FinalProjectGroup048-Fa21.ipynb

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   "source": [
    "# COGS 108 - Final Project: Vaccination Rates and Interest in Travel During the COVID-19 Pandemic in California "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [X] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project investigates the effects of COVID-19 vaccination rates on the interest for travel in California. The goal of this investigation is to see if higher vaccination rates correlate with greater interest in travel as measured by the popularity of travel related Google searches. Additionally, we wanted to see if there was a difference in interest in travel to California state parks and amusement parks. After conducting an exploratory data analysis, it was concluded that there was a correlation between vaccination rates and general travel interest, but there was no relationship for amusement parks and state parks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Abigail Koornwinder\n",
    "- Carolyn Yatco\n",
    "- Kristy Lee\n",
    "- Sophie Jensen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did the rate of COVID-19 vaccinations in California affect interest in travel within California? How does interest in traveling to state parks compare with interest in traveling to amusement parks?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One area that the COVID-19 pandemic greatly affected is travel. However, with the vaccine and mask mandates come looser restrictions allowing people to go out more compared to the beginning of the pandemic where the shelter-in-place order virtually limited all travel.  As people continue to get vaccinated, society has become more confident in venturing outside their homes.  Our group seeks to examine how trends in vaccination rate relate to travel interest in the state of California.\n",
    "\n",
    "Recent research has shown that trust in healthcare systems positively impacted travel intentions. The same study also showed that solidarity among people around the world as a result of going through the same experience of the pandemic led to more of an intent to travel (reference 1). It is not surprising that after a period of shelter-in-place and isolation, that people would want to explore and connect with new places and people. Media coverage on how governments are handling the crisis (via encouragement to get the vaccine or setting mask regulations) was also shown to impact intent to travel because people who trust how a government handles a situation are more likely to feel safe about traveling there (reference 1). The aforementioned study demonstrates that the pandemic has caused people to have a greater intent to travel, but how much are people actually willing to travel? And how far are they willing to go for a vacation? Since people are less inclined to take public transportation (airplane), are they looking into vacationing places that are close enough to drive in their own personal vehicle? To look into these topics, we can assess Google Trends to examine the use of keywords related to travel, the rate of incoming and outgoing flights, and also traffic patterns.  \n",
    "\n",
    "Surveyed Americans mentioned in January 2021 that travel experiences featuring outdoor scenic beauty were currently high on their minds (reference 2).  Perhaps this is a response to being stuck inside their homes for almost a whole year.  They also noted that high density areas such as cities and food experiences are low on their list because the virus still remains at large (reference 2).  The looming threat of covid is still in their minds but they are reaching their limit staying at home as a result Americans are seeking to travel to areas with open space in order to social distance..  The individuals who were surveyed also noted that having negative covid tests or showing proof of vaccination would make them feel more comfortable attending events (reference 2).  As a result we seek to dive deeper into this claim by examining if vaccine rates reveal anything about travel interest in the form of google searches.\n",
    "\n",
    "Additionally, government leaders have even used prospective travel and vacation as an incentive to get people to receive vaccines, implying that a great reward of getting vaccines is the ability to travel and prompting people to consider vacations. In June 2021, California governor Gavin Newsom announced that the government would be giving away “dream vacation packages” at random to those who have received vaccines. He stated that he hoped that vacation offers would incentivize people to get vaccines but also to boost the tourism industry, which had fallen drastically since the start of the pandemic. This connection between interest in vacations and vaccines made by Newsom suggests that there is a potential relationship between the two. Since the government has intentions to boost the tourism industry while promoting vaccinations, it is suggestable that this government support would affect Californian’s interest and trust in going on vacation after receiving vaccinations. With this form of encouragement from the governor to travel after receiving vaccinations, we intend to see if the rate of vaccines actually did have a tangible impact on interest in travel. \n",
    "\n",
    "References (include links):\n",
    "- 1) [Destination image during the COVID-19 pandemic and future travel behavior: The moderating role of past experience](https://www.sciencedirect.com/science/article/pii/S2212571X21000688)\n",
    "- 2) [Update on American Travel in the Period of Coronavirus—Week of January 18th](https://www.destinationanalysts.com/blog-update-on-american-travel-in-the-period-of-coronavirus-week-of-january-18th/)\n",
    "- 3) [California giving away vacations to those who get COVID-19 vaccines](https://www.desertsun.com/story/news/2021/06/14/california-giving-away-vacations-those-who-get-covid-19-vaccines/7695219002/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "We hypothesize that the rate of vaccinations per day does correlate with an increased interest in travel calculated by google search trends within California from December 20, 2020 to October 31, 2021. We make this assumption because as more people got vaccinated, people would feel more comfortable going outside and feel that they are less at risk to contracting the virus or getting seriously ill. Thus, this increased comfortability would lead people to consider travel, which was greatly restricted during the earlier pandemic. Additionally we hypothesize that the rate of interest in traveling to state parks would be greater compared to traveling to amusement parks. This is because we assume that people would still have concerns about being in mass crowds of people so they feel more comfortable traveling to state parks where there are less crowds compared to amusement parks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination trends\n",
    "- <b>Name:</b> Trends in number of COVID-19 Vaccinations.\n",
    "- <b>Link to the dataset:</b> [https://covid.cdc.gov/covid-data-tracker/#vaccination-trends_vacctrends-total-daily](https://covid.cdc.gov/covid-data-tracker/#vaccination-trends_vacctrends-total-daily)\n",
    "- <b>Number of observations:</b> 325\n",
    "- This data set contains trends in number of COVID-19 Vaccinations for all states in the US and is how we will look at the rate of vaccinations in CA. Using the filters on the website, we selected to see a data table containing (1) total doses administered and reported to the CDC by date administered for California, (2) the 7-day average for total doses daily for California, and (3) the 7-day average total doses for the US, from from December 14, 2020 to present day. \n",
    "\n",
    "### Interest in Travel\n",
    "- <b>Name:</b> Interest over time for search terms that indicate interest in travel\n",
    "- <b>Link to the dataset:</b> Google Trends links for specific search terms --> [california day trips](https://trends.google.com/trends/explore?geo=US-CA&q=day%20trips), [california destinations](https://trends.google.com/trends/explore?geo=US-CA&q=california%20destinations), [california road trips](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=california%20road%20trips), [california sightseeing](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=california%20sightseeing), [california tourism](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=california%20tourism), [california travel](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=california%20travel), [california trips](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=california%20trips), [california vacations](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=california%20vacations), [local travel](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=local%20travel), [places to go in california](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=places%20to%20go%20in%20california), [travel topic](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=travel%20topic)\n",
    "- <b>Number of observations:</b> 45 \n",
    "- In order to gather data for the interest of travel, we used the website Google Trends, which numerically categorizes the popularity of a search term for each week between a certain time range. This data was then downloaded as a csv file and uploaded to python as a dataframe. For our project, we specialized the region to California and set the time range from December 20, 2020 to October 31, 2021. To capture travel interest on google trends, we developed ten google search terms that we believe is representative of what people would search on google when they are interested in travelling. These phrases are \"california day trips\", \"california destinations\", \"california roadtrips\", \"california sightseeing\", \"california tourism\", \"california travel\", \"california trips\", \"california vacations\", \"local travel\", and \"places to go in california\". Additionally google trends also presented data on searches within the topic of \"travel\" which was contained to California. Since all of these search terms are related to travel and the region of the data is constricted to California, this is a good way to gather the interest of travel in California during the COVID-19 pandemic.\n",
    "\n",
    "\n",
    "### CA State Parks\n",
    "- <b>Name:</b> Interest over time for top 5 California state parks\n",
    "- <b>Link to the dataset:</b> Google Trends for top 5 CA state parks --> [Emerald_bay](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Emerald%20Bay%20State%20Park) [Julia_pfeiffer](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Julia%20Pfeiffer%20Burns%20State%20Park) [Point_lobos](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Point%20Lobos%20State%20Natural%20Reserve) [Humboldt_redwoods](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Point%20Lobos%20State%20Natural%20Reserve) [Burney_falls](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=McArthur-Burney%20Falls%20Memorial%20State%20Park)\n",
    "- <b>Number of observations:</b> 45\n",
