Pandas SARS-CoV-2 Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# <span style=\"color:cornflowerblue\">Pandas SARS-CoV-2 Project</span> \n",
    "#### <span style=\"color:cornflowerblue\">March 2022</span>\n",
    "***\n",
    "**Task:** Using the *time_series_covid19_confirmed_US.csv* CSV file provided and your knowledge of Pandas and Python, answer the questions contained within this notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### <span style=\"color:cornflowerblue\">Section One:</span>\n",
    "**Proccess:** Pandas was imported as variable *pd* and the csv file was read in as variable *confirmed*. Dataframe entries by were grouped by US state/territory using the sum of cases per day of that state/territory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
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       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>...</th>\n",
       "      <th>3/22/21</th>\n",
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       "      <th>3/27/21</th>\n",
       "      <th>3/28/21</th>\n",
       "      <th>3/29/21</th>\n",
       "      <th>3/30/21</th>\n",
       "      <th>3/31/21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>5796241491</td>\n",
       "      <td>57960</td>\n",
       "      <td>241491.0</td>\n",
       "      <td>2203.246784</td>\n",
       "      <td>-5809.578199</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>511779</td>\n",
       "      <td>511789</td>\n",
       "      <td>512711</td>\n",
       "      <td>513138</td>\n",
       "      <td>513580</td>\n",
       "      <td>514072</td>\n",
       "      <td>514391</td>\n",
       "      <td>514619</td>\n",
       "      <td>514980</td>\n",
       "      <td>515388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>2604232344</td>\n",
       "      <td>26040</td>\n",
       "      <td>232344.0</td>\n",
       "      <td>1747.177765</td>\n",
       "      <td>-4293.070291</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>61435</td>\n",
       "      <td>61611</td>\n",
       "      <td>61830</td>\n",
       "      <td>61976</td>\n",
       "      <td>62161</td>\n",
       "      <td>62161</td>\n",
       "      <td>62161</td>\n",
       "      <td>62161</td>\n",
       "      <td>62797</td>\n",
       "      <td>62797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-14.271000</td>\n",
       "      <td>-170.132000</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>1428230216</td>\n",
       "      <td>14280</td>\n",
       "      <td>230216.0</td>\n",
       "      <td>505.138555</td>\n",
       "      <td>-1671.948482</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>836737</td>\n",
       "      <td>837244</td>\n",
       "      <td>837849</td>\n",
       "      <td>837987</td>\n",
       "      <td>838558</td>\n",
       "      <td>839334</td>\n",
       "      <td>839888</td>\n",
       "      <td>840492</td>\n",
       "      <td>841078</td>\n",
       "      <td>841811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>6468550635</td>\n",
       "      <td>64680</td>\n",
       "      <td>550635.0</td>\n",
       "      <td>2618.391704</td>\n",
       "      <td>-6932.548370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>328707</td>\n",
       "      <td>328946</td>\n",
       "      <td>329177</td>\n",
       "      <td>329511</td>\n",
       "      <td>329695</td>\n",
       "      <td>329860</td>\n",
       "      <td>329929</td>\n",
       "      <td>330008</td>\n",
       "      <td>330186</td>\n",
       "      <td>330398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 440 columns</p>\n",
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      "text/plain": [
       "                       UID  code3      FIPS          Lat        Long_  \\\n",
       "Province_State                                                          \n",
       "Alabama         5796241491  57960  241491.0  2203.246784 -5809.578199   \n",
       "Alaska          2604232344  26040  232344.0  1747.177765 -4293.070291   \n",
       "American Samoa          16     16      60.0   -14.271000  -170.132000   \n",
       "Arizona         1428230216  14280  230216.0   505.138555 -1671.948482   \n",
       "Arkansas        6468550635  64680  550635.0  2618.391704 -6932.548370   \n",
       "\n",
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  ...  3/22/21  \\\n",
       "Province_State                                               ...            \n",
       "Alabama               0        0        0        0        0  ...   511779   \n",
       "Alaska                0        0        0        0        0  ...    61435   \n",
