diff --git "a/jupyterlite/notebooks/emendas/2 Cruzamento e an\303\241lise.ipynb" "b/jupyterlite/notebooks/emendas/2 Cruzamento e an\303\241lise.ipynb"
index ff42963..1615a71 100644
--- "a/jupyterlite/notebooks/emendas/2 Cruzamento e an\303\241lise.ipynb"	
+++ "b/jupyterlite/notebooks/emendas/2 Cruzamento e an\303\241lise.ipynb"	
@@ -31,7 +31,7 @@
     }
    ],
    "source": [
-    "%pip install -q fastparquet plotly nbformat>=4.2.0"
+    "%pip install -q fastparquet plotly nbformat"
    ]
   },
   {
@@ -41,10 +41,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import locale\n",
+    "\n",
     "import pandas as pd\n",
     "import plotly.graph_objects as go"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "15480ffb-1526-4eeb-a867-c06ed0ffa77c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'pt_BR.UTF-8'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "locale.setlocale(locale.LC_ALL, 'pt_BR.UTF-8')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9badbc8d-b669-41a9-b531-38681a6911b3",
@@ -57,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "24c52cf8-1e96-408e-9cdc-c5bf181212ed",
    "metadata": {},
    "outputs": [],
@@ -67,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "32f126d6-f712-4d20-94cf-3de8d5e2af08",
    "metadata": {},
    "outputs": [],
@@ -77,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "264ed6f0-5e78-4a0a-98bf-2bed63d1d3c0",
    "metadata": {},
    "outputs": [],
@@ -110,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "d1a4b261-410a-472e-9b2a-474cb68f5567",
    "metadata": {},
    "outputs": [
@@ -136,7 +159,7 @@
        " 'Silvio Antonio'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "db78bf30-82ec-402d-a559-37de46d20d4c",
    "metadata": {
     "colab": {
@@ -606,7 +629,7 @@
        "[10934 rows x 36 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -667,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "bf866b4f-6f29-442a-ad5b-769fd672779a",
    "metadata": {
     "colab": {
@@ -687,7 +710,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "803bc0c6-617e-4058-bd22-1ea91ab17009",
    "metadata": {},
    "outputs": [
@@ -729,7 +752,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -741,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "7f565fc7-a3ac-495b-b162-6c7302214e82",
    "metadata": {
     "scrolled": true
@@ -765,7 +788,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -785,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "c2117dea-8db7-48b2-a493-c52308d228f5",
    "metadata": {
     "id": "GLBrg7ptdt5Y"
@@ -800,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "6b96f916-f61f-4534-bd94-daab22b25549",
    "metadata": {
     "id": "CnbdggEberg1"
@@ -820,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "9dcad2c1-ba0e-4234-a435-e6e14696fa94",
    "metadata": {
     "colab": {
@@ -853,7 +876,7 @@
        "Name: valor_emenda, dtype: float64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -865,14 +888,62 @@
     "    .sum()\n",
     "    ['valor_emenda']\n",
     "    .sort_values(ascending=False)\n",
-    "    .head(15)\n",
     ")\n",
-    "df_emendas_deputados\n"
+    "df_emendas_deputados.head(15)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
+   "id": "855db066-ceb2-453e-82f0-98afe52ef499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bar_plot(df: pd.DataFrame, title: str, x_label: str, y_label: str):\n",
+    "    \"\"\"Função para criar o gráfico de barras usando o Plotly.\"\"\"\n",
+    "    # Criando o gráfico de barras horizontais\n",
+    "    fig = go.Figure(go.Bar(\n",
+    "        x=df.values,\n",
+    "        y=df.index.map(lambda x: f\"{x[0]} ({x[1]}/{x[2]})\"),\n",
+    "        orientation='h',\n",
