notebooks/notebook-2-02-example-better-code-population-dv.ipynb

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   "source": [
    "<style>\n",
    "*\n",
    "{\n",
    "\ttext-align: justify;\n",
    "\tline-height: 1.5;\n",
    "\tfont-family: \"Arial\", sans-serif;\n",
    "\tfont-size: 12px;\n",
    "}\n",
    "\n",
    "h2, h3, h4, h5, h6\n",
    "{\n",
    "\tfont-family: \"Arial\", sans-serif;\n",
    "\tfont-size: 12px;\n",
    "\tfont-weight: bold;\n",
    "}\n",
    "h2\n",
    "{\n",
    "\tfont-size: 14px;\n",
    "}\n",
    "h1\n",
    "{\n",
    "\tfont-family: \"Wingdings\", sans-serif;\n",
    "\tfont-size: 16px;\n",
    "}\n",
    "</style>"
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  {
   "cell_type": "markdown",
   "id": "c0c711a3",
   "metadata": {},
   "source": [
    "## Data Visualization of the Population Pyramid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e96ccf3",
   "metadata": {},
   "source": [
    "<!--\n",
    "import data_analytics.github as github\n",
    "print(github.create_jupyter_notebook_header(\"markcrowe-com\", \"data-analytics-project-template\", \"notebooks/notebook-2-02-example-better-code-population-dv.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-2-02-example-better-code-population-dv.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-2-02-example-better-code-population-dv.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": "009dce6f",
   "metadata": {},
   "source": [
    "### Objective"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c3324d",
   "metadata": {},
   "source": [
    "This objective is to display a population pyramid with the `population-1950-2021-eda-output.csv` file.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be1af8b",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d814b80c",
   "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": "129dbf10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-11T09:58:40.322837Z",
     "start_time": "2023-01-11T09:58:40.309841Z"
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   "outputs": [],
   "source": [
    "# Local\n",
    "#%pip install -r notebook-2-02-example-better-code-population-dv/requirements.txt --quiet\n",
    "# Remote option\n",
    "#%pip install -r https://github.com/markcrowe-com/data-analytics-project-template/blob/master/notebooks/notebook-2-02-example-better-code-population-dv/requirements.txt --quiet"
   ]
  },
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   "execution_count": 2,
   "id": "0e51174e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-11T09:58:45.618834Z",
     "start_time": "2023-01-11T09:58:40.324837Z"
    }
   },
   "outputs": [],
   "source": [
    "from altair.vegalite.v4.api import ConcatChart\n",
    "from data_analytics.graphs import display_caption\n",
    "from pandas import DataFrame\n",
    "from population_planning.dataframe_labels import *\n",
    "from population_planning.project_manager import ProjectArtifactManager\n",
    "import altair\n",
    "import data_analytics.github as github\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fad25d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-11T09:58:45.649837Z",
     "start_time": "2023-01-11T09:58:45.622838Z"
    }
   },
   "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/artifacts/population-1950-2021-eda-output.csv?raw=true\n"
     ]
    }
   ],
   "source": [
    "artifact_manager = ProjectArtifactManager()\n",
    "artifact_manager.is_remote = True\n",
    "github.display_jupyter_notebook_data_sources(\n",
    "    [artifact_manager.get_population_eda_filepath()])\n",
    "artifact_manager.is_remote = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d17bc6f",
   "metadata": {},
   "source": [
    "### Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e0c4a39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-11T09:58:45.680838Z",