    "- We decided to choose the top 5 state parks that came up on Google when searching “California state parks”. These datasets reveal the interest in our chosen keyword search terms. The numbers in the data set represent search interest relative to the highest point on the chart for the given region and time. So the number 100 would represent peak interest while 50 means the term is half as popular. For each of the search terms, Google Trends provides a CSV file that has a numerical measure of the popularity of each state park for a given week in our time frame. \n",
    "\n",
    "### Amusement Parks\n",
    "- <b>Name:</b> Interest over time for top 5 California amusement parks\n",
    "- <b>Link to the dataset:</b> Google Trends for top 5 CA amusement parks --> [Six Flags](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Six%20Flags), [Disneyland Park](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Disneyland%20Park), [Universal Studios Hollywood](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Universal%20Studios%20Hollywood), [Californias Great America](https://trends.google.com/trends/explore?date=2020-12-14%202021-11-02&geo=US-CA&q=Californias%20Great%20America)\n",
    "- <b>Number of observations:</b> 47\n",
    "- Same as the previous dataset, we used Google Trends to obtain data on the popularity for each of the top 5 CA amusement parks, narrowing down our scope to searches made in the state of California. For each of them, Google Trends provides a CSV file that has a numerical measure of the popularity of each amusement park for a given week in our time frame. \n",
    " \n",
    "\n",
    "<b>If you plan to use multiple datasets, add 1-2 sentences about how you plan to combine these datasets:</b>\n",
    "\n",
    "For each category (interest in travel, state parks, amusement parks) we plan on combining the data for each search term within the category in order to examine how they correlate to the vaccine rates data set. For example we will be comparing all the unique search terms under travel in general to the vaccination rates data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Vaccination Trends Data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what the original dataset for vaccination trends looks like: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "    Date Type        Date Location  Total Doses Administered Daily  \\\n",
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       "0                                    NaN                                  NaN   \n",
       "1                                   51.0                               2908.0   \n",
       "2                                    NaN                                  NaN   \n",
       "3                                 1326.0                               4253.0   \n",
       "4                                    NaN                                  NaN   \n",
       "..                                   ...                                  ...   \n",
       "644                                  NaN                                  NaN   \n",
       "645                                  4.0                           54511378.0   \n",
       "646                                  NaN                                  NaN   \n",
       "647                                  0.0                           54511378.0   \n",
       "648                                  NaN                                  NaN   \n",
       "\n",
       "     People Receiving 1 or More Doses Cumulative  \\\n",
       "0                                            NaN   \n",
       "1                                         2209.0   \n",
       "2                                            NaN   \n",
       "3                                         3535.0   \n",
       "4                                            NaN   \n",
       "..                                           ...   \n",
       "644                                          NaN   \n",
       "645                                   29488747.0   \n",
       "646                                          NaN   \n",
       "647                                   29488747.0   \n",
       "648                                          NaN   \n",
       "\n",
       "     7-Day Avg Daily Count Dose 1  7-Day Avg Total Doses Daily  \\\n",
       "0                             NaN                          NaN   \n",
       "1                          1105.0                       1454.0   \n",
       "2                             NaN                          NaN   \n",
       "3                          1178.0                       1417.0   \n",
       "4                             NaN                          NaN   \n",
       "..                            ...                          ...   \n",
       "644                           NaN                          NaN   \n",
       "645                       24815.0                     119197.0   \n",
       "646                           NaN                          NaN   \n",
       "647                       19449.0                      94554.0   \n",
       "648                           NaN                          NaN   \n",
       "\n",
       "     Total Doses Administered Daily Change  \\\n",
       "0                                      0.0   \n",
       "1                                      NaN   \n",
       "2                                      0.0   \n",
       "3                                      NaN   \n",
       "4                                      0.0   \n",
       "..                                     ...   \n",
       "644                               211972.0   \n",
       "645                                    NaN   \n",
       "646                               135060.0   \n",
       "647                                    NaN   \n",
       "648                                73133.0   \n",
       "\n",
       "     7-Day Avg Total Doses Administered Daily Change  \\\n",
       "0                                                0.0   \n",
       "1                                                NaN   \n",
       "2                                                0.0   \n",
       "3                                                NaN   \n",
       "4                                                0.0   \n",
       "..                                               ...   \n",
       "644                                         157048.0   \n",
       "645                                              NaN   \n",
       "646                                         164894.0   \n",
       "647                                              NaN   \n",
       "648                                         162888.0   \n",
       "\n",
       "     Daily Count of People Fully Vaccinated  \\\n",
       "0                                       NaN   \n",
       "1                                       8.0   \n",
       "2                                       NaN   \n",
       "3                                      24.0   \n",
       "4                                       NaN   \n",
       "..                                      ...   \n",
       "644                                     NaN   \n",
       "645                                    40.0   \n",
       "646                                     NaN   \n",
       "647                                     0.0   \n",
       "648                                     NaN   \n",
       "\n",
       "     People Fully Vaccinated Cumulative  \\\n",
       "0                                   NaN   \n",
       "1                                 865.0   \n",
       "2                                   NaN   \n",
       "3                                 889.0   \n",
       "4                                   NaN   \n",
       "..                                  ...   \n",
       "644                                 NaN   \n",
       "645                          24260599.0   \n",
       "646                                 NaN   \n",
       "647                          24260599.0   \n",
       "648                                 NaN   \n",
       "\n",
       "     7-Day Avg Daily Count of People Fully Vaccinated  \n",
       "0                                                 NaN  \n",
       "1                                               432.0  \n",
       "2                                                 NaN  \n",
       "3                                               296.0  \n",
       "4                                                 NaN  \n",
       "..                                                ...  \n",
       "644                                               NaN  \n",
       "645                                           19842.0  \n",
       "646                                               NaN  \n",
       "647                                           15733.0  \n",
       "648                                               NaN  \n",
       "\n",
       "[649 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset #1: vaccination trends data \n",
    "\n",
    "# Import vacc trends CSV \n",
    "df = pd.read_csv(\"vacc_trends_copy.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning process:\n",
    "\n",
    "Since we only plan on looking at the rate of COVID-19 vaccinations in California, we are only concerned about the columns with the date, the location, and the total doses administered that day. In the following cell, we removed all unneeded columns: 'Date type', '7-Day Avg Total Doses Daily', 'US 7-Day Total Doses Daily.'\n",
    "\n",
    "We also noticed how every day has an additional row of information where the Date type is a 'report.' These rows have NaN for our column of interest and the rest have information that is irrelevant to our question so we will drop these rows later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Total Doses Administered Daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>CA</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>CA</td>\n",
       "      <td>1345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>CA</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>2021-11-02</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>2021-11-02</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date Location  Total Doses Administered Daily\n",
       "0    2020-12-13       CA                             NaN\n",
       "1    2020-12-14       CA                            53.0\n",
       "2    2020-12-14       CA                             NaN\n",
       "3    2020-12-15       CA                          1345.0\n",
       "4    2020-12-15       CA                             NaN\n",
       "..          ...      ...                             ...\n",
       "644  2021-10-31       CA                             NaN\n",
       "645  2021-11-01       CA                            13.0\n",
       "646  2021-11-01       CA                             NaN\n",
       "647  2021-11-02       CA                             0.0\n",
       "648  2021-11-02       CA                             NaN\n",
       "\n",
       "[649 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove unneeded columns: Date type, 7-Day Avg Total Doses Daily, US 7-Day Total Doses Daily \n",
    "df = df.drop(labels=['Date Type'], axis = 1)\n",
    "df = df.iloc[:, :3]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can drop all the unnecessary rows that we noticed earlier. We do this by dropping all the rows with NaN."
