notebooks/04_data_design_and_data_wrangling.ipynb

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    "# Data Design & Data Wrangling\n",
    "\n",
    "'Data wrangling' is the mundane labor of collecting, loading, transforming, and preparing data for exploration and analysis. Although it is hard to put a number on it, [estimates](https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html) indicate that data scientists spend from 50 per cent to 80 per cent of their time on data wrangling. The importance of data wrangling has been recognised in [social sciencies](https://doi.org/10.1177/2378023118817378) as well. \n",
    "\n",
    "Although data wrangling may seem a-theoretical, some of the decisions you would make would in fact depend on how the data were generated or designed.\n",
    "\n",
    "In this lab, we will first discuss issues related to data design and will then practice hands-on data wrangling with `pandas` and other Python libraries for data analysis. We will wrangle open data sets, including the Google Covid-19 Community Mobility Reports and Apple Mobility Trends Reports.\n",
    "\n",
    "## Key themes\n",
    "* What are data? How are data generated (i.e., data design)? \n",
    "* Data wrangling with `pandas`: loading, selecting, and transforming data."
   ]
  },
  {
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   "source": [
    "## Learning resources\n",
    "\n",
    "<i class=\"fas fa-book\"></i> Matthew Salganik. [2.3 Ten common characteristics of big data](https://www.bitbybitbook.com/en/1st-ed/observing-behavior/). In _Bit by Bit: Social Research in the Digital Age_. [[Online version freely available]]((https://www.bitbybitbook.com/en/1st-ed/preface/))\n",
    "\n",
    "<i class=\"fas fa-book\"></i> Lisa Gitelman and Virginia Jackson. [“Raw Data” Is an Oxymoron. Introduction.](https://raley.english.ucsb.edu/wp-content/Engl800/RawData-excerpts.pdf) MIT Press.\n",
    "\n",
    "<i class=\"fas fa-scroll\"></i> Sabina Leonelli. [Scientific Research and Big Data.](https://plato.stanford.edu/cgi-bin/encyclopedia/archinfo.cgi?entry=science-big-data) The Stanford Encyclopedia of Philosophy.\n",
    "\n",
    "<i class=\"fas fa-book\"></i> <i class=\"fas fa-code\"></i> Sam Lau, Joey Gonzalez, and Deb Nolan. [The Data Science Lifecycle](https://www.textbook.ds100.org/ch/01/lifecycle_intro.html). In [_Principles and Techniques of Data Science_](https://www.textbook.ds100.org/intro.html).\n",
    "\n",
    "<i class=\"fas fa-scroll\"></i> Steve Lohr. [For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights](https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html). New York Times. \n",
    "\n",
    "<i class=\"fas fa-scroll\"></i> Alexander Kindel et al. [Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge](https://doi.org/10.1177/2378023118817378). \n",
    "Socius.  \n",
    "\n",
    "<i class=\"fas fa-play-circle\"></i> <i class=\"fas fa-code\"></i> [Introduction to Data Processing in Python with Pandas](https://www.youtube.com/watch?v=5rNu16O3YNE), SciPy 2019 Tutorial by Daniel Chen. See also freely available Jupyter notebooks: [01-intro.ipynb](https://github.com/valdanchev/scipy-2019-pandas/blob/master/notebooks/01-intro.ipynb) and [02-tidy.ipynb](https://github.com/valdanchev/scipy-2019-pandas/blob/master/notebooks/02-tidy.ipynb).\n",
    "\n",
    "<br>\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Activity: Discussing data biases\n",
    "After reading the section [Ten common characteristics of big data](https://www.bitbybitbook.com/en/1st-ed/observing-behavior/) in Matthew Salganik's book _Bit by Bit_, we will discuss how each characteristic of big data applies to the digital data sets we are analysing in this and next sections, including the [Google COVID-19 Community Mobility Reports](https://www.google.com/covid19/mobility/) and [Apple Mobility Trends Reports](https://covid19.apple.com/mobility).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/e/ed/Pandas_logo.svg\" title='Pandas Logo' width=\"200\" height=\"100\" align=\"right\" />\n",
    "\n",
    "# Get started with [`pandas`](https://pandas.pydata.org/docs/getting_started/index.html#getting-started)\n",
    "The `pandas` library:\n",
    "* is a fast, powerful, and flexible open source tool for doing real world data analysis in Python.\n",
    "* offers a diverse range of high-performance tools for data loading, cleaning, wrangling, merging, reshaping,  and summarising.\n",
    "* is the go-to data science library in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dv8lGggXc5Ez"
   },
   "source": [
    "# Importing `pandas`\n",
    "\n",
    "To use `pandas`, we first import the library via the Python's `import` command and give it the alias pd:"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.__version__\n",
    "\n",
    "# Suppress warnings to avoid potential confusion\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading your data\n",
    "\n",
    "Pandas supports many data file formats, including csv, excel, sql, json.\n",
    "For details, see [How do I read and write tabular data?](https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html#min-tut-02-read-write) For example, once you import pandas as pd, you can load a .csv file using the function `pd.read_csv()`, an Excel file using the function `pd.read_excel()`, and a JSON file using the function `pd.read_json()`.\n",
    "\n",
    "<img src=\"https://pandas.pydata.org/docs/_images/02_io_readwrite.svg\" width=\"800\" height=\"400\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Covid-19 data sets via URL \n",
    "\n",
    "\n",
    "<img src=\"https://www.google.com/covid19/static/reports-icon-grid.png\" title='Google Covid-19 Community Mobility Data' width=\"200\" height=\"100\" align=\"right\"/>\n",
    "\n",
    "What are the Google Covid-19 [Community Mobility Reports](https://www.google.com/covid19/mobility/)?\n",
    "\n",
    "* Aggregated and anonymized sets of freely available data that protect individual privacy.\n",
    "* Shows trends of human mobility over time by country and region, across six categories of places, including retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.\n",
    "* The data shows how visitors to (or time spent in) categorized places change compared to baseline days. A baseline day represents a normal value for that day of the week. The baseline day is the median value from the 5‑week period Jan 3 – Feb 6, 2020. \n",
    "\n",
    "An overview of the data from the mobility data can be found [here](https://www.google.com/covid19/mobility/).\n",
    "\n",
    "The code below loads the most recent version of the Covid-19 Google Community Mobility Reports as a `pandas` DataFrame object and assign the object to a variable called 'mobility_trends'"
   ]
  },
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   "source": [
    "mobility_trends = pd.read_csv(\n",
    "    \"https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv\",\n",
    "    parse_dates=[\"date\"],\n",
    ")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
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       "        country_region_code        country_region       sub_region_1  \\\n",
       "0                        AE  United Arab Emirates                NaN   \n",
       "1                        AE  United Arab Emirates                NaN   \n",
       "2                        AE  United Arab Emirates                NaN   \n",
       "3                        AE  United Arab Emirates                NaN   \n",
       "4                        AE  United Arab Emirates                NaN   \n",
       "...                     ...                   ...                ...   \n",
       "9900501                  ZW              Zimbabwe  Midlands Province   \n",
       "9900502                  ZW              Zimbabwe  Midlands Province   \n",
       "9900503                  ZW              Zimbabwe  Midlands Province   \n",
       "9900504                  ZW              Zimbabwe  Midlands Province   \n",
       "9900505                  ZW              Zimbabwe  Midlands Province   \n",
       "\n",
       "        sub_region_2 metro_area iso_3166_2_code  census_fips_code  \\\n",
       "0                NaN        NaN             NaN               NaN   \n",
       "1                NaN        NaN             NaN               NaN   \n",
       "2                NaN        NaN             NaN               NaN   \n",
       "3                NaN        NaN             NaN               NaN   \n",
       "4                NaN        NaN             NaN               NaN   \n",
       "...              ...        ...             ...               ...   \n",
       "9900501       Kwekwe        NaN             NaN               NaN   \n",
       "9900502       Kwekwe        NaN             NaN               NaN   \n",
       "9900503       Kwekwe        NaN             NaN               NaN   \n",
       "9900504       Kwekwe        NaN             NaN               NaN   \n",
       "9900505       Kwekwe        NaN             NaN               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "0        ChIJvRKrsd9IXj4RpwoIwFYv0zM 2020-02-15   \n",
       "1        ChIJvRKrsd9IXj4RpwoIwFYv0zM 2020-02-16   \n",
       "2        ChIJvRKrsd9IXj4RpwoIwFYv0zM 2020-02-17   \n",
       "3        ChIJvRKrsd9IXj4RpwoIwFYv0zM 2020-02-18   \n",
       "4        ChIJvRKrsd9IXj4RpwoIwFYv0zM 2020-02-19   \n",
       "...                              ...        ...   \n",
       "9900501  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-10   \n",
       "9900502  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-11   \n",
       "9900503  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-12   \n",
       "9900504  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-13   \n",
       "9900505  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-16   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "0                                                      0.0    \n",
       "1                                                      1.0    \n",
       "2                                                     -1.0    \n",
       "3                                                     -2.0    \n",
       "4                                                     -2.0    \n",
       "...                                                    ...    \n",
       "9900501                                                NaN    \n",
       "9900502                                                NaN    \n",
       "9900503                                                NaN    \n",
       "9900504                                                NaN    \n",
       "9900505                                                NaN    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "0                                                      4.0   \n",
       "1                                                      4.0   \n",
       "2                                                      1.0   \n",
       "3                                                      1.0   \n",
       "4                                                      0.0   \n",
       "...                                                    ...   \n",
       "9900501                                                NaN   \n",
       "9900502                                                NaN   \n",
       "9900503                                                NaN   \n",
       "9900504                                                NaN   \n",
       "9900505                                                NaN   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "0                                       5.0   \n",
       "1                                       4.0   \n",
       "2                                       5.0   \n",
       "3                                       5.0   \n",
       "4                                       4.0   \n",
       "...                                     ...   \n",
       "9900501                                 NaN   \n",
       "9900502                                 NaN   \n",
       "9900503                                 NaN   \n",
       "9900504                                 NaN   \n",
       "9900505                                 NaN   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "0                                                  0.0   \n",
       "1                                                  1.0   \n",
       "2                                                  1.0   \n",
       "3                                                  0.0   \n",
       "4                                                 -1.0   \n",
       "...                                                ...   \n",
       "9900501                                            NaN   \n",
       "9900502                                            NaN   \n",
       "9900503                                            NaN   \n",
       "9900504                                            NaN   \n",
       "9900505                                            NaN   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "0                                            2.0   \n",
       "1                                            2.0   \n",
       "2                                            2.0   \n",
       "3                                            2.0   \n",
       "4                                            2.0   \n",
       "...                                          ...   \n",
       "9900501                                    126.0   \n",
       "9900502                                    129.0   \n",
       "9900503                                    116.0   \n",
       "9900504                                    118.0   \n",
       "9900505                                    100.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "0                                             1.0  \n",
       "1                                             1.0  \n",
       "2                                             1.0  \n",
       "3                                             1.0  \n",
       "4                                             1.0  \n",
       "...                                           ...  \n",
       "9900501                                       NaN  \n",
       "9900502                                       NaN  \n",
       "9900503                                       NaN  \n",
       "9900504                                       NaN  \n",
       "9900505                                       NaN  \n",
       "\n",
