diff --git "a/Python Folder/All country's Total Debt/debt.ipynb" "b/Python Folder/All country's Total Debt/debt.ipynb"
new file mode 100644--- /dev/null
+++ "b/Python Folder/All country's Total Debt/debt.ipynb"
@@ -0,0 +1,563 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b2f15d4f",
+   "metadata": {},
+   "source": [
+    "Use Python to fetch country debt profiles. Use a horizontal bar chart, the y-axis is the list of all countries in the world (for example, Japan, Korea, Philippines, China, and more), and the x-axis is the total debt in $.  Make a ranking label on the outside and the right part of the bar, followed by the number of debt$ label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f892c8d3",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "ee785c18",
+   "metadata": {},
+   "source": [
+    "![image-2.png](attachment:image-2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "330397a2",
+   "metadata": {},
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "import re\n",
+    "\n",
+    "# -----------------------\n",
+    "# FETCH DATA\n",
+    "# -----------------------\n",
+    "\n",
+    "url = \"https://worldpopulationreview.com/country-rankings/countries-by-national-debt\"\n",
+    "html = requests.get(url).text\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "table = soup.find(\"table\")\n",
+    "rows = table.find_all(\"tr\")[1:]\n",
+    "\n",
+    "DEBT_MULT = {\"T\": 1e12, \"B\": 1e9, \"M\": 1e6}\n",
+    "\n",
+    "records = []\n",
+    "\n",
+    "for row in rows:\n",
+    "    cols = row.find_all(\"td\")\n",
+    "    if len(cols) < 5:\n",
+    "        continue\n",
+    "\n",
+    "    country = cols[1].text.strip()\n",
+    "    if country.upper() == \"TOTAL\":\n",
+    "        continue\n",
+    "\n",
+    "    debt_text = cols[2].text.strip()\n",
+    "    gdp_pct_text = cols[3].text.strip()\n",
+    "    per_capita_text = cols[4].text.strip()\n",
+    "\n",
+    "    # ---- NATIONAL DEBT ----\n",
+    "    m = re.match(r\"\\$(\\d+(\\.\\d+)?)([TBM])\", debt_text)\n",
+    "    if not m:\n",
+    "        continue\n",
+    "    debt_val = float(m.group(1))\n",
+    "    debt_unit = m.group(3)\n",
+    "    debt_usd = int(debt_val * DEBT_MULT[debt_unit])\n",
+    "\n",
+    "    # ---- DEBT % GDP ----\n",
+    "    debt_pct_gdp = (\n",
+    "        float(gdp_pct_text.replace(\"%\", \"\")) if \"%\" in gdp_pct_text else None\n",
+    "    )\n",
+    "\n",
+    "    # ---- DEBT PER CAPITA ----\n",
+    "    per_capita = None\n",
+    "    pc_text = per_capita_text.replace(\"$\", \"\").replace(\",\", \"\").strip()\n",
+    "    pc_match = re.match(r\"(\\d+(\\.\\d+)?)\\s*(Mn|Bn)?\", pc_text)\n",
+    "    if pc_match:\n",
+    "        pc_val = float(pc_match.group(1))\n",
+    "        pc_unit = pc_match.group(3)\n",
+    "        if pc_unit == \"Mn\":\n",
+    "            per_capita = int(pc_val * 1_000_000)\n",
+    "        elif pc_unit == \"Bn\":\n",
+    "            per_capita = int(pc_val * 1_000_000_000)\n",
+    "        else:\n",
+    "            per_capita = int(pc_val)\n",
+    "\n",
+    "    records.append({\n",
+    "        \"Country\": country,\n",
+    "        \"Debt Label\": debt_text,\n",
+    "        \"Debt USD\": debt_usd,\n",
+    "        \"Debt % GDP\": debt_pct_gdp,\n",
+    "        \"Debt Per Capita\": per_capita\n",
+    "    })\n",
+    "\n",
+    "df = pd.DataFrame(records)\n",