   ]
  },
  {
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Total Doses Administered Daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>CA</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>CA</td>\n",
       "      <td>1345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>CA</td>\n",
       "      <td>10031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>CA</td>\n",
       "      <td>30456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>2020-12-18</td>\n",
       "      <td>CA</td>\n",
       "      <td>50436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>639</td>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>CA</td>\n",
       "      <td>197822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>641</td>\n",
       "      <td>2021-10-30</td>\n",
       "      <td>CA</td>\n",
       "      <td>92334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>643</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>CA</td>\n",
       "      <td>29444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>645</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>CA</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>647</td>\n",
       "      <td>2021-11-02</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>324 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index        Date Location  Total Doses Administered Daily\n",
       "0        1  2020-12-14       CA                            53.0\n",
       "1        3  2020-12-15       CA                          1345.0\n",
       "2        5  2020-12-16       CA                         10031.0\n",
       "3        7  2020-12-17       CA                         30456.0\n",
       "4        9  2020-12-18       CA                         50436.0\n",
       "..     ...         ...      ...                             ...\n",
       "319    639  2021-10-29       CA                        197822.0\n",
       "320    641  2021-10-30       CA                         92334.0\n",
       "321    643  2021-10-31       CA                         29444.0\n",
       "322    645  2021-11-01       CA                            13.0\n",
       "323    647  2021-11-02       CA                             0.0\n",
       "\n",
       "[324 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take out rows with missing values and reset the index\n",
    "df = df.dropna().reset_index()\n",
    "# df = df.iloc[:, 2:]\n",
    "\n",
    "# Print out cleaned dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to drop the last 2 rows to stay in our time period. Also, since our vaccination data is daily but our data for google searches is weekly, we need to convert our vaccination rates data into weekly data:"
   ]
  },
  {
   "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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Total Doses Administered Daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location       Date  Total Doses Administered Daily\n",
       "0       CA 2020-12-20                        141193.0\n",
       "1       CA 2020-12-27                        228433.0\n",
       "2       CA 2021-01-03                        252093.0\n",
       "3       CA 2021-01-10                        498893.0\n",
       "4       CA 2021-01-17                        804624.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert vaccination rates data to weekly data\n",
    "df['Date'] = df['Date'].astype('datetime64[ns]')\n",
    "weekly_data = df.groupby(\"Location\").resample('W-Sun', label='right', closed = 'right', on='Date').sum().reset_index().sort_values(by='Date')\n",
    "weekly_data = weekly_data.drop(labels=['index'], axis = 1)\n",
    "\n",
    "# Drop the last 2 rows to our designated time frame\n",
    "weekly_data = weekly_data.iloc[:46, :]\n",
    "\n",
    "weekly_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Interest in Travel Data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of an uncleaned dataset for the \"daytrips\" keyword. Notice there is an unnecessary header 'Category: All categories.' We will use skiprows when importing the CSVs to get rid of that."
   ]
  },
  {
   "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>Category: All categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <td>california day trips: (California)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-20</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-27</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-03</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Category: All categories\n",
       "Week        california day trips: (California)\n",
       "2020-12-20                                  26\n",
       "2020-12-27                                  74\n",
       "2021-01-03                                  92\n",
       "2021-01-10                                  23"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daytrips_original = pd.read_csv('cadaytrips.csv')\n",
    "daytrips_original.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uploading the CSV files for each search term \n",
    "# Use skiprows to skip the unnecessary header\n",
    "daytrips = pd.read_csv('cadaytrips.csv', skiprows=2)\n",
    "destinations = pd.read_csv('cadestinations.csv', skiprows=2)\n",
    "roadtrips = pd.read_csv('caroadtrips.csv', skiprows=2)\n",
    "sightseeing = pd.read_csv('casightseeing.csv', skiprows=2)\n",
    "tourism = pd.read_csv('catourism.csv', skiprows=2)\n",
    "travel = pd.read_csv('catravel.csv', skiprows=2)\n",
    "trips = pd.read_csv('catrips.csv', skiprows=2)\n",
    "vacations = pd.read_csv('cavacations.csv', skiprows=2)\n",
    "local = pd.read_csv('localtravel.csv', skiprows=2)\n",
    "places = pd.read_csv('placestogoinca.csv', skiprows=2)\n",
    "traveltopic = pd.read_csv('traveltopic.csv', skiprows=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we removed the unneccesary headers, we rename the columns for each search term data frame to reflect the correct search term:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the columns of each search term data frame\n",
    "daytrips = daytrips.rename({'california day trips: (California)':'california day trips'}, axis='columns')\n",
    "destinations = destinations.rename({'california destinations: (California)':'california destinations'}, axis='columns')\n",
    "roadtrips = roadtrips.rename({'california road trips: (California)':'california road trips'}, axis='columns')\n",
    "sightseeing = sightseeing.rename({'california sightseeing: (California)':'california sightseeing'}, axis='columns')\n",
    "tourism = tourism.rename({'california tourism: (California)':'california tourism'}, axis='columns')\n",
    "travel = travel.rename({'california travel: (California)':'california travel'}, axis='columns')\n",
    "trips = trips.rename({'california trips: (California)':'california trips'}, axis='columns')\n",
    "vacations = vacations.rename({'california vacations: (California)':'california vacations'}, axis='columns')\n",
    "local = local.rename({'local travel: (California)':'local travel'}, axis='columns')\n",
    "places = places.rename({'places to go in california: (California)':'places to go in california'}, axis='columns')\n",
    "traveltopic = traveltopic.rename({'Travel: (California)':'travel'}, axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we combine the data from all the search terms into one dataframe so we can see all the data we want for when we analyze interest in travel. We also need to drop the last row so as to stay in our time period of analysis (12-20-2020 to 10-31-2021):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_travel = daytrips.join(destinations['california destinations']).join(roadtrips['california road trips']).join(sightseeing['california sightseeing']).join(tourism['california tourism']).join(travel['california travel']).join(trips['california trips']).join(vacations['california vacations']).join(local['local travel']).join(places['places to go in california']).join(traveltopic['travel'])\n",
    "\n",
    "# Drop the last row so as to stay in our time frame\n",
    "all_travel = all_travel.iloc[:46, :]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add a sum column that adds the values in each row so we get a measurement for interest in general travel in California for the given week:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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</th>\n",
       "      <th>california day trips</th>\n",
       "      <th>california destinations</th>\n",
       "      <th>california road trips</th>\n",
       "      <th>california sightseeing</th>\n",
       "      <th>california tourism</th>\n",
       "      <th>california travel</th>\n",
       "      <th>california trips</th>\n",
       "      <th>california vacations</th>\n",
       "      <th>local travel</th>\n",
       "      <th>places to go in california</th>\n",
       "      <th>travel</th>\n",
       "      <th>Travel Searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>54</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>56</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>74</td>\n",
       "      <td>14</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>48</td>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>56</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>92</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>72</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>61</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>80</td>\n",
       "      <td>33</td>\n",
       "      <td>57</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>93</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>58</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Week  california day trips  california destinations  \\\n",
       "0  2020-12-20                    26                       15   \n",
       "1  2020-12-27                    74                       14   \n",
       "2  2021-01-03                    92                       27   \n",
       "3  2021-01-10                    23                       27   \n",
       "4  2021-01-17                    93                       14   \n",
       "\n",
       "   california road trips  california sightseeing  california tourism  \\\n",
       "0                     54                      36                   0   \n",
       "1                     77                       0                  17   \n",
       "2                     24                       0                  16   \n",
       "3                     49                       0                  32   \n",
       "4                     24                       0                  48   \n",
       "\n",
       "   california travel  california trips  california vacations  local travel  \\\n",
       "0                 47                36                    11             0   \n",
       "1                 48                54                    10            17   \n",
       "2                 72                44                     9             0   \n",
       "3                 61                32                     9            80   \n",
       "4                 52                38                    28            16   \n",
       "\n",
       "   places to go in california  travel  Travel Searches  \n",
       "0                          27      56              308  \n",
       "1                          15      56              382  \n",
       "2                          52      58              394  \n",
       "3                          33      57              403  \n",
       "4                          29      58              400  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a sum column\n",
    "travel_sum = all_travel\n",
    "sum_col = travel_sum['california day trips'] + travel_sum['california destinations'] + travel_sum['california road trips'] + travel_sum['california sightseeing'] + travel_sum['california tourism'] + travel_sum['california travel'] + travel_sum['california trips'] + travel_sum['california vacations'] + travel_sum['local travel'] + travel_sum['places to go in california'] + travel_sum['travel']\n",
    "travel_sum['Travel Searches'] = sum_col\n",
    "travel_sum.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining vaccination rate data and travel search data into one dataframe completes the cleaning process for this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>total_doses</th>\n",