       "[9900506 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5bHsZdybYC48"
   },
   "source": [
    "`Pandas` load the data as a DataFrame object. We can confirm this by using the built-in function `type()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ium2qHENYBuZ",
    "outputId": "d8c5143f-268b-48ba-85ed-5a93fbdc2ef1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mobility_trends)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4vaYp1c5YwYY"
   },
   "source": [
    "## What is a [DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented)?\n",
    "\n",
    "\n",
    "> A DataFrame is a 2-dimensional data structure that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns.\n",
    "\n",
    "<img src=\"https://pandas.pydata.org/docs/_images/01_table_dataframe.svg\" title='Pandas DataFrame' width=\"400\" height=\"200\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x4XOELCTYTgX"
   },
   "source": [
    "# Viewing, Describing, and Accessing your Data\n",
    "\n",
    "Once we have our data as a DataFrame data structure, we can view, describe, and access the data by applying [basic functionalities](https://pandas.pydata.org/pandas-docs/stable/user_guide/basics.html) associated with that structure. For example, we can perform various operations by using the so called `methods`. Examples of `pandas` methods are `head()` and `tail()`; `head()` displays by default the top five rows of the data and `tail()` displays by default the last five rows. You can display a custom number of row by passing that number in the brackets.\n",
    "\n",
    "## View your 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>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_region_code        country_region sub_region_1 sub_region_2  \\\n",
       "0                  AE  United Arab Emirates          NaN          NaN   \n",
       "1                  AE  United Arab Emirates          NaN          NaN   \n",
       "2                  AE  United Arab Emirates          NaN          NaN   \n",
       "3                  AE  United Arab Emirates          NaN          NaN   \n",
       "4                  AE  United Arab Emirates          NaN          NaN   \n",
       "\n",
       "  metro_area iso_3166_2_code  census_fips_code                     place_id  \\\n",
       "0        NaN             NaN               NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM   \n",
       "1        NaN             NaN               NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM   \n",
       "2        NaN             NaN               NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM   \n",
       "3        NaN             NaN               NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM   \n",
       "4        NaN             NaN               NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM   \n",
       "\n",
       "        date  retail_and_recreation_percent_change_from_baseline  \\\n",
       "0 2020-02-15                                                0.0    \n",
       "1 2020-02-16                                                1.0    \n",
       "2 2020-02-17                                               -1.0    \n",
       "3 2020-02-18                                               -2.0    \n",
       "4 2020-02-19                                               -2.0    \n",
       "\n",
       "   grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "0                                                4.0   \n",
       "1                                                4.0   \n",
       "2                                                1.0   \n",
       "3                                                1.0   \n",
       "4                                                0.0   \n",
       "\n",
       "   parks_percent_change_from_baseline  \\\n",
       "0                                 5.0   \n",
       "1                                 4.0   \n",
       "2                                 5.0   \n",
       "3                                 5.0   \n",
       "4                                 4.0   \n",
       "\n",
       "   transit_stations_percent_change_from_baseline  \\\n",
       "0                                            0.0   \n",
       "1                                            1.0   \n",
       "2                                            1.0   \n",
       "3                                            0.0   \n",
       "4                                           -1.0   \n",
       "\n",
       "   workplaces_percent_change_from_baseline  \\\n",
       "0                                      2.0   \n",
       "1                                      2.0   \n",
       "2                                      2.0   \n",
       "3                                      2.0   \n",
       "4                                      2.0   \n",
       "\n",
       "   residential_percent_change_from_baseline  \n",
       "0                                       1.0  \n",
       "1                                       1.0  \n",
       "2                                       1.0  \n",
       "3                                       1.0  \n",
       "4                                       1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first five rows using the method DataFrame.head()\n",
    "mobility_trends.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9900501</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900502</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900503</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900504</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900505</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code country_region       sub_region_1 sub_region_2  \\\n",
       "9900501                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900502                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900503                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900504                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900505                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "\n",
       "        metro_area iso_3166_2_code  census_fips_code  \\\n",
       "9900501        NaN             NaN               NaN   \n",
       "9900502        NaN             NaN               NaN   \n",
       "9900503        NaN             NaN               NaN   \n",
       "9900504        NaN             NaN               NaN   \n",
       "9900505        NaN             NaN               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "9900501  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-10   \n",
       "9900502  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-11   \n",
       "9900503  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-12   \n",
       "9900504  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-13   \n",
       "9900505  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-16   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "9900501                                                NaN    \n",
       "9900502                                                NaN    \n",
       "9900503                                                NaN    \n",
       "9900504                                                NaN    \n",
       "9900505                                                NaN    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "9900501                                                NaN   \n",
       "9900502                                                NaN   \n",
       "9900503                                                NaN   \n",
       "9900504                                                NaN   \n",
       "9900505                                                NaN   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "9900501                                 NaN   \n",
       "9900502                                 NaN   \n",
       "9900503                                 NaN   \n",
       "9900504                                 NaN   \n",
       "9900505                                 NaN   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "9900501                                            NaN   \n",
       "9900502                                            NaN   \n",
       "9900503                                            NaN   \n",
       "9900504                                            NaN   \n",
       "9900505                                            NaN   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "9900501                                    126.0   \n",
       "9900502                                    129.0   \n",
       "9900503                                    116.0   \n",
       "9900504                                    118.0   \n",
       "9900505                                    100.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "9900501                                       NaN  \n",
       "9900502                                       NaN  \n",
       "9900503                                       NaN  \n",
       "9900504                                       NaN  \n",
       "9900505                                       NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the last five rows using the method DataFrame.tail()\n",
    "mobility_trends.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9900496</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900497</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900498</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900499</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900500</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900501</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900502</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900503</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900504</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9900505</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Midlands Province</td>\n",
       "      <td>Kwekwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJRcIZ3-FJNBkRRsj55IcLpfU</td>\n",
       "      <td>2022-05-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code country_region       sub_region_1 sub_region_2  \\\n",
       "9900496                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900497                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900498                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900499                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900500                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900501                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900502                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900503                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900504                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "9900505                  ZW       Zimbabwe  Midlands Province       Kwekwe   \n",
       "\n",
       "        metro_area iso_3166_2_code  census_fips_code  \\\n",
       "9900496        NaN             NaN               NaN   \n",
       "9900497        NaN             NaN               NaN   \n",
       "9900498        NaN             NaN               NaN   \n",
       "9900499        NaN             NaN               NaN   \n",
       "9900500        NaN             NaN               NaN   \n",
       "9900501        NaN             NaN               NaN   \n",
       "9900502        NaN             NaN               NaN   \n",
       "9900503        NaN             NaN               NaN   \n",
       "9900504        NaN             NaN               NaN   \n",
       "9900505        NaN             NaN               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "9900496  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-03   \n",
       "9900497  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-04   \n",
       "9900498  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-05   \n",
       "9900499  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-06   \n",
       "9900500  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-09   \n",
       "9900501  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-10   \n",
       "9900502  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-11   \n",
       "9900503  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-12   \n",
       "9900504  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-13   \n",
       "9900505  ChIJRcIZ3-FJNBkRRsj55IcLpfU 2022-05-16   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "9900496                                                NaN    \n",
       "9900497                                                NaN    \n",
       "9900498                                                NaN    \n",
       "9900499                                                NaN    \n",
       "9900500                                                NaN    \n",
       "9900501                                                NaN    \n",
       "9900502                                                NaN    \n",
       "9900503                                                NaN    \n",
       "9900504                                                NaN    \n",
       "9900505                                                NaN    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "9900496                                                NaN   \n",
       "9900497                                                NaN   \n",
       "9900498                                                NaN   \n",
       "9900499                                                NaN   \n",
       "9900500                                                NaN   \n",
       "9900501                                                NaN   \n",
       "9900502                                                NaN   \n",
       "9900503                                                NaN   \n",
       "9900504                                                NaN   \n",
       "9900505                                                NaN   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "9900496                                 NaN   \n",
       "9900497                                 NaN   \n",
       "9900498                                 NaN   \n",
       "9900499                                 NaN   \n",
       "9900500                                 NaN   \n",
       "9900501                                 NaN   \n",
       "9900502                                 NaN   \n",