+    "        marker_color='skyblue'\n",
+    "    ))\n",
+    "    \n",
+    "    # Adicionando título e rótulos aos eixos\n",
+    "    fig.update_layout(\n",
+    "        title=title,\n",
+    "        xaxis_title=x_label,\n",
+    "        yaxis_title=y_label,\n",
+    "        font_size=12\n",
+    "    )\n",
+    "    \n",
+    "    # Adicionando os valores nas barras\n",
+    "    for i, value in enumerate(df.values):\n",
+    "        fig.add_annotation(\n",
+    "            x=value,\n",
+    "            y=i,\n",
+    "            text=f'{value:,.2f}',\n",
+    "            textangle=0,\n",
+    "            font_size=10,\n",
+    "            showarrow=False,\n",
+    "            align='left',\n",
+    "            xanchor='right'\n",
+    "        )\n",
+    "    \n",
+    "    # Ajustando o layout\n",
+    "    fig.update_layout(\n",
+    "        height=600,\n",
+    "        width=800,\n",
+    "        bargap=0.1\n",
+    "    )\n",
+    "    \n",
+    "    # Exibindo o gráfico\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "id": "d5aee8ae-4d36-4055-8ab6-08de1828433e",
    "metadata": {
     "colab": {
@@ -1966,7 +2037,7 @@
          }
         },
         "title": {
-         "text": "Top 10 Deputados por Valor de Emendas"
+         "text": "15 dos Deputados que mais gastaram emendas"
         },
         "width": 800,
         "xaxis": {
@@ -1982,9 +2053,9 @@
        }
       },
       "text/html": [
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        "                            \n",
-       "var gd = document.getElementById('2450190c-bd54-4c81-94ca-0b3a4a2a8e44');\n",
+       "var gd = document.getElementById('10532db0-face-4774-b4f4-619bab764e31');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -2014,112 +2085,69 @@
     }
    ],
    "source": [
-    "# Criando o gráfico de barras horizontais\n",
-    "fig = go.Figure(go.Bar(\n",
-    "    x=df_emendas_deputados.values,\n",
-    "    y=df_emendas_deputados.index.map(lambda x: f\"{x[0]} ({x[1]}/{x[2]})\"),\n",
-    "    orientation='h',\n",
-    "    marker_color='skyblue'\n",
-    "))\n",
-    "\n",
-    "# Adicionando título e rótulos aos eixos\n",
-    "fig.update_layout(\n",
-    "    title='Top 10 Deputados por Valor de Emendas',\n",
-    "    xaxis_title='Valor Total das Emendas',\n",
-    "    yaxis_title='Deputados',\n",
-    "    font_size=12\n",
-    ")\n",
-    "\n",
-    "# Adicionando os valores nas barras\n",
-    "for i, value in enumerate(df_emendas_deputados.values):\n",
-    "    fig.add_annotation(\n",
-    "        x=value,\n",
-    "        y=i,\n",
-    "        text=f'{value:,.2f}',\n",
-    "        textangle=0,\n",
-    "        font_size=10,\n",
-    "        showarrow=False,\n",
-    "        align='left',\n",
-    "        xanchor='right'\n",
-    "    )\n",
-    "\n",
-    "# Ajustando o layout\n",
-    "fig.update_layout(\n",
-    "    height=600,\n",
-    "    width=800,\n",
-    "    bargap=0.1\n",
-    ")\n",
-    "\n",
-    "# Exibindo o gráfico\n",
-    "fig.show()\n"
+    "bar_plot(\n",
+    "    df_emendas_deputados.head(15),\n",
+    "    title=\"15 dos Deputados que mais gastaram emendas\",\n",
+    "    x_label=\"Valor Total das Emendas\",\n",
+    "    y_label=\"Deputados\"\n",
+    ")\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ba1d6d07-2ced-494c-8472-ae6ad692ccba",
+   "id": "d5e81888-ec8a-4347-9f00-159eb21568f8",
    "metadata": {},
    "source": [
-    "##### Senado"
+    "Como pode-se ver, muitos deputados gastam o valor máximo permitido\n",
+    "em emendas. Por isso, nem faz sentido o gráfico.\n",
+    "\n",
