ham_callsigns_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "import geopandas as gpd\n",
    "from geojson_rewind import rewind\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load relevant data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the dataset of argetinian amateur radio licenses.\n",
    "\n",
    "While ENACOM has an API, the dataset is provided by a URL, both in their website as in the API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titular de la Licencia</th>\n",
       "      <th>Señal Distintiva</th>\n",
       "      <th>Categoría</th>\n",
       "      <th>Expiración</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Localidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PROTECCION CIVIL (MIN. SEG. NACION)</td>\n",
       "      <td>LU0CD</td>\n",
       "      <td>SUPERIOR</td>\n",
       "      <td>2025-05-21</td>\n",
       "      <td>Ciudad de Buenos Aires</td>\n",
       "      <td>CIUDAD AUTONOMA DE BUENOS AIRES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARTURO JORGE PEYRU</td>\n",
       "      <td>LU1AAA</td>\n",
       "      <td>NOVICIO</td>\n",
       "      <td>2024-05-07</td>\n",
       "      <td>Ciudad de Buenos Aires</td>\n",
       "      <td>CIUDAD AUTONOMA DE BUENOS AIRES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHRISTIAN LUIS DIAZ</td>\n",
       "      <td>LU1AAB</td>\n",
       "      <td>NOVICIO</td>\n",
       "      <td>2024-04-26</td>\n",
       "      <td>Ciudad de Buenos Aires</td>\n",
       "      <td>CIUDAD AUTONOMA DE BUENOS AIRES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WENCESLAO BERNARDINO MOREL</td>\n",
       "      <td>LU1AAC</td>\n",
       "      <td>GENERAL</td>\n",
       "      <td>2024-05-16</td>\n",
       "      <td>Ciudad de Buenos Aires</td>\n",
       "      <td>CIUDAD AUTONOMA DE BUENOS AIRES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JUAN ANTONIO BILOTA</td>\n",
       "      <td>LU1AAD</td>\n",
       "      <td>NOVICIO</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Ciudad de Buenos Aires</td>\n",
       "      <td>CIUDAD AUTONOMA DE BUENOS AIRES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Titular de la Licencia Señal Distintiva Categoría Expiración  \\\n",
       "0  PROTECCION CIVIL (MIN. SEG. NACION)            LU0CD  SUPERIOR 2025-05-21   \n",
       "1                   ARTURO JORGE PEYRU           LU1AAA   NOVICIO 2024-05-07   \n",
       "2                  CHRISTIAN LUIS DIAZ           LU1AAB   NOVICIO 2024-04-26   \n",
       "3           WENCESLAO BERNARDINO MOREL           LU1AAC   GENERAL 2024-05-16   \n",
       "4                  JUAN ANTONIO BILOTA           LU1AAD   NOVICIO 2024-08-14   \n",
       "\n",
       "                Provincia                        Localidad  \n",
       "0  Ciudad de Buenos Aires  CIUDAD AUTONOMA DE BUENOS AIRES  \n",
       "1  Ciudad de Buenos Aires  CIUDAD AUTONOMA DE BUENOS AIRES  \n",
       "2  Ciudad de Buenos Aires  CIUDAD AUTONOMA DE BUENOS AIRES  \n",
       "3  Ciudad de Buenos Aires  CIUDAD AUTONOMA DE BUENOS AIRES  \n",
       "4  Ciudad de Buenos Aires  CIUDAD AUTONOMA DE BUENOS AIRES  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Switch to avoid downnloading XSLX each time I ran\n",
    "donwload_file = False\n",
    "\n",
    "# Define the name of the file to be saved locally\n",
    "filename = 'argentina_ham_callsigns.xls'\n",
    "\n",
    "if donwload_file:\n",
    "    # Define the URL with the file to be downlaoded\n",
    "    url = 'https://datosabiertos.enacom.gob.ar/datasets/197452-listado-de-radioaficionados.download/'\n",
    "\n",
    "    # Send the petition HTTP Get for obtaining the resource\n",
    "    data = requests.get(url)\n",
    "\n",
    "    # Save the file locally\n",
    "    with open(filename, 'wb') as file:\n",
    "        file.write(data.content)\n",
    "\n",
    "# Read CSV as pandas DataFrame\n",
    "df = pd.read_excel(filename)\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading a previously downloaded GeoJSON with the shapefile of Argentina's provinces (*[Source](https://www.kaggle.com/datasets/pablomgomez21/geojson-file-provincias-argentinas)*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('arg_provinces.geojson', 'rb') as file:\n",
    "    arg_prov_shape = json.load(file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, I'll load a CSV with some basic information of each province in Argentina.\n",
    "+ **province(index)**: Name of the province.\n",
    "+ **population**, in number of people (obatined by [provisional 2022 census results](https://www.indec.gob.ar/indec/web/Nivel4-Tema-2-41-165))\n",