       "9900503                                 NaN   \n",
       "9900504                                 NaN   \n",
       "9900505                                 NaN   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "9900496                                            NaN   \n",
       "9900497                                            NaN   \n",
       "9900498                                            NaN   \n",
       "9900499                                            NaN   \n",
       "9900500                                            NaN   \n",
       "9900501                                            NaN   \n",
       "9900502                                            NaN   \n",
       "9900503                                            NaN   \n",
       "9900504                                            NaN   \n",
       "9900505                                            NaN   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "9900496                                    119.0   \n",
       "9900497                                    116.0   \n",
       "9900498                                    110.0   \n",
       "9900499                                    124.0   \n",
       "9900500                                     98.0   \n",
       "9900501                                    126.0   \n",
       "9900502                                    129.0   \n",
       "9900503                                    116.0   \n",
       "9900504                                    118.0   \n",
       "9900505                                    100.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "9900496                                       NaN  \n",
       "9900497                                       NaN  \n",
       "9900498                                       NaN  \n",
       "9900499                                       NaN  \n",
       "9900500                                       NaN  \n",
       "9900501                                       NaN  \n",
       "9900502                                       NaN  \n",
       "9900503                                       NaN  \n",
       "9900504                                       NaN  \n",
       "9900505                                       NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the number of rows to return\n",
    "mobility_trends.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describing your DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9900506, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensionality — number of rows and columns — of a DataFrame\n",
    "mobility_trends.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "As of 2022-05-19 17:59:52.163517 The Google COVID-19 Community Mobility Reports contain 9900506 rows and 15 columns.\n"
     ]
    }
   ],
   "source": [
    "# Use the print function to display the number of rows and columns\n",
    "# in a DataFrame at the time of cell execution.\n",
    "# Import the 'datetime' module (used below to display the current date)\n",
    "import datetime\n",
    "\n",
    "print(\n",
    "    \"\\nAs of\",\n",
    "    datetime.datetime.now(),\n",
    "    \"The Google COVID-19 Community Mobility Reports contain\",\n",
    "    mobility_trends.shape[0],\n",
    "    \"rows and\",\n",
    "    mobility_trends.shape[1],\n",
    "    \"columns.\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country_region_code', 'country_region', 'sub_region_1', 'sub_region_2',\n",
       "       'metro_area', 'iso_3166_2_code', 'census_fips_code', 'place_id', 'date',\n",
       "       'retail_and_recreation_percent_change_from_baseline',\n",
       "       'grocery_and_pharmacy_percent_change_from_baseline',\n",
       "       'parks_percent_change_from_baseline',\n",
       "       'transit_stations_percent_change_from_baseline',\n",
       "       'workplaces_percent_change_from_baseline',\n",
       "       'residential_percent_change_from_baseline'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing columns using the DataFrame.columns attribute\n",
    "mobility_trends.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display a concise summary of our DataFrame using the method `info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9900506 entries, 0 to 9900505\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                              Dtype         \n",
      "---  ------                                              -----         \n",
      " 0   country_region_code                                 object        \n",
      " 1   country_region                                      object        \n",
      " 2   sub_region_1                                        object        \n",
      " 3   sub_region_2                                        object        \n",
      " 4   metro_area                                          object        \n",
      " 5   iso_3166_2_code                                     object        \n",
      " 6   census_fips_code                                    float64       \n",
      " 7   place_id                                            object        \n",
      " 8   date                                                datetime64[ns]\n",
      " 9   retail_and_recreation_percent_change_from_baseline  float64       \n",
      " 10  grocery_and_pharmacy_percent_change_from_baseline   float64       \n",
      " 11  parks_percent_change_from_baseline                  float64       \n",
      " 12  transit_stations_percent_change_from_baseline       float64       \n",
      " 13  workplaces_percent_change_from_baseline             float64       \n",
      " 14  residential_percent_change_from_baseline            float64       \n",
      "dtypes: datetime64[ns](1), float64(7), object(7)\n",
      "memory usage: 1.1+ GB\n"
     ]
    }
   ],
   "source": [
    "# Information about a DataFrame\n",
    "mobility_trends.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among other information, displayed are labels of the columns and the data type of each column. For example 'float64' refers to a floating point number, i.e., a number with a decimal point. In the Community Mobility Reports, floating point numbers are used to display the percentage mobility change for each category of places."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "38rf3fgFjRt9"
   },
   "source": [
    "# Accessing columns and rows in your data\n",
    "\n",
    "## Accessing columns\n",
    "\n",
    "You can can access columns via column name and column position.\n",
    "\n",
    "* *Accessing columns via column name*\n",
    "\n",
    "Let's access a single column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 232
    },
    "id": "ONie6YGhjRt-",
    "outputId": "829662d6-a4f9-43d7-d95e-11df646bbbd5"
   },
   "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>country_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country_region\n",
       "0  United Arab Emirates\n",
       "1  United Arab Emirates\n",
       "2  United Arab Emirates\n",
       "3  United Arab Emirates\n",
       "4  United Arab Emirates"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the country column and save it to its own variable\n",
    "# The double square bracket option `[[]]` gives DataFrame\n",
    "\n",
    "mobility_trends_countries = mobility_trends[[\"country_region\"]]\n",
    "mobility_trends_countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "GFoFwss3jRt-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the type of data structure\n",
    "\n",
    "type(mobility_trends_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "jf1deTDYjRt_"
   },
   "outputs": [],
   "source": [
    "# The single square braket `[]` option gives Series\n",
    "# pandas Series is a one-dimensional data structure or simply put, a single column\n",
    "\n",
    "mobility_trends_countries_series = mobility_trends[\"country_region\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Mnu3oQvQjRt_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the type of data structure\n",
    "type(mobility_trends_countries_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now access multiple columns. We can access multiple columns by using a Python list. We use square brakets to create a list, for example `['country_region', 'sub_region_1', 'date']`. Python lists are useful as they store multiple items in the same variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "tafzH0gxjRt_"
   },
   "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>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country_region sub_region_1       date\n",
       "0  United Arab Emirates          NaN 2020-02-15\n",
       "1  United Arab Emirates          NaN 2020-02-16\n",
       "2  United Arab Emirates          NaN 2020-02-17\n",
       "3  United Arab Emirates          NaN 2020-02-18\n",
       "4  United Arab Emirates          NaN 2020-02-19"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends[[\"country_region\", \"sub_region_1\", \"date\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sdKGG_oPjRuE"
   },
   "source": [
    "* *Accessing columns via column position*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "8Rkyb9h9jRuE"
   },
   "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>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>place_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country_region sub_region_1                     place_id\n",
       "0  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM\n",
       "1  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM\n",
       "2  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM\n",
       "3  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM\n",
       "4  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing columns 1, 2, adn 7 via column position and show the top five rows\n",
    "\n",
    "mobility_trends.iloc[:, [1, 2, 7]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "6wPYc8XxjRuE"
   },
   "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>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>place_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country_region sub_region_1                     place_id\n",
       "0  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM\n",
       "1  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM\n",
       "2  United Arab Emirates          NaN  ChIJvRKrsd9IXj4RpwoIwFYv0zM"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing a subset of rows and columns. We access the top 3 rows and columns 1, 2, and 7\n",
    "\n",
    "mobility_trends.iloc[0:3, [1, 2, 7]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x7NaEkLTjRuA"
   },
   "source": [
    "# Hands-on exercise\n",
    "\n",
    "1. Create a new code cell.\n",
    "2. Access the first 100 rows and the following columns from the DataFrame `mobility_trends`:\n",
    "\n",
    "    * `country_region_code`\n",
    "    * `date` \n",
    "    * `retail_recreation`\n",
    "\n",
    "3. Assign the output to a variable called `mobility_trends_subset`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "79fkz_RtjRuF"
   },
   "source": [
    "## Accessing rows\n",
    "\n",
    "Rows can be accessed via row labels `df.loc` and row index `df.iloc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tmGLpCG9jRuF",
    "outputId": "1b703e99-78da-4d85-ad9d-f05fe4ee27cf",
    "scrolled": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United Arab Emirates', 'Afghanistan', 'Antigua and Barbuda',\n",
       "       'Angola', 'Argentina', 'Austria', 'Australia', 'Aruba',\n",
       "       'Bosnia and Herzegovina', 'Barbados', 'Bangladesh', 'Belgium',\n",
       "       'Burkina Faso', 'Bulgaria', 'Bahrain', 'Benin', 'Bolivia',\n",
       "       'Brazil', 'The Bahamas', 'Botswana', 'Belarus', 'Belize', 'Canada',\n",
       "       'Switzerland', \"Côte d'Ivoire\", 'Chile', 'Cameroon', 'Colombia',\n",
       "       'Costa Rica', 'Cape Verde', 'Czechia', 'Germany', 'Denmark',\n",
       "       'Dominican Republic', 'Ecuador', 'Estonia', 'Egypt', 'Spain',\n",
       "       'Finland', 'Fiji', 'France', 'Gabon', 'United Kingdom', 'Georgia',\n",
       "       'Ghana', 'Greece', 'Guatemala', 'Guinea-Bissau', 'Hong Kong',\n",
       "       'Honduras', 'Croatia', 'Haiti', 'Hungary', 'Indonesia', 'Ireland',\n",
       "       'Israel', 'India', 'Iraq', 'Italy', 'Jamaica', 'Jordan', 'Japan',\n",
       "       'Kenya', 'Kyrgyzstan', 'Cambodia', 'South Korea', 'Kuwait',\n",
       "       'Kazakhstan', 'Laos', 'Lebanon', 'Liechtenstein', 'Sri Lanka',\n",
       "       'Lithuania', 'Luxembourg', 'Latvia', 'Libya', 'Morocco', 'Moldova',\n",
       "       'North Macedonia', 'Mali', 'Myanmar (Burma)', 'Mongolia', 'Malta',\n",
       "       'Mauritius', 'Mexico', 'Malaysia', 'Mozambique', 'Namibia',\n",
       "       'Niger', 'Nigeria', 'Nicaragua', 'Netherlands', 'Norway', 'Nepal',\n",
       "       'New Zealand', 'Oman', 'Panama', 'Peru', 'Papua New Guinea',\n",
       "       'Philippines', 'Pakistan', 'Poland', 'Puerto Rico', 'Portugal',\n",
       "       'Paraguay', 'Qatar', 'Réunion', 'Romania', 'Serbia', 'Russia',\n",
       "       'Rwanda', 'Saudi Arabia', 'Sweden', 'Singapore', 'Slovenia',\n",
       "       'Slovakia', 'Senegal', 'El Salvador', 'Togo', 'Thailand',\n",
       "       'Tajikistan', 'Turkey', 'Trinidad and Tobago', 'Taiwan',\n",
       "       'Tanzania', 'Ukraine', 'Uganda', 'United States', 'Uruguay',\n",
       "       'Venezuela', 'Vietnam', 'Yemen', 'South Africa', 'Zambia',\n",
       "       'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before accessing particular rows, let's see the names of all countries in the dataset.\n",
    "# We can achieve that by listing all unique values in the 'country_region' column.\n",
    "mobility_trends.country_region.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "id": "UjV-tmLIjRuF",
    "outputId": "812de40f-7f97-4d30-808d-17c5ee525f0a"
   },
   "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>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3647576</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647577</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647578</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647579</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647580</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code  country_region sub_region_1 sub_region_2  \\\n",
       "3647576                  GB  United Kingdom          NaN          NaN   \n",
       "3647577                  GB  United Kingdom          NaN          NaN   \n",