       "      <th>travel_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>1008428.0</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>1204047.0</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>1441269.0</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>1598479.0</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1576352.0</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1966881.0</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2154815.0</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>2211739.0</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-21</td>\n",
       "      <td>2449447.0</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>2539324.0</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2754122.0</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>3057982.0</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2837596.0</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>2700135.0</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>2266126.0</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2036990.0</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>1904638.0</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>1704929.0</td>\n",
       "      <td>693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>1098208.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>942860.0</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>947754.0</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>754804.0</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>601226.0</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>477418.0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-11</td>\n",
       "      <td>462152.0</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-18</td>\n",
       "      <td>464608.0</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>492903.0</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>526082.0</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>557420.0</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-15</td>\n",
       "      <td>588555.0</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-22</td>\n",
       "      <td>658795.0</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>680373.0</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-05</td>\n",
       "      <td>629085.0</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>513014.0</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>523667.0</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>530281.0</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>759547.0</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-10</td>\n",
       "      <td>682996.0</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>617421.0</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>667878.0</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>992928.0</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location       Date  total_doses  travel_searches\n",
       "0        CA 2020-12-20     141193.0              308\n",
       "1        CA 2020-12-27     228433.0              382\n",
       "2        CA 2021-01-03     252093.0              394\n",
       "3        CA 2021-01-10     498893.0              403\n",
       "4        CA 2021-01-17     804624.0              400\n",
       "5        CA 2021-01-24    1008428.0              390\n",
       "6        CA 2021-01-31    1204047.0              302\n",
       "7        CA 2021-02-07    1441269.0              530\n",
       "8        CA 2021-02-14    1598479.0              457\n",
       "9        CA 2021-02-21    1576352.0              433\n",
       "10       CA 2021-02-28    1966881.0              423\n",
       "11       CA 2021-03-07    2154815.0              493\n",
       "12       CA 2021-03-14    2211739.0              393\n",
       "13       CA 2021-03-21    2449447.0              569\n",
       "14       CA 2021-03-28    2539324.0              632\n",
       "15       CA 2021-04-04    2754122.0              595\n",
       "16       CA 2021-04-11    3057982.0              414\n",
       "17       CA 2021-04-18    2837596.0              377\n",
       "18       CA 2021-04-25    2700135.0              521\n",
       "19       CA 2021-05-02    2266126.0              378\n",
       "20       CA 2021-05-09    2036990.0              443\n",
       "21       CA 2021-05-16    1904638.0              561\n",
       "22       CA 2021-05-23    1704929.0              693\n",
       "23       CA 2021-05-30    1098208.0              520\n",
       "24       CA 2021-06-06     942860.0              493\n",
       "25       CA 2021-06-13     947754.0              555\n",
       "26       CA 2021-06-20     754804.0              441\n",
       "27       CA 2021-06-27     601226.0              551\n",
       "28       CA 2021-07-04     477418.0              512\n",
       "29       CA 2021-07-11     462152.0              556\n",
       "30       CA 2021-07-18     464608.0              547\n",
       "31       CA 2021-07-25     492903.0              497\n",
       "32       CA 2021-08-01     526082.0              406\n",
       "33       CA 2021-08-08     557420.0              396\n",
       "34       CA 2021-08-15     588555.0              282\n",
       "35       CA 2021-08-22     658795.0              298\n",
       "36       CA 2021-08-29     680373.0              436\n",
       "37       CA 2021-09-05     629085.0              407\n",
       "38       CA 2021-09-12     513014.0              311\n",
       "39       CA 2021-09-19     523667.0              270\n",
       "40       CA 2021-09-26     530281.0              320\n",
       "41       CA 2021-10-03     759547.0              324\n",
       "42       CA 2021-10-10     682996.0              379\n",
       "43       CA 2021-10-17     617421.0              324\n",
       "44       CA 2021-10-24     667878.0              423\n",
       "45       CA 2021-10-31     992928.0              226"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_col = travel_sum[\"Travel Searches\"]\n",
    "vacc_trav = weekly_data\n",
    "vacc_trav = vacc_trav.join(extracted_col)\n",
    "vacc_trav = vacc_trav.rename(columns={'Total Doses Administered Daily':'total_doses','Travel Searches':'travel_searches'})\n",
    "vacc_trav"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State parks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We follow the same process as above to clean our state parks dataset as it comes in the same format as the above travel search dataset. Below, you can see how we import the CSV's and drop the unnecessary header using skuprows, check for null vales, and then rename the columns with the appropriate key word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning dataset state park datasets for the sub question\n",
    "\n",
    "# Import respective state park CSV file\n",
    "Emerald_bay = pd.read_csv(\"Emerald_bay.csv\", skiprows=2)\n",
    "Julia_pfeiffer = pd.read_csv(\"Julia_pfeiffer.csv\", skiprows=2)\n",
    "Point_lobos = pd.read_csv(\"Point_lobos.csv\", skiprows=2)\n",
    "Humboldt_redwoods = pd.read_csv(\"Humboldt_redwoods.csv\", skiprows=2)\n",
    "Burney_falls = pd.read_csv(\"Burney_falls.csv\", skiprows=2)\n",
    "\n",
    "\n",
    "\n",
    "# Checking for null vlaues\n",
    "Emerald_bay = Emerald_bay.dropna()\n",
    "Julia_pfeiffer = Julia_pfeiffer.dropna()\n",
    "Point_lobos = Point_lobos.dropna()\n",
    "Humboldt_redwoods = Humboldt_redwoods.dropna()\n",
    "Burney_falls = Burney_falls.dropna()\n",
    "\n",
    "\n",
    "# Rename the columns \n",
    "Emerald_bay = Emerald_bay.rename({'Emerald Bay State Park: (California)':'Emerald Bay State Park'}, axis='columns')\n",
    "Julia_pfeiffer = Julia_pfeiffer.rename({'Julia Pfeiffer Burns State Park: (California)':'Julia Pfeiffer Burns State Park'}, axis='columns')\n",
    "Point_lobos = Point_lobos.rename({'Point Lobos State Natural Reserve: (California)':'Point Lobos State Natural Reserve'}, axis='columns')\n",
    "Humboldt_redwoods = Humboldt_redwoods.rename({'Humboldt Redwoods State Park: (California)':'Humboldt Redwoods State Park'}, axis='columns')\n",
    "Burney_falls = Burney_falls.rename({'McArthur-Burney Falls Memorial State Park: (California)':'McArthur-Burney Falls Memorial State Park'}, axis='columns')\n",
    "\n",
    "\n",
    "# There is no need to clean the data further since all rows and columns are essential\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we combine the data from all the search terms into one dataframe so we can see all the data we want for when we analyze interest in travel to CA state parks. We also drop the last row so we stay in our time frame (ending October 31, 2021) and create a sum column that adds up the values for each week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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</th>\n",
       "      <th>Emerald Bay State Park</th>\n",
       "      <th>Julia Pfeiffer Burns State Park</th>\n",
       "      <th>Point Lobos State Natural Reserve</th>\n",
       "      <th>Humboldt Redwoods State Park</th>\n",
       "      <th>McArthur-Burney Falls Memorial State Park</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>53</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Week  Emerald Bay State Park  Julia Pfeiffer Burns State Park  \\\n",
       "0  2020-12-20                       0                               42   \n",
       "1  2020-12-27                       0                               20   \n",
       "2  2021-01-03                      62                                0   \n",
       "3  2021-01-10                       0                               38   \n",
       "4  2021-01-17                      15                               37   \n",
       "\n",
       "   Point Lobos State Natural Reserve  Humboldt Redwoods State Park  \\\n",
       "0                                 53                            42   \n",
       "1                                  0                            60   \n",
       "2                                  0                            37   \n",
       "3                                  0                            19   \n",
       "4                                  0                            37   \n",
       "\n",
       "   McArthur-Burney Falls Memorial State Park  \n",
       "0                                          0  \n",
       "1                                          0  \n",
       "2                                          0  \n",
       "3                                          0  \n",
       "4                                          0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_parks = Emerald_bay.join(Julia_pfeiffer['Julia Pfeiffer Burns State Park']).join(Point_lobos['Point Lobos State Natural Reserve']).join(Humboldt_redwoods['Humboldt Redwoods State Park']).join(Burney_falls['McArthur-Burney Falls Memorial State Park'])\n",
    "\n",
    "# Drop the last row so as to stay in our time frame\n",
    "state_parks = state_parks.iloc[:46, :]\n",
    "\n",
    "state_parks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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</th>\n",
       "      <th>Emerald Bay State Park</th>\n",
       "      <th>Julia Pfeiffer Burns State Park</th>\n",
       "      <th>Point Lobos State Natural Reserve</th>\n",
       "      <th>Humboldt Redwoods State Park</th>\n",
       "      <th>McArthur-Burney Falls Memorial State Park</th>\n",
       "      <th>State Park Searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>53</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Week  Emerald Bay State Park  Julia Pfeiffer Burns State Park  \\\n",
       "0  2020-12-20                       0                               42   \n",
       "1  2020-12-27                       0                               20   \n",
       "2  2021-01-03                      62                                0   \n",
       "3  2021-01-10                       0                               38   \n",
       "4  2021-01-17                      15                               37   \n",
       "\n",
       "   Point Lobos State Natural Reserve  Humboldt Redwoods State Park  \\\n",
       "0                                 53                            42   \n",
       "1                                  0                            60   \n",
       "2                                  0                            37   \n",
       "3                                  0                            19   \n",
       "4                                  0                            37   \n",
       "\n",
       "   McArthur-Burney Falls Memorial State Park  State Park Searches  \n",
       "0                                          0                  137  \n",
