
    "                 tickformat = \"%b %Y\",\n",
    "                )\n",
    "\n",
    "fig.update_yaxes(title = 'Confirmed Cases')\n",
    "fig.update_xaxes(title = 'Date')\n",
    "\n",
    "fig.show()"
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   "id": "f0bea359",
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   "source": [
    "temp_df = pd.DataFrame(monkeypox_deprecated_df['Hospitalised (Y/N/NA)'].value_counts()).reset_index()\n",
    "n = temp_df['Hospitalised (Y/N/NA)'].sum()\n",
    "n_total = monkeypox_deprecated_df.shape[0]\n",
    "temp_df['index'] = np.where(temp_df['index'] == 'Y','Yes','No')\n",
    "\n",
    "\n",
    "fig = go.Figure(data=[go.Pie(labels = temp_df['index'],\n",
    "                             values = temp_df['Hospitalised (Y/N/NA)'],\n",
    "                             hole = .75,\n",
    "                             marker_colors = [\"#a60c0c\",\"#222222\"],\n",
    "                            )\n",
    "                     ])\n",
    "\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = \"Was the patient Hospitalized?<br><sub>Calculated on a sample of {} out of {} observations in the deprecated dataset - data up until 23/09/2022\".format(n, n_total),\n",
    "    template = 'plotly_dark',\n",
    "    height = 600,\n",
    "    annotations = [dict(text = 'Hospitalization',\n",
    "                      x = 0.5,\n",
    "                      y = 0.5,\n",
    "                      font_size = 20,\n",
    "                      showarrow = False,\n",
    "                     )])\n",
    "\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed0a4084",
   "metadata": {
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    "execution": {
     "iopub.execute_input": "2022-11-18T17:08:30.687614Z",
     "iopub.status.busy": "2022-11-18T17:08:30.687164Z",
     "iopub.status.idle": "2022-11-18T17:08:30.746993Z",
     "shell.execute_reply": "2022-11-18T17:08:30.746160Z"
    },
    "papermill": {
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     "end_time": "2022-11-18T17:08:30.749475",
     "exception": false,
     "start_time": "2022-11-18T17:08:30.678059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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Used to Confirm or Discart Case<br><sub>Calculated on a sample of 100 out of 69595 observations in the deprecated dataset - data up until 23/09/2022\"},\"height\":600,\"annotations\":[{\"showarrow\":false,\"text\":\"Confirmation Method\",\"x\":0.5,\"y\":0.5,\"font\":{\"size\":20}}]},                        {\"responsive\": true}                    ).then(function(){\n",
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       "// Listen for the removal of the full notebook cells\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rename_dict = {'RT-PCR':'RT-PCR',\n",
    "               'PCR':'PCR',\n",
    "               'Electron microscopy':'Electron microscopy',\n",
    "               'Sequencing':'Sequencing',\n",
    "               'Microbiological Test':'Microbiological test',\n",
    "               '\"Molecular and gene sequencing\"':'Sequencing',\n",
    "               'PCR and sequencing':'PCR',\n",
    "               'qPCR or RT-PCR':'PCR',\n",
    "               'confirmed positive for Monkeypox via Reverse Transcriptase Polymerase Chain Reaction or RT-PCR by the DOH Research Institute for Tropical Medicine (RITM) on August 18, 2022':'RT-PCR',\n",
    "               'confirmed positive for Monkeypox via RT-PCR by the DOH RITM on August 19, 2022':'RT-PCR',\n",
    "              }\n",
    "\n",
    "monkeypox_deprecated_df['Confirmation_method'] = monkeypox_deprecated_df['Confirmation_method'].apply(lambda x: rename_dict.get(x))\n",
    "\n",
    "temp_df = pd.DataFrame(monkeypox_deprecated_df['Confirmation_method'].value_counts()).reset_index()\n",
    "n = temp_df['Confirmation_method'].sum()\n",
    "n_total = monkeypox_deprecated_df.shape[0]\n",
    "\n",
    "fig = go.Figure(data = [go.Pie(labels = temp_df['index'],\n",
    "                             values = temp_df['Confirmation_method'],\n",