     "start_time": "2023-01-11T09:58:45.655839Z"
    }
   },
   "outputs": [],
   "source": [
    "def build_interactive_population_pyramid_chart(\n",
    "        population_dataframe: DataFrame,\n",
    "        age_field: str = \"Age\",\n",
    "        population_field: str = \"Population\",\n",
    "        sex_field: str = \"Sex\",\n",
    "        time_field: str = \"Year\",\n",
    "        male_value: str = \"Male\",\n",
    "        female_value: str = \"Female\",\n",
    "        male_color: str = \"darkblue\",\n",
    "        female_color: str = \"darkGreen\",\n",
    "        year_min: int = 1950,\n",
    "        year_max: int = 2021,\n",
    "        year_init: int = 2020) -> ConcatChart:\n",
    "    \"\"\"\n",
    "    Build a interactive population pyramid chart.\n",
    "    :param population_dataframe: The population dataframe to display.\n",
    "    :param age_field: The age field label.\n",
    "    :param population_field: The population field label.\n",
    "    :param sex_field: The sex field label.\n",
    "    :param time_field: The time field label.\n",
    "    :param male_value: The male value defined in the sex field.\n",
    "    :param female_value: The female value defined in the sex field.\n",
    "    :param male_color: The male color.\n",
    "    :param female_color: The female color.\n",
    "    :param year_min: The min year.\n",
    "    :param year_max: The max year.\n",
    "    :param year_init: The initial year to start the chart with.\n",
    "    :return: The interactive population pyramid Chart.\n",
    "    \"\"\"\n",
    "\n",
    "    year_bind_range = altair.binding_range(min=year_min,\n",
    "                                           max=year_max,\n",
    "                                           step=1,\n",
    "                                           name=\"Select Year\")\n",
    "    year_selection = altair.selection_single(bind=year_bind_range,\n",
    "                                             fields=[time_field],\n",
    "                                             init={time_field: year_init},\n",
    "                                             name=\"Irish Population\")\n",
    "    trunk = altair.Chart(population_dataframe, title=\"Age\").add_selection(\n",
    "        year_selection).transform_filter(year_selection).transform_calculate(\n",
    "            Sex=altair.datum[sex_field]).properties(width=300)\n",
    "\n",
    "    tree_color_scale = altair.Scale(domain=[male_value, female_value],\n",
    "                                    range=[male_color, female_color])\n",
    "\n",
    "    color = altair.Color(f\"{sex_field}:N\", scale=tree_color_scale)\n",
    "    scale = altair.Scale(domain=[0, 300.0])\n",
    "    sort_order = altair.SortOrder(\"descending\")\n",
    "\n",
    "    x_male = altair.X(f\"sum({population_field}):Q\",\n",
    "                      scale=scale,\n",
    "                      sort=sort_order,\n",
    "                      title=\"Male Population (1000)\")\n",
    "    x_female = altair.X(f\"sum({population_field}):Q\",\n",
    "                        scale=scale,\n",
    "                        title=\"Female Population (1000)\")\n",
    "    y = altair.Y(f\"{age_field}:O\", axis=None, sort=sort_order)\n",
    "\n",
    "    def sex_tree_lambda(value, title, x_axis):\n",
    "        return trunk.transform_filter(altair.datum[sex_field] == value).encode(\n",
    "            color=color, x=x_axis, y=y).mark_bar().properties(title=title)\n",
    "\n",
    "    female_tree = sex_tree_lambda(\"Female\", \"female\", x_female)\n",
    "    male_tree = sex_tree_lambda(\"Male\", \"male\", x_male)\n",
    "\n",
    "    y_trunk = trunk.encode(text=altair.Text(f\"{age_field}:O\"),\n",
    "                           y=y).mark_text().properties(width=20)\n",
    "\n",
