notebooks/notebook-1-01-example-bad-code-population.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96698039",
   "metadata": {},
   "source": [
    "## EDA of the population (1950 - 2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f0ae94",
   "metadata": {},
   "source": [
    "<!--\n",
    "import data_analytics.github as github\n",
    "print(github.create_jupyter_notebook_header(\"markcrowe-com\", \"data-analytics-project-template\", \"notebooks/notebook-1-01-example-bad-code-population.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-1-01-example-bad-code-population.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-1-01-example-bad-code-population.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": "691cdeca",
   "metadata": {},
   "source": [
    "A bad example of code and structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d846147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries and basic settings for ease of use\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import altair as alt\n",
    "\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # I suppressed the warnings\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_mode_interactivity = \"all\"\n",
    "\n",
    "#from pandas import read_csv\n",
    "#from pandas import datetime\n",
    "#from matplotlib import pyplot\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import kpss\n",
    "# pip install pmdarima\n",
    "import pmdarima as pm\n",
    "from statsmodels.tsa.arima_model import ARIMA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31cef164",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "616255b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read csv files into a pandas dataframe\n",
    "#df_BDM = pd.read_csv(\"D:/BDM_Ireland1960_2021.csv\")\n",
    "#df_B   = pd.read_csv(\"D:/BirthsByCounty1985_2020.csv\",sep=\"\\t\")\n",
    "#df_D   = pd.read_csv(\"D:/deaths-region-2007-2020.csv\")\n",
    "\n",
    "#df_P   = pd.read_csv(\"D:/population_est_1950-2021.csv\") # Commented out to remove bug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d1d4216",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction to make the program run\n",
    "df_P   = pd.read_csv(\"./../assets/2021-12Dec-11-population-estimates-1950-2021-pea01.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35f16f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values(df):\n",
    "    if (df.isnull().values.any()): print (df.isnull().sum()) # how many missing values?\n",
    "    else: print(\"no missing values\")\n",
    "\n",
    "# Augmented Dickey-Fuller Test - ADF Test\n",
    "def adf_test(series):\n",
    "    result = adfuller(series, autolag='AIC')\n",
    "    print(\"Augmented Dickey-Fuller Test (ADF)\\n==================================\")\n",
    "    print(f'T={series.count()}')\n",
    "    print(f'- ADF the test statistic:     {result[0]}')\n",
    "    print(f'- MacKinnons approx p-value:  {result[1]}')\n",
    "    print(f'- number of lags used:        {result[2]}')\n",
    "\n",
    "    print('\\nCritial Values:\\n---------------')\n",
    "    for key, value in result[4].items():\n",
    "        if (key==\"10%\"): print(f'{key}   {value}')\n",
    "        else: print(f' {key}   {value}')\n",
    "    print(\"\\n\")\n",
    "# Kwiatkowski–Phillips–Schmidt–Shin Test - KPSS Test \n",
    "def kpss_test(series, **kw):\n",
    "    statistic, p_value, n_lags, critical_values = kpss(series, **kw)\n",
    "    print(\"Kwiatkowski–Phillips–Schmidt–Shin Test (KPSS)\\n============================================\")    \n",
    "    print(f'T={series.count()}')\n",
    "    print(f'- KPSS the test statistic:   {statistic}')\n",
    "    print(f'- MacKinnons approx p-value: {p_value}')\n",
    "    print(f'- number of lags used:       {n_lags}')\n",
    "\n",
    "    print('\\nCritical Values:\\n---------------')\n",
    "    for key, value in critical_values.items():\n",
    "        if (key==\"2.5%\"): print(f'{key}   {value}')\n",
    "        elif (key==\"10%\"): print(f' {key}   {value}')\n",
    "        else: print(f'  {key}   {value}')\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3148b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# =================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e7ea975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of                                     Statistic  Year          Age Group  \\\n",
       "0     Population Estimates (Persons in April)  1950       Under 1 year   \n",
       "1     Population Estimates (Persons in April)  1950       Under 1 year   \n",
       "2     Population Estimates (Persons in April)  1950       Under 1 year   \n",
       "3     Population Estimates (Persons in April)  1950        0 - 4 years   \n",
       "4     Population Estimates (Persons in April)  1950        0 - 4 years   \n",
       "...                                       ...   ...                ...   \n",
       "5827  Population Estimates (Persons in April)  2021  85 years and over   \n",
       "5828  Population Estimates (Persons in April)  2021  85 years and over   \n",
       "5829  Population Estimates (Persons in April)  2021           All ages   \n",
