Day42 - Long vs. Wide Format.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Long vs. Wide Data Formats in Data Analysis**"
   ]
  },
  {
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   "source": [
    "## **Long Vs Wide Data**\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\"Long\" and \"wide\" are terms often used in data analysis and data reshaping in the context of data frames or tables, typically in software like R or Python. They describe two different ways of organizing and structuring data. \n",
    "\n",
    "#### **1. Long Format (also called \"Tidy\" or \"Melted\")**:\n",
    "   - In the long format, each row typically represents an individual observation or data point, and each column represents a variable or attribute. \n",
    "   - This format is useful when you have a dataset where you want to store multiple measurements or values for the same individuals or entities.\n",
    "   - Long data is often more convenient for various types of statistical analysis, and it can be easier to filter, subset, and manipulate the data.\n",
    "\n",
    "#### **2. Wide Format (also called \"Spread\" or \"Pivoted\"):**\n",
    "   - In the wide format, each row represents an individual, and multiple variables are stored in separate columns.\n",
    "   - This format is useful when you have a dataset with a few variables and you want to see the values for each individual at a glance, which can be more readable for humans.\n",
    "   - Wide data can be useful for simple summaries and initial data exploration.\n",
    "\n",
    "Here's a simple example to illustrate the difference:\n",
    "\n",
    "**Long Format:**\n",
    "\n",
    "| ID | Variable   | Value |\n",
    "|----|------------|-------|\n",
    "| 1  | Age        | 25    |\n",
    "| 1  | Height     | 175   |\n",
    "| 1  | Weight     | 70    |\n",
    "| 2  | Age        | 30    |\n",
    "| 2  | Height     | 160   |\n",
    "| 2  | Weight     | 60    |\n",
    "\n",
    "**Wide Format:**\n",
    "\n",
    "| ID | Age | Height | Weight |\n",
    "|----|-----|--------|--------|\n",
    "| 1  | 25  | 175    | 70     |\n",
    "| 2  | 30  | 160    | 60     |\n",
    "\n",
    "Converting data between long and wide formats is often necessary depending on the specific analysis or visualization tasks you want to perform. In software like R and Python, there are functions and libraries available for reshaping data between these formats, such as `tidyr` in R and `pivot` functions in Python's pandas library for moving from wide to long format, and `gather` in R and `melt` in pandas for moving from long to wide format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Conversion : melt**\n",
    "* wide is converted into long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   cse\n",
       "0  120"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'cse':[120]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variable  value\n",
       "0      cse    120"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'cse':[120]}).melt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cse</th>\n",
       "      <th>ece</th>\n",
       "      <th>mech</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cse  ece  mech\n",
       "0  120  100    50"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# melt -> branch with year\n",
    "pd.DataFrame({'cse':[120],'ece':[100],'mech':[50]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variable  value\n",
       "0      cse    120\n",
       "1      ece    100\n",
       "2     mech     50"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# melt -> branch with year\n",
    "pd.DataFrame({'cse':[120],'ece':[100],'mech':[50]}).melt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>branch</th>\n",
       "      <th>num_students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  branch  num_students\n",
       "0    cse           120\n",
       "1    ece           100\n",
       "2   mech            50"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# var_name and value_name\n",
    "pd.DataFrame({'cse':[120],'ece':[100],'mech':[50]}).melt(var_name='branch',value_name='num_students')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>branch</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  branch  2020  2021  2022\n",
       "0    cse   100   120   150\n",
       "1    ece   150   130   140\n",
       "2   mech    60    80    70"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'branch':['cse','ece','mech'],\n",
    "        '2020':[100,150,60],\n",
    "        '2021':[120,130,80],\n",
    "        '2022':[150,140,70]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>branch</td>\n",
       "      <td>cse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>branch</td>\n",
       "      <td>ece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>branch</td>\n",
       "      <td>mech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variable value\n",
       "0    branch   cse\n",
       "1    branch   ece\n",
       "2    branch  mech\n",
       "3      2020   100\n",
       "4      2020   150\n",
       "5      2020    60\n",
       "6      2021   120\n",
       "7      2021   130\n",
       "8      2021    80\n",
       "9      2022   150\n",
       "10     2022   140\n",
       "11     2022    70"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'branch':['cse','ece','mech'],\n",
    "        '2020':[100,150,60],\n",
    "        '2021':[120,130,80],\n",
    "        '2022':[150,140,70]\n",
    "    }\n",
    ").melt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>branch</th>\n",
       "      <th>year</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>2020</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>2020</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>2020</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cse</td>\n",
       "      <td>2021</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ece</td>\n",
       "      <td>2021</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mech</td>\n",