+    "\n",
+    "# -----------------------\n",
+    "# HELPER FUNCTION TO CREATE LABELS\n",
+    "# -----------------------\n",
+    "def create_rank_labels(df, value_col, value_format=\"raw\"):\n",
+    "    \"\"\"Create rank X, value_label format\"\"\"\n",
+    "    df_sorted = df.sort_values(value_col, ascending=False).reset_index(drop=True)\n",
+    "    df_sorted[\"Rank\"] = df_sorted.index + 1\n",
+    "\n",
+    "    def format_value(row):\n",
+    "        if value_format == \"debt\":\n",
+    "            return f\"{row['Debt Label']}\"\n",
+    "        elif value_format == \"pct\":\n",
+    "            return f\"{row[value_col]}%\"\n",
+    "        elif value_format == \"percap\":\n",
+    "            val = row[value_col]\n",
+    "            if val >= 1e9:\n",
+    "                return f\"${val/1e9:.2f} B\"\n",
+    "            elif val >= 1e6:\n",
+    "                return f\"${val/1e6:.2f} M\"\n",
+    "            else:\n",
+    "                return f\"${val}\"\n",
+    "        else:\n",
+    "            return str(row[value_col])\n",
+    "\n",
+    "    df_sorted[\"Plot Label\"] = df_sorted.apply(\n",
+    "        lambda x: f\"rank {x['Rank']}, {format_value(x)}\", axis=1\n",
+    "    )\n",
+    "    return df_sorted.sort_values(value_col)\n",
+    "\n",
+    "# -----------------------\n",
+    "# GRAPH 1 — NATIONAL DEBT\n",
+    "# -----------------------\n",
+    "df1_plot = create_rank_labels(df, \"Debt USD\", \"debt\")\n",
+    "fig1 = go.Figure(go.Bar(\n",
+    "    x=df1_plot[\"Debt USD\"],\n",
+    "    y=df1_plot[\"Country\"],\n",
+    "    orientation=\"h\",\n",
+    "    text=df1_plot[\"Plot Label\"],\n",
+    "    textposition=\"outside\"\n",
+    "))\n",
+    "fig1.update_layout(\n",
+    "    title=\"National Debt by Country\",\n",
+    "    xaxis_title=\"National Debt (USD)\",\n",
+    "    height=max(600, len(df1_plot)*28),\n",
+    "    margin=dict(l=220, r=320),\n",
+    "    xaxis=dict(range=[0, df1_plot[\"Debt USD\"].max()*1.3]),\n",
+    "    template=\"plotly_white\"\n",
+    ")\n",
+    "fig1.show()\n",
+    "\n",
+    "# -----------------------\n",
+    "# GRAPH 2 — DEBT % OF GDP\n",
+    "# -----------------------\n",
+    "df2 = df.dropna(subset=[\"Debt % GDP\"])\n",
+    "df2_plot = create_rank_labels(df2, \"Debt % GDP\", \"pct\")\n",
+    "fig2 = go.Figure(go.Bar(\n",
+    "    x=df2_plot[\"Debt % GDP\"],\n",
+    "    y=df2_plot[\"Country\"],\n",
+    "    orientation=\"h\",\n",
+    "    text=df2_plot[\"Plot Label\"],\n",
+    "    textposition=\"outside\"\n",
+    "))\n",
+    "fig2.update_layout(\n",
+    "    title=\"Debt as Percentage of GDP\",\n",
+    "    xaxis_title=\"Debt (% of GDP)\",\n",
+    "    height=max(600, len(df2_plot)*28),\n",
+    "    margin=dict(l=220, r=200),\n",
+    "    xaxis=dict(range=[0, df2_plot[\"Debt % GDP\"].max()*1.2]),\n",
+    "    template=\"plotly_white\"\n",
+    ")\n",
+    "fig2.show()\n",
+    "\n",
+    "# -----------------------\n",
+    "# GRAPH 3 — DEBT PER CAPITA\n",
+    "# -----------------------\n",
+    "df3 = df.dropna(subset=[\"Debt Per Capita\"])\n",
+    "df3_plot = create_rank_labels(df3, \"Debt Per Capita\", \"percap\")\n",
+    "fig3 = go.Figure(go.Bar(\n",
+    "    x=df3_plot[\"Debt Per Capita\"],\n",
+    "    y=df3_plot[\"Country\"],\n",
+    "    orientation=\"h\",\n",
+    "    text=df3_plot[\"Plot Label\"],\n",