    "    return altair.concat(male_tree, y_trunk, female_tree, spacing=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abd60a6",
   "metadata": {},
   "source": [
    "Create Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c063a28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-11T09:58:46.130840Z",
     "start_time": "2023-01-11T09:58:45.682839Z"
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   },
   "outputs": [
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       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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\"05-09\", \"Sex\": \"Male\", \"Population\": 142.3}, {\"Year\": 1950, \"Age\": \"05-09\", \"Sex\": \"Female\", \"Population\": 136.6}, {\"Year\": 1950, \"Age\": \"10-14\", \"Sex\": \"Male\", \"Population\": 133.4}, {\"Year\": 1950, \"Age\": \"10-14\", \"Sex\": \"Female\", \"Population\": 128.7}, {\"Year\": 1950, \"Age\": \"15-19\", \"Sex\": \"Male\", \"Population\": 126.6}, {\"Year\": 1950, \"Age\": \"15-19\", \"Sex\": \"Female\", \"Population\": 117.4}, {\"Year\": 1950, \"Age\": \"20-24\", \"Sex\": \"Male\", \"Population\": 108.3}, {\"Year\": 1950, \"Age\": \"20-24\", \"Sex\": \"Female\", \"Population\": 100.3}, {\"Year\": 1950, \"Age\": \"25-29\", \"Sex\": \"Male\", \"Population\": 100.9}, {\"Year\": 1950, \"Age\": \"25-29\", \"Sex\": \"Female\", \"Population\": 100.2}, {\"Year\": 1950, \"Age\": \"30-34\", \"Sex\": \"Male\", \"Population\": 98.3}, {\"Year\": 1950, \"Age\": \"30-34\", \"Sex\": \"Female\", \"Population\": 96.5}, {\"Year\": 1950, \"Age\": \"35-39\", \"Sex\": \"Male\", 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\"Population\": 121.7}, {\"Year\": 1951, \"Age\": \"05-09\", \"Sex\": \"Male\", \"Population\": 143.5}, {\"Year\": 1951, \"Age\": \"05-09\", \"Sex\": \"Female\", \"Population\": 137.6}, {\"Year\": 1951, \"Age\": \"10-14\", \"Sex\": \"Male\", \"Population\": 132.7}, {\"Year\": 1951, \"Age\": \"10-14\", \"Sex\": \"Female\", \"Population\": 128.2}, {\"Year\": 1951, \"Age\": \"15-19\", \"Sex\": \"Male\", \"Population\": 125.7}, {\"Year\": 1951, \"Age\": \"15-19\", \"Sex\": \"Female\", \"Population\": 115.4}, {\"Year\": 1951, \"Age\": \"20-24\", \"Sex\": \"Male\", \"Population\": 105.4}, {\"Year\": 1951, \"Age\": \"20-24\", \"Sex\": \"Female\", \"Population\": 96.8}, {\"Year\": 1951, \"Age\": \"25-29\", \"Sex\": \"Male\", \"Population\": 99.6}, {\"Year\": 1951, \"Age\": \"25-29\", \"Sex\": \"Female\", \"Population\": 98.8}, {\"Year\": 1951, \"Age\": \"30-34\", \"Sex\": \"Male\", \"Population\": 96.4}, {\"Year\": 1951, \"Age\": \"30-34\", \"Sex\": \"Female\", \"Population\": 95.1}, 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\"Population\": 165.5}, {\"Year\": 2017, \"Age\": \"10-14\", \"Sex\": \"Female\", \"Population\": 157.8}, {\"Year\": 2017, \"Age\": \"15-19\", \"Sex\": \"Male\", \"Population\": 157.6}, {\"Year\": 2017, \"Age\": \"15-19\", \"Sex\": \"Female\", \"Population\": 151.0}, {\"Year\": 2017, \"Age\": \"20-24\", \"Sex\": \"Male\", \"Population\": 140.4}, {\"Year\": 2017, \"Age\": \"20-24\", \"Sex\": \"Female\", \"Population\": 135.9}, {\"Year\": 2017, \"Age\": \"25-29\", \"Sex\": \"Male\", \"Population\": 144.3}, {\"Year\": 2017, \"Age\": \"25-29\", \"Sex\": \"Female\", \"Population\": 148.2}, {\"Year\": 2017, \"Age\": \"30-34\", \"Sex\": \"Male\", \"Population\": 166.3}, {\"Year\": 2017, \"Age\": \"30-34\", \"Sex\": \"Female\", \"Population\": 181.6}, {\"Year\": 2017, \"Age\": \"35-39\", \"Sex\": \"Male\", \"Population\": 192.7}, {\"Year\": 2017, \"Age\": \"35-39\", \"Sex\": \"Female\", \"Population\": 201.9}, {\"Year\": 2017, \"Age\": \"40-44\", \"Sex\": \"Male\", \"Population\": 180.1}, {\"Year\": 2017, \"Age\": \"40-44\", \"Sex\": \"Female\", \"Population\": 183.0}, {\"Year\": 2017, \"Age\": \"45-49\", \"Sex\": \"Male\", \"Population\": 166.2}, {\"Year\": 2017, \"Age\": \"45-49\", \"Sex\": \"Female\", \"Population\": 166.9}, {\"Year\": 