       "3647578                  GB  United Kingdom          NaN          NaN   \n",
       "3647579                  GB  United Kingdom          NaN          NaN   \n",
       "3647580                  GB  United Kingdom          NaN          NaN   \n",
       "\n",
       "        metro_area iso_3166_2_code  census_fips_code  \\\n",
       "3647576        NaN             NaN               NaN   \n",
       "3647577        NaN             NaN               NaN   \n",
       "3647578        NaN             NaN               NaN   \n",
       "3647579        NaN             NaN               NaN   \n",
       "3647580        NaN             NaN               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "3647576  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-15   \n",
       "3647577  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-16   \n",
       "3647578  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-17   \n",
       "3647579  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-18   \n",
       "3647580  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-19   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "3647576                                              -12.0    \n",
       "3647577                                               -7.0    \n",
       "3647578                                               10.0    \n",
       "3647579                                                7.0    \n",
       "3647580                                                6.0    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "3647576                                               -7.0   \n",
       "3647577                                               -6.0   \n",
       "3647578                                                1.0   \n",
       "3647579                                               -1.0   \n",
       "3647580                                               -2.0   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "3647576                               -35.0   \n",
       "3647577                               -28.0   \n",
       "3647578                                24.0   \n",
       "3647579                                20.0   \n",
       "3647580                                 8.0   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "3647576                                          -12.0   \n",
       "3647577                                           -7.0   \n",
       "3647578                                           -2.0   \n",
       "3647579                                           -3.0   \n",
       "3647580                                           -4.0   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "3647576                                     -4.0   \n",
       "3647577                                     -3.0   \n",
       "3647578                                    -14.0   \n",
       "3647579                                    -14.0   \n",
       "3647580                                    -14.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "3647576                                       2.0  \n",
       "3647577                                       1.0  \n",
       "3647578                                       2.0  \n",
       "3647579                                       2.0  \n",
       "3647580                                       3.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing specific rows from a DataFrame.\n",
    "# We are interested in the data about the United Kingdom.\n",
    "\n",
    "mobility_trends_UK = mobility_trends[\n",
    "    mobility_trends[\"country_region\"] == \"United Kingdom\"\n",
    "]\n",
    "\n",
    "mobility_trends_UK.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "id": "GKfHOp_JjRuG",
    "outputId": "c7f54b53-a307-44cf-e8dc-adc935ad95a6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3174838</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJa76xwh5ymkcRW-WRjmtd6HU</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3174839</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJa76xwh5ymkcRW-WRjmtd6HU</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3174840</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJa76xwh5ymkcRW-WRjmtd6HU</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3174841</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJa76xwh5ymkcRW-WRjmtd6HU</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3174842</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJa76xwh5ymkcRW-WRjmtd6HU</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code country_region sub_region_1 sub_region_2  \\\n",
       "3174838                  DE        Germany          NaN          NaN   \n",
       "3174839                  DE        Germany          NaN          NaN   \n",
       "3174840                  DE        Germany          NaN          NaN   \n",
       "3174841                  DE        Germany          NaN          NaN   \n",
       "3174842                  DE        Germany          NaN          NaN   \n",
       "\n",
       "        metro_area iso_3166_2_code  census_fips_code  \\\n",
       "3174838        NaN             NaN               NaN   \n",
       "3174839        NaN             NaN               NaN   \n",
       "3174840        NaN             NaN               NaN   \n",
       "3174841        NaN             NaN               NaN   \n",
       "3174842        NaN             NaN               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "3174838  ChIJa76xwh5ymkcRW-WRjmtd6HU 2020-02-15   \n",
       "3174839  ChIJa76xwh5ymkcRW-WRjmtd6HU 2020-02-16   \n",
       "3174840  ChIJa76xwh5ymkcRW-WRjmtd6HU 2020-02-17   \n",
       "3174841  ChIJa76xwh5ymkcRW-WRjmtd6HU 2020-02-18   \n",
       "3174842  ChIJa76xwh5ymkcRW-WRjmtd6HU 2020-02-19   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "3174838                                                6.0    \n",
       "3174839                                                7.0    \n",
       "3174840                                                2.0    \n",
       "3174841                                                2.0    \n",
       "3174842                                                3.0    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "3174838                                                1.0   \n",
       "3174839                                               10.0   \n",
       "3174840                                                2.0   \n",
       "3174841                                                2.0   \n",
       "3174842                                                0.0   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "3174838                                45.0   \n",
       "3174839                                 9.0   \n",
       "3174840                                 7.0   \n",
       "3174841                                10.0   \n",
       "3174842                                 6.0   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "3174838                                           10.0   \n",
       "3174839                                            6.0   \n",
       "3174840                                            1.0   \n",
       "3174841                                            1.0   \n",
       "3174842                                           -1.0   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "3174838                                      0.0   \n",
       "3174839                                     -1.0   \n",
       "3174840                                     -2.0   \n",
       "3174841                                     -1.0   \n",
       "3174842                                     -1.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "3174838                                      -1.0  \n",
       "3174839                                       0.0  \n",
       "3174840                                       0.0  \n",
       "3174841                                       1.0  \n",
       "3174842                                       1.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing data about multiple countries using the isin() method.\n",
    "mobility_trends_countries = mobility_trends[\n",
    "    mobility_trends[\"country_region\"].isin(\n",
    "        [\"United Kingdom\", \"Germany\", \"Italy\", \"Sweden\"]\n",
    "    )\n",
    "]\n",
    "\n",
    "mobility_trends_countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kn-__LBLjRuG",
    "outputId": "9b41bd40-839f-43f1-eeae-cfeaa64ef1c3",
    "scrolled": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Aberdeen City', 'Aberdeenshire', 'Angus Council',\n",
       "       'Antrim and Newtownabbey', 'Ards and North Down',\n",
       "       'Argyll and Bute Council', 'Armagh City, Banbridge and Craigavon',\n",
       "       'Bath and North East Somerset', 'Bedford', 'Belfast',\n",
       "       'Blackburn with Darwen', 'Blackpool', 'Blaenau Gwent',\n",
       "       'Borough of Halton', 'Bracknell Forest', 'Bridgend County Borough',\n",
       "       'Brighton and Hove', 'Bristol City', 'Buckinghamshire',\n",
       "       'Caerphilly County Borough', 'Cambridgeshire', 'Cardiff',\n",
       "       'Carmarthenshire', 'Causeway Coast and Glens',\n",
       "       'Central Bedfordshire', 'Ceredigion', 'Cheshire East',\n",
       "       'Cheshire West and Chester', 'Clackmannanshire',\n",
       "       'Conwy Principal Area', 'Cornwall', 'County Durham', 'Cumbria',\n",
       "       'Darlington', 'Denbighshire', 'Derby', 'Derbyshire',\n",
       "       'Derry and Strabane', 'Devon', 'Dorset', 'Dumfries and Galloway',\n",
       "       'Dundee City Council', 'East Ayrshire Council',\n",
       "       'East Dunbartonshire Council', 'East Lothian Council',\n",
       "       'East Renfrewshire Council', 'East Riding of Yorkshire',\n",
       "       'East Sussex', 'Edinburgh', 'Essex', 'Falkirk',\n",
       "       'Fermanagh and Omagh', 'Fife', 'Flintshire', 'Glasgow City',\n",
       "       'Gloucestershire', 'Greater London', 'Greater Manchester',\n",
       "       'Gwynedd', 'Hampshire', 'Hartlepool', 'Herefordshire',\n",
       "       'Hertfordshire', 'Highland Council', 'Inverclyde',\n",
       "       'Isle of Anglesey', 'Isle of Wight', 'Kent', 'Kingston upon Hull',\n",
       "       'Lancashire', 'Leicester', 'Leicestershire', 'Lincolnshire',\n",
       "       'Lisburn and Castlereagh', 'Luton', 'Medway', 'Merseyside',\n",
       "       'Merthyr Tydfil County Borough', 'Mid and East Antrim',\n",
       "       'Mid Ulster', 'Middlesbrough', 'Midlothian', 'Milton Keynes',\n",
       "       'Monmouthshire', 'Moray', 'Na h-Eileanan an Iar',\n",
       "       'Neath Port Talbot Principle Area', 'Newport',\n",
       "       'Newry, Mourne and Down', 'Norfolk', 'North Ayrshire Council',\n",
       "       'North East Lincolnshire', 'North Lanarkshire',\n",
       "       'North Lincolnshire', 'North Somerset', 'North Yorkshire',\n",
       "       'Northamptonshire', 'Northumberland', 'Nottingham',\n",
       "       'Nottinghamshire', 'Orkney', 'Oxfordshire', 'Pembrokeshire',\n",
       "       'Perth and Kinross', 'Peterborough', 'Plymouth', 'Portsmouth',\n",
       "       'Powys', 'Reading', 'Redcar and Cleveland', 'Renfrewshire',\n",
       "       'Rhondda Cynon Taff', 'Rutland', 'Scottish Borders',\n",
       "       'Shetland Islands', 'Shropshire', 'Slough', 'Somerset',\n",
       "       'South Ayrshire Council', 'South Gloucestershire',\n",
       "       'South Lanarkshire', 'South Yorkshire', 'Southampton',\n",
       "       'Southend-on-Sea', 'Staffordshire', 'Stirling', 'Stockton-on-Tees',\n",
       "       'Stoke-on-Trent', 'Suffolk', 'Surrey', 'Swansea', 'Swindon',\n",
       "       'Thurrock', 'Torbay', 'Torfaen Principal Area', 'Tyne and Wear',\n",
       "       'Vale of Glamorgan', 'Warrington', 'Warwickshire',\n",
       "       'West Berkshire', 'West Dunbartonshire Council', 'West Lothian',\n",
       "       'West Midlands', 'West Sussex', 'West Yorkshire', 'Wiltshire',\n",
       "       'Windsor and Maidenhead', 'Wokingham', 'Worcestershire',\n",
       "       'Wrexham Principal Area', 'York'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter by two conditions — country and county — simultenously\n",
    "# First let's see the list of counties in the dataset\n",
    "\n",
    "mobility_trends[\n",
    "    mobility_trends[\"country_region\"] == \"United Kingdom\"\n",
    "].sub_region_1.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "id": "yGO3ZJTJjRuG",
    "outputId": "c0e1a914-3c6f-4f47-8de2-8eba03587a34"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3720375</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Essex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-ESS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJ0w2H_idW2EcReVDuRzjLV0I</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3720376</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Essex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-ESS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJ0w2H_idW2EcReVDuRzjLV0I</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3720377</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Essex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-ESS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJ0w2H_idW2EcReVDuRzjLV0I</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3720378</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Essex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-ESS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJ0w2H_idW2EcReVDuRzjLV0I</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3720379</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Essex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-ESS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJ0w2H_idW2EcReVDuRzjLV0I</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code  country_region sub_region_1 sub_region_2  \\\n",
       "3720375                  GB  United Kingdom        Essex          NaN   \n",
       "3720376                  GB  United Kingdom        Essex          NaN   \n",
       "3720377                  GB  United Kingdom        Essex          NaN   \n",
       "3720378                  GB  United Kingdom        Essex          NaN   \n",
       "3720379                  GB  United Kingdom        Essex          NaN   \n",
       "\n",
       "        metro_area iso_3166_2_code  census_fips_code  \\\n",
       "3720375        NaN          GB-ESS               NaN   \n",
       "3720376        NaN          GB-ESS               NaN   \n",