    "+ **area**, in $km^{2}$. [Source](https://en.wikipedia.org/wiki/Provinces_of_Argentina)\n",
    "+ **earnings**, which refers to the monthly mean individual earning for each province for the month of february 2023. [Source](https://datos.gob.ar/dataset/produccion-salarios-promedio-mediano-por-provincia-sector-actividad/archivo/produccion_8ed16580-cc11-48ba-8ba3-81ab3c95b6da)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>salaries</th>\n",
       "      <th>pop_density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Buenos Aires</th>\n",
       "      <td>17569053</td>\n",
       "      <td>307571</td>\n",
       "      <td>244345</td>\n",
       "      <td>57.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catamarca</th>\n",
       "      <td>429556</td>\n",
       "      <td>102602</td>\n",
       "      <td>186419</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chaco</th>\n",
       "      <td>1142963</td>\n",
       "      <td>99633</td>\n",
       "      <td>186228</td>\n",
       "      <td>11.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chubut</th>\n",
       "      <td>603120</td>\n",
       "      <td>224686</td>\n",
       "      <td>368289</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ciudad Autónoma de Buenos Aires</th>\n",
       "      <td>3120612</td>\n",
       "      <td>203</td>\n",
       "      <td>323670</td>\n",
       "      <td>15372.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 population    area  salaries  pop_density\n",
       "province                                                                  \n",
       "Buenos Aires                       17569053  307571    244345        57.12\n",
       "Catamarca                            429556  102602    186419         4.19\n",
       "Chaco                               1142963   99633    186228        11.47\n",
       "Chubut                               603120  224686    368289         2.68\n",
       "Ciudad Autónoma de Buenos Aires     3120612     203    323670     15372.47"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prov_data = pd.read_csv('arg_provinces_data.csv', delimiter=';').set_index('province')\n",
    "\n",
    "# Create a propulation density column\n",
    "prov_data['pop_density'] = (prov_data['population'] / prov_data['area']).round(2)\n",
    "prov_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Buenos Aires', 'Catamarca', 'Chaco', 'Chubut',\n",
       "       'Ciudad Autónoma de Buenos Aires', 'Corrientes', 'Córdoba',\n",
       "       'Entre Ríos', 'Formosa', 'Jujuy', 'La Pampa', 'La Rioja', 'Mendoza',\n",
       "       'Misiones', 'Neuquén', 'Río Negro', 'Salta', 'San Juan', 'San Luis',\n",
       "       'Santa Cruz', 'Santa Fe', 'Santiago del Estero',\n",
       "       'Tierra del Fuego, Antártida e Islas del Atlántico Sur', 'Tucumán'],\n",
       "      dtype='object', name='province')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prov_data.index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the columns for ease of use\n",
    "df = df.rename(columns={\n",
    "    'Titular de la Licencia': 'license_owner',\n",
    "    'Señal Distintiva': 'callsign',\n",
    "    'Categoría': 'category',\n",
    "    'Expiración': 'expiration_date',\n",
    "    'Provincia': 'province',\n",
    "    'Localidad': 'city'\n",
    "})\n",
    "\n",
    "# Remove leading or trailing spaces for all object columns\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == object:\n",
    "        df[column] = df[column].str.strip() \n",
    "\n",
    "# Apply same format to category and city column\n",
    "df['category'] = df['category'].str.title()\n",
    "df['city'] = df['city'].str.title()\n",
    "\n",
    "# Rename province names to adhere to a standard\n",
    "df['province'] = df['province'].replace({\n",
    "    'Provincia de Buenos Aires': 'Buenos Aires',\n",
    "    'Ciudad de Buenos Aires': 'Ciudad Autónoma de Buenos Aires',\n",
    "    'Tierra del Fuego A. e I.A.S.': 'Tierra del Fuego, Antártida e Islas del Atlántico Sur'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ciudad Autónoma de Buenos Aires', 'Buenos Aires', 'Santa Fe',\n",
       "       'Chaco', 'Formosa', 'Córdoba', 'Misiones', 'Entre Ríos', 'Tucumán',\n",
       "       'Corrientes', 'Mendoza', 'Santiago del Estero', 'Salta',\n",
       "       'San Juan', 'San Luis', 'Catamarca', 'La Rioja', 'Jujuy',\n",
       "       'La Pampa', 'Río Negro', 'Chubut', 'Santa Cruz',\n",