       "1                                          0                   80  \n",
       "2                                          0                   99  \n",
       "3                                          0                   57  \n",
       "4                                          0                   89  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_park = state_parks\n",
    "sum_col = s_park['Emerald Bay State Park'] + s_park['Julia Pfeiffer Burns State Park'] + s_park['Point Lobos State Natural Reserve'] + s_park['Humboldt Redwoods State Park'] + s_park['McArthur-Burney Falls Memorial State Park']\n",
    "s_park['State Park Searches'] = sum_col\n",
    "s_park.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
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       "    }\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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>total_doses</th>\n",
       "      <th>stprk_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>1008428.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>1204047.0</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>1441269.0</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>1598479.0</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1576352.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1966881.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2154815.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>2211739.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-21</td>\n",
       "      <td>2449447.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>2539324.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2754122.0</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>3057982.0</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2837596.0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>2700135.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>2266126.0</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2036990.0</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>1904638.0</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>1704929.0</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>1098208.0</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>942860.0</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>947754.0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>754804.0</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>601226.0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>477418.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-11</td>\n",
       "      <td>462152.0</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-18</td>\n",
       "      <td>464608.0</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>492903.0</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>526082.0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>557420.0</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-15</td>\n",
       "      <td>588555.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-22</td>\n",
       "      <td>658795.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>680373.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-05</td>\n",
       "      <td>629085.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>513014.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>523667.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>530281.0</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>759547.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-10</td>\n",
       "      <td>682996.0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>617421.0</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>667878.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>992928.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location       Date  total_doses  stprk_searches\n",
       "0        CA 2020-12-20     141193.0             137\n",
       "1        CA 2020-12-27     228433.0              80\n",
       "2        CA 2021-01-03     252093.0              99\n",
       "3        CA 2021-01-10     498893.0              57\n",
       "4        CA 2021-01-17     804624.0              89\n",
       "5        CA 2021-01-24    1008428.0              99\n",
       "6        CA 2021-01-31    1204047.0             103\n",
       "7        CA 2021-02-07    1441269.0              84\n",
       "8        CA 2021-02-14    1598479.0             151\n",
       "9        CA 2021-02-21    1576352.0              35\n",
       "10       CA 2021-02-28    1966881.0              39\n",
       "11       CA 2021-03-07    2154815.0              49\n",
       "12       CA 2021-03-14    2211739.0              68\n",
       "13       CA 2021-03-21    2449447.0             110\n",
       "14       CA 2021-03-28    2539324.0              39\n",
       "15       CA 2021-04-04    2754122.0             160\n",
       "16       CA 2021-04-11    3057982.0             181\n",
       "17       CA 2021-04-18    2837596.0             180\n",
       "18       CA 2021-04-25    2700135.0              70\n",
       "19       CA 2021-05-02    2266126.0             135\n",
       "20       CA 2021-05-09    2036990.0             192\n",
       "21       CA 2021-05-16    1904638.0             174\n",
       "22       CA 2021-05-23    1704929.0             269\n",
       "23       CA 2021-05-30    1098208.0             160\n",
       "24       CA 2021-06-06     942860.0             317\n",
       "25       CA 2021-06-13     947754.0             173\n",
       "26       CA 2021-06-20     754804.0             337\n",
       "27       CA 2021-06-27     601226.0             154\n",
       "28       CA 2021-07-04     477418.0              97\n",
       "29       CA 2021-07-11     462152.0             217\n",
       "30       CA 2021-07-18     464608.0             121\n",
       "31       CA 2021-07-25     492903.0             132\n",
       "32       CA 2021-08-01     526082.0             154\n",
       "33       CA 2021-08-08     557420.0              88\n",
       "34       CA 2021-08-15     588555.0              56\n",
       "35       CA 2021-08-22     658795.0             110\n",
       "36       CA 2021-08-29     680373.0              40\n",
       "37       CA 2021-09-05     629085.0              17\n",
       "38       CA 2021-09-12     513014.0              69\n",
       "39       CA 2021-09-19     523667.0              40\n",
       "40       CA 2021-09-26     530281.0             179\n",
       "41       CA 2021-10-03     759547.0              57\n",
       "42       CA 2021-10-10     682996.0              38\n",
       "43       CA 2021-10-17     617421.0             193\n",
       "44       CA 2021-10-24     667878.0              41\n",
       "45       CA 2021-10-31     992928.0               0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_col = s_park[\"State Park Searches\"]\n",
    "vacc_stprk = weekly_data\n",
    "vacc_stprk = vacc_stprk.join(extracted_col)\n",
    "vacc_stprk = vacc_stprk.rename(columns={'Total Doses Administered Daily':'total_doses','State Park Searches':'stprk_searches'})\n",
    "vacc_stprk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amusement Parks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We follow the same process to clean our amusement park data as we did above for the travel and state park datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning Amusement park datasets for the sub question \n",
    "\n",
    "# Cleaning dataset for \"disneyland\" search term\n",
    "disney = pd.read_csv(\"disneyland.csv\")\n",
    "\n",
    "# Checking for null vlaues\n",
    "disney = disney.dropna() \n",
    "\n",
    "# rename columns\n",
    "disney = disney.rename({'Category: All categories':'Week', 'Unnamed: 1':'Disneyland'}, axis='columns')\n",
    "\n",
    "# dropping the first row \n",
    "disney = disney.iloc[1:, :]\n",
    "\n",
    "# Follow the same cleaning process for the other amusement park search terms\n",
    "six_flags = pd.read_csv(\"sixflags.csv\")\n",
    "six_flags = six_flags.dropna()\n",
    "six_flags = six_flags.rename({'Category: All categories':'Week', 'Unnamed: 1':'Six Flags'}, axis='columns')\n",
    "six_flags = six_flags.iloc[1:, :]\n",
    "\n",
    "great_america = pd.read_csv(\"greatamerica.csv\")\n",
    "great_america = great_america.dropna()\n",
    "great_america = great_america.rename({'Category: All categories':'Week', 'Unnamed: 1':'Great America'}, axis='columns')\n",
    "great_america = great_america.iloc[1:, :]\n",
    "\n",
    "legoland = pd.read_csv(\"legoland.csv\")\n",
    "legoland = legoland.dropna()\n",
    "legoland = legoland.rename({'Category: All categories':'Week', 'Unnamed: 1':'Legoland'}, axis='columns')\n",
    "legoland = legoland.iloc[1:, :]\n",
    "\n",
    "universal = pd.read_csv(\"universalstudios.csv\")\n",
    "universal = universal.dropna()\n",
    "universal = universal.rename({'Category: All categories':'Week', 'Unnamed: 1':'Universal Studios'}, axis='columns')\n",
    "universal = universal.iloc[1:, :]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we combine the data from all the search terms into one dataframe so we can see all the data we want for when we analyze interest in travel to amusement parks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we notice that something is wrong with our value types! We checked the datatypes using dtypes and saw that search popularity values were objects and not ints which we need them to be. So, we need to go ahead and convert the data types of all the search term columns from objects to ints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Week                 object\n",
       "Disneyland           object\n",
       "Six Flags            object\n",
       "Great America        object\n",
       "Legoland             object\n",
       "Universal Studios    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_a_parks = disney.join(six_flags['Six Flags']).join(great_america['Great America']).join(legoland['Legoland']).join(universal['Universal Studios']).reset_index()\n",
    "all_a_parks = all_a_parks.drop(columns=['index'])\n",
    "all_a_parks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Week                       object\n",
       "Disneyland                  int64\n",
       "Six Flags                   int64\n",
       "Great America               int64\n",
       "Legoland                    int64\n",
       "Universal Studios           int64\n",
       "Amusement Park Searches     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the datatypes of all the columns to ints so we can perform numerical operations\n",
    "all_a_parks['Disneyland'] = all_a_parks['Disneyland'].astype(str).astype(int)\n",
    "all_a_parks['Six Flags'] = all_a_parks['Six Flags'].astype(str).astype(int)\n",
    "all_a_parks['Great America'] = all_a_parks['Great America'].astype(str).astype(int)\n",
    "all_a_parks['Legoland'] = all_a_parks['Legoland'].astype(str).astype(int)\n",
    "all_a_parks['Universal Studios'] = all_a_parks['Universal Studios'].astype(str).astype(int)\n",
    "\n",
    "\n",
    "a_park = all_a_parks\n",
    "sum_col = all_a_parks['Disneyland'] + all_a_parks['Six Flags'] + all_a_parks['Great America'] + all_a_parks['Legoland'] + all_a_parks['Universal Studios']\n",
    "a_park['Amusement Park Searches'] = sum_col\n",
    "a_park.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>total_doses</th>\n",
       "      <th>amprk_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location       Date  total_doses  amprk_searches\n",
       "0       CA 2020-12-20     141193.0              36\n",
       "1       CA 2020-12-27     228433.0              49\n",
       "2       CA 2021-01-03     252093.0              53\n",
       "3       CA 2021-01-10     498893.0              46\n",
       "4       CA 2021-01-17     804624.0              50"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_col = all_a_parks[\"Amusement Park Searches\"]\n",
    "vacc_amprk = weekly_data\n",
    "vacc_amprk = vacc_amprk.join(extracted_col)\n",
    "vacc_amprk = vacc_amprk.rename(columns={'Total Doses Administered Daily':'total_doses','Amusement Park Searches':'amprk_searches'})\n",
    "vacc_amprk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As a summary, we will working with the following 4 dataframes in our EDA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Total Doses Administered Daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>1008428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>1204047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>1441269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>1598479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1576352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1966881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2154815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>2211739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-21</td>\n",