+    "    textposition=\"outside\"\n",
+    "))\n",
+    "fig3.update_layout(\n",
+    "    title=\"Debt Per Capita by Country\",\n",
+    "    xaxis_title=\"Debt Per Capita (USD)\",\n",
+    "    height=max(600, len(df3_plot)*28),\n",
+    "    margin=dict(l=220, r=240),\n",
+    "    xaxis=dict(range=[0, df3_plot[\"Debt Per Capita\"].max()*1.2]),\n",
+    "    template=\"plotly_white\"\n",
+    ")\n",
+    "fig3.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a53218a",
+   "metadata": {},
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "import re\n",
+    "\n",
+    "# -----------------------\n",
+    "# FETCH DATA\n",
+    "# -----------------------\n",
+    "url = \"https://worldpopulationreview.com/country-rankings/countries-by-national-debt\"\n",
+    "html = requests.get(url).text\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "table = soup.find(\"table\")\n",
+    "rows = table.find_all(\"tr\")[1:]\n",
+    "\n",
+    "DEBT_MULT = {\"T\": 1e12, \"B\": 1e9, \"M\": 1e6}\n",
+    "\n",
+    "records = []\n",
+    "\n",
+    "for row in rows:\n",
+    "    cols = row.find_all(\"td\")\n",
+    "    if len(cols) < 5:\n",
+    "        continue\n",
+    "\n",
+    "    country = cols[1].text.strip()\n",
+    "    if country.upper() == \"TOTAL\":\n",
+    "        continue\n",
+    "\n",
+    "    debt_text = cols[2].text.strip()\n",
+    "    gdp_pct_text = cols[3].text.strip()\n",
+    "    per_capita_text = cols[4].text.strip()\n",
+    "\n",
+    "    # National Debt\n",
+    "    m = re.match(r\"\\$(\\d+(\\.\\d+)?)([TBM])\", debt_text)\n",
+    "    if not m:\n",
+    "        continue\n",
+    "    debt_val = float(m.group(1))\n",
+    "    debt_unit = m.group(3)\n",
+    "    debt_usd = int(debt_val * DEBT_MULT[debt_unit])\n",
+    "\n",
+    "    # Debt % GDP\n",
+    "    debt_pct_gdp = float(gdp_pct_text.replace(\"%\", \"\")) if \"%\" in gdp_pct_text else None\n",
+    "\n",
+    "    # Debt per Capita\n",
+    "    per_capita = None\n",
+    "    pc_text = per_capita_text.replace(\"$\", \"\").replace(\",\", \"\").strip()\n",
+    "    pc_match = re.match(r\"(\\d+(\\.\\d+)?)\\s*(Mn|Bn)?\", pc_text)\n",
+    "    if pc_match:\n",
+    "        pc_val = float(pc_match.group(1))\n",
+    "        pc_unit = pc_match.group(3)\n",
+    "        if pc_unit == \"Mn\":\n",
+    "            per_capita = int(pc_val * 1_000_000)\n",
+    "        elif pc_unit == \"Bn\":\n",
+    "            per_capita = int(pc_val * 1_000_000_000)\n",
+    "        else:\n",
+    "            per_capita = int(pc_val)\n",
+    "\n",
+    "    records.append({\n",
+    "        \"Country\": country,\n",
+    "        \"Debt Label\": debt_text,\n",
+    "        \"Debt USD\": debt_usd,\n",
+    "        \"Debt % GDP\": debt_pct_gdp,\n",
+    "        \"Debt Per Capita\": per_capita\n",
+    "    })\n",
+    "\n",
+    "df = pd.DataFrame(records)\n",
+    "\n",
+    "# -----------------------\n",
+    "# COUNTRY → ISO2 FLAG MAPPING\n",
+    "# -----------------------\n",
+    "# Minimal mapping; for more countries, add ISO2 codes\n",
+    "country_to_iso2 = {\n",
+    "    \"United States\": \"us\",\n",
+    "    \"China\": \"cn\",\n",
+    "    \"Japan\": \"jp\",\n",
+    "    \"Germany\": \"de\",\n",
+    "    \"United Kingdom\": \"gb\",\n",
+    "    \"France\": \"fr\",\n",
+    "    \"India\": \"in\",\n",
+    "    \"Italy\": \"it\",\n",