       "American Samoa        0        0        0        0        0  ...        0   \n",
       "Arizona               0        0        0        0        1  ...   836737   \n",
       "Arkansas              0        0        0        0        0  ...   328707   \n",
       "\n",
       "                3/23/21  3/24/21  3/25/21  3/26/21  3/27/21  3/28/21  3/29/21  \\\n",
       "Province_State                                                                  \n",
       "Alabama          511789   512711   513138   513580   514072   514391   514619   \n",
       "Alaska            61611    61830    61976    62161    62161    62161    62161   \n",
       "American Samoa        0        0        0        0        0        0        0   \n",
       "Arizona          837244   837849   837987   838558   839334   839888   840492   \n",
       "Arkansas         328946   329177   329511   329695   329860   329929   330008   \n",
       "\n",
       "                3/30/21  3/31/21  \n",
       "Province_State                    \n",
       "Alabama          514980   515388  \n",
       "Alaska            62797    62797  \n",
       "American Samoa        0        0  \n",
       "Arizona          841078   841811  \n",
       "Arkansas         330186   330398  \n",
       "\n",
       "[5 rows x 440 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "confirmed = pd.read_csv('time_series_covid19_confirmed_US.csv')\n",
    "ByTerr = confirmed.groupby('Province_State').sum()\n",
    "ByTerr.head()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "Initially, only summer months were required for the query, so only columns 166 (July 1st 2020) through 258 (September 30th 2020) were kept in dataframe *ByTerrSummer*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "      <th></th>\n",
       "      <th>7/1/20</th>\n",
       "      <th>7/2/20</th>\n",
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       "      <th>Province_State</th>\n",
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       "      <th>Alabama</th>\n",
       "      <td>39647</td>\n",
       "      <td>41340</td>\n",
       "      <td>42255</td>\n",
       "      <td>43216</td>\n",
       "      <td>44022</td>\n",
       "      <td>44932</td>\n",
       "      <td>46348</td>\n",
       "      <td>48149</td>\n",
       "      <td>49695</td>\n",
       "      <td>51329</td>\n",
       "      <td>...</td>\n",
       "      <td>145202</td>\n",
       "      <td>145859</td>\n",
       "      <td>146897</td>\n",
       "      <td>149263</td>\n",
       "      <td>150239</td>\n",
       "      <td>150965</td>\n",
       "      <td>151604</td>\n",
       "      <td>152350</td>\n",
       "      <td>153584</td>\n",
       "      <td>154772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>1184</td>\n",
       "      <td>1234</td>\n",
       "      <td>1294</td>\n",
       "      <td>1349</td>\n",
       "      <td>1382</td>\n",
       "      <td>1410</td>\n",
       "      <td>1434</td>\n",
       "      <td>1485</td>\n",
       "      <td>1537</td>\n",
       "      <td>1591</td>\n",
       "      <td>...</td>\n",
       "      <td>7862</td>\n",
       "      <td>7922</td>\n",
       "      <td>7999</td>\n",
       "      <td>8142</td>\n",
       "      <td>8269</td>\n",
       "      <td>8388</td>\n",
       "      <td>8500</td>\n",
       "      <td>8617</td>\n",
       "      <td>8739</td>\n",
       "      <td>8845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>84105</td>\n",
       "      <td>87445</td>\n",
       "      <td>91872</td>\n",
       "      <td>94567</td>\n",
       "      <td>98103</td>\n",
       "      <td>101455</td>\n",
       "      <td>105094</td>\n",
       "      <td>108614</td>\n",
       "      <td>112671</td>\n",
       "      <td>116892</td>\n",
       "      <td>...</td>\n",
       "      <td>214251</td>\n",
       "      <td>214846</td>\n",
       "      <td>215284</td>\n",
       "      <td>215852</td>\n",
       "      <td>216367</td>\n",
       "      <td>216826</td>\n",
       "      <td>217237</td>\n",
       "      <td>217510</td>\n",
       "      <td>218184</td>\n",
       "      <td>218507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>21197</td>\n",
       "      <td>22075</td>\n",
       "      <td>22622</td>\n",
       "      <td>23209</td>\n",
       "      <td>23814</td>\n",
       "      <td>24253</td>\n",
       "      <td>24512</td>\n",
       "      <td>25246</td>\n",
       "      <td>26052</td>\n",
       "      <td>26803</td>\n",
       "      <td>...</td>\n",
       "      <td>76364</td>\n",
       "      <td>76981</td>\n",
       "      <td>77963</td>\n",
       "      <td>79049</td>\n",
       "      <td>79946</td>\n",