2017, \"Age\": \"50-54\", \"Sex\": \"Male\", \"Population\": 150.1}, {\"Year\": 2017, \"Age\": \"50-54\", \"Sex\": \"Female\", \"Population\": 153.2}, {\"Year\": 2017, \"Age\": \"55-59\", \"Sex\": \"Male\", \"Population\": 135.9}, {\"Year\": 2017, \"Age\": \"55-59\", \"Sex\": \"Female\", \"Population\": 138.2}, {\"Year\": 2017, \"Age\": \"60-64\", \"Sex\": \"Male\", \"Population\": 120.5}, {\"Year\": 2017, \"Age\": \"60-64\", \"Sex\": \"Female\", \"Population\": 121.8}, {\"Year\": 2017, \"Age\": \"65-69\", \"Sex\": \"Male\", \"Population\": 104.8}, {\"Year\": 2017, \"Age\": \"65-69\", \"Sex\": \"Female\", \"Population\": 106.4}, {\"Year\": 2017, \"Age\": \"70-74\", \"Sex\": \"Male\", \"Population\": 82.9}, {\"Year\": 2017, \"Age\": \"70-74\", \"Sex\": \"Female\", \"Population\": 86.3}, {\"Year\": 2017, \"Age\": \"75-79\", \"Sex\": \"Male\", \"Population\": 55.1}, {\"Year\": 2017, \"Age\": \"75-79\", \"Sex\": \"Female\", \"Population\": 61.7}, {\"Year\": 2017, \"Age\": \"80-84\", \"Sex\": \"Male\", \"Population\": 36.2}, {\"Year\": 2017, \"Age\": \"80-84\", \"Sex\": \"Female\", \"Population\": 46.6}, {\"Year\": 2017, \"Age\": \"85+\", \"Sex\": \"Male\", \"Population\": 24.3}, {\"Year\": 2017, \"Age\": \"85+\", \"Sex\": \"Female\", \"Population\": 45.3}, {\"Year\": 2018, \"Age\": \"00\", \"Sex\": \"Male\", \"Population\": 31.3}, {\"Year\": 2018, \"Age\": \"00\", \"Sex\": \"Female\", \"Population\": 30.3}, {\"Year\": 2018, \"Age\": \"01-04\", \"Sex\": \"Male\", \"Population\": 132.0}, {\"Year\": 2018, \"Age\": \"01-04\", \"Sex\": \"Female\", \"Population\": 125.8}, {\"Year\": 2018, \"Age\": \"05-09\", \"Sex\": \"Male\", \"Population\": 182.1}, {\"Year\": 2018, \"Age\": \"05-09\", \"Sex\": \"Female\", \"Population\": 174.7}, {\"Year\": 2018, \"Age\": \"10-14\", \"Sex\": \"Male\", \"Population\": 170.5}, {\"Year\": 2018, \"Age\": \"10-14\", \"Sex\": \"Female\", \"Population\": 162.1}, {\"Year\": 2018, \"Age\": \"15-19\", \"Sex\": \"Male\", \"Population\": 161.2}, {\"Year\": 2018, \"Age\": \"15-19\", \"Sex\": \"Female\", \"Population\": 155.0}, {\"Year\": 2018, \"Age\": \"20-24\", \"Sex\": \"Male\", \"Population\": 147.3}, {\"Year\": 2018, \"Age\": \"20-24\", \"Sex\": \"Female\", \"Population\": 142.0}, {\"Year\": 2018, \"Age\": \"25-29\", \"Sex\": \"Male\", \"Population\": 145.2}, {\"Year\": 2018, \"Age\": \"25-29\", \"Sex\": \"Female\", \"Population\": 145.9}, {\"Year\": 2018, \"Age\": \"30-34\", \"Sex\": \"Male\", \"Population\": 161.1}, {\"Year\": 2018, \"Age\": \"30-34\", \"Sex\": \"Female\", \"Population\": 174.8}, {\"Year\": 2018, \"Age\": \"35-39\", \"Sex\": \"Male\", \"Population\": 193.1}, {\"Year\": 2018, \"Age\": \"35-39\", \"Sex\": \"Female\", \"Population\": 205.2}, {\"Year\": 2018, \"Age\": \"40-44\", \"Sex\": \"Male\", \"Population\": 183.2}, {\"Year\": 2018, \"Age\": \"40-44\", \"Sex\": \"Female\", \"Population\": 186.7}, {\"Year\": 2018, \"Age\": \"45-49\", \"Sex\": \"Male\", \"Population\": 170.2}, {\"Year\": 2018, \"Age\": \"45-49\", \"Sex\": \"Female\", \"Population\": 171.0}, {\"Year\": 2018, \"Age\": \"50-54\", \"Sex\": \"Male\", \"Population\": 152.0}, {\"Year\": 2018, \"Age\": \"50-54\", \"Sex\": \"Female\", \"Population\": 154.6}, {\"Year\": 2018, \"Age\": \"55-59\", \"Sex\": \"Male\", \"Population\": 138.6}, {\"Year\": 2018, \"Age\": \"55-59\", \"Sex\": \"Female\", \"Population\": 141.3}, {\"Year\": 2018, \"Age\": \"60-64\", \"Sex\": \"Male\", \"Population\": 122.4}, {\"Year\": 2018, \"Age\": \"60-64\", \"Sex\": \"Female\", \"Population\": 124.1}, {\"Year\": 2018, \"Age\": \"65-69\", \"Sex\": \"Male\", \"Population\": 106.8}, {\"Year\": 2018, \"Age\": \"65-69\", \"Sex\": \"Female\", \"Population\": 108.9}, {\"Year\": 2018, \"Age\": \"70-74\", \"Sex\": \"Male\", \"Population\": 87.4}, {\"Year\": 2018, \"Age\": \"70-74\", \"Sex\": \"Female\", \"Population\": 90.2}, {\"Year\": 2018, \"Age\": \"75-79\", \"Sex\": \"Male\", \"Population\": 