+    "    \"Canada\": \"ca\",\n",
+    "    \"South Korea\": \"kr\",\n",
+    "    \"Brazil\": \"br\",\n",
+    "    # Add more as needed...\n",
+    "}\n",
+    "\n",
+    "def get_flag_url(country):\n",
+    "    code = country_to_iso2.get(country)\n",
+    "    if code:\n",
+    "        return f\"https://flagcdn.com/w20/{code}.png\"\n",
+    "    return None\n",
+    "\n",
+    "# -----------------------\n",
+    "# HELPER FUNCTION TO CREATE LABELS\n",
+    "# -----------------------\n",
+    "def create_rank_labels(df, value_col, value_format=\"raw\"):\n",
+    "    df_sorted = df.sort_values(value_col, ascending=False).reset_index(drop=True)\n",
+    "    df_sorted[\"Rank\"] = df_sorted.index + 1\n",
+    "    def format_value(row):\n",
+    "        if value_format == \"debt\":\n",
+    "            return f\"{row['Debt Label']}\"\n",
+    "        elif value_format == \"pct\":\n",
+    "            return f\"{row[value_col]}%\"\n",
+    "        elif value_format == \"percap\":\n",
+    "            val = row[value_col]\n",
+    "            if val >= 1e9:\n",
+    "                return f\"${val/1e9:.2f} B\"\n",
+    "            elif val >= 1e6:\n",
+    "                return f\"${val/1e6:.2f} M\"\n",
+    "            else:\n",
+    "                return f\"${val}\"\n",
+    "        else:\n",
+    "            return str(row[value_col])\n",
+    "    df_sorted[\"Plot Label\"] = df_sorted.apply(\n",
+    "        lambda x: f\"rank {x['Rank']}, {format_value(x)}\", axis=1\n",
+    "    )\n",
+    "    return df_sorted.sort_values(value_col)\n",
+    "\n",
+    "# -----------------------\n",
+    "# PLOT FUNCTION WITH FLAGS\n",
+    "# -----------------------\n",
+    "def plot_bar_with_flags(df_plot, value_col, title, xaxis_title, value_format):\n",
+    "    fig = go.Figure()\n",
+    "\n",
+    "    fig.add_trace(go.Bar(\n",
+    "        x=df_plot[value_col],\n",
+    "        y=df_plot[\"Country\"],\n",
+    "        orientation=\"h\",\n",
+    "        text=df_plot[\"Plot Label\"],\n",
+    "        textposition=\"outside\"\n",
+    "    ))\n",
+    "\n",
+    "    # Add flags\n",
+    "    for idx, row in df_plot.iterrows():\n",
+    "        flag_url = get_flag_url(row[\"Country\"])\n",
+    "        if flag_url:\n",
+    "            fig.add_layout_image(\n",
+    "                dict(\n",
+    "                    source=flag_url,\n",
+    "                    xref=\"paper\",\n",
+    "                    yref=\"y\",\n",
+    "                    x=0,  # left side\n",
+    "                    y=row[\"Country\"],\n",
+    "                    xanchor=\"right\",\n",
+    "                    yanchor=\"middle\",\n",
+    "                    sizex=0.03,\n",
+    "                    sizey=0.03,\n",
+    "                    layer=\"above\"\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title=title,\n",
+    "        xaxis_title=xaxis_title,\n",
+    "        height=max(600, len(df_plot)*28),\n",
+    "        margin=dict(l=220, r=320),\n",
+    "        xaxis=dict(range=[0, df_plot[value_col].max()*1.3]),\n",
+    "        template=\"plotly_white\"\n",
+    "    )\n",
+    "    fig.show()\n",
+    "\n",
+    "# -----------------------\n",
+    "# GRAPH 1 — NATIONAL DEBT\n",
+    "# -----------------------\n",
+    "df1_plot = create_rank_labels(df, \"Debt USD\", \"debt\")\n",