       "      <td>80755</td>\n",
       "      <td>81242</td>\n",
       "      <td>82049</td>\n",
       "      <td>82755</td>\n",
       "      <td>83697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
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      ],
      "text/plain": [
       "                7/1/20  7/2/20  7/3/20  7/4/20  7/5/20  7/6/20  7/7/20  \\\n",
       "Province_State                                                           \n",
       "Alabama          39647   41340   42255   43216   44022   44932   46348   \n",
       "Alaska            1184    1234    1294    1349    1382    1410    1434   \n",
       "American Samoa       0       0       0       0       0       0       0   \n",
       "Arizona          84105   87445   91872   94567   98103  101455  105094   \n",
       "Arkansas         21197   22075   22622   23209   23814   24253   24512   \n",
       "\n",
       "                7/8/20  7/9/20  7/10/20  ...  9/21/20  9/22/20  9/23/20  \\\n",
       "Province_State                           ...                              \n",
       "Alabama          48149   49695    51329  ...   145202   145859   146897   \n",
       "Alaska            1485    1537     1591  ...     7862     7922     7999   \n",
       "American Samoa       0       0        0  ...        0        0        0   \n",
       "Arizona         108614  112671   116892  ...   214251   214846   215284   \n",
       "Arkansas         25246   26052    26803  ...    76364    76981    77963   \n",
       "\n",
       "                9/24/20  9/25/20  9/26/20  9/27/20  9/28/20  9/29/20  9/30/20  \n",
       "Province_State                                                                 \n",
       "Alabama          149263   150239   150965   151604   152350   153584   154772  \n",
       "Alaska             8142     8269     8388     8500     8617     8739     8845  \n",
       "American Samoa        0        0        0        0        0        0        0  \n",
       "Arizona          215852   216367   216826   217237   217510   218184   218507  \n",
       "Arkansas          79049    79946    80755    81242    82049    82755    83697  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grabs all rows in columns #166-258 and puts subdata in a new dataframe\n",
    "ByTerrSummer = ByTerr.iloc[:,166:258]\n",
    "ByTerrSummer.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**1) Which state/territory had the highest numerical increase in confirmed cases over the summer months (July 1st through the end of September)?**\n",
    "\n",
    ">**Proccess:** A new column was added named *Increase*, which held the difference between the total cases on the last day of summer and the total cases on the first day of summer for each province; essencially, the numerical increase in confirmed cases over the summer months. The dataframe was sorted in descending order by this column, so that the states/territories with the highest numerical increases in confirmed cases over the summer months could be found using head().\n",
    "\n",
    ">**Answer:** Texas was the state/territory that had the highest numerical increase in confirmed cases over the summer months with a total increase of 605198 cases, diriving from 172859 cases on 7/1/20 to 778057 cases on 9/30/20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>7/1/20</th>\n",
       "      <th>9/30/20</th>\n",
       "      <th>Increase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>172859</td>\n",
       "      <td>778057</td>\n",
       "      <td>605198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>247317</td>\n",
       "      <td>822191</td>\n",
       "      <td>574874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>158997</td>\n",
       "      <td>706516</td>\n",
       "      <td>547519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>84242</td>\n",
       "      <td>318026</td>\n",
       "      <td>233784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>144013</td>\n",
       "      <td>295720</td>\n",
       "      <td>151707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                7/1/20  9/30/20  Increase\n",
       "Province_State                           \n",
       "Texas           172859   778057    605198\n",
       "California      247317   822191    574874\n",
       "Florida         158997   706516    547519\n",
       "Georgia          84242   318026    233784\n",
       "Illinois        144013   295720    151707"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new column of difference between cases on 9/30 and cases on 7/1\n",
    "ByTerrSummer['Increase'] = ByTerrSummer['9/30/20'] - ByTerrSummer['7/1/20']\n",