57.6}, {\"Year\": 2018, \"Age\": \"75-79\", \"Sex\": \"Female\", \"Population\": 64.2}, {\"Year\": 2018, \"Age\": \"80-84\", \"Sex\": \"Male\", \"Population\": 37.5}, {\"Year\": 2018, \"Age\": \"80-84\", \"Sex\": \"Female\", \"Population\": 47.6}, {\"Year\": 2018, \"Age\": \"85+\", \"Sex\": \"Male\", \"Population\": 26.2}, {\"Year\": 2018, \"Age\": \"85+\", \"Sex\": \"Female\", \"Population\": 46.8}, {\"Year\": 2019, \"Age\": \"00\", \"Sex\": \"Male\", \"Population\": 31.2}, {\"Year\": 2019, \"Age\": \"00\", \"Sex\": \"Female\", \"Population\": 30.2}, {\"Year\": 2019, \"Age\": \"01-04\", \"Sex\": \"Male\", \"Population\": 129.9}, {\"Year\": 2019, \"Age\": \"01-04\", \"Sex\": \"Female\", \"Population\": 124.0}, {\"Year\": 2019, \"Age\": \"05-09\", \"Sex\": \"Male\", \"Population\": 180.2}, {\"Year\": 2019, \"Age\": \"05-09\", \"Sex\": \"Female\", \"Population\": 172.0}, {\"Year\": 2019, \"Age\": \"10-14\", \"Sex\": \"Male\", \"Population\": 175.2}, {\"Year\": 2019, \"Age\": \"10-14\", \"Sex\": \"Female\", \"Population\": 166.2}, {\"Year\": 2019, \"Age\": \"15-19\", \"Sex\": \"Male\", \"Population\": 162.3}, {\"Year\": 2019, \"Age\": \"15-19\", \"Sex\": \"Female\", \"Population\": 157.6}, {\"Year\": 2019, \"Age\": \"20-24\", \"Sex\": \"Male\", \"Population\": 151.5}, {\"Year\": 2019, \"Age\": \"20-24\", \"Sex\": \"Female\", \"Population\": 146.6}, {\"Year\": 2019, \"Age\": \"25-29\", \"Sex\": \"Male\", \"Population\": 145.3}, {\"Year\": 2019, \"Age\": \"25-29\", \"Sex\": \"Female\", \"Population\": 143.9}, {\"Year\": 2019, \"Age\": \"30-34\", \"Sex\": \"Male\", \"Population\": 159.8}, {\"Year\": 2019, \"Age\": \"30-34\", \"Sex\": \"Female\", \"Population\": 171.0}, {\"Year\": 2019, \"Age\": \"35-39\", \"Sex\": \"Male\", \"Population\": 192.0}, {\"Year\": 2019, \"Age\": \"35-39\", \"Sex\": \"Female\", \"Population\": 205.1}, {\"Year\": 2019, \"Age\": \"40-44\", \"Sex\": \"Male\", \"Population\": 187.4}, {\"Year\": 2019, \"Age\": \"40-44\", \"Sex\": \"Female\", \"Population\": 192.5}, {\"Year\": 2019, \"Age\": \"45-49\", \"Sex\": \"Male\", \"Population\": 174.7}, {\"Year\": 2019, \"Age\": \"45-49\", \"Sex\": \"Female\", \"Population\": 176.4}, {\"Year\": 2019, \"Age\": \"50-54\", \"Sex\": \"Male\", \"Population\": 154.5}, {\"Year\": 2019, \"Age\": \"50-54\", \"Sex\": \"Female\", \"Population\": 156.0}, {\"Year\": 2019, \"Age\": \"55-59\", \"Sex\": \"Male\", \"Population\": 141.1}, {\"Year\": 2019, \"Age\": \"55-59\", \"Sex\": \"Female\", \"Population\": 144.8}, {\"Year\": 2019, \"Age\": \"60-64\", \"Sex\": \"Male\", \"Population\": 126.0}, {\"Year\": 2019, \"Age\": \"60-64\", \"Sex\": \"Female\", \"Population\": 127.8}, {\"Year\": 2019, \"Age\": \"65-69\", \"Sex\": \"Male\", \"Population\": 108.4}, {\"Year\": 2019, \"Age\": \"65-69\", \"Sex\": \"Female\", \"Population\": 110.9}, {\"Year\": 2019, \"Age\": \"70-74\", \"Sex\": \"Male\", \"Population\": 90.8}, {\"Year\": 2019, \"Age\": \"70-74\", \"Sex\": \"Female\", \"Population\": 94.1}, {\"Year\": 2019, \"Age\": \"75-79\", \"Sex\": \"Male\", \"Population\": 60.6}, {\"Year\": 2019, \"Age\": \"75-79\", \"Sex\": \"Female\", \"Population\": 66.9}, {\"Year\": 2019, \"Age\": \"80-84\", \"Sex\": \"Male\", \"Population\": 38.7}, {\"Year\": 2019, \"Age\": \"80-84\", \"Sex\": \"Female\", \"Population\": 48.7}, {\"Year\": 2019, \"Age\": \"85+\", \"Sex\": \"Male\", \"Population\": 28.3}, {\"Year\": 2019, \"Age\": \"85+\", \"Sex\": \"Female\", \"Population\": 49.0}, {\"Year\": 2020, \"Age\": \"00\", \"Sex\": \"Male\", \"Population\": 29.9}, {\"Year\": 2020, \"Age\": \"00\", \"Sex\": \"Female\", \"Population\": 28.4}, {\"Year\": 2020, \"Age\": \"01-04\", \"Sex\": \"Male\", \"Population\": 128.3}, {\"Year\": 2020, \"Age\": \"01-04\", \"Sex\": \"Female\", \"Population\": 122.9}, {\"Year\": 2020, \"Age\": \"05-09\", \"Sex\": \"Male\", \"Population\": 176.3}, {\"Year\": 