       "      <td>2021</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cse</td>\n",
       "      <td>2022</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ece</td>\n",
       "      <td>2022</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mech</td>\n",
       "      <td>2022</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  branch  year  students\n",
       "0    cse  2020       100\n",
       "1    ece  2020       150\n",
       "2   mech  2020        60\n",
       "3    cse  2021       120\n",
       "4    ece  2021       130\n",
       "5   mech  2021        80\n",
       "6    cse  2022       150\n",
       "7    ece  2022       140\n",
       "8   mech  2022        70"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# id_vars ->  we don't want to convert\n",
    "pd.DataFrame(\n",
    "    {\n",
    "        'branch':['cse','ece','mech'],\n",
    "        '2020':[100,150,60],\n",
    "        '2021':[120,130,80],\n",
    "        '2022':[150,140,70]\n",
    "    }\n",
    ").melt(id_vars=['branch'],var_name='year',value_name='students')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real-World Example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In the context of COVID-19 data, data for deaths and confirmed cases are initially stored in wide formats.\n",
    "* The data is converted to long format, making it easier to conduct analyses.\n",
    "* In the long format, each row represents a specific location, date, and the corresponding number of deaths or confirmed cases.\n",
    "This format allows for efficient merging and analysis, as it keeps related data in one place and facilitates further data exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# melt -> real world example\n",
    "death = pd.read_csv('Data\\Day42\\death_covid.csv')\n",
    "confirm = pd.read_csv('Data\\Day42\\Confirmed_covid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Country/Region</th>\n",
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       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
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       "      <th>12/25/22</th>\n",
       "      <th>12/26/22</th>\n",
       "      <th>12/27/22</th>\n",
       "      <th>12/28/22</th>\n",
       "      <th>12/29/22</th>\n",
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       "      <th>12/31/22</th>\n",
       "      <th>1/1/23</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7845</td>\n",
       "      <td>7846</td>\n",
       "      <td>7846</td>\n",
       "      <td>7846</td>\n",
       "      <td>7846</td>\n",
       "      <td>7847</td>\n",
       "      <td>7847</td>\n",
       "      <td>7849</td>\n",
       "      <td>7849</td>\n",
       "      <td>7849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>3595</td>\n",
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       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</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>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</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>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1081 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  12/24/22  12/25/22  12/26/22  \\\n",
       "0        0        0        0        0  ...      7845      7846      7846   \n",
       "1        0        0        0        0  ...      3595      3595      3595   \n",
       "2        0        0        0        0  ...      6881      6881      6881   \n",
       "3        0        0        0        0  ...       165       165       165   \n",
       "4        0        0        0        0  ...      1928      1928      1928   \n",
       "\n",
       "   12/27/22  12/28/22  12/29/22  12/30/22  12/31/22  1/1/23  1/2/23  \n",
       "0      7846      7846      7847      7847      7849    7849    7849  \n",
       "1      3595      3595      3595      3595      3595    3595    3595  \n",
       "2      6881      6881      6881      6881      6881    6881    6881  \n",
       "3       165       165       165       165       165     165     165  \n",
       "4      1930      1930      1930      1930      1930    1930    1930  \n",
       "\n",
       "[5 rows x 1081 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>47751</td>\n",
       "      <td>47751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</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>104973</td>\n",
       "      <td>104973</td>\n",
       "      <td>104973</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1081 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  12/24/22  12/25/22  12/26/22  \\\n",
       "0        0        0        0        0  ...    207310    207399    207438   \n",
       "1        0        0        0        0  ...    333749    333749    333751   \n",
       "2        0        0        0        0  ...    271194    271198    271198   \n",
       "3        0        0        0        0  ...     47686     47686     47686   \n",
       "4        0        0        0        0  ...    104973    104973    104973   \n",
       "\n",
       "   12/27/22  12/28/22  12/29/22  12/30/22  12/31/22  1/1/23  1/2/23  \n",
       "0    207460    207493    207511    207550    207559  207616  207627  \n",
       "1    333751    333776    333776    333806    333806  333811  333812  \n",
       "2    271202    271208    271217    271223    271228  271229  271229  \n",
       "3     47686     47751     47751     47751     47751   47751   47751  \n",
       "4    105095    105095    105095    105095    105095  105095  105095  \n",
       "\n",
       "[5 rows x 1081 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "death = death.melt(id_vars=['Province/State','Country/Region','Lat','Long'],var_name='date',value_name='num_deaths')\n",
    "confirm = confirm.melt(id_vars=['Province/State','Country/Region','Lat','Long'],var_name='date',value_name='num_cases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>date</th>\n",
       "      <th>num_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long     date  num_deaths\n",