    "NumSummer = ByTerrSummer.sort_values(by='Increase',ascending=False)\n",
    "NumSummer.loc[:,['7/1/20','9/30/20','Increase']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**2) Which state/territory had the highest percentage increase in confirmed cases over the same time period?**\n",
    "\n",
    ">**Proccess:** A new column was added named *% Increase*, with its contents being calculated by dividing the numerical increase by the original number of cases on day one (7/1/20), and multiplying that value by 100. The dataframe was sorted in descending order by this column, so that the states/territories with the highest percentage increases in confirmed cases over the summer months could be found using head().\n",
    "\n",
    ">**Answer:** The Virgin Islands were the state/territory that had the highest percentage increase in confirmed cases over the summer months, with a total percent increase of 1364.44% from a total numeric increase of 1228 cases, going from 90 cases on 7/1/20 to 1318 cases on 9/30/20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>7/1/20</th>\n",
       "      <th>9/30/20</th>\n",
       "      <th>% Increase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>90</td>\n",
       "      <td>1318</td>\n",
       "      <td>1364.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Rico</th>\n",
       "      <td>1729</td>\n",
       "      <td>24000</td>\n",
       "      <td>1288.085599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>926</td>\n",
       "      <td>12597</td>\n",
       "      <td>1260.367171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>1016</td>\n",
       "      <td>13071</td>\n",
       "      <td>1186.515748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guam</th>\n",
       "      <td>267</td>\n",
       "      <td>2488</td>\n",
       "      <td>831.835206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                7/1/20  9/30/20   % Increase\n",
       "Province_State                              \n",
       "Virgin Islands      90     1318  1364.444444\n",
       "Puerto Rico       1729    24000  1288.085599\n",
       "Hawaii             926    12597  1260.367171\n",
       "Montana           1016    13071  1186.515748\n",
       "Guam               267     2488   831.835206"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ByTerrSummer['% Increase'] = (ByTerrSummer['Increase'] / ByTerrSummer['7/1/20']) * 100 #New column of quotient of increase and 7/1 columns\n",
    "PercSummer = ByTerrSummer.sort_values(by='% Increase',ascending=False)\n",
    "PercSummer.loc[:,['7/1/20','9/30/20','% Increase']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**3) What was the 7-day average increase in cases per state? (mean of cases per day over a seven day period for 8 weeks, starting on June 1st.)**\n",
    "\n",
    ">**Proccess:** Next, because only 8 weeks, starting June 1st, were required for the query, only columns 136 (June 1st 2020) through 192 (July 22nd 2020) were kept in dataframe *ByTerr8Week*.\n",
    "<br> A new, blank dataframe was created named *dfWeeklyInc*, along with a counting variable starting at 0. A for-loop was created to range from the 7th day in the 8 week period to the 56th day, going by steps of 7. Each time it looped, the count was raised by 1 for the naming variable, variables j and k changed depending on the value of i, and new columns in the dataframe *dfWeeklyInc* were created by taking the average of all days in each week for each province. Next, each of those weekly means for each province were averaged, creating column *Avg Weekly Avg Inc* in the dataframe. For observational purposes, the dataframe was sorted from highest increase to lowest by province, and the head() function was used for easy viewing.\n",
    "\n",
    ">**Answer:** New York had the highest average weekly average increase, with the average weekly average increase of 395351.39 cases over the specified 8 week period. The runner up was California with an average weekly increase of 251589.61 cases, followed by Texas with 185274.93, Florida with 180836.43, and New Jersey with 172010.96."
   ]
  },
  {
   "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",
       "    }\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>Week 1 Avg</th>\n",
       "      <th>Week 2 Avg</th>\n",
       "      <th>Week 3 Avg</th>\n",
       "      <th>Week 4 Avg</th>\n",
       "      <th>Week 5 Avg</th>\n",
       "      <th>Week 6 Avg</th>\n",
       "      <th>Week 7 Avg</th>\n",
       "      <th>Week 8 Avg</th>\n",
       "      <th>Avg Weekly Avg Inc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>377895.571429</td>\n",
       "      <td>383629.571429</td>\n",