2020, \"Age\": \"05-09\", \"Sex\": \"Female\", \"Population\": 167.8}, {\"Year\": 2020, \"Age\": \"10-14\", \"Sex\": \"Male\", \"Population\": 179.4}, {\"Year\": 2020, \"Age\": \"10-14\", \"Sex\": \"Female\", \"Population\": 170.6}, {\"Year\": 2020, \"Age\": \"15-19\", \"Sex\": \"Male\", \"Population\": 164.7}, {\"Year\": 2020, \"Age\": \"15-19\", \"Sex\": \"Female\", \"Population\": 159.3}, {\"Year\": 2020, \"Age\": \"20-24\", \"Sex\": \"Male\", \"Population\": 156.0}, {\"Year\": 2020, \"Age\": \"20-24\", \"Sex\": \"Female\", \"Population\": 151.2}, {\"Year\": 2020, \"Age\": \"25-29\", \"Sex\": \"Male\", \"Population\": 147.4}, {\"Year\": 2020, \"Age\": \"25-29\", \"Sex\": \"Female\", \"Population\": 144.8}, {\"Year\": 2020, \"Age\": \"30-34\", \"Sex\": \"Male\", \"Population\": 157.5}, {\"Year\": 2020, \"Age\": \"30-34\", \"Sex\": \"Female\", \"Population\": 166.5}, {\"Year\": 2020, \"Age\": \"35-39\", \"Sex\": \"Male\", \"Population\": 186.4}, {\"Year\": 2020, \"Age\": \"35-39\", \"Sex\": \"Female\", \"Population\": 200.1}, {\"Year\": 2020, \"Age\": \"40-44\", \"Sex\": \"Male\", \"Population\": 193.8}, {\"Year\": 2020, \"Age\": \"40-44\", \"Sex\": \"Female\", \"Population\": 199.9}, {\"Year\": 2020, \"Age\": \"45-49\", \"Sex\": \"Male\", \"Population\": 177.7}, {\"Year\": 2020, \"Age\": \"45-49\", \"Sex\": \"Female\", \"Population\": 180.8}, {\"Year\": 2020, \"Age\": \"50-54\", \"Sex\": \"Male\", \"Population\": 157.4}, {\"Year\": 2020, \"Age\": \"50-54\", \"Sex\": \"Female\", \"Population\": 158.7}, {\"Year\": 2020, \"Age\": \"55-59\", \"Sex\": \"Male\", \"Population\": 144.1}, {\"Year\": 2020, \"Age\": \"55-59\", \"Sex\": \"Female\", \"Population\": 148.6}, {\"Year\": 2020, \"Age\": \"60-64\", \"Sex\": \"Male\", \"Population\": 128.0}, {\"Year\": 2020, \"Age\": \"60-64\", \"Sex\": \"Female\", \"Population\": 130.9}, {\"Year\": 2020, \"Age\": \"65-69\", \"Sex\": \"Male\", \"Population\": 111.0}, {\"Year\": 2020, \"Age\": \"65-69\", \"Sex\": \"Female\", \"Population\": 113.3}, {\"Year\": 2020, \"Age\": \"70-74\", \"Sex\": \"Male\", \"Population\": 93.5}, {\"Year\": 2020, \"Age\": \"70-74\", \"Sex\": \"Female\", \"Population\": 97.4}, {\"Year\": 2020, \"Age\": \"75-79\", \"Sex\": \"Male\", \"Population\": 64.0}, {\"Year\": 2020, \"Age\": \"75-79\", \"Sex\": \"Female\", \"Population\": 70.1}, {\"Year\": 2020, \"Age\": \"80-84\", \"Sex\": \"Male\", \"Population\": 40.1}, {\"Year\": 2020, \"Age\": \"80-84\", \"Sex\": \"Female\", \"Population\": 49.6}, {\"Year\": 2020, \"Age\": \"85+\", \"Sex\": \"Male\", \"Population\": 30.2}, {\"Year\": 2020, \"Age\": \"85+\", \"Sex\": \"Female\", \"Population\": 51.0}, {\"Year\": 2021, \"Age\": \"00\", \"Sex\": \"Male\", \"Population\": 28.5}, {\"Year\": 2021, \"Age\": \"00\", \"Sex\": \"Female\", \"Population\": 27.0}, {\"Year\": 2021, \"Age\": \"01-04\", \"Sex\": \"Male\", \"Population\": 121.1}, {\"Year\": 2021, \"Age\": \"01-04\", \"Sex\": \"Female\", \"Population\": 247.1}, {\"Year\": 2021, \"Age\": \"05-09\", \"Sex\": \"Male\", \"Population\": 171.6}, {\"Year\": 2021, \"Age\": \"05-09\", \"Sex\": \"Female\", \"Population\": 163.4}, {\"Year\": 2021, \"Age\": \"10-14\", \"Sex\": \"Male\", \"Population\": 183.0}, {\"Year\": 2021, \"Age\": \"10-14\", \"Sex\": \"Female\", \"Population\": 175.0}, {\"Year\": 2021, \"Age\": \"15-19\", \"Sex\": \"Male\", \"Population\": 166.0}, {\"Year\": 2021, \"Age\": \"15-19\", \"Sex\": \"Female\", \"Population\": 159.3}, {\"Year\": 2021, \"Age\": \"20-24\", \"Sex\": \"Male\", \"Population\": 158.1}, {\"Year\": 2021, \"Age\": \"20-24\", \"Sex\": \"Female\", \"Population\": 152.2}, {\"Year\": 2021, \"Age\": \"25-29\", \"Sex\": \"Male\", \"Population\": 146.9}, {\"Year\": 2021, \"Age\": \"25-29\", \"Sex\": \"Female\", \"Population\": 144.7}, {\"Year\": 2021, \"Age\": \"30-34\", \"Sex\": \"Male\", \"Population\": 155.4}, {\"Year\": 2021, \"Age\": \"30-34\", \"Sex\": \"Female\", \"Population\": 