       "5830  Population Estimates (Persons in April)  2021           All ages   \n",
       "5831  Population Estimates (Persons in April)  2021           All ages   \n",
       "\n",
       "             Sex      UNIT   VALUE  \n",
       "0     Both sexes  Thousand    61.1  \n",
       "1           Male  Thousand    31.4  \n",
       "2         Female  Thousand    29.7  \n",
       "3     Both sexes  Thousand     NaN  \n",
       "4           Male  Thousand     NaN  \n",
       "...          ...       ...     ...  \n",
       "5827        Male  Thousand    31.9  \n",
       "5828      Female  Thousand    52.7  \n",
       "5829  Both sexes  Thousand  5011.5  \n",
       "5830        Male  Thousand  2481.1  \n",
       "5831      Female  Thousand  2530.3  \n",
       "\n",
       "[5832 rows x 6 columns]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_P.info # Structure and dimensions of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf646087",
   "metadata": {},
   "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>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",
       "    <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 Group         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  VALUE  \n",
       "0  Thousand   61.1  \n",
       "1  Thousand   31.4  \n",
       "2  Thousand   29.7  \n",
       "3  Thousand    NaN  \n",
       "4  Thousand    NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_P.head() # first view of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "754ecb92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Population Estimates (Persons in April)\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimension reduction:::\n",
    "# Seems, Columns Statistic & UNIT cannot be used because they contain only one value.\n",
    "# Examining the values in the Statistic column\n",
    "pd.Series(df_P[\"Statistic\"].unique()).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84cd95b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Thousand\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining the values in the UNIT column\n",
    "pd.Series(df_P[\"UNIT\"].unique()).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9062b1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only one value - cannot be used for analysis\n",
    "# Deleting the Statistic column \n",
    "# Deleting the UNIT column \n",
    "df_P = df_P.drop([\"Statistic\"], axis='columns')\n",
    "df_P = df_P.drop([\"UNIT\"], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b24f3ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Year                Age         Sex  population\n",
       "0     1950       Under 1 year  Both sexes        61.1\n",
       "1     1950       Under 1 year        Male        31.4\n",
       "2     1950       Under 1 year      Female        29.7\n",
       "3     1950        0 - 4 years  Both sexes         NaN\n",
       "4     1950        0 - 4 years        Male         NaN\n",
       "...    ...                ...         ...         ...\n",
       "5827  2021  85 years and over        Male        31.9\n",
       "5828  2021  85 years and over      Female        52.7\n",
       "5829  2021           All ages  Both sexes      5011.5\n",
       "5830  2021           All ages        Male      2481.1\n",
       "5831  2021           All ages      Female      2530.3\n",
       "\n",
       "[5832 rows x 4 columns]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename the columns\n",
    "new_titles = {\"Year\":\"Year\", \"Age Group\":\"Age\", \"Sex\":\"Sex\", \"VALUE\":\"population\"}\n",
    "df_P = df_P.rename(columns=new_titles)\n",
    "df_P.info  # Structure and dimensions of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e1b2dd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year            0\n",
      "Age             0\n",
      "Sex             0\n",
      "population    114\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# any missing values?\n",
    "missing_values(df_P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "681143a9",
   "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>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>Under 1 year</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>Under 1 year</td>\n",
       "      <td>Male</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950</td>\n",
       "      <td>Under 1 year</td>\n",
       "      <td>Female</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>0 - 4 years</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>0 - 4 years</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year           Age         Sex  population\n",
       "0  1950  Under 1 year  Both sexes        61.1\n",
       "1  1950  Under 1 year        Male        31.4\n",
       "2  1950  Under 1 year      Female        29.7\n",
       "3  1950   0 - 4 years  Both sexes         NaN\n",
       "4  1950   0 - 4 years        Male         NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_P.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "517239c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Year                Age         Sex  population\n",
       "0     1950       Under 1 year  Both sexes        61.1\n",