       "3720377        NaN          GB-ESS               NaN   \n",
       "3720378        NaN          GB-ESS               NaN   \n",
       "3720379        NaN          GB-ESS               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "3720375  ChIJ0w2H_idW2EcReVDuRzjLV0I 2020-02-15   \n",
       "3720376  ChIJ0w2H_idW2EcReVDuRzjLV0I 2020-02-16   \n",
       "3720377  ChIJ0w2H_idW2EcReVDuRzjLV0I 2020-02-17   \n",
       "3720378  ChIJ0w2H_idW2EcReVDuRzjLV0I 2020-02-18   \n",
       "3720379  ChIJ0w2H_idW2EcReVDuRzjLV0I 2020-02-19   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "3720375                                               -9.0    \n",
       "3720376                                              -16.0    \n",
       "3720377                                                9.0    \n",
       "3720378                                               10.0    \n",
       "3720379                                                9.0    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "3720375                                               -7.0   \n",
       "3720376                                              -12.0   \n",
       "3720377                                               -1.0   \n",
       "3720378                                               -4.0   \n",
       "3720379                                               -3.0   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "3720375                               -27.0   \n",
       "3720376                               -45.0   \n",
       "3720377                                23.0   \n",
       "3720378                                15.0   \n",
       "3720379                                10.0   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "3720375                                          -11.0   \n",
       "3720376                                           -9.0   \n",
       "3720377                                           -1.0   \n",
       "3720378                                            0.0   \n",
       "3720379                                            1.0   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "3720375                                     -3.0   \n",
       "3720376                                     -5.0   \n",
       "3720377                                    -16.0   \n",
       "3720378                                    -16.0   \n",
       "3720379                                    -16.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "3720375                                       2.0  \n",
       "3720376                                       2.0  \n",
       "3720377                                       3.0  \n",
       "3720378                                       2.0  \n",
       "3720379                                       2.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access data about UK and Essex\n",
    "mobility_trends_UK_Essex = mobility_trends[\n",
    "    (mobility_trends[\"country_region\"] == \"United Kingdom\")\n",
    "    & (mobility_trends[\"sub_region_1\"] == \"Essex\")\n",
    "]\n",
    "\n",
    "mobility_trends_UK_Essex.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, you could simply type in  \n",
    "```\n",
    "mobility_trends[mobility_trends['sub_region_1']=='Essex']\n",
    "```\n",
    "The command will return the same result as there is no region called 'Essex' outside the UK. However, there are instances in the data where regions in different countries have the same name. When this is the case, not specifying the country would return confusing outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing multiple rows and columns and conditioning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3647576</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647577</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647578</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647579</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647580</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990325</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990326</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990327</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-14</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990328</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-15</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990329</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-16</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342754 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code  country_region sub_region_1 sub_region_2  \\\n",
       "3647576                  GB  United Kingdom          NaN          NaN   \n",
       "3647577                  GB  United Kingdom          NaN          NaN   \n",
       "3647578                  GB  United Kingdom          NaN          NaN   \n",
       "3647579                  GB  United Kingdom          NaN          NaN   \n",
       "3647580                  GB  United Kingdom          NaN          NaN   \n",
       "...                     ...             ...          ...          ...   \n",
       "3990325                  GB  United Kingdom         York          NaN   \n",
       "3990326                  GB  United Kingdom         York          NaN   \n",
       "3990327                  GB  United Kingdom         York          NaN   \n",
       "3990328                  GB  United Kingdom         York          NaN   \n",
       "3990329                  GB  United Kingdom         York          NaN   \n",
       "\n",
       "        metro_area iso_3166_2_code  census_fips_code  \\\n",
       "3647576        NaN             NaN               NaN   \n",
       "3647577        NaN             NaN               NaN   \n",
       "3647578        NaN             NaN               NaN   \n",
       "3647579        NaN             NaN               NaN   \n",
       "3647580        NaN             NaN               NaN   \n",
       "...            ...             ...               ...   \n",
       "3990325        NaN          GB-YOR               NaN   \n",
       "3990326        NaN          GB-YOR               NaN   \n",
       "3990327        NaN          GB-YOR               NaN   \n",
       "3990328        NaN          GB-YOR               NaN   \n",
       "3990329        NaN          GB-YOR               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "3647576  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-15   \n",
       "3647577  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-16   \n",
       "3647578  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-17   \n",
       "3647579  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-18   \n",
       "3647580  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-19   \n",
       "...                              ...        ...   \n",
       "3990325  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-12   \n",
       "3990326  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-13   \n",
       "3990327  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-14   \n",
       "3990328  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-15   \n",
       "3990329  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-16   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "3647576                                              -12.0    \n",
       "3647577                                               -7.0    \n",
       "3647578                                               10.0    \n",
       "3647579                                                7.0    \n",
       "3647580                                                6.0    \n",
       "...                                                    ...    \n",
       "3990325                                               -3.0    \n",
       "3990326                                               -9.0    \n",
       "3990327                                              -15.0    \n",
       "3990328                                               -8.0    \n",
       "3990329                                               -5.0    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "3647576                                               -7.0   \n",
       "3647577                                               -6.0   \n",
       "3647578                                                1.0   \n",
       "3647579                                               -1.0   \n",
       "3647580                                               -2.0   \n",
       "...                                                    ...   \n",
       "3990325                                               10.0   \n",
       "3990326                                                8.0   \n",
       "3990327                                                6.0   \n",
       "3990328                                                9.0   \n",
       "3990329                                                9.0   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "3647576                               -35.0   \n",
       "3647577                               -28.0   \n",
       "3647578                                24.0   \n",
       "3647579                                20.0   \n",
       "3647580                                 8.0   \n",
       "...                                     ...   \n",
       "3990325                                91.0   \n",
       "3990326                                71.0   \n",
       "3990327                                23.0   \n",
       "3990328                                 7.0   \n",
       "3990329                                12.0   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "3647576                                          -12.0   \n",
       "3647577                                           -7.0   \n",
       "3647578                                           -2.0   \n",
       "3647579                                           -3.0   \n",
       "3647580                                           -4.0   \n",
       "...                                                ...   \n",
       "3990325                                          -20.0   \n",
       "3990326                                          -12.0   \n",
       "3990327                                           -7.0   \n",
       "3990328                                          -10.0   \n",
       "3990329                                          -24.0   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "3647576                                     -4.0   \n",
       "3647577                                     -3.0   \n",
       "3647578                                    -14.0   \n",
       "3647579                                    -14.0   \n",
       "3647580                                    -14.0   \n",
       "...                                          ...   \n",
       "3990325                                    -25.0   \n",
       "3990326                                    -26.0   \n",
       "3990327                                     -5.0   \n",
       "3990328                                     -5.0   \n",
       "3990329                                    -30.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "3647576                                       2.0  \n",
       "3647577                                       1.0  \n",
       "3647578                                       2.0  \n",
       "3647579                                       2.0  \n",
       "3647580                                       3.0  \n",
       "...                                           ...  \n",
       "3990325                                       5.0  \n",
       "3990326                                       5.0  \n",
       "3990327                                      -1.0  \n",
       "3990328                                       0.0  \n",
       "3990329                                       6.0  \n",
       "\n",
       "[342754 rows x 15 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends_UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>sub_region_1</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3739259</th>\n",
       "      <td>Gloucestershire</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741716</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3763910</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742538</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3758978</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948491</th>\n",
       "      <td>Tyne and Wear</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3885110</th>\n",
       "      <td>Nottinghamshire</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3772952</th>\n",
       "      <td>Greater Manchester</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930454</th>\n",
       "      <td>Suffolk</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3803289</th>\n",
       "      <td>Kent</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               sub_region_1  \\\n",
       "3739259     Gloucestershire   \n",
       "3741716      Greater London   \n",
       "3763910      Greater London   \n",
       "3742538      Greater London   \n",
       "3758978      Greater London   \n",
       "...                     ...   \n",
       "3948491       Tyne and Wear   \n",
       "3885110     Nottinghamshire   \n",
       "3772952  Greater Manchester   \n",
       "3930454             Suffolk   \n",
       "3803289                Kent   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \n",
       "3739259                                              -16.0   \n",
       "3741716                                              -14.0   \n",
       "3763910                                              -13.0   \n",
       "3742538                                              -12.0   \n",
       "3758978                                              -11.0   \n",
       "...                                                    ...   \n",
       "3948491                                               -1.0   \n",
       "3885110                                               -1.0   \n",
       "3772952                                               -1.0   \n",
       "3930454                                               -1.0   \n",
       "3803289                                               -1.0   \n",
       "\n",
       "[167 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see which UK counties had the lower retail and recreation mobility\n",
    "# the day after Italy went in lockdown on 9 March 2020\n",