       "       'Tierra del Fuego, Antártida e Islas del Atlántico Sur', 'Neuquén',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['province'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14995 entries, 0 to 14994\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   license_owner    14995 non-null  object        \n",
      " 1   callsign         14995 non-null  object        \n",
      " 2   category         14995 non-null  object        \n",
      " 3   expiration_date  14995 non-null  datetime64[ns]\n",
      " 4   province         14992 non-null  object        \n",
      " 5   city             14989 non-null  object        \n",
      "dtypes: datetime64[ns](1), object(5)\n",
      "memory usage: 703.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we perform some basic Data Quality operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "license_owner      0\n",
       "callsign           0\n",
       "category           0\n",
       "expiration_date    0\n",
       "province           3\n",
       "city               6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change dtype of Expiration date colunm\n",
    "df['expiration_date'] = pd.to_datetime(df['expiration_date'])\n",
    "\n",
    "# Check number of NaNs\n",
    "df.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some instances were the province or the city are not defined. While we can't do anything about the city, we can find out the corresponding state by looking at the callsign, in which the suffix (usually by the first letter) responds to the province of the station.\n",
    "\n",
    "Let's take a look into the missing ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>license_owner</th>\n",
       "      <th>callsign</th>\n",
       "      <th>category</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5322</th>\n",
       "      <td>JUAN CARLOS CHEHADHI</td>\n",
       "      <td>LU3OAB</td>\n",
       "      <td>Novicio</td>\n",
       "      <td>2023-10-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8205</th>\n",
       "      <td>ESTEBAN MANDUCI</td>\n",
       "      <td>LU6EEM</td>\n",
       "      <td>Novicio</td>\n",
       "      <td>2023-09-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9083</th>\n",
       "      <td>LEONARDO ANTONIO PAGLIARO</td>\n",
       "      <td>LU7DNI</td>\n",
       "      <td>Novicio</td>\n",
       "      <td>2023-07-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  license_owner callsign category expiration_date province  \\\n",
       "5322       JUAN CARLOS CHEHADHI   LU3OAB  Novicio      2023-10-16      NaN   \n",
       "8205            ESTEBAN MANDUCI   LU6EEM  Novicio      2023-09-14      NaN   \n",
       "9083  LEONARDO ANTONIO PAGLIARO   LU7DNI  Novicio      2023-07-13      NaN   \n",
       "\n",
       "     city  \n",
       "5322  NaN  \n",
       "8205  NaN  \n",
       "9083  NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['province'].isna(), :]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a function that fills NaN values province based on the callsign structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaNs in \"province\" column: 0\n"
     ]
    }
   ],
   "source": [
    "def fix_province_nan(callsign):\n",
    "    # State is defined by the first letter (and sometimes also second) of the suffix\n",
    "    first_letter = callsign[3]\n",
    "    second_letter = callsign[4]\n",
    "\n",
    "    match first_letter:\n",
    "        case 'A' | 'B' | 'C':\n",
    "            return 'Ciudad Autónoma de Buenos Aires'\n",
    "        case 'D' | 'E':\n",
    "            return 'Buenos Aires'\n",
    "        case 'F':\n",
    "            return 'Santa Fe'\n",
    "        case 'G':\n",
    "            if second_letter in [chr(i) for i in range(ord('A'), ord('P'))]:\n",
    "                return 'Chaco'\n",
    "            else:\n",
    "                return 'Formosa'\n",
    "        case 'H':\n",
    "            return 'Córdoba'\n",
    "        case 'I':\n",
    "            return 'Misiones'\n",
    "        case 'J':\n",
    "            return 'Entre Ríos'\n",
    "        case 'K':\n",
    "            return 'Tucumán'\n",
    "        case 'L':\n",
    "            return 'Corrientes'\n",
    "        case 'M':\n",
    "            return 'Mendoza'\n",
    "        case 'N':\n",
    "            return 'Santiago del Estero'\n",
    "        case 'O':\n",
    "            return 'Salta'\n",
    "        case 'P':\n",
    "            return 'San Juan'\n",
    "        case 'Q':\n",
    "            return 'San Luis'\n",
    "        case 'R':\n",
    "            return 'Catamarca'\n",
    "        case 'S':\n",
    "            return 'La Rioja'\n",