       "1     1950       Under 1 year        Male        31.4\n",
       "2     1950       Under 1 year      Female        29.7\n",
       "9     1950        1 - 4 years  Both sexes       249.1\n",
       "10    1950        1 - 4 years        Male       127.5\n",
       "...    ...                ...         ...         ...\n",
       "5824  2021      80 - 84 years        Male        41.0\n",
       "5825  2021      80 - 84 years      Female        50.5\n",
       "5826  2021  85 years and over  Both sexes        84.6\n",
       "5827  2021  85 years and over        Male        31.9\n",
       "5828  2021  85 years and over      Female        52.7\n",
       "\n",
       "[4104 rows x 4 columns]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PO = df_P.drop(df_P[ (df_P[\"Age\"] == \"0 - 4 years\") |\n",
    "                        (df_P[\"Age\"] == \"0 - 14 years\") |\n",
    "                        (df_P[\"Age\"] == \"15 - 24 years\") |\n",
    "                        (df_P[\"Age\"] == \"15 years and over\") |\n",
    "                        (df_P[\"Age\"] == \"25 - 44 years\") |\n",
    "                        (df_P[\"Age\"] == \"45 - 64 years\") |\n",
    "                        (df_P[\"Age\"] == \"65 years and over\") |\n",
    "                        (df_P[\"Age\"] == \"All ages\") ].index)\n",
    "df_PO.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16330d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Year                Age         Sex  population\n",
       "0     1950       Under 1 year  Both sexes        61.1\n",
       "1     1950       Under 1 year        Male        31.4\n",
       "2     1950       Under 1 year      Female        29.7\n",
       "3     1950        1 - 4 years  Both sexes       249.1\n",
       "4     1950        1 - 4 years        Male       127.5\n",
       "...    ...                ...         ...         ...\n",
       "4099  2021      80 - 84 years        Male        41.0\n",
       "4100  2021      80 - 84 years      Female        50.5\n",
       "4101  2021  85 years and over  Both sexes        84.6\n",
       "4102  2021  85 years and over        Male        31.9\n",
       "4103  2021  85 years and over      Female        52.7\n",
       "\n",
       "[4104 rows x 4 columns]>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PO = df_PO.reset_index(drop=True)\n",
    "df_PO.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c9d19c00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no missing values\n"
     ]
    }
   ],
   "source": [
    "missing_values(df_PO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "503bf6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_PP = df_PO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07e9e730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19       13.3\n",
       "56       14.0\n",
       "75       14.5\n",
       "93       15.1\n",
       "111      15.7\n",
       "        ...  \n",
       "881     399.8\n",
       "911     400.6\n",
       "1076    402.1\n",
       "898     408.3\n",
       "1         NaN\n",
       "Length: 1083, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(df_PP[\"population\"].where(df_PP[\"Sex\"] == \"Both sexes\").unique()).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "406da700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year          1368\n",
       "Age           1368\n",
       "Sex           1368\n",
       "population    1368\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the \"Both sexes\" rows\n",
    "df_PP.where(df_PP[\"Sex\"] == \"Both sexes\").count(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff4d71df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Both sexes\" - not need for the population pyramids\n",
    "# Deleting the \"Both sexes\" rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b1386e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Year                Age     Sex  population\n",
       "1     1950       Under 1 year    Male        31.4\n",
       "2     1950       Under 1 year  Female        29.7\n",
       "4     1950        1 - 4 years    Male       127.5\n",
       "5     1950        1 - 4 years  Female       121.6\n",
       "7     1950        5 - 9 years    Male       142.3\n",
       "...    ...                ...     ...         ...\n",
       "4097  2021      75 - 79 years  Female        74.5\n",
       "4099  2021      80 - 84 years    Male        41.0\n",
       "4100  2021      80 - 84 years  Female        50.5\n",
       "4102  2021  85 years and over    Male        31.9\n",
       "4103  2021  85 years and over  Female        52.7\n",
       "\n",
       "[2736 rows x 4 columns]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PP = df_PP.drop(df_PP[(df_PP[\"Sex\"] == \"Both sexes\")].index)\n",
    "df_PP.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8c86b6b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year          0\n",
       "Age           0\n",
       "Sex           0\n",
       "population    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the rows\n",
    "df_PP.where(df_PP[\"Sex\"] == \"Both sexes\").count(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5f206225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Year                Age     Sex  population\n",