    "                             hole = .75,\n",
    "                             marker_colors = px.colors.sequential.Emrld_r,\n",
    "                            )\n",
    "                     ])\n",
    "\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = \"Method Used to Confirm or Discart Case<br><sub>Calculated on a sample of {} out of {} observations in the deprecated dataset - data up until 23/09/2022\".format(n, n_total),\n",
    "    template = 'plotly_dark',\n",
    "    height = 600,\n",
    "    annotations = [dict(text = 'Confirmation Method',\n",
    "                      x = 0.5,\n",
    "                      y = 0.5,\n",
    "                      font_size = 20,\n",
    "                      showarrow = False,\n",
    "                     )])\n",
    "\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e4b24cf",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-11-18T17:08:30.766434Z",
     "iopub.status.busy": "2022-11-18T17:08:30.766001Z",
     "iopub.status.idle": "2022-11-18T17:08:30.926081Z",
     "shell.execute_reply": "2022-11-18T17:08:30.925115Z"
    },
    "papermill": {
     "duration": 0.171264,
     "end_time": "2022-11-18T17:08:30.928420",
     "exception": false,
     "start_time": "2022-11-18T17:08:30.757156",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
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   ],
   "source": [
    "filtered_deprecated_df = monkeypox_deprecated_df.loc[monkeypox_deprecated_df['Status'] == 'confirmed']\n",
    "filtered_deprecated_df['Gender'] = filtered_deprecated_df['Gender'].str.capitalize().str.strip()\n",
    "temp_df = filtered_deprecated_df[['Gender','ID']].groupby('Gender').agg('count').reset_index()\n",
    "temp_df.rename({'ID':'Count'}, axis = 1, inplace = True)\n",
    "temp_df['Percentage'] = (temp_df['Count']/temp_df['Count'].sum() * 100).round(2)\n",
    "temp_df['Percentage'] = temp_df['Percentage'].astype('str') + '%'\n",
    "n = temp_df['Count'].sum()\n",
    "\n",
    "fig = px.bar(temp_df,\n",
    "             y = 'Gender',\n",
    "             x = 'Count',\n",
    "             title = 'Distribution of Sex Among Confirmed Cases<br><sub>Calculated on a sample of {} out of {} observations in the deprecated dataset - data up until 23/09/2022</sub>'.format(n,n_total),\n",
    "             color = 'Gender',\n",
    "             text = 'Percentage',\n",
    "             template = 'plotly_dark',\n",
    "             opacity = 0.8,\n",
    "             height = 800,\n",
    "             color_discrete_sequence = ['#dbdbdb','#38cae0']\n",
    "            )\n",
    "\n",
    "\n",
    "fig.update_yaxes(title = 'Count of Occurences')\n",
    "fig.update_xaxes(title = 'Sex')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "235b6395",
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     "iopub.status.idle": "2022-11-18T17:08:30.990701Z",
     "shell.execute_reply": "2022-11-18T17:08:30.989594Z"
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       "            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",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = pd.DataFrame(monkeypox_deprecated_df['Symptoms'].value_counts()).reset_index()\n",
    "temp_df = temp_df.append(pd.DataFrame({'index':'multiple or other', 'Symptoms':temp_df.loc[temp_df['Symptoms'] < 5]['Symptoms'].sum()},index = [0]))\n",
    "temp_df = temp_df.loc[temp_df['Symptoms'] > 4]\n",
    "\n",
    "\n",
    "fig = go.Figure(data = [go.Pie(labels = temp_df['index'],\n",
    "                               values = temp_df['Symptoms'],\n",
    "                               hole = .75,\n",
    "                               #title = '% of Symptons',\n",
    "                               marker_colors = px.colors.sequential.algae_r,\n",
    "                              )\n",
    "                       ])\n",
    "\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = \"Distribution of Symptoms<br><sub>Calculated on a sample in the deprecated dataset - data up until 23/09/2022\",\n",
    "    template = 'plotly_dark',\n",
    "    height = 600,\n",
    "    annotations = [dict(text = 'Symptoms',\n",
