{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84130e7d",
   "metadata": {},
   "source": [
    "# Interpreting Used DJI Drone Listed Price Ranges\n",
    "\n",
    "## tl;dr\n",
    "\n",
    "This reproducible companion validates **43 aircraft-model aggregates**\n",
    "summarizing **251 published catalog configurations** in the\n",
    "Reboot Hub Q3 2026 dataset. The values are **listed prices, not completed-sale prices**.\n",
    "Configuration counts describe **configuration coverage, not sales volume or market demand**.\n",
    "\n",
    "The notebook shows how to read low, median and high listed-price points without\n",
    "treating a one-date catalog snapshot as a transaction index or a time series.\n",
    "\n",
    "- Maintained source: [https://reboot-hub.com/pages/reboot-hub-data](https://reboot-hub.com/pages/reboot-hub-data)\n",
    "- Exact version DOI: [https://doi.org/10.5281/zenodo.21387578](https://doi.org/10.5281/zenodo.21387578)\n",
    "- GitHub release: [https://github.com/Reboot-Hub/dji-drone-specs-used-price-index/releases/tag/v0.2.0](https://github.com/Reboot-Hub/dji-drone-specs-used-price-index/releases/tag/v0.2.0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe49c9d",
   "metadata": {},
   "source": [
    "## Context & Methods\n",
    "\n",
    "### Key assumptions and boundaries\n",
    "\n",
    "- Snapshot date: **2026-07-07**; release: **2026-Q3**.\n",
    "- Grain: one aircraft model-level aggregate, not one sale or one customer order.\n",
    "- Price ranges can reflect condition, controller, battery, accessory and bundle\n",
    "  differences. A wide range is not proof of price volatility.\n",
    "- The underlying configuration rows are not included in this public release.\n",
    "- A single snapshot cannot establish a price trend. Trend comparisons require a\n",
    "  later quarterly release generated with the same method.\n",
    "- Reboot Hub is **not affiliated with, endorsed by, or officially authorized by DJI**.\n",
    "\n",
    "The analysis uses only the public CC BY 4.0 release. It does not contain customer,\n",
    "supplier, repair-order, cost or credential data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa8d79a",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "### 1. Load the versioned public CSV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c45479f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-16T13:37:42.446280Z",
     "iopub.status.busy": "2026-07-16T13:37:42.446280Z",
     "iopub.status.idle": "2026-07-16T13:37:43.007012Z",
     "shell.execute_reply": "2026-07-16T13:37:43.006509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 43 rows from ..\\model_price_summary_2026_q3.csv\n"
     ]
    }
   ],
   "source": [
    "import base64\n",
    "from html import escape\n",
    "from io import BytesIO\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from IPython.display import HTML, display\n",
    "\n",
    "DATA_FILENAME = 'model_price_summary_2026_q3.csv'\n",
    "RAW_RELEASE_URL = 'https://raw.githubusercontent.com/Reboot-Hub/dji-drone-specs-used-price-index/v0.2.0/model_price_summary_2026_q3.csv'\n",
    "EXPECTED_MODEL_ROWS = 43\n",
    "EXPECTED_CONFIGURATION_COUNT = 251\n",
    "\n",
    "\n",
    "def display_figure(fig, alt_text):\n",
    "    buffer = BytesIO()\n",
    "    fig.savefig(buffer, format=\"png\", dpi=144, bbox_inches=\"tight\")\n",
    "    encoded = base64.b64encode(buffer.getvalue()).decode(\"ascii\")\n",
    "    safe_alt = escape(alt_text, quote=True)\n",
    "    display(\n",
    "        HTML(\n",
    "            f'<img src=\"data:image/png;base64,{encoded}\" alt=\"{safe_alt}\" '\n",
    "            'style=\"max-width:100%;height:auto;\">'\n",
    "        )\n",
    "    )\n",
    "    plt.close(fig)\n",
    "\n",
    "local_candidates = [\n",
    "    Path(DATA_FILENAME),\n",
    "    Path(\"..\") / DATA_FILENAME,\n",
    "    Path(\"/kaggle/input/dji-drone-specs-used-price-index\") / DATA_FILENAME,\n",
    "]\n",
    "local_source = next((path for path in local_candidates if path.exists()), None)\n",
    "data_source = local_source if local_source is not None else RAW_RELEASE_URL\n",
    "data = pd.read_csv(data_source)\n",
    "\n",
    "print(f\"Loaded {len(data)} rows from {data_source}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ece0a43",
   "metadata": {},
   "source": [
    "### 2. Re-run the publication gates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddeec64f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-16T13:37:43.008016Z",