       "      <td>2449447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>2539324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2754122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>3057982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2837596.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>2700135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>2266126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2036990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>1904638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>1704929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>1098208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>942860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>947754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>754804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>601226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>477418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-11</td>\n",
       "      <td>462152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-18</td>\n",
       "      <td>464608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>492903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>526082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>557420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-15</td>\n",
       "      <td>588555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-22</td>\n",
       "      <td>658795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>680373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-05</td>\n",
       "      <td>629085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>513014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>523667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>530281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>759547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-10</td>\n",
       "      <td>682996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>617421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>667878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>992928.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location       Date  Total Doses Administered Daily\n",
       "0        CA 2020-12-20                        141193.0\n",
       "1        CA 2020-12-27                        228433.0\n",
       "2        CA 2021-01-03                        252093.0\n",
       "3        CA 2021-01-10                        498893.0\n",
       "4        CA 2021-01-17                        804624.0\n",
       "5        CA 2021-01-24                       1008428.0\n",
       "6        CA 2021-01-31                       1204047.0\n",
       "7        CA 2021-02-07                       1441269.0\n",
       "8        CA 2021-02-14                       1598479.0\n",
       "9        CA 2021-02-21                       1576352.0\n",
       "10       CA 2021-02-28                       1966881.0\n",
       "11       CA 2021-03-07                       2154815.0\n",
       "12       CA 2021-03-14                       2211739.0\n",
       "13       CA 2021-03-21                       2449447.0\n",
       "14       CA 2021-03-28                       2539324.0\n",
       "15       CA 2021-04-04                       2754122.0\n",
       "16       CA 2021-04-11                       3057982.0\n",
       "17       CA 2021-04-18                       2837596.0\n",
       "18       CA 2021-04-25                       2700135.0\n",
       "19       CA 2021-05-02                       2266126.0\n",
       "20       CA 2021-05-09                       2036990.0\n",
       "21       CA 2021-05-16                       1904638.0\n",
       "22       CA 2021-05-23                       1704929.0\n",
       "23       CA 2021-05-30                       1098208.0\n",
       "24       CA 2021-06-06                        942860.0\n",
       "25       CA 2021-06-13                        947754.0\n",
       "26       CA 2021-06-20                        754804.0\n",
       "27       CA 2021-06-27                        601226.0\n",
       "28       CA 2021-07-04                        477418.0\n",
       "29       CA 2021-07-11                        462152.0\n",
       "30       CA 2021-07-18                        464608.0\n",
       "31       CA 2021-07-25                        492903.0\n",
       "32       CA 2021-08-01                        526082.0\n",
       "33       CA 2021-08-08                        557420.0\n",
       "34       CA 2021-08-15                        588555.0\n",
       "35       CA 2021-08-22                        658795.0\n",
       "36       CA 2021-08-29                        680373.0\n",
       "37       CA 2021-09-05                        629085.0\n",
       "38       CA 2021-09-12                        513014.0\n",
       "39       CA 2021-09-19                        523667.0\n",
       "40       CA 2021-09-26                        530281.0\n",
       "41       CA 2021-10-03                        759547.0\n",
       "42       CA 2021-10-10                        682996.0\n",
       "43       CA 2021-10-17                        617421.0\n",
       "44       CA 2021-10-24                        667878.0\n",
       "45       CA 2021-10-31                        992928.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total doses administered daily weekly from 2020-12-20 to 2021-10-31\n",
    "weekly_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>total_doses</th>\n",
       "      <th>travel_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>1008428.0</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>1204047.0</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>1441269.0</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>1598479.0</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1576352.0</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1966881.0</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2154815.0</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>2211739.0</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-21</td>\n",
       "      <td>2449447.0</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>2539324.0</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2754122.0</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>3057982.0</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2837596.0</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>2700135.0</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>2266126.0</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2036990.0</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>1904638.0</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>1704929.0</td>\n",
       "      <td>693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>1098208.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>942860.0</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>947754.0</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>754804.0</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>601226.0</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>477418.0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-11</td>\n",
       "      <td>462152.0</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-18</td>\n",
       "      <td>464608.0</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>492903.0</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>526082.0</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>557420.0</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-15</td>\n",
       "      <td>588555.0</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-22</td>\n",
       "      <td>658795.0</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>680373.0</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-05</td>\n",
       "      <td>629085.0</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>513014.0</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>523667.0</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>530281.0</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>759547.0</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-10</td>\n",
       "      <td>682996.0</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>617421.0</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>667878.0</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>992928.0</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location       Date  total_doses  travel_searches\n",
       "0        CA 2020-12-20     141193.0              308\n",
       "1        CA 2020-12-27     228433.0              382\n",
       "2        CA 2021-01-03     252093.0              394\n",
       "3        CA 2021-01-10     498893.0              403\n",
       "4        CA 2021-01-17     804624.0              400\n",
       "5        CA 2021-01-24    1008428.0              390\n",
       "6        CA 2021-01-31    1204047.0              302\n",
       "7        CA 2021-02-07    1441269.0              530\n",
       "8        CA 2021-02-14    1598479.0              457\n",
       "9        CA 2021-02-21    1576352.0              433\n",
       "10       CA 2021-02-28    1966881.0              423\n",
       "11       CA 2021-03-07    2154815.0              493\n",
       "12       CA 2021-03-14    2211739.0              393\n",
       "13       CA 2021-03-21    2449447.0              569\n",
       "14       CA 2021-03-28    2539324.0              632\n",
       "15       CA 2021-04-04    2754122.0              595\n",
       "16       CA 2021-04-11    3057982.0              414\n",
       "17       CA 2021-04-18    2837596.0              377\n",
       "18       CA 2021-04-25    2700135.0              521\n",
       "19       CA 2021-05-02    2266126.0              378\n",
       "20       CA 2021-05-09    2036990.0              443\n",
       "21       CA 2021-05-16    1904638.0              561\n",
       "22       CA 2021-05-23    1704929.0              693\n",
       "23       CA 2021-05-30    1098208.0              520\n",
       "24       CA 2021-06-06     942860.0              493\n",
       "25       CA 2021-06-13     947754.0              555\n",
       "26       CA 2021-06-20     754804.0              441\n",
       "27       CA 2021-06-27     601226.0              551\n",
       "28       CA 2021-07-04     477418.0              512\n",
       "29       CA 2021-07-11     462152.0              556\n",
       "30       CA 2021-07-18     464608.0              547\n",
       "31       CA 2021-07-25     492903.0              497\n",
       "32       CA 2021-08-01     526082.0              406\n",
       "33       CA 2021-08-08     557420.0              396\n",
       "34       CA 2021-08-15     588555.0              282\n",
       "35       CA 2021-08-22     658795.0              298\n",
       "36       CA 2021-08-29     680373.0              436\n",
       "37       CA 2021-09-05     629085.0              407\n",
       "38       CA 2021-09-12     513014.0              311\n",
       "39       CA 2021-09-19     523667.0              270\n",
       "40       CA 2021-09-26     530281.0              320\n",
       "41       CA 2021-10-03     759547.0              324\n",
       "42       CA 2021-10-10     682996.0              379\n",
       "43       CA 2021-10-17     617421.0              324\n",
       "44       CA 2021-10-24     667878.0              423\n",
       "45       CA 2021-10-31     992928.0              226"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vaccination rate data and popularity of searches related to travel in california by week\n",
    "vacc_trav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>total_doses</th>\n",
       "      <th>stprk_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>1008428.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>1204047.0</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>1441269.0</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>1598479.0</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1576352.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1966881.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2154815.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>2211739.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-21</td>\n",
