SARS_COV2_DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes\n",
    "#### Useful links\n",
    "- blog about data sources: https://towardsdatascience.com/a-short-review-of-covid-19-data-sources-ba7f7aa1c342\n",
    "- GitHub repo of John Hopkinns CSSE: https://github.com/CSSEGISandData/COVID-19\n",
    "#### Learning pandas\n",
    "- useful link to learn pandas: https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/\n",
    "- pandas is derived from the term \"panel data\", an econometrics term for data sets that include observations over multiple time periods for the same individuals\n",
    "- pandas is built on top of the NumPy package, meaning a lot of the structure of NumPy is used or replicated in Pandas. Data in pandas is often used to feed statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn\n",
    "- a Series is essentially a column, and a DataFrame is a multi-dimensional table made up of a collection of Series\n",
    "- two primary components of pandas are: series and dataframes\n",
    "- handling missing data values: most commonly you'll see Python's 'None' or NumPy's 'np.nan', each of which are handled differently in some situations\n",
    "- two options to deal with missing data values:get rid of them or replace them (called imputation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imported modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import odeint\n",
    "import json, requests\n",
    "import seaborn as sb\n",
    "from scipy import stats\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading COVID19 infection spread data from JHCSSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'COVID-19 cases')"
      ]
     },
     "execution_count": 179,
     "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": [
    "cases='csse_covid_19_time_series/time_series_covid19_confirmed_global.csv' #confirmed cases\n",
    "deaths='csse_covid_19_time_series/time_series_covid19_deaths_global.csv' #deaths\n",
    "data=cases\n",
    "jhCsseURL='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/'\n",
    "frm=pd.read_csv(jhCsseURL+data,index_col=1)\n",
    "frm=frm.transpose() #switching to have countries in columns\n",
    "\n",
    "frmDict={}\n",
    "for i in frm:\n",
    "    r=list(frm[i].iloc[0:1])\n",
    "    #r=float(frm[i][0])\n",
    "    if len(r)>1:\n",
    "        for q in range(len(r)):\n",
    "            j=str(i)+':'+str(list(frm[i].iloc[0])[q])\n",
    "            m=np.asanyarray(frm[i].iloc[3:]).T[q]\n",
    "            frmDict[j]=m\n",
    "    else:\n",
    "        j=i\n",
    "        k=frm[i].keys()[3:]\n",
    "        m=frm[i][k]\n",
    "        frmDict[j]=m\n",
    "#for i in frmDict:\n",
    "#    X=list(pd.Series(frmDict[i]).index.values)\n",
    "#    Y=list(frmDict[i])\n",
    "#    plt.plot(X,Y)\n",
    "#X=list(pd.Series(frmDict['China:Hubei']).index.values)\n",
    "Y=list(frmDict['China:Hubei'])\n",
    "plt.plot(Y)\n",
    "plt.xlabel('Time (days)')\n",
    "plt.ylabel('#Infected')\n",
    "plt.title('COVID-19 cases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 138 entries, Province/State to 6/4/20\n",
      "Columns: 266 entries, Afghanistan to Lesotho\n",
      "dtypes: object(266)\n",
      "memory usage: 287.9+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading data from GitHub\n",
    "cases='csse_covid_19_time_series/time_series_covid19_confirmed_global.csv' #confirmed cases\n",
    "deaths='csse_covid_19_time_series/time_series_covid19_deaths_global.csv' #deaths\n",
    "data=cases\n",
    "jhCsseURL='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/'+data\n",
    "frm=pd.read_csv(jhCsseURL,index_col=1)\n",
    "frm=frm.transpose() #switching to have countries in columns\n",
    "\n",
    "#dataset information\n",
    "frm.shape\n",
    "frm.head()\n",
    "frm.tail()\n",
    "frm.info()\n",
    "\n",
    "'''\n",
    "#data cleaning\n",
    "##remove duplicates\n",
    "frm.shape\n",
    "tempfrm=frm\n",
    "tempfrm.drop_duplicates(inplace=True) # OR tempfrm=tempfrm.drop_duplicates()\n",
    "#tempfrm.drop_duplicates(inplace=True,keep='first') #remove all duplicates except first (DEFAULT)\n",
    "#tempfrm.drop_duplicates(inplace=True,keep='last') #remove all duplicates except last\n",