       "      <td>388240.142857</td>\n",
       "      <td>392794.714286</td>\n",
       "      <td>397463.000000</td>\n",
       "      <td>402276.714286</td>\n",
       "      <td>407700.571429</td>\n",
       "      <td>412810.857143</td>\n",
       "      <td>395351.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>123810.285714</td>\n",
       "      <td>144871.000000</td>\n",
       "      <td>169996.857143</td>\n",
       "      <td>207017.857143</td>\n",
       "      <td>252728.857143</td>\n",
       "      <td>308857.714286</td>\n",
       "      <td>370406.571429</td>\n",
       "      <td>435027.714286</td>\n",
       "      <td>251589.607143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>70883.428571</td>\n",
       "      <td>82866.285714</td>\n",
       "      <td>101847.714286</td>\n",
       "      <td>134834.142857</td>\n",
       "      <td>179313.285714</td>\n",
       "      <td>236149.857143</td>\n",
       "      <td>304593.285714</td>\n",
       "      <td>371711.428571</td>\n",
       "      <td>185274.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>60201.142857</td>\n",
       "      <td>69633.571429</td>\n",
       "      <td>86702.285714</td>\n",
       "      <td>117618.857143</td>\n",
       "      <td>170805.000000</td>\n",
       "      <td>235030.714286</td>\n",
       "      <td>315215.142857</td>\n",
       "      <td>391484.714286</td>\n",
       "      <td>180836.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>163024.714286</td>\n",
       "      <td>166227.142857</td>\n",
       "      <td>168756.285714</td>\n",
       "      <td>171084.571429</td>\n",
       "      <td>173380.285714</td>\n",
       "      <td>175673.428571</td>\n",
       "      <td>177959.571429</td>\n",
       "      <td>179981.714286</td>\n",
       "      <td>172010.964286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Week 1 Avg     Week 2 Avg     Week 3 Avg     Week 4 Avg  \\\n",
       "Province_State                                                               \n",
       "New York        377895.571429  383629.571429  388240.142857  392794.714286   \n",
       "California      123810.285714  144871.000000  169996.857143  207017.857143   \n",
       "Texas            70883.428571   82866.285714  101847.714286  134834.142857   \n",
       "Florida          60201.142857   69633.571429   86702.285714  117618.857143   \n",
       "New Jersey      163024.714286  166227.142857  168756.285714  171084.571429   \n",
       "\n",
       "                   Week 5 Avg     Week 6 Avg     Week 7 Avg     Week 8 Avg  \\\n",
       "Province_State                                                               \n",
       "New York        397463.000000  402276.714286  407700.571429  412810.857143   \n",
       "California      252728.857143  308857.714286  370406.571429  435027.714286   \n",
       "Texas           179313.285714  236149.857143  304593.285714  371711.428571   \n",
       "Florida         170805.000000  235030.714286  315215.142857  391484.714286   \n",
       "New Jersey      173380.285714  175673.428571  177959.571429  179981.714286   \n",
       "\n",
       "                Avg Weekly Avg Inc  \n",
       "Province_State                      \n",
       "New York             395351.392857  \n",
       "California           251589.607143  \n",
       "Texas                185274.928571  \n",
       "Florida              180836.428571  \n",
       "New Jersey           172010.964286  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ByTerr8Week = ByTerr.iloc[:,136:192]\n",
    "\n",
    "dfWeeklyInc = pd.DataFrame()\n",
    "count = 0\n",
    "for i in range(7,57,7): # starting at column 7 to column 56, grouping by 7 (days in each week)\n",
    "    count += 1\n",
    "    j = i - 1 # stop column spot\n",
    "    k = i - 7 # start column spot\n",
    "    name = \"Week \" + str(count) + \" Avg\" # new column naming\n",
    "    dfWeeklyInc[name] = ByTerr8Week.iloc[:,k:j+1].mean(axis=1) #mean of rows for each column in this range\n",
    "dfWeeklyInc['Avg Weekly Avg Inc'] = dfWeeklyInc.mean(axis=1) #new column for means of means\n",
    "Top5AvgWeeklyInc = dfWeeklyInc.sort_values(by='Avg Weekly Avg Inc',ascending=False)\n",
    "Top5AvgWeeklyInc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**4) What proportion of cases nationally had occurred in NY State at the start of each month, from March 2020 to August 2020?**\n",
    "\n",
    ">**Proccess:** Next, since only the first days of spring months were required for the query, only columns '3/1/20', '4/1/20', '5/1/20', '6/1/20', '7/1/20', and '8/1/20' were kept for new dataframe *SprMonths*, and these columns were transposed to rows.\n",