       "      <td>2449447.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>2539324.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2754122.0</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>3057982.0</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2837596.0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>2700135.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>2266126.0</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2036990.0</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>1904638.0</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>1704929.0</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>1098208.0</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>942860.0</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>947754.0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>754804.0</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>601226.0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>477418.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-11</td>\n",
       "      <td>462152.0</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-18</td>\n",
       "      <td>464608.0</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>492903.0</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>526082.0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>557420.0</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-15</td>\n",
       "      <td>588555.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-22</td>\n",
       "      <td>658795.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>680373.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-05</td>\n",
       "      <td>629085.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>513014.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>523667.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>530281.0</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>759547.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-10</td>\n",
       "      <td>682996.0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>617421.0</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>667878.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>992928.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location       Date  total_doses  stprk_searches\n",
       "0        CA 2020-12-20     141193.0             137\n",
       "1        CA 2020-12-27     228433.0              80\n",
       "2        CA 2021-01-03     252093.0              99\n",
       "3        CA 2021-01-10     498893.0              57\n",
       "4        CA 2021-01-17     804624.0              89\n",
       "5        CA 2021-01-24    1008428.0              99\n",
       "6        CA 2021-01-31    1204047.0             103\n",
       "7        CA 2021-02-07    1441269.0              84\n",
       "8        CA 2021-02-14    1598479.0             151\n",
       "9        CA 2021-02-21    1576352.0              35\n",
       "10       CA 2021-02-28    1966881.0              39\n",
       "11       CA 2021-03-07    2154815.0              49\n",
       "12       CA 2021-03-14    2211739.0              68\n",
       "13       CA 2021-03-21    2449447.0             110\n",
       "14       CA 2021-03-28    2539324.0              39\n",
       "15       CA 2021-04-04    2754122.0             160\n",
       "16       CA 2021-04-11    3057982.0             181\n",
       "17       CA 2021-04-18    2837596.0             180\n",
       "18       CA 2021-04-25    2700135.0              70\n",
       "19       CA 2021-05-02    2266126.0             135\n",
       "20       CA 2021-05-09    2036990.0             192\n",
       "21       CA 2021-05-16    1904638.0             174\n",
       "22       CA 2021-05-23    1704929.0             269\n",
       "23       CA 2021-05-30    1098208.0             160\n",
       "24       CA 2021-06-06     942860.0             317\n",
       "25       CA 2021-06-13     947754.0             173\n",
       "26       CA 2021-06-20     754804.0             337\n",
       "27       CA 2021-06-27     601226.0             154\n",
       "28       CA 2021-07-04     477418.0              97\n",
       "29       CA 2021-07-11     462152.0             217\n",
       "30       CA 2021-07-18     464608.0             121\n",
       "31       CA 2021-07-25     492903.0             132\n",
       "32       CA 2021-08-01     526082.0             154\n",
       "33       CA 2021-08-08     557420.0              88\n",
       "34       CA 2021-08-15     588555.0              56\n",
       "35       CA 2021-08-22     658795.0             110\n",
       "36       CA 2021-08-29     680373.0              40\n",
       "37       CA 2021-09-05     629085.0              17\n",
       "38       CA 2021-09-12     513014.0              69\n",
       "39       CA 2021-09-19     523667.0              40\n",
       "40       CA 2021-09-26     530281.0             179\n",
       "41       CA 2021-10-03     759547.0              57\n",
       "42       CA 2021-10-10     682996.0              38\n",
       "43       CA 2021-10-17     617421.0             193\n",
       "44       CA 2021-10-24     667878.0              41\n",
       "45       CA 2021-10-31     992928.0               0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vaccination rate data and popularity of searches related to state parks in california by week\n",
    "vacc_stprk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>total_doses</th>\n",
       "      <th>amprk_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>141193.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>228433.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>252093.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>498893.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>804624.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>1008428.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>1204047.0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>1441269.0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>1598479.0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1576352.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1966881.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2154815.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>2211739.0</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-21</td>\n",
       "      <td>2449447.0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>2539324.0</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2754122.0</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>3057982.0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2837596.0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>2700135.0</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>2266126.0</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2036990.0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>1904638.0</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>1704929.0</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>1098208.0</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>942860.0</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>947754.0</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>754804.0</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>601226.0</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>477418.0</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-11</td>\n",
       "      <td>462152.0</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-18</td>\n",
       "      <td>464608.0</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>492903.0</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>526082.0</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>557420.0</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-15</td>\n",
       "      <td>588555.0</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-22</td>\n",
       "      <td>658795.0</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>680373.0</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-05</td>\n",
       "      <td>629085.0</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>513014.0</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>523667.0</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>530281.0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>759547.0</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-10</td>\n",
       "      <td>682996.0</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>617421.0</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>667878.0</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CA</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>992928.0</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location       Date  total_doses  amprk_searches\n",
       "0        CA 2020-12-20     141193.0              36\n",
       "1        CA 2020-12-27     228433.0              49\n",
       "2        CA 2021-01-03     252093.0              53\n",
       "3        CA 2021-01-10     498893.0              46\n",
       "4        CA 2021-01-17     804624.0              50\n",
       "5        CA 2021-01-24    1008428.0              69\n",
       "6        CA 2021-01-31    1204047.0              55\n",
       "7        CA 2021-02-07    1441269.0              52\n",
       "8        CA 2021-02-14    1598479.0              65\n",
       "9        CA 2021-02-21    1576352.0              56\n",
       "10       CA 2021-02-28    1966881.0              69\n",
       "11       CA 2021-03-07    2154815.0              70\n",
       "12       CA 2021-03-14    2211739.0             104\n",
       "13       CA 2021-03-21    2449447.0             111\n",
       "14       CA 2021-03-28    2539324.0             126\n",
       "15       CA 2021-04-04    2754122.0             149\n",
       "16       CA 2021-04-11    3057982.0             154\n",
       "17       CA 2021-04-18    2837596.0             182\n",
       "18       CA 2021-04-25    2700135.0             257\n",
       "19       CA 2021-05-02    2266126.0             219\n",
       "20       CA 2021-05-09    2036990.0             256\n",
       "21       CA 2021-05-16    1904638.0             277\n",
       "22       CA 2021-05-23    1704929.0             260\n",
       "23       CA 2021-05-30    1098208.0             273\n",
       "24       CA 2021-06-06     942860.0             327\n",
       "25       CA 2021-06-13     947754.0             341\n",
       "26       CA 2021-06-20     754804.0             352\n",
       "27       CA 2021-06-27     601226.0             405\n",
       "28       CA 2021-07-04     477418.0             378\n",
       "29       CA 2021-07-11     462152.0             409\n",
       "30       CA 2021-07-18     464608.0             448\n",
       "31       CA 2021-07-25     492903.0             424\n",
       "32       CA 2021-08-01     526082.0             418\n",
       "33       CA 2021-08-08     557420.0             386\n",
       "34       CA 2021-08-15     588555.0             379\n",
       "35       CA 2021-08-22     658795.0             323\n",
       "36       CA 2021-08-29     680373.0             266\n",
       "37       CA 2021-09-05     629085.0             264\n",
       "38       CA 2021-09-12     513014.0             251\n",
       "39       CA 2021-09-19     523667.0             274\n",
       "40       CA 2021-09-26     530281.0             256\n",
       "41       CA 2021-10-03     759547.0             273\n",
       "42       CA 2021-10-10     682996.0             300\n",
       "43       CA 2021-10-17     617421.0             305\n",
       "44       CA 2021-10-24     667878.0             285\n",
       "45       CA 2021-10-31     992928.0             265"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vaccination rate data and popularity of searches related to amusement parks in california by week\n",
    "vacc_amprk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination Rate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Total Doses Administered Daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.184968e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.259867e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.411930e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.370658e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.820855e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.854711e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.057982e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Doses Administered Daily\n",
       "count                    4.600000e+01\n",
       "mean                     1.184968e+06\n",
       "std                      8.259867e+05\n",
       "min                      1.411930e+05\n",