    "mobility_trends_UK_10MARCH2020 = mobility_trends_UK.loc[\n",
    "    (mobility_trends_UK[\"date\"] == \"2020-03-10\")\n",
    "    & (mobility_trends_UK[\"retail_and_recreation_percent_change_from_baseline\"] < 0),\n",
    "    [\"sub_region_1\", \"retail_and_recreation_percent_change_from_baseline\"],\n",
    "]\n",
    "\n",
    "# Sort in decreasing order\n",
    "mobility_trends_UK_10MARCH2020.sort_values(\n",
    "    by=\"retail_and_recreation_percent_change_from_baseline\", ascending=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3741730</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742552</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3747484</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3767212</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3756526</th>\n",
       "      <td>Greater London</td>\n",
       "      <td>-83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3969853</th>\n",
       "      <td>West Sussex</td>\n",
       "      <td>-56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3686799</th>\n",
       "      <td>Cumbria</td>\n",
       "      <td>-56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3808235</th>\n",
       "      <td>Kent</td>\n",
       "      <td>-54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3809879</th>\n",
       "      <td>Kent</td>\n",
       "      <td>-54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3842701</th>\n",
       "      <td>Merseyside</td>\n",
       "      <td>-50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sub_region_1  retail_and_recreation_percent_change_from_baseline\n",
       "3741730  Greater London                                              -94.0 \n",
       "3742552  Greater London                                              -92.0 \n",
       "3747484  Greater London                                              -87.0 \n",
       "3767212  Greater London                                              -84.0 \n",
       "3756526  Greater London                                              -83.0 \n",
       "...                 ...                                                ... \n",
       "3969853     West Sussex                                              -56.0 \n",
       "3686799         Cumbria                                              -56.0 \n",
       "3808235            Kent                                              -54.0 \n",
       "3809879            Kent                                              -54.0 \n",
       "3842701      Merseyside                                              -50.0 \n",
       "\n",
       "[417 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# UK counties with the lower retail and recreation\n",
    "# the day after UK went in lockdown on 23 March 2020\n",
    "mobility_trends_UK_24MARCH2020 = mobility_trends_UK.loc[\n",
    "    (mobility_trends_UK[\"date\"] == \"2020-03-24\")\n",
    "    & (mobility_trends_UK[\"retail_and_recreation_percent_change_from_baseline\"] < 0),\n",
    "    [\"sub_region_1\", \"retail_and_recreation_percent_change_from_baseline\"],\n",
    "]\n",
    "\n",
    "# Sort in decreasing order\n",
    "mobility_trends_UK_24MARCH2020.sort_values(\n",
    "    by=\"retail_and_recreation_percent_change_from_baseline\", ascending=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's identify the places in the UK with the highest reduction of workplaces mobility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kM9NB4Xdf8sg",
    "outputId": "80f6ed2e-bba9-4d1d-b87f-084ecfaedede"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3756546"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need to first find the index of the row\n",
    "# with the highest reduction of workplaces mobility\n",
    "mobility_trends_UK_workplaces = mobility_trends_UK[\n",
    "    \"workplaces_percent_change_from_baseline\"\n",
    "].idxmin()\n",
    "\n",
    "mobility_trends_UK_workplaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 131
    },
    "id": "OXX4MLOGf8mT",
    "outputId": "4a66da44-875e-4b12-8523-2accaac80b0e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3756546</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>London Borough of Islington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-ISL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJ5Tvr7V0bdkgRyWXsGMuF8FQ</td>\n",
       "      <td>2020-04-13</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-51.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code  country_region    sub_region_1  \\\n",
       "3756546                  GB  United Kingdom  Greater London   \n",
       "\n",
       "                        sub_region_2 metro_area iso_3166_2_code  \\\n",
       "3756546  London Borough of Islington        NaN          GB-ISL   \n",
       "\n",
       "         census_fips_code                     place_id       date  \\\n",
       "3756546               NaN  ChIJ5Tvr7V0bdkgRyWXsGMuF8FQ 2020-04-13   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "3756546                                              -88.0    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "3756546                                              -51.0   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "3756546                               -28.0   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "3756546                                          -90.0   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "3756546                                    -91.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "3756546                                      40.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the above index to access the row\n",
    "# with the highest reduction of workplaces mobility\n",
    "mobility_trends.iloc[[mobility_trends_UK_workplaces]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rtNow4jWwwRf"
   },
   "source": [
    "In our basic exploratory data analysis, we found that as of 21 April 2021 the largest percentage mobility change in workplaces was in the London Borough of Islington, Greater London, on 13 April 2020 when the workplaces mobility was reduced with 91 per cent compared to a baseline period. This result is only for exploratory purposes. Note that [the same mobility in two different days would result in different percentage changes](https://support.google.com/covid19-mobility/answer/9824897?hl=en&ref_topic=9822927) due to the way in which actual mobility is compared to a baseline in the Google Covid-19 Community Mobility Reports."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Missing Values\n",
    "Many data sets have missing values and the mobility data is not an exception. Handling missing values is a very common problem in data analysis. We will cover two simple ways of handling missing values in data: \n",
    "* Deletion — missing observations are simply dropped from the data set. \n",
    "    * Likewise - removes all data for an observation that has one or more missing values.     \n",
    "    * Pairwise — removes observations with missing values only for the variables used in the current procedure (e.g., model or technique).\n",
    "* Imputation — missing observations are replaced with inferred values. We will cover simple imputation methods in which missing values are replaced with column mean or row mean.\n",
    "\n",
    "For more advanced methods and functionality, including interpolation methods, see the section ['Working with missing data'](https://pandas.pydata.org/pandas-docs/version/0.19.1/missing_data.html) in Pandas documentation.\n",
    "\n",
    "## Detecting missing values\n",
    "In `pandas`, missing values are represented as `NaN`, which stands for Not a Number. To detect missing values, we can use the function `isna()` (other functions, for example `isnull()` can be used to the same effect). The function returns boolean values (False, True) indicating whether each corresponding value in a DataFrame is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <th>9900504</th>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>9900505</th>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9900506 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country_region_code  country_region  sub_region_1  sub_region_2  \\\n",
       "0                      False           False          True          True   \n",
       "1                      False           False          True          True   \n",
       "2                      False           False          True          True   \n",
       "3                      False           False          True          True   \n",
       "4                      False           False          True          True   \n",
       "...                      ...             ...           ...           ...   \n",
       "9900501                False           False         False         False   \n",
       "9900502                False           False         False         False   \n",
       "9900503                False           False         False         False   \n",
       "9900504                False           False         False         False   \n",
       "9900505                False           False         False         False   \n",
       "\n",
       "         metro_area  iso_3166_2_code  census_fips_code  place_id   date  \\\n",
       "0              True             True              True     False  False   \n",
       "1              True             True              True     False  False   \n",
       "2              True             True              True     False  False   \n",
       "3              True             True              True     False  False   \n",
       "4              True             True              True     False  False   \n",
       "...             ...              ...               ...       ...    ...   \n",
       "9900501        True             True              True     False  False   \n",
       "9900502        True             True              True     False  False   \n",
       "9900503        True             True              True     False  False   \n",
       "9900504        True             True              True     False  False   \n",
       "9900505        True             True              True     False  False   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "0                                                    False    \n",
       "1                                                    False    \n",
       "2                                                    False    \n",
       "3                                                    False    \n",
       "4                                                    False    \n",
       "...                                                    ...    \n",
       "9900501                                               True    \n",
       "9900502                                               True    \n",
       "9900503                                               True    \n",
       "9900504                                               True    \n",
       "9900505                                               True    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "0                                                    False   \n",
       "1                                                    False   \n",
       "2                                                    False   \n",
       "3                                                    False   \n",
       "4                                                    False   \n",
       "...                                                    ...   \n",
       "9900501                                               True   \n",
       "9900502                                               True   \n",
       "9900503                                               True   \n",
       "9900504                                               True   \n",
       "9900505                                               True   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "0                                     False   \n",
       "1                                     False   \n",
       "2                                     False   \n",
       "3                                     False   \n",
       "4                                     False   \n",
       "...                                     ...   \n",
       "9900501                                True   \n",
       "9900502                                True   \n",
       "9900503                                True   \n",
       "9900504                                True   \n",
       "9900505                                True   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "0                                                False   \n",
       "1                                                False   \n",
       "2                                                False   \n",
       "3                                                False   \n",
       "4                                                False   \n",
       "...                                                ...   \n",
       "9900501                                           True   \n",
       "9900502                                           True   \n",
       "9900503                                           True   \n",
       "9900504                                           True   \n",
       "9900505                                           True   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "0                                          False   \n",
       "1                                          False   \n",
       "2                                          False   \n",
       "3                                          False   \n",
       "4                                          False   \n",
       "...                                          ...   \n",
       "9900501                                    False   \n",
       "9900502                                    False   \n",
       "9900503                                    False   \n",
       "9900504                                    False   \n",
       "9900505                                    False   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "0                                           False  \n",
       "1                                           False  \n",
       "2                                           False  \n",
       "3                                           False  \n",
       "4                                           False  \n",
       "...                                           ...  \n",