    "        case 'T':\n",
    "            return 'Jujuy'\n",
    "        case 'U':\n",
    "            return 'La Pampa'\n",
    "        case 'V':\n",
    "            return 'Río Negro' \n",
    "        case 'W':\n",
    "            return 'Chubut'\n",
    "        case 'X':\n",
    "            if second_letter in [chr(i) for i in range(ord('A'), ord('P'))]:\n",
    "                return 'Santa Cruz'\n",
    "            else:\n",
    "                return 'Tierra del Fuego, Antártida e Islas del Atlántico Sur'\n",
    "        case 'Y':\n",
    "            return 'Neuquén'\n",
    "        case 'Z':\n",
    "            return 'Tierra del Fuego, Antártida e Islas del Atlántico Sur'\n",
    "\n",
    "df['province'] = df.apply(lambda row: fix_province_nan(row['callsign']) if pd.isna(row['province']) else row['province'], axis=1)\n",
    "\n",
    "# Check if the function corrected the NaN\n",
    "print(f'NaNs in \"province\" column: {df[\"province\"].isna().sum()}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Analysis\n",
    "\n",
    "First of all, I'm going to create a GeoDataFrame. This way, I can link the provinces geography from the GeoJSON with the province data and the amateur radio callsigns database.\n",
    "\n",
    "Had to rewind GeoJSON because if not, ot rendered properly. More info [here](https://pypi.org/project/geojson-rewind/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rewinded GeoJSON\n",
    "rewinded = rewind(arg_prov_shape, rfc7946=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>salaries</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>latitude</th>\n",
       "      <th>total_licenses</th>\n",
       "      <th>novicio</th>\n",
       "      <th>general</th>\n",
       "      <th>superior</th>\n",
       "      <th>especial</th>\n",
       "      <th>licenses_perc_pop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>Tucumán</th>\n",
       "      <td>POLYGON ((-66.20361 -26.62782, -66.11572 -26.2...</td>\n",
       "      <td>1703186</td>\n",
       "      <td>22524</td>\n",
       "      <td>152862</td>\n",
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       "      <td>116</td>\n",
       "      <td>49</td>\n",
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       "      <th>Santiago del Estero</th>\n",
       "      <td>POLYGON ((-64.42108 -26.28356, -64.22333 -25.6...</td>\n",
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       "      <td>94</td>\n",
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       "    <tr>\n",
       "      <th>Salta</th>\n",
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       "      <td>155488</td>\n",
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       "      <td>118</td>\n",
       "      <td>54</td>\n",
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       "      <td>27</td>\n",
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       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000034</td>\n",
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       "    <tr>\n",
       "      <th>Formosa</th>\n",
       "      <td>POLYGON ((-62.35840 -24.08659, -62.40234 -22.4...</td>\n",
       "      <td>604278</td>\n",
       "      <td>72066</td>\n",
       "      <td>194757</td>\n",
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       "                                                              geometry  \\\n",
       "province                                                                 \n",
       "Tucumán              POLYGON ((-66.20361 -26.62782, -66.11572 -26.2...   \n",
       "Santiago del Estero  POLYGON ((-64.42108 -26.28356, -64.22333 -25.6...   \n",
       "Salta                POLYGON ((-66.11572 -26.21459, -66.21460 -26.1...   \n",
       "Jujuy                POLYGON ((-67.22534 -23.69483, -67.02759 -22.9...   \n",
       "Formosa              POLYGON ((-62.35840 -24.08659, -62.40234 -22.4...   \n",
       "\n",
       "                     population    area  salaries  pop_density   latitude  \\\n",
       "province                                                                    \n",
       "Tucumán                 1703186   22524    152862        75.62  26.953960   \n",
       "Santiago del Estero     1054028  136351    170466         7.73  27.771537   \n",
       "Salta                   1440672  155488    241973         9.27  24.310458   \n",
       "Jujuy                    797955   53219    153811        14.99  23.301782   \n",
       "Formosa                  604278   72066    194757         8.39  24.847571   \n",
       "\n",
