notebooks/notebook-2-01-example-better-code-population-eda.ipynb

{
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   "cell_type": "markdown",
   "id": "f10f1dea",
   "metadata": {},
   "source": [
    "<style>\n",
    "*\n",
    "{\n",
    "\ttext-align: justify;\n",
    "\tline-height: 1.5;\n",
    "\tfont-family: \"Arial\", sans-serif;\n",
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    "h2, h3, h4, h5, h6\n",
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  {
   "cell_type": "markdown",
   "id": "bc595d50",
   "metadata": {},
   "source": [
    "## EDA of the population (1950 - 2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5bb8dc",
   "metadata": {},
   "source": [
    "<!--\n",
    "import data_analytics.github as github\n",
    "print(github.create_jupyter_notebook_header(\"markcrowe-com\", \"data-analytics-project-template\", \"notebooks/notebook-2-01-example-better-code-population-eda.ipynb\", \"master\"))\n",
    "-->\n",
    "<table style=\"margin: auto;\"><tr><td><a href=\"https://mybinder.org/v2/gh/markcrowe-com/data-analytics-project-template/master?filepath=notebooks/notebook-2-01-example-better-code-population-eda.ipynb\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Open In Binder\"/></a></td><td>online editors</td><td><a href=\"https://colab.research.google.com/github/markcrowe-com/data-analytics-project-template/blob/master/notebooks/notebook-2-01-example-better-code-population-eda.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "835bf4a6",
   "metadata": {},
   "source": [
    "### Objective"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38eee430",
   "metadata": {},
   "source": [
    "The objective is to provide an Exploratory Data Analysis (EDA) of the `2021-12Dec-11-population-estimates-1950-2021-pea01.csv` file provided by the <a href=\"https://data.cso.ie/table/PEA01\" target=\"_new\">CSO: PEA01 Table</a>. The EDA is performed to investigate and clean the data, to spot anomalies.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f098a21",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1caa871c",
   "metadata": {},
   "source": [
    "Import required third party Python libraries, import supporting functions and sets up data source file paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b6683cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local\n",
    "#%pip install -r notebook-2-01-example-better-code-population-eda/requirements.txt --quiet\n",
    "# Remote option\n",
    "#%pip install -r https://github.com/markcrowe-com/data-analytics-project-template/blob/master/notebooks/notebook-2-01-example-better-code-population-eda/requirements.txt --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56370af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from population_planning.dataframe_labels import *\n",
    "from population_planning.project_manager import ProjectArtifactManager, ProjectAssetManager\n",
    "import data_analytics.github as github\n",
    "import data_analytics.exploratory_data_analysis_reports as eda_reports\n",
    "import os\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d846147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>Data Sources</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p>Data Sources available at</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://github.com/markcrowe-com/population-planning-data-analytics/blob/master/assets/2021-12Dec-11-population-estimates-1950-2021-pea01.csv?raw=true\n"
     ]
    }
   ],
   "source": [
    "artifact_manager: ProjectArtifactManager = ProjectArtifactManager()\n",
    "asset_manager: ProjectAssetManager = ProjectAssetManager()\n",
    "artifact_manager.is_remote = asset_manager.is_remote = True\n",
    "github.display_jupyter_notebook_data_sources(\n",
    "    [asset_manager.get_population_estimates_filepath()])\n",
    "artifact_manager.is_remote = asset_manager.is_remote = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379cf5ae",
   "metadata": {},
   "source": [
    "### Working with population estimates CSV file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123516fd",
   "metadata": {},
   "source": [
    "#### Create Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2385c72",
   "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>Statistic</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Statistic, Year, Age Group, Sex, UNIT, VALUE]\n",
       "Index: []"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath: str = asset_manager.get_population_estimates_filepath()\n",
    "population_dataframe = pandas.read_csv(filepath)\n",
    "population_dataframe.head(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b999db",
   "metadata": {},
   "source": [
    "#### Renaming Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0535d40",
   "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>Statistic</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Statistic, Year, Age, Sex, UNIT, Population]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename the columns\n",
    "old_to_new_column_names_dictionary = {\"Age Group\": AGE, \"VALUE\": POPULATION}\n",
    "population_dataframe = population_dataframe.rename(\n",
    "    columns=old_to_new_column_names_dictionary)\n",
    "population_dataframe.head(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d8fe71",
   "metadata": {},
   "source": [
    "### Data Type Analysis Quick View"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c59e56",
   "metadata": {},
   "source": [
    "Print an analysis report of each dataset.  \n",
    "- Show the top five rows of the data frame as a quick sample.\n",
    "- Show the data types of each column.\n",
    "- Report the count of any duplicate rows.\n",
    "- Report the counts of any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b24f3ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>2021-12Dec-11-population-estimates-1950-2021-pea01.csv</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p>Row, Column Count: (5832, 6)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h5>Sample: Top five rows</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Statistic</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Population Estimates (Persons in April)</td>\n",
       "      <td>1950</td>\n",
       "      <td>Under 1 year</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>Thousand</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population Estimates (Persons in April)</td>\n",
       "      <td>1950</td>\n",
       "      <td>Under 1 year</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thousand</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Population Estimates (Persons in April)</td>\n",
       "      <td>1950</td>\n",
       "      <td>Under 1 year</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thousand</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Population Estimates (Persons in April)</td>\n",
       "      <td>1950</td>\n",
       "      <td>0 - 4 years</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>Thousand</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Population Estimates (Persons in April)</td>\n",
       "      <td>1950</td>\n",
       "      <td>0 - 4 years</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thousand</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Statistic  Year           Age         Sex  \\\n",
       "0  Population Estimates (Persons in April)  1950  Under 1 year  Both sexes   \n",