    "#tempfrm.drop_duplicates(inplace=True,keep=False) #remove all duplicates\n",
    "tempfrm.shape\n",
    "##cleaning columns\n",
    "frm.columns\n",
    "frm.rename(columns={'Province/State':'Area'},inplace=True) #renaming to simpler names\n",
    "frm.columns\n",
    "frm.columns=[col.lower() for col in frm.columns] #lowercase for all colnames\n",
    "frm.columns\n",
    "##dealing missing data values\n",
    "frm.isnull() #return true/false for each cell\n",
    "frm.isnull().sum() #counts the total number of true\n",
    "#frm.dropna(inplace=True)#will delete any row with at least a single null value (only use if very few na)\n",
    "#frm.dropna(axis=1,inplace=True)#will delete any column with at least a single null value (only use if very few na)\n",
    "area=frm['area']\n",
    "area.head()\n",
    "area.fillna('Unknown',inplace=True) #imputing null values with 'missing'\n",
    "frm.isnull().sum()\n",
    "frm.head()\n",
    "'''\n",
    "\n",
    "#data statistics\n",
    "#distibution\n",
    "#frm.describe()\n",
    "#frm['India'].describe() #can be performed on categorical data also\n",
    "#frm['India'].value_counts() #gives frequency of all the categories\n",
    "#correlation\n",
    "#frm.corr() #generates correlation matrix\n",
    "\n",
    "frm.head()\n",
    "frm['India']\n",
    "frm['China']\n",
    "frm['Italy']\n",
    "series_Italy=frm['Italy']\n",
    "series_Italy.shape\n",
    "#np.asarray(series_Italy)[40]\n",
    "series_Italy.keys()[32]\n",
    "series_Italy[series_Italy.keys()[33]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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": [
    "##set country\n",
    "def spreadPlotter(Data_Series,Start_Point,Plot_Colour):\n",
    "#    Data_Series=frm[Country_Name]\n",
    "    Time_Points=[]\n",
    "    Infected_Cases=[]\n",
    "    for i in Data_Series.keys()[Start_Point:]:\n",
    "        [m,d,y]=i.split('/')\n",
    "        y=2000+int(y)\n",
    "        m=int(m)\n",
    "        d=int(d)\n",
    "        #print(y,m,d)\n",
    "        Day_Count=dt.datetime(y,m,d).strftime('%j')\n",
    "        Day_Count=int(Day_Count)\n",
    "        Time_Points.append(Day_Count)\n",
    "        Infected_Cases.append(Data_Series[i])\n",
    "    Time_Points=[i-Time_Points[0] for i in Time_Points]\n",
    "    x_Ticks=list(Data_Series.keys()[Start_Point:])\n",
    "    Display_Points=np.arange(min(Time_Points),max(Time_Points)+1,10)\n",
    "    Display_Ticks=[x_Ticks[i] for i in Display_Points]\n",
    "    plt.plot(Time_Points,Infected_Cases,Plot_Colour)\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('#Infected')\n",
    "    plt.title('Trend Plot')\n",
    "    plt.xticks(Display_Points,Display_Ticks,rotation=45)\n",
    "    return Time_Points,Infected_Cases\n",
    "\n",
    "#Country_Name='Italy'\n",
    "#x_Data,y_Data=spreadPlotter(frm[Country_Name],33,'r')\n",
    "\n",
    "Country_Name='United Kingdom'\n",
    "Province='nan'\n",
    "SubData=frm[Country_Name]\n",
    "NewLabel=list(np.asanyarray(SubData.iloc[0:1]))[0]\n",
    "NewLabel=[str(i) for i in NewLabel]\n",
    "NewIndex=list(SubData.index)[3:]\n",
    "NewData=np.asanyarray(SubData.iloc[3:])\n",
    "SubDataFrame=pd.DataFrame(NewData,index=NewIndex,columns=NewLabel)\n",
    "x_Data,y_Data=spreadPlotter(SubDataFrame[Province],3,'r')\n",
    "\n",
    "'''Country_Name='China'\n",
    "Province='Hubei'\n",
    "SubData=frm[Country_Name]\n",
    "NewLabel=list(np.asanyarray(SubData.iloc[0:1]))[0]\n",
    "NewLabel=[str(i) for i in NewLabel]\n",
    "NewIndex=list(SubData.index)[3:]\n",
    "NewData=np.asanyarray(SubData.iloc[3:])\n",
    "SubDataFrame=pd.DataFrame(NewData,index=NewIndex,columns=NewLabel)\n",
    "x_Data,y_Data=spreadPlotter(SubDataFrame[Province],3,'m')\n",
    "'''\n",
    "Country_Name='Italy'\n",
    "x_Data,y_Data=spreadPlotter(frm[Country_Name],3,'b')\n",
    "\n",
    "Country_Name='US'\n",
    "x_Data,y_Data=spreadPlotter(frm[Country_Name],3,'k')\n",
    "\n",
    "Country_Name='India'\n",
    "x_Data,y_Data=spreadPlotter(frm[Country_Name],3,'g')\n",
    "\n",
    "Country_Name='Iran'\n",
    "x_Data,y_Data=spreadPlotter(frm[Country_Name],3,'y')\n",
    "\n",
    "#print(len(x_Data),len(y_Data))\n",
    "\n"
   ]
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}