       "25%                      5.370658e+05\n",
       "50%                      7.820855e+05\n",
       "75%                      1.854711e+06\n",
       "max                      3.057982e+06"
      ]
     },
     "execution_count": 24,
     "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": [
    "weekly_data = weekly_data.sort_values('Date', ascending=True)\n",
    "\n",
    "#weekly_data.hist(column='Total Doses Administered Daily')\n",
    "sns.histplot(data=weekly_data, x=\"Total Doses Administered Daily\", bins=10)\n",
    "\n",
    "# Print out summary statistics \n",
    "weekly_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total doses adminstered daily as a line plot\n",
    "plt.figure(figsize = (10,6))\n",
    "ax = sns.lineplot(data=weekly_data, x=\"Date\", y=\"Total Doses Administered Daily\")\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line plot for vaccination rates shows that there is a steady increase of vaccination rates from December to 2020 to around mid April 2021. From end of April 2021, the line plot reveals a decline in the number of vaccinations administered daily, with some fluctuations in September 2021 to November 2021. As expected, there was an increase in the total doses administered daily when the vaccination was first available. It is interesting that after April, there was a sharp decrease in the total number of doses administered.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination Rate and General Interest in Travel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>total_doses</th>\n",
       "      <th>travel_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.184968e+06</td>\n",
       "      <td>434.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.259867e+05</td>\n",
       "      <td>103.798413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.411930e+05</td>\n",
       "      <td>226.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.370658e+05</td>\n",
       "      <td>378.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.820855e+05</td>\n",
       "      <td>418.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.854711e+06</td>\n",
       "      <td>518.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.057982e+06</td>\n",
       "      <td>693.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_doses  travel_searches\n",
       "count  4.600000e+01        46.000000\n",
       "mean   1.184968e+06       434.021739\n",
       "std    8.259867e+05       103.798413\n",
       "min    1.411930e+05       226.000000\n",
       "25%    5.370658e+05       378.250000\n",
       "50%    7.820855e+05       418.500000\n",
       "75%    1.854711e+06       518.000000\n",
       "max    3.057982e+06       693.000000"
      ]
     },
     "execution_count": 26,
     "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": [
    "vacc_trav.plot.scatter(x='total_doses', y='travel_searches', c='DarkGreen');\n",
    "\n",
    "\n",
    "#vacc_trav.hist(column='travel_searches')\n",
    "\n",
    "# Print out summary statistics \n",
    "vacc_trav.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "sns.histplot(data=vacc_trav, x=\"travel_searches\", bins=10, color='DarkGreen');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatterplot above compares vaccination rates and travel searches. It shows there is a positive relationship between the two variables. \n",
    "\n",
    "The histogram further demonstrates that there is a normal distribution in the number of travel searches. \n",
    "\n",
    "From the graphs, we can interpret that there is a relationship between vaccination rates and travel searches, eventhough it is slim. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination Rate and Interest in Travel to State Parks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>total_doses</th>\n",
       "      <th>stprk_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.184968e+06</td>\n",
       "      <td>117.173913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.259867e+05</td>\n",
       "      <td>75.143508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.411930e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.370658e+05</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.820855e+05</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.854711e+06</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.057982e+06</td>\n",
       "      <td>337.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_doses  stprk_searches\n",
       "count  4.600000e+01       46.000000\n",
       "mean   1.184968e+06      117.173913\n",
       "std    8.259867e+05       75.143508\n",
       "min    1.411930e+05        0.000000\n",
       "25%    5.370658e+05       57.000000\n",
       "50%    7.820855e+05      101.000000\n",
       "75%    1.854711e+06      160.000000\n",
       "max    3.057982e+06      337.000000"
      ]
     },
     "execution_count": 28,
     "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": [
    "vacc_stprk.plot.scatter(x='total_doses', y='stprk_searches', c='DarkBlue');\n",
    "\n",
    "#vacc_stprk.hist(column='stprk_searches')\n",
    "\n",
    "# Print out summary statistics \n",
    "vacc_stprk.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "sns.histplot(data=vacc_stprk, x=\"stprk_searches\", bins=10, color='DarkBlue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatter plot shows that there is a slight relationship between total doses and state park searches. The scatterplot also shows there is a lot of variability between the two plots. \n",
    "\n",
    "Further, the histogram of total state park searches demonstrates a bimodal distribution. The distribution of total state park searches is not a normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination Rate and Interest in Travel to Amusement Parks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>total_doses</th>\n",
       "      <th>amprk_searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.184968e+06</td>\n",
       "      <td>225.369565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.259867e+05</td>\n",
       "      <td>128.688143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.411930e+05</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.370658e+05</td>\n",
       "      <td>78.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.820855e+05</td>\n",
       "      <td>258.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.854711e+06</td>\n",
       "      <td>318.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.057982e+06</td>\n",
       "      <td>448.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_doses  amprk_searches\n",
       "count  4.600000e+01       46.000000\n",
       "mean   1.184968e+06      225.369565\n",
       "std    8.259867e+05      128.688143\n",
       "min    1.411930e+05       36.000000\n",
       "25%    5.370658e+05       78.500000\n",
       "50%    7.820855e+05      258.500000\n",
       "75%    1.854711e+06      318.500000\n",
       "max    3.057982e+06      448.000000"
      ]
     },
     "execution_count": 30,
     "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": [
    "vacc_amprk.plot.scatter(x='total_doses', y='amprk_searches', c='DarkRed');\n",
    "\n",
    "#vacc_amprk.hist(column='amprk_searches')\n",
    "\n",
    "\n",
    "# Print out summary statistics \n",
    "vacc_amprk.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "sns.histplot(data=vacc_amprk, x=\"amprk_searches\", bins=10, color='DarkRed');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram for total amusement parks searches shows that there is a bimodal distribution, where there are two frequent peaks with the same exact value. Ultimately, the scatterplot and histogram show that there is no relationship between vaccination rates and amusment park searches. We can also see that there is high variability in the amusement park data because the stardard deviation of 128.69 is quite high. This helps further explain the shape of the scatterplot as it being more dispersed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our research project will comply with the ethics and privacy standards because all data used is freely available to the public and no personal information such as names were recorded.  We viewed our data holistically, and as a result there was no need for personal names or search history.  However we do acknowledge that there are biases in our data set.  Not every individual in California has access to internet and google, therefore it does not reflect the “whole” population but only the majority.  Furthermore, not every individual has access to the vaccine due to issues with transportation and less affluent neighborhoods.  To detect these certain areas, further research will be needed to acknowledge which specific areas and populations had less rates due to external factors and limitations.  Lastly, we must also acknowledge that the datasets for California do not reflect what is happening in the rest of the United States, or the world overall because every government has different responses and populations to deal with.\n",
    "\n",
    "While our group has harmless intentions by analyzing datasets in order to see travel interest in society, the data could be used to manipulate the public.  Individuals noted in our background articles that knowledge of society getting increasingly fully vaccinated boosted their confidence in traveling.  If the wrong person had access to manipulating vaccination data to the public, they could create false reassurances and encourage individuals to travel by lying about high vaccination rates.  The travel industry heavily suffered with the appearance of Covid-19, by manipulating vaccination data they could encourage society to travel before it’s safe to do so because individuals will feel reassured by high vaccination rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project studied the relationship between vaccination rates and interest in travel in California during the COVID-19 pandemic. We hypothesized that there would be a positive correlation between vaccination rates and interest in travel. Additionally, we hypothesized that there would be a stronger correlation between vaccination rates and interest in traveling to state parks as opposed to traveling to amusement parks. We gathered vaccination rate data from the CDC and interest in travel data from Google Trends. After performing an exploratory data analysis through visualizing the datasets as histograms, line plots, scatter plots, and outputting summary statistics, we were able to conclude that there was evidence of correlation between vaccination rates and general interest in travel. However, there was not evidence to support our hypothesis for our subquestion as there appeared to be no correlation between vaccination rates and specific searches for state parks or amusement parks. \n",
    "\n",
    "A limitation to our project is that our quantification of interest in travel may not fully include the entire population that we are interested in. For example, we only have ten search terms to represent interest in general travel when there could be more. Also, not everyone has access to the internet or uses Google as a search engine so this is further not reflective of the entire population of California. \n",
    "\n",
    "The impact of this study on society is that it brings to light how as more people get vaccinated, people will start to be more confident and interested in travel. The pandemic of COVID-19 caused disruption in society where people were confined to their homes/city. Our project brings more awareness to the general population of California that vaccinations are increasing confidence in going out and therefore providing more safety to travel. Further potential research can analyze the relationship between vaccination rates and how much people actually travel (via automobile, airplane, etc.) in addition to interest in travel which we have done here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All sections were worked on together as a team, while certain team members took the lead on some specific sections. \n",
    "- Overview: Everyone\n",
    "- Background: Abby, Kristy, Carolyn\n",
    "- Data Cleaning: Abby cleaned vaccination data, Carolyn cleaned general travel search data, Sophie cleaned amusement park search data, and Kristy cleaned state park search data\n",
    "- EDA: Everyone\n",
    "- Analysis write up: Sophie\n",
    "- Conclusion & Discussion: Everyone\n",
    "- Ethics & Privacy: Kristy"
   ]
  }
 ],
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