161.8}, {\"Year\": 2021, \"Age\": \"35-39\", \"Sex\": \"Male\", \"Population\": 180.0}, {\"Year\": 2021, \"Age\": \"35-39\", \"Sex\": \"Female\", \"Population\": 195.6}, {\"Year\": 2021, \"Age\": \"40-44\", \"Sex\": \"Male\", \"Population\": 196.7}, {\"Year\": 2021, \"Age\": \"40-44\", \"Sex\": \"Female\", \"Population\": 205.5}, {\"Year\": 2021, \"Age\": \"45-49\", \"Sex\": \"Male\", \"Population\": 180.4}, {\"Year\": 2021, \"Age\": \"45-49\", \"Sex\": \"Female\", \"Population\": 184.0}, {\"Year\": 2021, \"Age\": \"50-54\", \"Sex\": \"Male\", \"Population\": 161.6}, {\"Year\": 2021, \"Age\": \"50-54\", \"Sex\": \"Female\", \"Population\": 162.4}, {\"Year\": 2021, \"Age\": \"55-59\", \"Sex\": \"Male\", \"Population\": 146.4}, {\"Year\": 2021, \"Age\": \"55-59\", \"Sex\": \"Female\", \"Population\": 150.7}, {\"Year\": 2021, \"Age\": \"60-64\", \"Sex\": \"Male\", \"Population\": 131.2}, {\"Year\": 2021, \"Age\": \"60-64\", \"Sex\": \"Female\", \"Population\": 134.7}, {\"Year\": 2021, \"Age\": \"65-69\", \"Sex\": \"Male\", \"Population\": 113.1}, {\"Year\": 2021, \"Age\": \"65-69\", \"Sex\": \"Female\", \"Population\": 116.1}, {\"Year\": 2021, \"Age\": \"70-74\", \"Sex\": \"Male\", \"Population\": 95.1}, {\"Year\": 2021, \"Age\": \"70-74\", \"Sex\": \"Female\", \"Population\": 99.1}, {\"Year\": 2021, \"Age\": \"75-79\", \"Sex\": \"Male\", \"Population\": 68.3}, {\"Year\": 2021, \"Age\": \"75-79\", \"Sex\": \"Female\", \"Population\": 74.5}, {\"Year\": 2021, \"Age\": \"80-84\", \"Sex\": \"Male\", \"Population\": 41.0}, {\"Year\": 2021, \"Age\": \"80-84\", \"Sex\": \"Female\", \"Population\": 50.5}, {\"Year\": 2021, \"Age\": \"85+\", \"Sex\": \"Male\", \"Population\": 31.9}, {\"Year\": 2021, \"Age\": \"85+\", \"Sex\": \"Female\", \"Population\": 52.7}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.ConcatChart(...)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dataframe = pandas.read_csv(\n",
    "    artifact_manager.get_population_eda_filepath())\n",
    "build_interactive_population_pyramid_chart(population_dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b3b2389",
   "metadata": {},
   "source": [
    "<p class=\"Caption\">Interactive Irish Population Pyramid</p>  \n",
    "<a href=\"https://vega.github.io/editor/#/gist/f60264d419560ac188af4f6dfa5c57dd/vega-irish-population-pyramid-graph.json\" target=\"_new\"> Click here to open graph online</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58c3b7a",
   "metadata": {},
   "source": [
    "### Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58224cba",
   "metadata": {},
   "source": [
    "There is something unexpected in the data for the years 2012 to 2017 for females in the age bracket 1 to 4 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39e580a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-11T09:58:46.178837Z",
     "start_time": "2023-01-11T09:58:46.134843Z"
    }
   },
   "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>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>2011</td>\n",
       "      <td>01-04</td>\n",
       "      <td>138.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2359</th>\n",
       "      <td>2012</td>\n",
       "      <td>01-04</td>\n",
       "      <td>280.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>2013</td>\n",
       "      <td>01-04</td>\n",
       "      <td>277.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>2014</td>\n",
       "      <td>01-04</td>\n",
       "      <td>278.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>2015</td>\n",
       "      <td>01-04</td>\n",
       "      <td>279.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2475</th>\n",
       "      <td>2015</td>\n",
       "      <td>05-09</td>\n",
       "      <td>170.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2511</th>\n",
       "      <td>2016</td>\n",
       "      <td>01-04</td>\n",