       "                     total_licenses  novicio  general  superior  especial  \\\n",
       "province                                                                    \n",
       "Tucumán                         116       49       26        37         4   \n",
       "Santiago del Estero              94       62       16        14         2   \n",
       "Salta                           118       54       33        27         4   \n",
       "Jujuy                            27       17        1         8         1   \n",
       "Formosa                          27       15        8         4         0   \n",
       "\n",
       "                     licenses_perc_pop  \n",
       "province                                \n",
       "Tucumán                       0.000068  \n",
       "Santiago del Estero           0.000089  \n",
       "Salta                         0.000082  \n",
       "Jujuy                         0.000034  \n",
       "Formosa                       0.000045  "
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create GeoDataFrame\n",
    "gdf = gpd.GeoDataFrame.from_features(rewinded)\n",
    "gdf = gdf.rename(columns={'nombre': 'province'})\n",
    "gdf = gdf.set_index('province')\n",
    "\n",
    "# Merge province data previously loaded from CSV\n",
    "gdf = gdf.merge(prov_data, on='province')\n",
    "gdf['latitude'] = abs(gdf['geometry'].centroid.y)\n",
    "\n",
    "# Auxiliary groupby DataFrame\n",
    "grouped_by_df = df.groupby(['province', 'category']).size().unstack().fillna(0)\n",
    "\n",
    "# Count number of licenses for each province\n",
    "gdf['total_licenses'] = df.groupby('province').size()\n",
    "\n",
    "# Count number of licenses of each category for each province\n",
    "for category in ['Novicio', 'General', 'Superior', 'Especial']:\n",
    "    gdf[category.lower()] = grouped_by_df[category].astype(int)\n",
    "\n",
    "# Generate an additional column with the licenses as percentage of population\n",
    "gdf['licenses_perc_pop'] = gdf['total_licenses'] / gdf['population']\n",
    "\n",
    "gdf.head()"
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           "zerolinecolor": "rgb(36,36,36)"
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    "fig1 = px.bar(gdf['total_licenses'].sort_values(ascending=False), title='Total Amateur Radio Licenses per Province', template='simple_white')\n",
    "fig1.update_layout(height=600, font_family='Ubuntu', title_font_size=20, xaxis_title=None, yaxis_title=None, showlegend=False)\n",
    "fig1.update_traces(marker_color='chocolate', marker_line_color='DarkSlateGrey', marker_line_width=1, hovertemplate='Licenses: %{y}<extra></extra>')\n",
    "fig1.update_layout(hovermode='x unified')\n",
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    "fig1.write_json('./json/ham_graph1.json')"
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  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
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   "source": [
    "aux = gdf['total_licenses'].sort_values(ascending=False).cumsum() / df.shape[0]\n",
    "fig2 = px.line(aux, title='Cumulative Amateru Radio Licenses', template='simple_white', markers=True)\n",
    "fig2.update_layout(height=600, font_family='Ubuntu', title_font_size=20, xaxis_title=None, yaxis_title=None, showlegend=False)\n",
    "fig2.update_traces(line_color='goldenrod', line_width=2.5, marker_size=10, marker_color='chocolate', marker_line_color='DarkSlateGrey', marker_line_width=2, hovertemplate='With it %{y:.2%} of Total Licenses<extra></extra>')\n",
    "fig2.update_layout(hovermode='x unified')\n",
    "fig2.update_layout(yaxis={'tickformat': ',.0%',\n",
    "                    'range': [0,1.05]})\n",
    "fig2.add_hline(y=0.5, opacity=1, line_width=2, line_dash='dash', line_color='navy')\n",
    "fig2.add_hline(y=0.75, opacity=1, line_width=2, line_dash='dash', line_color='navy')\n",
    "fig2.add_annotation(text='50% of all licenses',\n",
    "                  xref='paper', yref='paper',\n",
    "                  x=1, y=0.50, showarrow=False)\n",
    "fig2.add_annotation(text='75% of all licenses',\n",
    "                  xref='paper', yref='paper',\n",
    "                  x=1, y=0.76, showarrow=False)\n",
    "\n",
    "fig2.show()\n",
    "fig2.write_json('./json/ham_graph2.json')"
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  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
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          "title": {
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         "font": {
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     },