     "iopub.status.busy": "2026-07-16T13:37:43.008016Z",
     "iopub.status.idle": "2026-07-16T13:37:43.016387Z",
     "shell.execute_reply": "2026-07-16T13:37:43.016387Z"
    }
   },
   "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>check</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aircraft-model rows</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>published configurations summarized</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>duplicate model names</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>invalid low/median/high ranges</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>snapshot dates</td>\n",
       "      <td>2026-07-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 check      result\n",
       "0                  aircraft-model rows          43\n",
       "1  published configurations summarized         251\n",
       "2                duplicate model names           0\n",
       "3       invalid low/median/high ranges           0\n",
       "4                       snapshot dates  2026-07-07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "required_columns = {\n",
    "    \"model\",\n",
    "    \"configurations_tracked\",\n",
    "    \"listed_price_low_usd\",\n",
    "    \"listed_price_high_usd\",\n",
    "    \"median_listed_price_usd\",\n",
    "    \"snapshot_date\",\n",
    "    \"release_quarter\",\n",
    "    \"observation_unit\",\n",
    "    \"quality_status\",\n",
    "    \"source_url\",\n",
    "    \"methodology_note\",\n",
    "    \"non_affiliation_note\",\n",
    "}\n",
    "\n",
    "assert required_columns.issubset(data.columns)\n",
    "assert len(data) == EXPECTED_MODEL_ROWS\n",
    "assert int(data[\"configurations_tracked\"].sum()) == EXPECTED_CONFIGURATION_COUNT\n",
    "assert data[\"model\"].str.casefold().is_unique\n",
    "assert (data[\"configurations_tracked\"] > 0).all()\n",
    "assert (data[\"listed_price_low_usd\"] <= data[\"median_listed_price_usd\"]).all()\n",
    "assert (data[\"median_listed_price_usd\"] <= data[\"listed_price_high_usd\"]).all()\n",
    "assert data[\"snapshot_date\"].nunique() == 1\n",
    "assert data[\"release_quarter\"].nunique() == 1\n",
    "assert (data[\"quality_status\"] == \"passed_publication_gate\").all()\n",
    "\n",
    "validation_summary = pd.DataFrame(\n",
    "    {\n",
    "        \"check\": [\n",
    "            \"aircraft-model rows\",\n",
    "            \"published configurations summarized\",\n",
    "            \"duplicate model names\",\n",
    "            \"invalid low/median/high ranges\",\n",
    "            \"snapshot dates\",\n",
    "        ],\n",
    "        \"result\": [\n",
    "            len(data),\n",
    "            int(data[\"configurations_tracked\"].sum()),\n",
    "            int(data[\"model\"].str.casefold().duplicated().sum()),\n",
    "            int(\n",
    "                (\n",
    "                    (data[\"listed_price_low_usd\"] > data[\"median_listed_price_usd\"])\n",
    "                    | (data[\"median_listed_price_usd\"] > data[\"listed_price_high_usd\"])\n",
    "                ).sum()\n",
    "            ),\n",
    "            \", \".join(sorted(data[\"snapshot_date\"].astype(str).unique())),\n",
    "        ],\n",
    "    }\n",
    ")\n",
    "display(validation_summary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48ca2bcf",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "### 3. Inspect configuration coverage\n",
    "\n",
    "`configurations_tracked` counts the catalog configurations summarized for a\n",
    "model. It is useful for judging how much listed-price variation was observed,\n",
    "but it is **not** a proxy for sales, searches, installed base or popularity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eca6e80d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-16T13:37:43.017391Z",
     "iopub.status.busy": "2026-07-16T13:37:43.017391Z",
     "iopub.status.idle": "2026-07-16T13:37:43.141334Z",
     "shell.execute_reply": "2026-07-16T13:37:43.141334Z"
    }
   },
   "outputs": [
    {
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\" alt=\"Bar chart of configuration coverage by DJI model in the Q3 2026 listed-price snapshot.\" style=\"max-width:100%;height:auto;\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coverage = (\n",
    "    data[[\"model\", \"configurations_tracked\"]]\n",