       "0     1950       Under 1 year    Male        31.4\n",
       "1     1950       Under 1 year  Female        29.7\n",
       "2     1950        1 - 4 years    Male       127.5\n",
       "3     1950        1 - 4 years  Female       121.6\n",
       "4     1950        5 - 9 years    Male       142.3\n",
       "...    ...                ...     ...         ...\n",
       "2731  2021      75 - 79 years  Female        74.5\n",
       "2732  2021      80 - 84 years    Male        41.0\n",
       "2733  2021      80 - 84 years  Female        50.5\n",
       "2734  2021  85 years and over    Male        31.9\n",
       "2735  2021  85 years and over  Female        52.7\n",
       "\n",
       "[2736 rows x 4 columns]>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PP = df_PP.reset_index(drop=True)\n",
    "df_PP.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e63fd3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year          144\n",
       "Age           144\n",
       "Sex           144\n",
       "population    144\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PP.where(df_PP[\"Age\"] == \"Under 1 year\").count(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "32bb1862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Under 1 year\n",
       "1           1 - 4 years\n",
       "2           5 - 9 years\n",
       "3         10 - 14 years\n",
       "4         15 - 19 years\n",
       "5         20 - 24 years\n",
       "6         25 - 29 years\n",
       "7         30 - 34 years\n",
       "8         35 - 39 years\n",
       "9         40 - 44 years\n",
       "10        45 - 49 years\n",
       "11        50 - 54 years\n",
       "12        55 - 59 years\n",
       "13        60 - 64 years\n",
       "14        65 - 69 years\n",
       "15        70 - 74 years\n",
       "16        75 - 79 years\n",
       "17        80 - 84 years\n",
       "18    85 years and over\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining the values in the Age Group column\n",
    "pd.Series(df_PP[\"Age\"].unique()) #.sort_values()\n",
    "#df_PP[\"Age Group\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f397921",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_PP=df_PP.replace(\"Under 1 year\",\" 0-\")\n",
    "df_PP=df_PP.replace(\"1 - 4 years\",\" 1-\")\n",
    "df_PP=df_PP.replace(\"5 - 9 years\",\" 5-\")\n",
    "df_PP=df_PP.replace(\"10 - 14 years\",\"10-\")\n",
    "df_PP=df_PP.replace(\"15 - 19 years\",\"15-\")\n",
    "df_PP=df_PP.replace(\"20 - 24 years\",\"20-\")\n",
    "df_PP=df_PP.replace(\"25 - 29 years\",\"25-\")\n",
    "df_PP=df_PP.replace(\"30 - 34 years\",\"30-\")\n",
    "df_PP=df_PP.replace(\"35 - 39 years\",\"35-\")\n",
    "df_PP=df_PP.replace(\"40 - 44 years\",\"40-\")\n",
    "df_PP=df_PP.replace(\"45 - 49 years\",\"45-\")\n",
    "df_PP=df_PP.replace(\"50 - 54 years\",\"50-\")\n",
    "df_PP=df_PP.replace(\"55 - 59 years\",\"55-\")\n",
    "df_PP=df_PP.replace(\"60 - 64 years\",\"60-\")\n",
    "df_PP=df_PP.replace(\"65 - 69 years\",\"65-\")\n",
    "df_PP=df_PP.replace(\"70 - 74 years\",\"70-\")\n",
    "df_PP=df_PP.replace(\"75 - 79 years\",\"75-\")\n",
    "df_PP=df_PP.replace(\"80 - 84 years\",\"80-\")\n",
    "df_PP=df_PP.replace(\"85 years and over\",\"85-\")\n",
    "#df_PP.where(df_PP[\"Age Group\"] == \"Under 1 year\")[\"Age Group\"]=\"0\"\n",
    "#df_PP = df_PP.drop(df_PP[(df_PP[\"Sex\"] == \"Both sexes\")].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "75ccaf14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Year  Age     Sex  population\n",
       "0     1950   0-    Male        31.4\n",
       "1     1950   0-  Female        29.7\n",
       "2     1950   1-    Male       127.5\n",
       "3     1950   1-  Female       121.6\n",
       "4     1950   5-    Male       142.3\n",
       "...    ...  ...     ...         ...\n",
       "2731  2021  75-  Female        74.5\n",
       "2732  2021  80-    Male        41.0\n",
       "2733  2021  80-  Female        50.5\n",
       "2734  2021  85-    Male        31.9\n",
       "2735  2021  85-  Female        52.7\n",
       "\n",
       "[2736 rows x 4 columns]>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PP = df_PP.reset_index(drop=True)\n",
    "df_PP.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9b4cc51d",
   "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>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>0-</td>\n",
       "      <td>Male</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>0-</td>\n",
       "      <td>Female</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950</td>\n",
       "      <td>1-</td>\n",
       "      <td>Male</td>\n",
       "      <td>127.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>1-</td>\n",
       "      <td>Female</td>\n",
       "      <td>121.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>5-</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",
       "0  1950   0-    Male        31.4\n",
       "1  1950   0-  Female        29.7\n",
       "2  1950   1-    Male       127.5\n",
       "3  1950   1-  Female       121.6\n",