     "metadata": {},
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   ],
   "source": [
    "fig3 = px.choropleth_mapbox(gdf, \n",
    "                           geojson=gdf.geometry, \n",
    "                           locations=gdf.index,\n",
    "                           color='total_licenses',\n",
    "                           color_continuous_scale = px.colors.sequential.Oranges,\n",
    "                           mapbox_style='carto-positron', \n",
    "                           center={'lat': -40.7, 'lon': -65.12695},\n",
    "                           zoom=3,\n",
    "                           opacity=0.6,\n",
    "                           labels={'total_licenses': 'Total Licenses'},\n",
    "                           hover_name=gdf.index,\n",
    "                           title='Total Amateur Radio Licenses')\n",
    "fig3.update_layout(height=700, width=900, margin={'b':0}, font_family='Ubuntu', title_font_size=20)\n",
    "fig3.update_traces(hovertemplate='<b>%{hovertext}</b><br>Total Licenses: %{z}<extra></extra>')\n",
    "fig3.update_layout(coloraxis=dict(colorbar=dict(orientation='h')))\n",
    "fig3.show()\n",
    "fig3.write_json('./json/ham_graph3.json')"
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  {
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         "tickformat": ",.2%",
         "title": {}
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     "metadata": {},
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   ],
   "source": [
    "fig4 = px.bar((gdf['licenses_perc_pop']).sort_values(ascending=False), title='Amateur Radio Licenses Relative to Province Population')\n",
    "fig4.update_layout(height=600, font_family='Ubuntu', title_font_size=20, xaxis_title=None, yaxis_title=None, showlegend=False)\n",
    "fig4.update_traces(marker_color='chocolate', marker_line_color='DarkSlateGrey', marker_line_width=1, hovertemplate='Licenses: %{y:0.4%}<extra></extra>')\n",
    "fig4.update_layout(hovermode='x unified')\n",
    "fig4.update_layout(yaxis={'tickformat': ',.2%'})\n",
    "                    #'range': [0,1.05]})\n",
    "fig4.show()\n",
    "fig4.write_json('./json/ham_graph4.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
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   "source": [
    "fig5 = px.choropleth_mapbox(gdf, \n",
    "                           geojson=gdf.geometry, \n",
    "                           locations=gdf.index,\n",
    "                           color='licenses_perc_pop',\n",
    "                           color_continuous_scale = px.colors.sequential.Oranges,\n",
    "                           mapbox_style='carto-positron', \n",
    "                           center={'lat': -40.7, 'lon': -65.12695},\n",
    "                           zoom=3,\n",
    "                           opacity=0.6,\n",
    "                           labels={'licenses_perc_pop': 'Licenses as % of population'},\n",
    "                           title='Amateur Radio Licenses Relative to Population',\n",
    "                           hover_name=gdf.index)\n",
    "fig5.update_layout(height=700, width=900, margin={'b':0}, font_family='Ubuntu', title_font_size=20)\n",
    "fig5.update_traces(hovertemplate='<b>%{hovertext}</b><br>Licenses as % of popoulation: %{z:.4%}<extra></extra>')\n",
    "fig5.update_layout(coloraxis=dict(colorbar=dict(orientation='h', tickformat=',.2%')))\n",
    "fig5.show()\n",
    "fig5.write_json('./json/ham_graph5.json')"
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  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
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   "source": [
    "fig6 = px.scatter(gdf, x='salaries', y='licenses_perc_pop', trendline='ols', title='Salaries vs. Amateur Radio Licenses Relative to Population', hover_name=gdf.index)\n",
    "fig6.update_layout(height=600, font_family='Ubuntu', title_font_size=20, xaxis_title='Mean Monthly Salary in ARS', yaxis_title='Amateur Radio Licenses Relative to Population', showlegend=False)\n",
    "fig6.update_traces(marker_color='chocolate', marker_size=10, marker_line_color='DarkSlateGrey', marker_line_width=1, hovertemplate='<b>%{hovertext}</b><br><br>Area: %{x} km^2<br>Licenses: %{y} of population<extra></extra>')\n",
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    "fig6.update_traces(line_color='dodgerblue', hoverinfo=None, selector = dict(mode='lines'))\n",
    "fig6.show()\n",
    "fig6.write_json('./json/ham_graph6.json')"
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   "cell_type": "code",