       "1  Population Estimates (Persons in April)  1950  Under 1 year        Male   \n",
       "2  Population Estimates (Persons in April)  1950  Under 1 year      Female   \n",
       "3  Population Estimates (Persons in April)  1950   0 - 4 years  Both sexes   \n",
       "4  Population Estimates (Persons in April)  1950   0 - 4 years        Male   \n",
       "\n",
       "       UNIT  Population  \n",
       "0  Thousand        61.1  \n",
       "1  Thousand        31.4  \n",
       "2  Thousand        29.7  \n",
       "3  Thousand         NaN  \n",
       "4  Thousand         NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h5>Data Types</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Statistic      object\n",
       "Year            int64\n",
       "Age            object\n",
       "Sex            object\n",
       "UNIT           object\n",
       "Population    float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h5>Duplicates</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p>Number of duplicate rows: 0</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h5>Null Values</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistic       0\n",
      "Year            0\n",
      "Age             0\n",
      "Sex             0\n",
      "UNIT            0\n",
      "Population    114\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h5>Single Value Columns</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ul><li>The column `Statistic` has only one value. Recommend removing.</li><li>The column `UNIT` has only one value. Recommend removing.</li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h5>Columns: Unique value counts</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ul><li>The column \"Statistic\" has 1 unique values.</li><li>The column \"Year\" has 72 unique values.</li><li>The column \"Age\" has 27 unique values.</li><li>The column \"Sex\" has 3 unique values.</li><li>The column \"UNIT\" has 1 unique values.</li><li>The column \"Population\" has 3305 unique values.</li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h5>Outlier Analysis</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ul><li>The column \"Population\" has 635 outliers. Recommend removing outliers.</li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = os.path.basename(filepath)\n",
    "eda_reports.print_dataframe_analysis_report(population_dataframe, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9062b1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "population_dataframe = population_dataframe.drop([\"Statistic\"], axis=\"columns\")\n",
    "population_dataframe = population_dataframe.drop([\"UNIT\"], axis=\"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5646f3a6",
   "metadata": {},
   "source": [
    "### Remove duplicate Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "517239c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year          0\n",
      "Age           0\n",
      "Sex           0\n",
      "Population    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "duplicate_age_range_labels = [\n",
    "    \"0 - 4 years\", \"0 - 14 years\", \"15 - 24 years\", \"15 years and over\",\n",
    "    \"25 - 44 years\", \"45 - 64 years\", \"65 years and over\", \"All ages\"\n",
    "]\n",
    "\n",
    "population_dataframe = population_dataframe.drop(population_dataframe[(\n",
    "    population_dataframe[AGE].isin(duplicate_age_range_labels))].index)\n",
    "print(population_dataframe.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658aa4bf",
   "metadata": {},
   "source": [
    "'Both sexes' - is not needed for the population pyramids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b1386e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year          0\n",
       "Age           0\n",
       "Sex           0\n",
       "Population    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dataframe = population_dataframe.drop(\n",
    "    population_dataframe[(population_dataframe[SEX] == \"Both sexes\")].index)\n",
    "population_dataframe.where(population_dataframe[SEX] == \"Both sexes\").count(\n",
    "    axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47bd7903",
   "metadata": {},
   "source": [
    "### Rename age brackets for chart labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32bb1862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Under 1 year', '1 - 4 years', '5 - 9 years', '10 - 14 years',\n",
       "       '15 - 19 years', '20 - 24 years', '25 - 29 years', '30 - 34 years',\n",
       "       '35 - 39 years', '40 - 44 years', '45 - 49 years', '50 - 54 years',\n",
       "       '55 - 59 years', '60 - 64 years', '65 - 69 years', '70 - 74 years',\n",
       "       '75 - 79 years', '80 - 84 years', '85 years and over'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dataframe[AGE].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dad33506",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename_age_bracket(label: str) -> str:\n",
    "    if label == \"Under 1 year\":\n",
    "        return \"00\"\n",
    "    if label == \"85 years and over\":\n",
    "        return \"85+\"\n",
    "    label = label.replace(\" years\", \"\")\n",
    "    if label == \"1 - 4\" or label == \"5 - 9\":\n",
    "        return \"0\" + label.replace(\" - \", \"-0\")\n",
    "    return label.replace(\" \", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f397921",
   "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>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>00</td>\n",
       "      <td>Male</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950</td>\n",
       "      <td>00</td>\n",
       "      <td>Female</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1950</td>\n",
       "      <td>01-04</td>\n",
       "      <td>Male</td>\n",
       "      <td>127.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1950</td>\n",
       "      <td>01-04</td>\n",
       "      <td>Female</td>\n",
       "      <td>121.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1950</td>\n",
       "      <td>05-09</td>\n",
       "      <td>Male</td>\n",
       "      <td>142.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year    Age     Sex  Population\n",
       "1   1950     00    Male        31.4\n",
       "2   1950     00  Female        29.7\n",
       "10  1950  01-04    Male       127.5\n",
       "11  1950  01-04  Female       121.6\n",
       "13  1950  05-09    Male       142.3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for value in population_dataframe[AGE].unique():\n",
    "    population_dataframe[AGE] = population_dataframe[AGE].replace(\n",
    "        value, rename_age_bracket(value))\n",
    "\n",
    "population_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74be102e",
   "metadata": {},
   "source": [
    "### Save Artifact\n",
    "Saving the output of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a86813a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "population_dataframe.to_csv(artifact_manager.get_population_eda_filepath(),\n",
    "                            index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ddde460",
   "metadata": {},
   "source": [
    "Author &copy; 2021 <a href=\"https://github.com/markcrowe-com\" target=\"_parent\">Mark Crowe</a>. All rights reserved."
   ]
  }
 ],
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