{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# World Languages - Interactive Demo\n",
        "\n",
        "Explore **7,130 world languages** with geographic coordinates, speaker populations, and language family classification.\n",
        "\n",
        "**Dataset Highlights:**\n",
        "- Geographic coordinates for mapping\n",
        "- Speaker population estimates\n",
        "- Language family classification (Glottolog)\n",
        "- Bible translation status (Joshua Project)\n",
        "- ISO 639-3 codes as primary keys"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Uncomment to install dependencies\n",
        "# !pip install pandas matplotlib seaborn folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from collections import Counter\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "plt.style.use('seaborn-v0_8-darkgrid')\n",
        "sns.set_palette('husl')\n",
        "\n",
        "print('Libraries loaded')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open('world_languages_integrated.json') as f:\n",
        "    data = json.load(f)\n",
        "\n",
        "print(f'Total languages: {len(data):,}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Flatten the nested structure for analysis\n",
        "records = []\n",
        "for lang in data:\n",
        "    record = {\n",
        "        'iso_639_3': lang.get('iso_639_3'),\n",
        "        'name': lang.get('name'),\n",
        "        'family': lang.get('glottolog', {}).get('family_name', 'Unknown'),\n",
        "        'macroarea': lang.get('glottolog', {}).get('macroarea', 'Unknown'),\n",
        "        'latitude': lang.get('glottolog', {}).get('latitude'),\n",
        "        'longitude': lang.get('glottolog', {}).get('longitude'),\n",
        "        'glottocode': lang.get('glottolog', {}).get('glottocode'),\n",
        "        'speakers': lang.get('speaker_count', {}).get('count') if lang.get('speaker_count') else None,\n",
        "        'religion': lang.get('joshua_project', {}).get('primary_religion', ''),\n",
        "        'bible_status': lang.get('joshua_project', {}).get('bible_status', 0),\n",
        "    }\n",
        "    records.append(record)\n",
        "\n",
        "df = pd.DataFrame(records)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Dataset Overview"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print('Dataset Overview:')\n",
        "print('=' * 50)\n",
        "print(f'Total languages: {len(df):,}')\n",
        "print(f'With coordinates: {df[\"latitude\"].notna().sum():,} ({df[\"latitude\"].notna().sum()/len(df)*100:.1f}%)')\n",
        "print(f'With speaker counts: {df[\"speakers\"].notna().sum():,} ({df[\"speakers\"].notna().sum()/len(df)*100:.1f}%)')\n",
        "print(f'Unique families: {df[\"family\"].nunique()}')\n",
        "print(f'Unique macroareas: {df[\"macroarea\"].nunique()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. Language Family Distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Top 15 language families\n",
        "family_counts = df['family'].value_counts().head(15)\n",
        "\n",
        "print('Top 15 Language Families:')\n",
        "print('=' * 50)\n",
        "for family, count in family_counts.items():\n",
        "    pct = count / len(df) * 100\n",
        "    bar = '|' * int(pct)\n",
        "    print(f'{family:30s} {count:5,} ({pct:5.1f}%) {bar}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "family_counts.plot(kind='barh', ax=ax, color='steelblue')\n",
        "ax.set_xlabel('Number of Languages', fontsize=12)\n",
        "ax.set_ylabel('Language Family', fontsize=12)\n",
        "ax.set_title('Top 15 Language Families', fontsize=14, fontweight='bold')\n",
        "ax.grid(axis='x', alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. Geographic Distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Macroarea distribution\n",
        "macroarea_counts = df['macroarea'].value_counts()\n",
        "\n",
        "print('Languages by Macroarea:')\n",
        "print('=' * 50)\n",
        "for area, count in macroarea_counts.items():\n",
        "    pct = count / len(df) * 100\n",
        "    print(f'{area:20s} {count:5,} ({pct:5.1f}%)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Plot languages on world map\n",
        "valid_coords = df[df['latitude'].notna() & df['longitude'].notna()]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "scatter = ax.scatter(\n",
        "    valid_coords['longitude'],\n",
        "    valid_coords['latitude'],\n",
        "    c=pd.factorize(valid_coords['macroarea'])[0],\n",
        "    alpha=0.5,\n",
        "    s=5,\n",
        "    cmap='tab10'\n",
        ")\n",
        "ax.set_xlabel('Longitude', fontsize=12)\n",
        "ax.set_ylabel('Latitude', fontsize=12)\n",