    "    .sort_values([\"configurations_tracked\", \"model\"], ascending=[False, True])\n",
    "    .head(12)\n",
    "    .sort_values(\"configurations_tracked\")\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5.5))\n",
    "ax.barh(coverage[\"model\"], coverage[\"configurations_tracked\"], color=\"#d71920\")\n",
    "ax.set_title(\"Highest configuration coverage in the Q3 2026 snapshot\")\n",
    "ax.set_xlabel(\"Published catalog configurations summarized\")\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlim(left=0)\n",
    "ax.grid(axis=\"x\", alpha=0.2)\n",
    "plt.tight_layout()\n",
    "display_figure(\n",
    "    fig,\n",
    "    \"Bar chart of configuration coverage by DJI model in the Q3 2026 listed-price snapshot.\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e3f346",
   "metadata": {},
   "source": [
    "### 4. Read listed-price ranges with their median points\n",
    "\n",
    "The next view is limited to the same twelve models with the greatest configuration\n",
    "coverage. Horizontal lines show observed low-to-high listed prices, while dots\n",
    "show the model-level median. The chart compares catalog ranges only; it does not\n",
    "measure transaction value, depreciation or market volatility.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b299f995",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-16T13:37:43.142886Z",
     "iopub.status.busy": "2026-07-16T13:37:43.142369Z",
     "iopub.status.idle": "2026-07-16T13:37:43.244269Z",
     "shell.execute_reply": "2026-07-16T13:37:43.244269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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alt=\"Range chart of low, median and high listed prices for the twelve models with the highest configuration coverage.\" style=\"max-width:100%;height:auto;\">"
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#dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mini 3</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$280 to $530</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$405</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mavic 2 Pro</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$276 to $700</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$455</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mavic 2</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$247 to $700</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$465</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mavic Air 2</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$241 to $750</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$470</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mini 3 Pro</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$380 to $740</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$516</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Air 2S</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$391 to $776</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$543</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mini 4 Pro</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$461 to $850</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$672</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Air 3</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$591 to $1,129</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$828</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Air 3S</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$790 to $1,320</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$1,066</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mavic 3</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$1,481 to $2,601</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$1,953</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mavic 3 Classic</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$2,140 to $2,721</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$2,450</td></tr><tr><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:32%;\">DJI Mavic 3 Pro</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:13%;\">10</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:35%;\">$1,712 to $3,354</td><td style=\"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;white-space:normal;overflow-wrap:anywhere;width:20%;\">$2,486</td></tr></tbody></table>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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   ],