+    "plot_bar_with_flags(df1_plot, \"Debt USD\", \"National Debt by Country\", \"National Debt (USD)\", \"debt\")\n",
+    "\n",
+    "# -----------------------\n",
+    "# GRAPH 2 — DEBT % OF GDP\n",
+    "# -----------------------\n",
+    "df2 = df.dropna(subset=[\"Debt % GDP\"])\n",
+    "df2_plot = create_rank_labels(df2, \"Debt % GDP\", \"pct\")\n",
+    "plot_bar_with_flags(df2_plot, \"Debt % GDP\", \"Debt as Percentage of GDP\", \"Debt (% of GDP)\", \"pct\")\n",
+    "\n",
+    "# -----------------------\n",
+    "# GRAPH 3 — DEBT PER CAPITA\n",
+    "# -----------------------\n",
+    "df3 = df.dropna(subset=[\"Debt Per Capita\"])\n",
+    "df3_plot = create_rank_labels(df3, \"Debt Per Capita\", \"percap\")\n",
+    "plot_bar_with_flags(df3_plot, \"Debt Per Capita\", \"Debt Per Capita by Country\", \"Debt Per Capita (USD)\", \"percap\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e79a83c9",
+   "metadata": {},
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "import re\n",
+    "\n",
+    "# fetch the webpage and parse the table\n",
+    "url = \"https://worldpopulationreview.com/country-rankings/countries-by-national-debt\"\n",
+    "html = requests.get(url).text\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "table = soup.find(\"table\")\n",
+    "rows = table.find_all(\"tr\")[1:]\n",
+    "\n",
+    "# multipliers for debt units\n",
+    "DEBT_MULT = {\"T\": 1e12, \"B\": 1e9, \"M\": 1e6}\n",
+    "\n",
+    "records = []\n",
+    "\n",
+    "for row in rows:\n",
+    "    cols = row.find_all(\"td\")\n",
+    "    if len(cols) < 5:\n",
+    "        continue\n",
+    "\n",
+    "    country = cols[1].text.strip()\n",
+    "    if country.upper() == \"TOTAL\":\n",
+    "        continue\n",
+    "\n",
+    "    debt_text = cols[2].text.strip()\n",
+    "    gdp_pct_text = cols[3].text.strip()\n",
+    "    per_capita_text = cols[4].text.strip()\n",
+    "\n",
+    "    # get the debt in USD\n",
+    "    m = re.match(r\"\\$(\\d+(\\.\\d+)?)([TBM])\", debt_text)\n",
+    "    if not m:\n",
+    "        continue\n",
+    "    debt_val = float(m.group(1))\n",
+    "    debt_unit = m.group(3)\n",
+    "    debt_usd = int(debt_val * DEBT_MULT[debt_unit])\n",
+    "\n",
+    "    # get debt as % of GDP\n",
+    "    debt_pct_gdp = float(gdp_pct_text.replace(\"%\", \"\")) if \"%\" in gdp_pct_text else None\n",
+    "\n",
+    "    # get debt per person\n",
+    "    per_capita = None\n",
+    "    pc_text = per_capita_text.replace(\"$\", \"\").replace(\",\", \"\").strip()\n",
+    "    pc_match = re.match(r\"(\\d+(\\.\\d+)?)\\s*(Mn|Bn)?\", pc_text)\n",
+    "    if pc_match:\n",
+    "        pc_val = float(pc_match.group(1))\n",
+    "        pc_unit = pc_match.group(3)\n",
+    "        if pc_unit == \"Mn\":\n",
+    "            per_capita = int(pc_val * 1_000_000)\n",
+    "        elif pc_unit == \"Bn\":\n",
+    "            per_capita = int(pc_val * 1_000_000_000)\n",
+    "        else:\n",
+    "            per_capita = int(pc_val)\n",
+    "\n",
+    "    records.append({\n",
+    "        \"Country\": country,\n",
+    "        \"Debt Label\": debt_text,\n",
+    "        \"Debt USD\": debt_usd,\n",
+    "        \"Debt % GDP\": debt_pct_gdp,\n",
+    "        \"Debt Per Capita\": per_capita\n",
+    "    })\n",
+    "\n",
+    "df = pd.DataFrame(records)\n",
+    "\n",
+    "# helper to make rank labels for the bars\n",
+    "def create_rank_labels(df, value_col, value_format=\"raw\"):\n",