       "      <td>281.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549</th>\n",
       "      <td>2017</td>\n",
       "      <td>01-04</td>\n",
       "      <td>286.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551</th>\n",
       "      <td>2017</td>\n",
       "      <td>05-09</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2587</th>\n",
       "      <td>2018</td>\n",
       "      <td>01-04</td>\n",
       "      <td>125.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2589</th>\n",
       "      <td>2018</td>\n",
       "      <td>05-09</td>\n",
       "      <td>174.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year    Age  Population\n",
       "2321  2011  01-04       138.5\n",
       "2359  2012  01-04       280.7\n",
       "2397  2013  01-04       277.3\n",
       "2435  2014  01-04       278.7\n",
       "2473  2015  01-04       279.7\n",
       "2475  2015  05-09       170.8\n",
       "2511  2016  01-04       281.9\n",
       "2549  2017  01-04       286.8\n",
       "2551  2017  05-09       176.0\n",
       "2587  2018  01-04       125.8\n",
       "2589  2018  05-09       174.7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strange_values_dataframe = population_dataframe[[YEAR, AGE, POPULATION]]\n",
    "strange_values_dataframe = strange_values_dataframe[(\n",
    "    (population_dataframe[AGE] == \"01-04\") &\n",
    "    (population_dataframe[SEX] == \"Female\") &\n",
    "    (population_dataframe[YEAR] >= 2011) &\n",
    "    (population_dataframe[YEAR] <= 2018)) | (\n",
    "        (population_dataframe[AGE] == \"05-09\") &\n",
    "        (population_dataframe[SEX] == \"Female\") &\n",
    "        (population_dataframe[YEAR].isin([2015, 2017, 2018])))]\n",
    "strange_values_dataframe"
   ]
  },
  {
   "attachments": {
    "notebook-2-05-sa-population-pyramid-2011-2012.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "2c1ff836",
   "metadata": {},
   "source": [
    "![notebook-2-05-sa-population-pyramid-2011-2012.png](attachment:notebook-2-05-sa-population-pyramid-2011-2012.png)\n",
    "<!--![Population pyramid for years 2011 and 2012](./../artifacts/notebook-2-05-sa-population-pyramid-2011-2012.png?raw=1)-->  \n",
    "<p class=\"Caption\">Population pyramid for years 2011 and 2012</p>"
   ]
  },
  {
   "attachments": {
    "notebook-2-05-sa-population-pyramid-2017-2018.png": {
     "image/png": 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   "cell_type": "markdown",
   "id": "35a7da2a",
   "metadata": {},
   "source": [
    "![notebook-2-05-sa-population-pyramid-2017-2018.png](attachment:notebook-2-05-sa-population-pyramid-2017-2018.png)\n",
    "<!--![Population pyramid for years 2017 and 2018](./../artifacts/notebook-2-05-sa-population-pyramid-2017-2018.png?raw=1)-->  \n",
    "<p class=\"Caption\">Population pyramid for years 2017 and 2018</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6561f83e",
   "metadata": {},
   "source": [
    "After 2011 the population of females in the age bracket 1 to 4 years jumps from 138.5 thousand to 280.7 thousand without cause. This value remains in this range for the next 6 years. Then in 2018 is drops to 125.8 thousand. Also, if the numbers were accurate, we would expect that after 4 years the females in the age bracket 1 to 4 years would almost completely move into the age bracket 5 to 9 years in 2015. In 2015 the population for females in the age bracket 5 to 9 years is 170.8 thousand, 109 thousand short of the expected 280 thousand.  These 109 thousand females just disappear without cause in the records from 2018 on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4568dbe",
   "metadata": {},
   "source": [
    "Author &copy; 2021 <a href=\"https://github.com/markcrowe-com\" target=\"_parent\">Mark Crowe</a>. All rights reserved."
   ]
  }
 ],
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