    "<br> A new column was added named *Date Total*, which held the sum of each of the dataframe *SprMonths's* rows. A second column was added named *NY Proportion*, with its contents being calculated by dividing contents of each row in the *New York* column by the contents of each row in the *Date Total* column to find the proportion of cases nationally that occurred in NY State at the start of each month.\n",
    "\n",
    ">**Answer:** The proportion of cases nationally that occurred in NY State on March 1st was 0, April 1st was 0.402, May 1st was 0.280, June 1st was 0.206, July 1st was 0.147, and August 1st was 0.091."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>3/1/20</th>\n",
       "      <th>4/1/20</th>\n",
       "      <th>5/1/20</th>\n",
       "      <th>6/1/20</th>\n",
       "      <th>7/1/20</th>\n",
       "      <th>8/1/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NY Proportion</th>\n",
       "      <td>0</td>\n",
       "      <td>0.402491</td>\n",
       "      <td>0.280299</td>\n",
       "      <td>0.20638</td>\n",
       "      <td>0.147163</td>\n",
       "      <td>0.0906001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              3/1/20    4/1/20    5/1/20   6/1/20    7/1/20     8/1/20\n",
       "NY Proportion      0  0.402491  0.280299  0.20638  0.147163  0.0906001"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new df of only these columns but all rows\n",
    "SprMonths = ByTerr[['3/1/20','4/1/20','5/1/20','6/1/20','7/1/20','8/1/20']]\n",
    "SprMonths = SprMonths.reset_index(drop=False)\n",
    "SprMonths = SprMonths.T\n",
    "SprMonths = SprMonths.rename(columns=SprMonths.iloc[0]).drop(SprMonths.index[0])\n",
    "\n",
    "# new column of date sums\n",
    "SprMonths['Date Total'] = SprMonths.sum(axis=\"columns\")\n",
    "# new column of quotient of new york total and date totals\n",
    "SprMonths['NY Proportion'] = (SprMonths['New York'] / SprMonths['Date Total'])\n",
    "\n",
    "SprMonths.loc[:,['NY Proportion']].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### <span style=\"color:cornflowerblue\">Section Two:</span>\n",
    "**Proccess:** Plots were provided to show the results of each question in Part 1. \n",
    "\n",
    "*Hint: It is up to you to determine how best to demonstrate that result.*\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) Which state/territory had the highest numerical increase in confirmed cases over the summer months (July 1st through the end of September.)**\n",
    "\n",
    "The line plot below shows the number of cases on the y-axis and the top 5 US provinces on the x-axis. The data taken for this plot was from dataframe *NumSummer* and consists of the increase of cases over the summer months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cases')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1a = NumSummer.loc[:,['Increase']].head().plot(kind=\"line\",cmap='Paired',figsize=(10,5))\n",
    "plot1a.figure.suptitle(\"Top 5 US Provinces With The Highest Numerical Confirmed Case Increase\",fontweight=\"bold\")\n",
    "plot1a.set_xlabel(\"Province\",fontweight=\"bold\")\n",
    "plot1a.set_ylabel(\"Cases\",fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The area plot below shows the number of cases on the y-axis and the top 5 US provinces on the x-axis. The data taken for this plot was from dataframe *NumSummer* and consists of the cases on 7/1/20, increase of cases over the summer months, and the cases on 9/30/20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cases')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1b = NumSummer.loc[:,['7/1/20','Increase','9/30/20']].head().plot(kind=\"area\",cmap='Pastel1',figsize=(10,5))\n",
    "plot1b.figure.suptitle(\"Confirmed Case Numbers For Top 5 US Provinces With Highest Case Number Increases\",fontweight=\"bold\")\n",
    "plot1b.set_xlabel(\"Province\",fontweight=\"bold\")\n",
    "plot1b.set_ylabel(\"Cases\",fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) Which state/territory had the highest percentage increase in confirmed cases over the same time period?**\n",
    "\n",
    "The line plot below shows the case increase percentage on the y-axis and the top 5 US provinces on the x-axis. The data taken for this plot was from dataframe *PercSummer* and consists of the percent increase of cases over the summer months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cases Increase %')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2a = PercSummer.loc[:,['% Increase']].head().plot(kind=\"line\",cmap='Paired',figsize=(10,5))\n",
    "plot2a.figure.suptitle(\"Top 5 US Provinces With The Highest Percentage Confirmed Case Increase\",fontweight=\"bold\")\n",