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    "fig7 = px.scatter(gdf, x='pop_density', y='licenses_perc_pop', log_x=True, title='Population Density vs. Amateur Radio Licenses Relative to Population', hover_name=gdf.index)\n",
    "fig7.update_layout(height=600, font_family='Ubuntu', title_font_size=20, xaxis_title='Population Density', yaxis_title='Amateur Radio Licenses Relative to Population', showlegend=False)\n",
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    "fig7.update_layout(hovermode='x unified')\n",
    "fig7.show()\n",
    "fig7.write_json('./json/ham_graph7.json')"
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   "cell_type": "code",
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   "source": [
    "outliers = ['Ciudad Autónoma de Buenos Aires', 'Buenos Aires', 'Tierra del Fuego, Antártida e Islas del Atlántico Sur']\n",
    "gdf_without_outliers = gdf.drop(outliers)\n",
    "fig8 = px.scatter(gdf_without_outliers, x='area', y='licenses_perc_pop', trendline='ols', title='Province Area vs. Amateur Radio Licenses Relative to Population', hover_name=gdf_without_outliers.index)\n",
    "fig8.update_layout(height=600, font_family='Ubuntu', title_font_size=20, xaxis_title='Province Area [km^2]', yaxis_title='Amateur Radio Licenses Relative to Population', showlegend=False)\n",
    "fig8.update_traces(marker_color='chocolate', marker_size=10, marker_line_color='DarkSlateGrey', marker_line_width=1, hovertemplate='<b>%{hovertext}</b><br><br>Area: %{x} km^2<br>Licenses: %{y} of population<extra></extra>')\n",
    "fig8.add_scatter(name='outliers', x=gdf.loc[outliers,'area'], y=gdf.loc[outliers,'licenses_perc_pop'], mode='markers', marker_color='dodgerblue', hovertext=gdf.loc[outliers,'licenses_perc_pop'].index, marker_size=10, marker_line_color='DarkSlateGrey', marker_line_width=1, hovertemplate='<b>%{hovertext}</b><br><br>Area: %{x} km^2<br>Licenses: %{y} of population<extra></extra>')\n",
    "fig8.update_traces(line_color='dodgerblue', hoverinfo=None, selector = dict(mode='lines'))\n",
    "fig8.update_layout(yaxis={'tickformat': ',.2%'})\n",
    "fig8.show()\n",
    "fig8.write_json('./json/ham_graph8.json')"
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  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
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        },
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   "source": [
    "gdf_without_outliers = gdf.drop(['Ciudad Autónoma de Buenos Aires', 'Buenos Aires'])\n",
    "fig9 = px.scatter(gdf, x='latitude', y='licenses_perc_pop', trendline='ols', title='Latitude vs. Amateur Radio Licenses Relative to Population', hover_name=gdf.index)\n",
    "fig9.update_layout(height=600, font_family='Ubuntu', title_font_size=20, xaxis_title='Latitude [°S]', yaxis_title='Amateur Radio Licenses Relative to Population', showlegend=False)\n",
    "fig9.update_traces(marker_color='chocolate', marker_size=10, marker_line_color='DarkSlateGrey', marker_line_width=1, hovertemplate='<b>%{hovertext}</b><br><br>Area: %{x} km^2<br>Licenses: %{y} of population<extra></extra>', selector = dict(mode='markers'))\n",
    "fig9.update_traces(line_color='dodgerblue', hoverinfo='skip', selector = dict(mode='lines'))\n",
    "fig9.update_layout(yaxis={'tickformat': ',.2%'})\n",
    "fig9.show()\n",
    "fig9.write_json('./json/ham_graph9.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get number of licenses for each category\n",
    "total_per_category = df.groupby('category').size()\n",
    "stacked_per_category = total_per_category.sort_values(ascending=False).cumsum()"
   ]
  },
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   "source": [
    "fig10 = px.bar(total_per_category.sort_values(ascending=True), orientation='h', title='National Amateur Radio Licenses by Category')\n",
    "fig10.update_layout(height=500, width=1200, font_family='Ubuntu', title_font_size=20, xaxis_title=None, yaxis_title=None, showlegend=False)\n",
    "fig10.update_traces(marker_color='chocolate', marker_line_color='DarkSlateGrey', marker_line_width=1, hovertemplate='Licenses: %{x}<extra></extra>')\n",
    "fig10.update_layout(hovermode='y unified')\n",
    "fig10.update_layout(margin={'pad': 10})\n",
    "fig10.show()\n",
    "fig10.write_json('./json/ham_graph10.json')"
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   "source": [
    "## 4. Conclusions\n",
    "\n",
    "## 5. Possible future work\n",
    "\n",
    "1. Geolocation of corrected cities.\n",
    "2. Download from API names of Argentina, to differentiate institutions from people"
   ]
  }
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