       "0            NaN    Afghanistan  33.93911  67.709953  1/22/20           0\n",
       "1            NaN        Albania  41.15330  20.168300  1/22/20           0\n",
       "2            NaN        Algeria  28.03390   1.659600  1/22/20           0\n",
       "3            NaN        Andorra  42.50630   1.521800  1/22/20           0\n",
       "4            NaN         Angola -11.20270  17.873900  1/22/20           0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>num_cases</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>1/22/20</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long     date  num_cases\n",
       "0            NaN    Afghanistan  33.93911  67.709953  1/22/20          0\n",
       "1            NaN        Albania  41.15330  20.168300  1/22/20          0\n",
       "2            NaN        Algeria  28.03390   1.659600  1/22/20          0\n",
       "3            NaN        Andorra  42.50630   1.521800  1/22/20          0\n",
       "4            NaN         Angola -11.20270  17.873900  1/22/20          0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Province/State</th>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311248</th>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>31.952200</td>\n",
       "      <td>35.233200</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>703228</td>\n",
       "      <td>5708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311249</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Winter Olympics 2022</td>\n",
       "      <td>39.904200</td>\n",
       "      <td>116.407400</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311250</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>11945</td>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311251</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>334661</td>\n",
       "      <td>4024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311252</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>259981</td>\n",
       "      <td>5637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311253 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Province/State        Country/Region        Lat        Long     date  \\\n",
       "0                 NaN           Afghanistan  33.939110   67.709953  1/22/20   \n",
       "1                 NaN               Albania  41.153300   20.168300  1/22/20   \n",
       "2                 NaN               Algeria  28.033900    1.659600  1/22/20   \n",
       "3                 NaN               Andorra  42.506300    1.521800  1/22/20   \n",
       "4                 NaN                Angola -11.202700   17.873900  1/22/20   \n",
       "...               ...                   ...        ...         ...      ...   \n",
       "311248            NaN    West Bank and Gaza  31.952200   35.233200   1/2/23   \n",
       "311249            NaN  Winter Olympics 2022  39.904200  116.407400   1/2/23   \n",
       "311250            NaN                 Yemen  15.552727   48.516388   1/2/23   \n",
       "311251            NaN                Zambia -13.133897   27.849332   1/2/23   \n",
       "311252            NaN              Zimbabwe -19.015438   29.154857   1/2/23   \n",
       "\n",
       "        num_cases  num_deaths  \n",
       "0               0           0  \n",
       "1               0           0  \n",
       "2               0           0  \n",
       "3               0           0  \n",
       "4               0           0  \n",
       "...           ...         ...  \n",
       "311248     703228        5708  \n",
       "311249        535           0  \n",
       "311250      11945        2159  \n",
       "311251     334661        4024  \n",
       "311252     259981        5637  \n",
       "\n",
       "[311253 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm.merge(death,on=['Province/State','Country/Region','Lat','Long','date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>311248</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>703228</td>\n",
       "      <td>5708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311249</th>\n",
       "      <td>Winter Olympics 2022</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311250</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>11945</td>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311251</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>334661</td>\n",
       "      <td>4024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311252</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>259981</td>\n",
       "      <td>5637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311253 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country/Region     date  num_cases  num_deaths\n",
       "0                Afghanistan  1/22/20          0           0\n",
       "1                    Albania  1/22/20          0           0\n",
       "2                    Algeria  1/22/20          0           0\n",
       "3                    Andorra  1/22/20          0           0\n",
       "4                     Angola  1/22/20          0           0\n",
       "...                      ...      ...        ...         ...\n",
       "311248    West Bank and Gaza   1/2/23     703228        5708\n",
       "311249  Winter Olympics 2022   1/2/23        535           0\n",
       "311250                 Yemen   1/2/23      11945        2159\n",
       "311251                Zambia   1/2/23     334661        4024\n",
       "311252              Zimbabwe   1/2/23     259981        5637\n",
       "\n",
       "[311253 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm.merge(death,on=['Province/State','Country/Region','Lat','Long','date'])[['Country/Region','date','num_cases','num_deaths']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The choice between long and wide data formats depends on the nature of the dataset and the specific analysis or visualization tasks you want to perform. Converting data between these formats can help optimize data organization for different analytical needs."
   ]
  }
 ],
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