    "plot2a.set_xlabel(\"Province\",fontweight=\"bold\")\n",
    "plot2a.set_ylabel(\"Cases Increase %\",fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The area plot below shows the number of cases on the y-axis and the top 5 US provinces on the x-axis. The data taken for this plot was from dataframe *PercSummer* and consists of the cases on 7/1/20 and the increase of cases over the summer months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cases')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2b = PercSummer.loc[:,['7/1/20','Increase', '9/30/20']].head().plot(kind=\"area\",cmap='Pastel1',figsize=(10,5))\n",
    "plot2b.figure.suptitle(\"Initial Case And Total Increase Values For The Top 5 US Provinces With The Highest Percentage Confirmed Case Increase\",fontweight=\"bold\")\n",
    "plot2b.set_xlabel(\"Province\",fontweight=\"bold\")\n",
    "plot2b.set_ylabel(\"Cases\",fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) What was the 7-day average increase in cases per state? (mean of cases per day over a seven day period for 8 weeks, starting on June 1st.)**\n",
    "\n",
    "The line plot below shows the average number of weekly cases on the y-axis and the top 5 US provinces on the x-axis. The data taken for this plot was from dataframe *Top5AvgWeeklyInc* and consists of the average weekly average increase of cases over the 8 weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Avg Weekly Case Inc Avg')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot3a = Top5AvgWeeklyInc.loc[:,['Avg Weekly Avg Inc']].head().plot(kind=\"line\",cmap='Paired',figsize=(10,5))\n",
    "plot3a.figure.suptitle(\"Top 5 US Provinces With The Highest Average Weekly Case Increase Average\",fontweight=\"bold\")\n",
    "plot3a.set_xlabel(\"Province\",fontweight=\"bold\")\n",
    "plot3a.set_ylabel(\"Avg Weekly Case Inc Avg\",fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line graph below shows the average number of cases on the y-axis and the weeks on the x-axis. The data taken for this plot was from dataframe *Top5AvgWeeklyInc* and consists of the average weekly number of cases over the 8 weeks for the top 5 US provinces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Case Average')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot3b = Top5AvgWeeklyInc.iloc[:,:-1].head().T.plot(kind=\"line\",cmap='Pastel1',figsize=(15,5))\n",
    "plot3b.figure.suptitle(\"Weekly Case Average For Top 5 US Provinces\",fontweight=\"bold\")\n",
    "plot3b.set_xlabel(\"Weeks\",fontweight=\"bold\")\n",
    "plot3b.set_ylabel(\"Case Average\",fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4) What proportion of cases nationally had occurred in NY State at the start of each month, from March 2020 to August 2020?**\n",
    "\n",
    "The line graph below shows the number of cases on the y-axis and the dates on the x-axis. The data taken for this plot was from dataframe *SprMonths* and consists of the number of cases on each date for NY city alone compared to the total number of national cases for all provinces on each date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cases')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot4a = SprMonths.loc[:,['New York', 'Date Total']].head().plot(kind=\"line\",cmap='Paired',figsize=(10,5))\n",
    "plot4a.figure.suptitle(\"Confirmed New York State Cases VS National Cases\",fontweight=\"bold\")\n",
    "plot4a.set_xlabel(\"Date\",fontweight=\"bold\")\n",
    "plot4a.set_ylabel(\"Cases\",fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The area plot below shows the number of proportion out of 1 the y-axis and the dates on the x-axis. The data taken for this plot was from dataframe *NYdf* and consists of the proportion of cases on each date for 4 US provinces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proportion')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NYdf = pd.DataFrame()\n",
    "NYdf[\"NY Proportion\"] = SprMonths['NY Proportion']\n",
    "NYdf[\"NJ Proportion\"] = (SprMonths['New Jersey'] / SprMonths['Date Total'])\n",
    "NYdf[\"MA Proportion\"] = (SprMonths['Massachusetts'] / SprMonths['Date Total'])\n",
    "NYdf[\"MI Proportion\"] = (SprMonths['Michigan'] / SprMonths['Date Total'])\n",
    "\n",
    "plot4b = NYdf.loc[:,['MI Proportion', 'MA Proportion', 'NJ Proportion', 'NY Proportion']].plot(kind=\"area\",ylim=(0,1),cmap='Pastel1',figsize=(10,5))\n",
    "plot4b.figure.suptitle(\"Proportions Of Confirmed Cases For Top 5 US Provinces\",fontweight=\"bold\")\n",
    "plot4b.set_xlabel(\"Date\",fontweight=\"bold\")\n",
    "plot4b.set_ylabel(\"Proportion\",fontweight=\"bold\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}