       "9900501                                      True  \n",
       "9900502                                      True  \n",
       "9900503                                      True  \n",
       "9900504                                      True  \n",
       "9900505                                      True  \n",
       "\n",
       "[9900506 rows x 15 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends.isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's determine the number of missing values for each column in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_region_code                                      6183\n",
       "country_region                                              0\n",
       "sub_region_1                                           163754\n",
       "sub_region_2                                          1610543\n",
       "metro_area                                            9847293\n",
       "iso_3166_2_code                                       8173140\n",
       "census_fips_code                                      7793263\n",
       "place_id                                                18182\n",
       "date                                                        0\n",
       "retail_and_recreation_percent_change_from_baseline    3769374\n",
       "grocery_and_pharmacy_percent_change_from_baseline     4048994\n",
       "parks_percent_change_from_baseline                    5217709\n",
       "transit_stations_percent_change_from_baseline         4988015\n",
       "workplaces_percent_change_from_baseline                354374\n",
       "residential_percent_change_from_baseline              3846476\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be useful to quantify the percentage of values that are missing per column. To achieve this, we simply divide the number of missing values in a column by the number of rows in the DataFrame (and multiply the result by 100)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_region_code                                    0.062451\n",
       "country_region                                         0.000000\n",
       "sub_region_1                                           1.653996\n",
       "sub_region_2                                          16.267280\n",
       "metro_area                                            99.462522\n",
       "iso_3166_2_code                                       82.552750\n",
       "census_fips_code                                      78.715805\n",
       "place_id                                               0.183647\n",
       "date                                                   0.000000\n",
       "retail_and_recreation_percent_change_from_baseline    38.072539\n",
       "grocery_and_pharmacy_percent_change_from_baseline     40.896839\n",
       "parks_percent_change_from_baseline                    52.701438\n",
       "transit_stations_percent_change_from_baseline         50.381415\n",
       "workplaces_percent_change_from_baseline                3.579352\n",
       "residential_percent_change_from_baseline              38.851307\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends.isna().sum() / mobility_trends.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now repeat the procedure for the UK mobility data only: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_region_code                                     0.000000\n",
       "country_region                                          0.000000\n",
       "sub_region_1                                            0.239822\n",
       "sub_region_2                                           36.107237\n",
       "metro_area                                            100.000000\n",
       "iso_3166_2_code                                        49.442166\n",
       "census_fips_code                                      100.000000\n",
       "place_id                                                0.000000\n",
       "date                                                    0.000000\n",
       "retail_and_recreation_percent_change_from_baseline      2.760289\n",
       "grocery_and_pharmacy_percent_change_from_baseline       2.684724\n",
       "parks_percent_change_from_baseline                     21.118353\n",
       "transit_stations_percent_change_from_baseline           3.234390\n",
       "workplaces_percent_change_from_baseline                 0.518739\n",
       "residential_percent_change_from_baseline                5.692713\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends_UK.isna().sum() / mobility_trends_UK.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output shows that the missing values in most mobility categories are below 5 per cent except the missing values for residential mobility (8%) and parks mobility (21.7%).\n",
    "\n",
    "## Removing missing values\n",
    "\n",
    "We can remove missing values using the Pandas function [`dropna()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html). The default functionality removes rows that contain at least one missing value. The `dropna()` function provides other capabilities as well. For example, it is possible to remove columns (instead of rows) that contain missing values by specifying `axis = 1` within the brackets. Also, we can specify under what conditions rows or columns are removed: use `how = any` if you would like to drop rows or columns that contain any missing value, which is the default functionality; use `how = all` if you would like to drop rows or columns in which all values are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country_region_code, country_region, sub_region_1, sub_region_2, metro_area, iso_3166_2_code, census_fips_code, place_id, date, retail_and_recreation_percent_change_from_baseline, grocery_and_pharmacy_percent_change_from_baseline, parks_percent_change_from_baseline, transit_stations_percent_change_from_baseline, workplaces_percent_change_from_baseline, residential_percent_change_from_baseline]\n",
       "Index: []"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends_UK.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We did not specify particular columns on which to perform the `dropna()` function. Because we have a column ('metro_area') for which all values are missing, the `dropna()` function removed all observations from the DataFrame. This is analogous to a likewise deletion of missing values.\n",
    "\n",
    "A more informative approach would be to specify the columns we are going to use in our analysis and drop missing values only for this subset of variables. We can drop rows that contain any missing values in the six mobility categories by passing the labels of those categories to the parameter `subset` as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>country_region_code</th>\n",
       "      <th>country_region</th>\n",
       "      <th>sub_region_1</th>\n",
       "      <th>sub_region_2</th>\n",
       "      <th>metro_area</th>\n",
       "      <th>iso_3166_2_code</th>\n",
       "      <th>census_fips_code</th>\n",
       "      <th>place_id</th>\n",
       "      <th>date</th>\n",
       "      <th>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3647576</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647577</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647578</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647579</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647580</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJqZHHQhE7WgIReiWIMkOg-MQ</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3990325</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>3990326</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>3990327</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-14</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990328</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-15</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990329</th>\n",
       "      <td>GB</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB-YOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChIJh-IigLwxeUgRAKFv7Z75DAM</td>\n",
       "      <td>2022-05-16</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>255595 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_region_code  country_region sub_region_1 sub_region_2  \\\n",
       "3647576                  GB  United Kingdom          NaN          NaN   \n",
       "3647577                  GB  United Kingdom          NaN          NaN   \n",
       "3647578                  GB  United Kingdom          NaN          NaN   \n",
       "3647579                  GB  United Kingdom          NaN          NaN   \n",
       "3647580                  GB  United Kingdom          NaN          NaN   \n",
       "...                     ...             ...          ...          ...   \n",
       "3990325                  GB  United Kingdom         York          NaN   \n",
       "3990326                  GB  United Kingdom         York          NaN   \n",
       "3990327                  GB  United Kingdom         York          NaN   \n",
       "3990328                  GB  United Kingdom         York          NaN   \n",
       "3990329                  GB  United Kingdom         York          NaN   \n",
       "\n",
       "        metro_area iso_3166_2_code  census_fips_code  \\\n",
       "3647576        NaN             NaN               NaN   \n",
       "3647577        NaN             NaN               NaN   \n",
       "3647578        NaN             NaN               NaN   \n",
       "3647579        NaN             NaN               NaN   \n",
       "3647580        NaN             NaN               NaN   \n",
       "...            ...             ...               ...   \n",
       "3990325        NaN          GB-YOR               NaN   \n",
       "3990326        NaN          GB-YOR               NaN   \n",
       "3990327        NaN          GB-YOR               NaN   \n",
       "3990328        NaN          GB-YOR               NaN   \n",
       "3990329        NaN          GB-YOR               NaN   \n",
       "\n",
       "                            place_id       date  \\\n",
       "3647576  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-15   \n",
       "3647577  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-16   \n",
       "3647578  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-17   \n",
       "3647579  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-18   \n",
       "3647580  ChIJqZHHQhE7WgIReiWIMkOg-MQ 2020-02-19   \n",
       "...                              ...        ...   \n",
       "3990325  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-12   \n",
       "3990326  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-13   \n",
       "3990327  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-14   \n",
       "3990328  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-15   \n",
       "3990329  ChIJh-IigLwxeUgRAKFv7Z75DAM 2022-05-16   \n",
       "\n",
       "         retail_and_recreation_percent_change_from_baseline  \\\n",
       "3647576                                              -12.0    \n",
       "3647577                                               -7.0    \n",
       "3647578                                               10.0    \n",
       "3647579                                                7.0    \n",
       "3647580                                                6.0    \n",
       "...                                                    ...    \n",
       "3990325                                               -3.0    \n",
       "3990326                                               -9.0    \n",
       "3990327                                              -15.0    \n",
       "3990328                                               -8.0    \n",
       "3990329                                               -5.0    \n",
       "\n",
       "         grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "3647576                                               -7.0   \n",
       "3647577                                               -6.0   \n",
       "3647578                                                1.0   \n",
       "3647579                                               -1.0   \n",
       "3647580                                               -2.0   \n",
       "...                                                    ...   \n",
       "3990325                                               10.0   \n",
       "3990326                                                8.0   \n",
       "3990327                                                6.0   \n",
       "3990328                                                9.0   \n",
       "3990329                                                9.0   \n",
       "\n",
       "         parks_percent_change_from_baseline  \\\n",
       "3647576                               -35.0   \n",
       "3647577                               -28.0   \n",
       "3647578                                24.0   \n",
       "3647579                                20.0   \n",
       "3647580                                 8.0   \n",
       "...                                     ...   \n",
       "3990325                                91.0   \n",
       "3990326                                71.0   \n",
       "3990327                                23.0   \n",
       "3990328                                 7.0   \n",
       "3990329                                12.0   \n",
       "\n",
       "         transit_stations_percent_change_from_baseline  \\\n",
       "3647576                                          -12.0   \n",
       "3647577                                           -7.0   \n",
       "3647578                                           -2.0   \n",
       "3647579                                           -3.0   \n",
       "3647580                                           -4.0   \n",
       "...                                                ...   \n",
       "3990325                                          -20.0   \n",
       "3990326                                          -12.0   \n",
       "3990327                                           -7.0   \n",
       "3990328                                          -10.0   \n",
       "3990329                                          -24.0   \n",
       "\n",
       "         workplaces_percent_change_from_baseline  \\\n",
       "3647576                                     -4.0   \n",
       "3647577                                     -3.0   \n",
       "3647578                                    -14.0   \n",