   "source": [
    "range_view = (\n",
    "    data.merge(coverage[[\"model\"]], on=\"model\", how=\"inner\")\n",
    "    .sort_values(\"median_listed_price_usd\")\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "range_view[\"listed_price_spread_usd\"] = (\n",
    "    range_view[\"listed_price_high_usd\"] - range_view[\"listed_price_low_usd\"]\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6.5))\n",
    "positions = range(len(range_view))\n",
    "ax.hlines(\n",
    "    positions,\n",
    "    range_view[\"listed_price_low_usd\"],\n",
    "    range_view[\"listed_price_high_usd\"],\n",
    "    color=\"#59636e\",\n",
    "    linewidth=3,\n",
    ")\n",
    "ax.scatter(\n",
    "    range_view[\"median_listed_price_usd\"],\n",
    "    list(positions),\n",
    "    color=\"#d71920\",\n",
    "    s=45,\n",
    "    zorder=3,\n",
    "    label=\"Median listed price\",\n",
    ")\n",
    "ax.set_yticks(list(positions), range_view[\"model\"])\n",
    "ax.set_title(\"Observed listed-price ranges for the most-covered models\")\n",
    "ax.set_xlabel(\"Listed price (USD)\")\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlim(left=0)\n",
    "ax.grid(axis=\"x\", alpha=0.2)\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.tight_layout()\n",
    "display_figure(\n",
    "    fig,\n",
    "    \"Range chart of low, median and high listed prices for the twelve models with the highest configuration coverage.\",\n",
    ")\n",
    "\n",
    "cell_style = (\n",
    "    \"border:1px solid #dfe3e8;padding:5px;text-align:left;vertical-align:top;\"\n",
    "    \"white-space:normal;overflow-wrap:anywhere;\"\n",
    ")\n",
    "header_style = cell_style + \"background:#111827;color:#fff;font-weight:700;\"\n",
    "table_rows = []\n",
    "for _, row in range_view.iterrows():\n",
    "    listed_range = (\n",
    "        f\"${row['listed_price_low_usd']:,.0f} to \"\n",
    "        f\"${row['listed_price_high_usd']:,.0f}\"\n",
    "    )\n",
    "    median = f\"${row['median_listed_price_usd']:,.0f}\"\n",
    "    table_rows.append(\n",
    "        \"<tr>\"\n",
    "        f'<td style=\"{cell_style}width:32%;\">{escape(str(row[\"model\"]))}</td>'\n",
    "        f'<td style=\"{cell_style}width:13%;\">{int(row[\"configurations_tracked\"])}</td>'\n",
    "        f'<td style=\"{cell_style}width:35%;\">{escape(listed_range)}</td>'\n",
    "        f'<td style=\"{cell_style}width:20%;\">{escape(median)}</td>'\n",
    "        \"</tr>\"\n",
    "    )\n",
    "\n",
    "table_html = (\n",
    "    '<table style=\"width:100%;max-width:100%;table-layout: fixed;'\n",
    "    'border-collapse:collapse;font-size:12px;line-height:1.35;\">'\n",
    "    \"<thead><tr>\"\n",
    "    f'<th style=\"{header_style}width:32%;\">Model</th>'\n",
    "    f'<th style=\"{header_style}width:13%;\">Configs</th>'\n",
    "    f'<th style=\"{header_style}width:35%;\">Listed range (USD)</th>'\n",
    "    f'<th style=\"{header_style}width:20%;\">Median</th>'\n",
    "    \"</tr></thead><tbody>\"\n",
    "    + \"\".join(table_rows)\n",
    "    + \"</tbody></table>\"\n",
    ")\n",
    "display(HTML(table_html))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf7966a",
   "metadata": {},
   "source": [
    "## Takeaways\n",
    "\n",
    "1. The public release contains **43 aircraft-model aggregates** covering\n",
    "   **251 published catalog configurations** after documented\n",
    "   exclusion and normalization gates.\n",
    "2. A model name alone does not explain its observed listed-price range. Bundle,\n",
    "   controller, battery, accessories and condition can all move the endpoints.\n",
    "3. Configuration coverage is a data-coverage measure, not sales volume or market\n",
    "   demand. Listed prices are not completed-sale prices.\n",
    "4. The Q3 2026 release is a baseline snapshot. No depreciation or trend claim is\n",
    "   supportable until a later, methodologically comparable quarter exists.\n",
    "5. For formal reuse, cite **Reboot Hub DJI Drone Specs and Used Price Index v0.2.0**\n",
    "   using DOI [10.5281/zenodo.21387578](https://doi.org/10.5281/zenodo.21387578) and retain the methodology\n",
    "   boundary from the [maintained source](https://reboot-hub.com/pages/reboot-hub-data).\n",
    "\n",
    "License: CC BY 4.0. Reboot Hub is not affiliated with, endorsed by, or officially\n",
    "authorized by DJI.\n"
   ]
  }
 ],
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