+    "    df_sorted = df.sort_values(value_col, ascending=False).reset_index(drop=True)\n",
+    "    df_sorted[\"Rank\"] = df_sorted.index + 1\n",
+    "\n",
+    "    def format_value(row):\n",
+    "        if value_format == \"debt\":\n",
+    "            return f\"{row['Debt Label']}\"\n",
+    "        elif value_format == \"pct\":\n",
+    "            return f\"{row[value_col]}%\"\n",
+    "        elif value_format == \"percap\":\n",
+    "            val = row[value_col]\n",
+    "            if val >= 1e9:\n",
+    "                return f\"${val/1e9:.2f} B\"\n",
+    "            elif val >= 1e6:\n",
+    "                return f\"${val/1e6:.2f} M\"\n",
+    "            else:\n",
+    "                return f\"${val}\"\n",
+    "        else:\n",
+    "            return str(row[value_col])\n",
+    "\n",
+    "    df_sorted[\"Plot Label\"] = df_sorted.apply(\n",
+    "        lambda x: f\"rank {x['Rank']}, {format_value(x)}\", axis=1\n",
+    "    )\n",
+    "    return df_sorted.sort_values(value_col)\n",
+    "\n",
+    "# national debt chart\n",
+    "df1_plot = create_rank_labels(df, \"Debt USD\", \"debt\")\n",
+    "fig1 = go.Figure(go.Bar(\n",
+    "    x=df1_plot[\"Debt USD\"],\n",
+    "    y=df1_plot[\"Country\"],\n",
+    "    orientation=\"h\",\n",
+    "    text=df1_plot[\"Plot Label\"],\n",
+    "    textposition=\"outside\"\n",
+    "))\n",
+    "fig1.update_layout(\n",
+    "    title=\"National Debt by Country\",\n",
+    "    xaxis_title=\"National Debt (USD)\",\n",
+    "    height=max(600, len(df1_plot)*28),\n",
+    "    margin=dict(l=220, r=320),\n",
+    "    xaxis=dict(range=[0, df1_plot[\"Debt USD\"].max()*1.3]),\n",
+    "    template=\"plotly_white\"\n",
+    ")\n",
+    "fig1.show()\n",
+    "\n",
+    "# debt as % of GDP chart\n",
+    "df2 = df.dropna(subset=[\"Debt % GDP\"])\n",
+    "df2_plot = create_rank_labels(df2, \"Debt % GDP\", \"pct\")\n",
+    "fig2 = go.Figure(go.Bar(\n",
+    "    x=df2_plot[\"Debt % GDP\"],\n",
+    "    y=df2_plot[\"Country\"],\n",
+    "    orientation=\"h\",\n",
+    "    text=df2_plot[\"Plot Label\"],\n",
+    "    textposition=\"outside\"\n",
+    "))\n",
+    "fig2.update_layout(\n",
+    "    title=\"Debt as Percentage of GDP\",\n",
+    "    xaxis_title=\"Debt (% of GDP)\",\n",
+    "    height=max(600, len(df2_plot)*28),\n",
+    "    margin=dict(l=220, r=200),\n",
+    "    xaxis=dict(range=[0, df2_plot[\"Debt % GDP\"].max()*1.2]),\n",
+    "    template=\"plotly_white\"\n",
+    ")\n",
+    "fig2.show()\n",
+    "\n",
+    "# debt per capita chart\n",
+    "df3 = df.dropna(subset=[\"Debt Per Capita\"])\n",
+    "df3_plot = create_rank_labels(df3, \"Debt Per Capita\", \"percap\")\n",
+    "fig3 = go.Figure(go.Bar(\n",
+    "    x=df3_plot[\"Debt Per Capita\"],\n",
+    "    y=df3_plot[\"Country\"],\n",
+    "    orientation=\"h\",\n",
+    "    text=df3_plot[\"Plot Label\"],\n",
+    "    textposition=\"outside\"\n",
+    "))\n",
+    "fig3.update_layout(\n",
+    "    title=\"Debt Per Capita by Country\",\n",
+    "    xaxis_title=\"Debt Per Capita (USD)\",\n",
+    "    height=max(600, len(df3_plot)*28),\n",
+    "    margin=dict(l=220, r=240),\n",
+    "    xaxis=dict(range=[0, df3_plot[\"Debt Per Capita\"].max()*1.2]),\n",
+    "    template=\"plotly_white\"\n",
+    ")\n",
+    "fig3.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}