       "3647579                                    -14.0   \n",
       "3647580                                    -14.0   \n",
       "...                                          ...   \n",
       "3990325                                    -25.0   \n",
       "3990326                                    -26.0   \n",
       "3990327                                     -5.0   \n",
       "3990328                                     -5.0   \n",
       "3990329                                    -30.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "3647576                                       2.0  \n",
       "3647577                                       1.0  \n",
       "3647578                                       2.0  \n",
       "3647579                                       2.0  \n",
       "3647580                                       3.0  \n",
       "...                                           ...  \n",
       "3990325                                       5.0  \n",
       "3990326                                       5.0  \n",
       "3990327                                      -1.0  \n",
       "3990328                                       0.0  \n",
       "3990329                                       6.0  \n",
       "\n",
       "[255595 rows x 15 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends_UK.dropna(subset=mobility_trends_UK.iloc[:, 9:15].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(342754, 15)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends_UK.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing the rows that contain missing values in the six mobility categories/columns, we are left with 137992 rows out of 191064 rows, a reduction of approximately 28%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replacing missing values\n",
    "\n",
    "Let’s replace NaN values with the mean of non-missing values. For example, we can replace missing values in the columns 'workplaces_percent_change_from_baseline' and 'residential_percent_change_from_baseline' by the mean of values in the two columns as determined by the Pandas `mean()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3647576</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647577</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647578</th>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647579</th>\n",
       "      <td>-14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647580</th>\n",
       "      <td>-14.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990325</th>\n",
       "      <td>-25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990326</th>\n",
       "      <td>-26.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990327</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990328</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990329</th>\n",
       "      <td>-30.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342754 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         workplaces_percent_change_from_baseline  \\\n",
       "3647576                                     -4.0   \n",
       "3647577                                     -3.0   \n",
       "3647578                                    -14.0   \n",
       "3647579                                    -14.0   \n",
       "3647580                                    -14.0   \n",
       "...                                          ...   \n",
       "3990325                                    -25.0   \n",
       "3990326                                    -26.0   \n",
       "3990327                                     -5.0   \n",
       "3990328                                     -5.0   \n",
       "3990329                                    -30.0   \n",
       "\n",
       "         residential_percent_change_from_baseline  \n",
       "3647576                                       2.0  \n",
       "3647577                                       1.0  \n",
       "3647578                                       2.0  \n",
       "3647579                                       2.0  \n",
       "3647580                                       3.0  \n",
       "...                                           ...  \n",
       "3990325                                       5.0  \n",
       "3990326                                       5.0  \n",
       "3990327                                      -1.0  \n",
       "3990328                                       0.0  \n",
       "3990329                                       6.0  \n",
       "\n",
       "[342754 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the NaNs in the columns 'workplaces_percent_change_from_baseline'\n",
    "# and 'residential_percent_change_from_baseline'\n",
    "# by the mean of the non-missing values.\n",
    "mobility_trends_UK[\n",
    "    [\n",
    "        \"workplaces_percent_change_from_baseline\",\n",
    "        \"residential_percent_change_from_baseline\",\n",
    "    ]\n",
    "].fillna(\n",
    "    value=mobility_trends_UK[\n",
    "        [\n",
    "            \"workplaces_percent_change_from_baseline\",\n",
    "            \"residential_percent_change_from_baseline\",\n",
    "        ]\n",
    "    ].mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the same number of rows (191064) as the original mobility_trends_UK DataFrame, with each missing value replaced by the mean value for the respective column. More advanced (and practical) approaches of replacing missing values can take into account groupings in the data and replace missing values with the mean value of a group of counties or the mean value of a time period; those operations would require more advanced Pandas functionality such as `groupby()`, which we will use in the next lab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2dtD0m7K7F-v"
   },
   "source": [
    "<img src=\"https://covid19-static.cdn-apple.com/applications/covid19-mobility/current/static/og_image_v1.png\" width=\"200\" height=\"100\" align=\"right\"/>\n",
    "\n",
    "# Compare mobility data formats\n",
    "\n",
    "A related aggregate, privacy-preserving, and freely available mobility data set is the Apple's [Mobility Trends Reports](https://covid19.apple.com/mobility). The data is updated daily and reflect in aggregate requests for directions in Apple Maps for select countries/regions, sub-regions and cities.  \n",
    "\n",
    "The Mobility Trends Reports data can be downloaded from the website https://covid19.apple.com/mobility. However, because the data file is not at a stable location (URL), we cannot load the data from the website but instead access the data from a [COVID-19 mobility data aggregator on GitHub](https://github.com/ActiveConclusion/COVID19_mobility)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "IOGd24x-yt9Q",
    "outputId": "35f3ea17-589f-4c44-cb01-e0ccbc3bdc6a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>geo_type</th>\n",
       "      <th>region</th>\n",
       "      <th>transportation_type</th>\n",
       "      <th>alternative_name</th>\n",
       "      <th>sub-region</th>\n",
       "      <th>country</th>\n",
       "      <th>2020-01-13</th>\n",
       "      <th>2020-01-14</th>\n",
       "      <th>2020-01-15</th>\n",
       "      <th>2020-01-16</th>\n",
       "      <th>...</th>\n",
       "      <th>2022-04-03</th>\n",
       "      <th>2022-04-04</th>\n",
       "      <th>2022-04-05</th>\n",
       "      <th>2022-04-06</th>\n",
       "      <th>2022-04-07</th>\n",
       "      <th>2022-04-08</th>\n",
       "      <th>2022-04-09</th>\n",
       "      <th>2022-04-10</th>\n",
       "      <th>2022-04-11</th>\n",
       "      <th>2022-04-12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>country/region</td>\n",
       "      <td>Albania</td>\n",
       "      <td>driving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.30</td>\n",
       "      <td>101.43</td>\n",
       "      <td>97.20</td>\n",
       "      <td>...</td>\n",
       "      <td>149.58</td>\n",
       "      <td>156.76</td>\n",
       "      <td>156.76</td>\n",
       "      <td>151.02</td>\n",
       "      <td>157.86</td>\n",
       "      <td>185.12</td>\n",
       "      <td>196.62</td>\n",
       "      <td>173.11</td>\n",
       "      <td>173.21</td>\n",
       "      <td>171.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>country/region</td>\n",
       "      <td>Albania</td>\n",
       "      <td>walking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.68</td>\n",
       "      <td>98.93</td>\n",
       "      <td>98.46</td>\n",
       "      <td>...</td>\n",
       "      <td>161.71</td>\n",
       "      <td>215.31</td>\n",
       "      <td>221.62</td>\n",
       "      <td>211.34</td>\n",
       "      <td>189.25</td>\n",
       "      <td>244.22</td>\n",
       "      <td>231.90</td>\n",
       "      <td>154.93</td>\n",
       "      <td>223.97</td>\n",
       "      <td>223.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>country/region</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>driving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.07</td>\n",
       "      <td>102.45</td>\n",
       "      <td>111.21</td>\n",
       "      <td>...</td>\n",
       "      <td>93.32</td>\n",
       "      <td>97.74</td>\n",
       "      <td>102.63</td>\n",
       "      <td>106.26</td>\n",
       "      <td>111.81</td>\n",
       "      <td>145.45</td>\n",
       "      <td>158.56</td>\n",
       "      <td>99.20</td>\n",
       "      <td>100.82</td>\n",
       "      <td>114.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>country/region</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>walking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.11</td>\n",
       "      <td>101.37</td>\n",
       "      <td>112.67</td>\n",
       "      <td>...</td>\n",
       "      <td>88.58</td>\n",
       "      <td>126.43</td>\n",
       "      <td>133.06</td>\n",
       "      <td>135.64</td>\n",
       "      <td>142.40</td>\n",
       "      <td>178.26</td>\n",
       "      <td>172.83</td>\n",
       "      <td>98.96</td>\n",
       "      <td>126.57</td>\n",
       "      <td>139.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>country/region</td>\n",
       "      <td>Australia</td>\n",
       "      <td>driving</td>\n",
       "      <td>AU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.98</td>\n",
       "      <td>104.21</td>\n",
       "      <td>108.63</td>\n",
       "      <td>...</td>\n",
       "      <td>123.95</td>\n",
       "      <td>116.32</td>\n",
       "      <td>132.29</td>\n",
       "      <td>134.97</td>\n",
       "      <td>145.09</td>\n",
       "      <td>146.34</td>\n",
       "      <td>125.79</td>\n",
       "      <td>136.60</td>\n",
       "      <td>139.01</td>\n",
       "      <td>146.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 827 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         geo_type     region transportation_type alternative_name sub-region  \\\n",
       "0  country/region    Albania             driving              NaN        NaN   \n",
       "1  country/region    Albania             walking              NaN        NaN   \n",
       "2  country/region  Argentina             driving              NaN        NaN   \n",
       "3  country/region  Argentina             walking              NaN        NaN   \n",
       "4  country/region  Australia             driving               AU        NaN   \n",
       "\n",
       "  country  2020-01-13  2020-01-14  2020-01-15  2020-01-16  ...  2022-04-03  \\\n",
       "0     NaN       100.0       95.30      101.43       97.20  ...      149.58   \n",
       "1     NaN       100.0      100.68       98.93       98.46  ...      161.71   \n",
       "2     NaN       100.0       97.07      102.45      111.21  ...       93.32   \n",
       "3     NaN       100.0       95.11      101.37      112.67  ...       88.58   \n",
       "4     NaN       100.0      102.98      104.21      108.63  ...      123.95   \n",
       "\n",
       "   2022-04-04  2022-04-05  2022-04-06  2022-04-07  2022-04-08  2022-04-09  \\\n",
       "0      156.76      156.76      151.02      157.86      185.12      196.62   \n",
       "1      215.31      221.62      211.34      189.25      244.22      231.90   \n",
       "2       97.74      102.63      106.26      111.81      145.45      158.56   \n",
       "3      126.43      133.06      135.64      142.40      178.26      172.83   \n",
       "4      116.32      132.29      134.97      145.09      146.34      125.79   \n",
       "\n",
       "   2022-04-10  2022-04-11  2022-04-12  \n",
       "0      173.11      173.21      171.09  \n",
       "1      154.93      223.97      223.45  \n",
       "2       99.20      100.82      114.56  \n",
       "3       98.96      126.57      139.29  \n",
       "4      136.60      139.01      146.75  \n",
       "\n",
       "[5 rows x 827 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_trends_covid_apple = pd.read_csv(\n",
    "    \"https://raw.githubusercontent.com/ActiveConclusion/COVID19_mobility/master/apple_reports/applemobilitytrends.csv\"\n",
    ")\n",
    "mobility_trends_covid_apple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Activity: Discussing data formats\n",
    "Compare the format of the data in the _Apple Mobility Trends Reports_ to the format of the data in the _Google COVID-19 Community Mobility Reports_ and in _Our World in Data COVID-19 data_. Focus in particular on similarities and differences in how time series data is represented across data sets.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recording dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas: 1.1.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Install the watermark extension\n",
    "!pip install -q watermark\n",
    "\n",
    "# Load the watermark extension\n",
    "%load_ext watermark\n",
    "\n",
    "# Show packages that were imported\n",
    "%watermark --iversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save a list of the packages (and their versions) used in the current notebook to a file named `requirements.txt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pipreqs  --savepath ../notebooks/requirements.txt .//__temp_pipreqsnb_folder/\n",
      "INFO: Successfully saved requirements file in ../notebooks/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "# Run pipreqsnb after specifying the path to the notebook\n",
    "!pipreqsnb ../notebooks/04_data_design_and_data_wrangling.ipynb"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "data_design_wrangling.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}