    "                      x = 0.5,\n",
    "                      y = 0.5,\n",
    "                      font_size = 20,\n",
    "                      showarrow = False\n",
    "                     )])\n",
    "\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aaa5f0fa",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-11-18T17:08:31.011671Z",
     "iopub.status.busy": "2022-11-18T17:08:31.011250Z",
     "iopub.status.idle": "2022-11-18T17:08:31.374505Z",
     "shell.execute_reply": "2022-11-18T17:08:31.373504Z"
    },
    "papermill": {
     "duration": 0.375563,
     "end_time": "2022-11-18T17:08:31.376878",
     "exception": false,
     "start_time": "2022-11-18T17:08:31.001315",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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   "source": [
    "# Create dataframe counting NaN values per column\n",
    "nan_df = pd.DataFrame(monkeypox_df.isna().sum()).reset_index()\n",
    "nan_df.columns  = ['Column', 'NaN_Count']\n",
    "nan_df['NaN_Count'] = nan_df['NaN_Count'].astype('int')\n",
    "nan_df['NaN_%'] = round(nan_df['NaN_Count']/monkeypox_df.shape[0] * 100,1)\n",
    "nan_df['Type']  = 'Missingness'\n",
    "nan_df.sort_values('NaN_%', inplace = True)\n",
    "\n",
    "# Add completeness\n",
    "for i in range(nan_df.shape[0]):\n",
    "    complete_df = pd.DataFrame([nan_df.loc[i,'Column'],monkeypox_df.shape[0] - nan_df.loc[i,'NaN_Count'],100 - nan_df.loc[i,'NaN_%'], 'Completeness']).T\n",
    "    complete_df.columns  = ['Column','NaN_Count','NaN_%','Type']\n",
    "    complete_df['NaN_%'] = complete_df['NaN_%'].astype('int')\n",
    "    complete_df['NaN_Count'] = complete_df['NaN_Count'].astype('int')\n",
    "    nan_df = nan_df.append(complete_df, sort = True)\n",
    "    \n",
    "\n",
    "# Missingness Plot\n",
    "fig = px.bar(nan_df,\n",
    "             x = 'Column',\n",
    "             y = 'NaN_%',\n",
    "             title = 'Missingness within this Dataset',\n",
    "             color = 'Type',\n",
    "             template = 'plotly_dark',\n",
    "             opacity = 0.6,\n",
    "             height = 800,\n",
    "             color_discrete_sequence = ['#dbdbdb','#38cae0']\n",
    "            )\n",
    "\n",
    "\n",
    "fig.update_yaxes(title = 'Percentage of NaNs')\n",
    "fig.update_xaxes(title = 'Column Name')\n",
    "\n",
    "fig.show()"
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   "source": [
    "In case you found this dataset and analysis sueful, you might be intered in a related dataset:\n",
    "\n",
    " <font size=\"+1\">Monkeypox Scientific Literature</font>\n",
    "\n",
    "\n",
    "<a href=\"https://www.kaggle.com/datasets/andrewmvd/monkeypox-scientific-literature\">\n",
    "<img src=\"https://github.com/Larxel/Assets/blob/main/monkeypox__abstractsbutton.png?raw=true\" alt=\"Monkeypox Scientific Literature\" style=\"float:left\" >\n",
    "</a>"
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     "start_time": "2022-11-18T17:08:31.402924",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"color:white;\n",
    "           display:fill;\n",
    "           border-radius:5px;\n",
    "           background-color:#38cae0;\n",
    "           font-size:110%;\n",
    "           font-family:Roboto;\n",
    "           letter-spacing:0.5px\">\n",
    "\n",
    "<p style=\"padding: 10px;\n",
    "              color:white;\">       For more resources on monkeypox, please refer to the WHO website for trustworthy information.\n",
    "    \n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n",
    "<div style=\"color:white;\n",
    "           display:fill;\n",
    "           border-radius:5px;\n",
    "           background-color:#111111;\n",
    "           font-size:110%;\n",
    "           font-family:Roboto;\n",
    "           letter-spacing:0.5px\">\n",
    "\n",
    "<p style=\"padding: 10px;\n",
    "              color:white;\">       Thank you for reading.\n",
    "    \n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n"
   ]
  }
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