        "ax.set_title('Global Distribution of Languages', fontsize=14, fontweight='bold')\n",
        "ax.grid(alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. Speaker Populations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Top 20 languages by speakers\n",
        "with_speakers = df[df['speakers'].notna()].copy()\n",
        "top_speakers = with_speakers.nlargest(20, 'speakers')[['name', 'family', 'speakers']]\n",
        "\n",
        "print('Top 20 Languages by Speaker Count:')\n",
        "print('=' * 60)\n",
        "for idx, row in top_speakers.iterrows():\n",
        "    speakers_m = row['speakers'] / 1_000_000\n",
        "    print(f\"{row['name']:25s} {row['family']:25s} {speakers_m:8.1f}M\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Speaker distribution (log scale)\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "with_speakers['speakers'].apply(np.log10).hist(bins=50, ax=ax, color='teal', edgecolor='white')\n",
        "ax.set_xlabel('Speakers (log10 scale)', fontsize=12)\n",
        "ax.set_ylabel('Number of Languages', fontsize=12)\n",
        "ax.set_title('Distribution of Speaker Populations', fontsize=14, fontweight='bold')\n",
        "ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
        "ax.set_xticklabels(['1', '10', '100', '1K', '10K', '100K', '1M', '10M', '100M', '1B'])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 6. Bible Translation Status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Bible translation status\n",
        "bible_labels = {\n",
        "    0: 'Unspecified',\n",
        "    1: 'Translation Needed',\n",
        "    2: 'Translation Started',\n",
        "    3: 'Portions Available',\n",
        "    4: 'New Testament',\n",
        "    5: 'Complete Bible'\n",
        "}\n",
        "\n",
        "df['bible_label'] = df['bible_status'].map(bible_labels)\n",
        "bible_counts = df['bible_label'].value_counts()\n",
        "\n",
        "print('Bible Translation Status:')\n",
        "print('=' * 50)\n",
        "for status, count in bible_counts.items():\n",
        "    pct = count / len(df) * 100\n",
        "    print(f'{status:25s} {count:5,} ({pct:5.1f}%)')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 7. Interactive Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import folium\n",
        "from folium.plugins import MarkerCluster\n",
        "\n",
        "# Sample for performance\n",
        "sample = valid_coords.sample(min(1000, len(valid_coords)))\n",
        "\n",
        "m = folium.Map(location=[20, 0], zoom_start=2, tiles='CartoDB positron')\n",
        "marker_cluster = MarkerCluster().add_to(m)\n",
        "\n",
        "for idx, row in sample.iterrows():\n",
        "    popup = f\"<b>{row['name']}</b><br>Family: {row['family']}<br>Macroarea: {row['macroarea']}\"\n",
        "    folium.CircleMarker(\n",
        "        location=[row['latitude'], row['longitude']],\n",
        "        radius=4,\n",
        "        popup=popup,\n",
        "        color='steelblue',\n",
        "        fill=True,\n",
        "        fillOpacity=0.6\n",
        "    ).add_to(marker_cluster)\n",
        "\n",
        "m.save('languages_map.html')\n",
        "print('Map saved to languages_map.html')\n",
        "m"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 8. Query Examples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Find all Indo-European languages\n",
        "indo_european = df[df['family'] == 'Indo-European']\n",
        "print(f'Indo-European languages: {len(indo_european):,}')\n",
        "print(indo_european[['name', 'macroarea', 'speakers']].head(10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Find endangered languages (small speaker populations)\n",
        "endangered = df[(df['speakers'].notna()) & (df['speakers'] < 1000)]\n",
        "print(f'Languages with <1000 speakers: {len(endangered):,}')\n",
        "print(endangered[['name', 'family', 'speakers']].head(10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Languages in Africa\n",
        "africa = df[df['macroarea'] == 'Africa']\n",
        "africa_families = africa['family'].value_counts().head(10)\n",
        "print(f'Languages in Africa: {len(africa):,}')\n",
        "print('\\nTop families in Africa:')\n",
        "print(africa_families)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Conclusion\n",
        "\n",
        "This notebook demonstrated:\n",
        "\n",
        "- Loading and exploring 7,130 world languages\n",
        "- Analyzing language family distributions\n",
        "- Mapping geographic distributions\n",
        "- Exploring speaker populations\n",
        "- Querying by region and attributes\n",
        "\n",
        "**Author**: Luke Steuber | luke@lukesteuber.com | @lukesteuber.com (Bluesky)"
      ]
    }
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