       "4  1950   5-    Male       142.3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96e029c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========================================================================================\n",
    "# interactive population pyramid code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f124e0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "year_selector = alt.binding_range(min=1950, max=2021, step=1, name= \"year selector\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "18dc1db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "select_year = alt.selection_single(name=\"population\",fields=[\"Year\"],\n",
    "                                   bind=year_selector, init={\"Year\":2020} )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "10407409",
   "metadata": {},
   "outputs": [],
   "source": [
    "trunk = alt.Chart(df_PP, title=\"Age\").add_selection(\n",
    "    select_year).transform_filter(\n",
    "    select_year).transform_calculate(\n",
    "    gender=alt.datum.Sex).properties(width=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "91471725",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_color = alt.Scale(domain=['Male', 'Female'], range=['blue', 'violet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fca34152",
   "metadata": {},
   "outputs": [],
   "source": [
    "male_tree = trunk.transform_filter(alt.datum.gender == 'Male').encode(\n",
    "    y=alt.Y('Age:O', axis=None, sort=alt.SortOrder('descending')),\n",
    "    x=alt.X('sum(population):Q', scale=alt.Scale(domain=[0,300.0]),\n",
    "    title='male population (1000)', sort=alt.SortOrder('descending')),\n",
    "    color=alt.Color('gender:N', scale=tree_color)\n",
    "    ).mark_bar().properties(title='Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d303dcc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_trunk = trunk.encode(\n",
    "    y=alt.Y('Age:O', axis=None, sort=alt.SortOrder('descending')),\n",
    "        text=alt.Text('Age:O')).mark_text().properties(width=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "51f43271",
   "metadata": {},
   "outputs": [],
   "source": [
    "female_tree = trunk.transform_filter(alt.datum.gender == 'Female').encode(\n",
    "    y=alt.Y('Age:O', axis=None, sort=alt.SortOrder('descending')),\n",
    "    x=alt.X('sum(population):Q', scale=alt.Scale(domain=[0,300.0]),\n",
    "    title='female population (1000)'),\n",
    "    color=alt.Color('gender:N', scale=tree_color)\n",
    "    ).mark_bar().properties(title='Female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93d73632",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-c232455eda0e42d4a9e4cfb52d9e88e2\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-c232455eda0e42d4a9e4cfb52d9e88e2\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-c232455eda0e42d4a9e4cfb52d9e88e2\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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       "    function showError(err) {\n",
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       "    function displayChart(vegaEmbed) {\n",
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       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "      displayChart(vegaEmbed);\n",
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       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"concat\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"gender\", \"scale\": {\"domain\": [\"Male\", \"Female\"], \"range\": [\"blue\", \"violet\"]}}, \"x\": {\"type\": \"quantitative\", \"aggregate\": \"sum\", \"field\": \"population\", \"scale\": {\"domain\": [0, 300.0]}, \"sort\": \"descending\", \"title\": \"male population (1000)\"}, \"y\": {\"type\": \"ordinal\", \"axis\": null, \"field\": \"Age\", \"sort\": \"descending\"}}, \"selection\": {\"population\": {\"type\": \"single\", \"fields\": [\"Year\"], \"bind\": {\"input\": \"range\", \"max\": 2021, \"min\": 1950, \"name\": \"year selector\", \"step\": 1}, \"init\": {\"Year\": 2020}}}, \"title\": \"Male\", \"transform\": [{\"filter\": {\"selection\": \"population\"}}, {\"calculate\": \"datum.Sex\", \"as\": \"gender\"}, {\"filter\": \"(datum.gender === 'Male')\"}], \"width\": 300}, {\"mark\": 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{\"population\": {\"type\": \"single\", \"fields\": [\"Year\"], \"bind\": {\"input\": \"range\", \"max\": 2021, \"min\": 1950, \"name\": \"year selector\", \"step\": 1}, \"init\": {\"Year\": 2020}}}, \"title\": \"Female\", \"transform\": [{\"filter\": {\"selection\": \"population\"}}, {\"calculate\": \"datum.Sex\", \"as\": \"gender\"}, {\"filter\": \"(datum.gender === 'Female')\"}], \"width\": 300}], \"data\": {\"name\": \"data-4f5943022faa3328e686d4d9c88b1a29\"}, \"spacing\": 2, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-4f5943022faa3328e686d4d9c88b1a29\": [{\"Year\": 1950, \"Age\": \" 0-\", \"Sex\": \"Male\", \"population\": 31.4}, {\"Year\": 1950, \"Age\": \" 0-\", \"Sex\": \"Female\", \"population\": 29.7}, {\"Year\": 1950, \"Age\": \" 1-\", \"Sex\": \"Male\", \"population\": 127.5}, {\"Year\": 1950, \"Age\": \" 1-\", \"Sex\": \"Female\", \"population\": 121.6}, {\"Year\": 1950, \"Age\": \" 5-\", \"Sex\": \"Male\", \"population\": 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