+    "Mas, então, quantos deputados gastam o valor máximo?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "670d5ac6-a81a-48f7-8258-f657bc54b0ca",
+   "execution_count": 17,
+   "id": "35cf0252-689b-48e6-89d8-bb0fd4d8a9f2",
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 429
-    },
-    "id": "KuO38U6Aewyl",
-    "outputId": "075c698e-6cc2-4ec1-fb4c-d54e33c9f9b0"
+    "scrolled": true
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "nome             partido  uf\n",
-       "Marcos Rogério   PL       RO    34817425.0\n",
-       "Jussara Lima     PSD      PI    34817425.0\n",
-       "Jayme Campos     UNIÃO    MT    34817425.0\n",
-       "Davi Alcolumbre  UNIÃO    AP    34815000.0\n",
-       "Otto Alencar     PSD      BA    34634850.0\n",
-       "Ciro Nogueira    PP       PI    34634850.0\n",
-       "Giordano         MDB      SP    34500000.0\n",
-       "Eliziane Gama    PSD      MA    34317425.0\n",
-       "Fernando Farias  MDB      AL    34317425.0\n",
-       "Angelo Coronel   PSD      BA    34134850.0\n",
-       "Name: valor_emenda, dtype: float64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valor máximo: R$ 18.935.792,00\n",
+      "Quantidade de deputados que usaram este valor:  21\n"
+     ]
     }
    ],
    "source": [
-    "df_emendas_senadores = (\n",
-    "    df_emendas[df_emendas['cargo']=='senador']\n",
-    "    .groupby(colunas_agregacao)\n",
-    "    .sum()\n",
-    "    ['valor_emenda']\n",
-    "    .sort_values(ascending=False)\n",
-    "    .head(10)\n",
-    ")\n",
-    "df_emendas_senadores"
+    "print(\n",
+    "    f\"Valor máximo: R$ {locale.format_string('%.2f', df_emendas_deputados.max(), grouping=True)}\\n\"\n",
+    "    \"Quantidade de deputados que usaram este valor: \",\n",
+    "    len(df_emendas_deputados[df_emendas_deputados.values == df_emendas_deputados.values.max()])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a0cfc44-99ba-45ea-9dc8-b7feaa544850",
+   "metadata": {},
+   "source": [
+    "E quais deputados gastaram menos em emendas?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "901ad9fa-27da-4750-8e9b-c5c2372da6ed",
+   "execution_count": 18,
+   "id": "a456b992-0fa1-4ad8-b4ee-b02d1763b1c5",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 429
+     "height": 427
     },
-    "id": "a6stdnTjeyqZ",
-    "outputId": "5579a341-b234-47c0-a98f-b2432012ebbe"
+    "id": "hZaJoAxEe0qt",
+    "outputId": "05fef776-6f17-42f9-bc32-5f7bb5072b30"
    },
    "outputs": [
     {
@@ -2136,28 +2164,38 @@
          "orientation": "h",
          "type": "bar",
          "x": [
-          34817425,
-          34817425,
-          34817425,
-          34815000,
-          34634850,
-          34634850,
-          34500000,
-          34317425,
-          34317425,
-          34134850
+          1000000,
+          1000000,
+          1000000,
+          1000000,
+          740000,
+          550000,
+          500000,
+          400000,
+          380000,
+          300000,
+          215000,
+          200000,
+          200000,
+          200000,
+          150000
          ],
          "y": [
-          "Marcos Rogério (PL/RO)",
-          "Jussara Lima (PSD/PI)",
-          "Jayme Campos (UNIÃO/MT)",
-          "Davi Alcolumbre (UNIÃO/AP)",
-          "Otto Alencar (PSD/BA)",
-          "Ciro Nogueira (PP/PI)",
-          "Giordano (MDB/SP)",
-          "Eliziane Gama (PSD/MA)",
-          "Fernando Farias (MDB/AL)",
-          "Angelo Coronel (PSD/BA)"
+          "Altineu Côrtes (PL/RJ)",
+          "Jandira Feghali (PCdoB/RJ)",
+          "Carlos Jordy (PL/RJ)",
+          "Helio Lopes (PL/RJ)",
+          "Chris Tonietto (PL/RJ)",
+          "Duda Salabert (PDT/MG)",
+          "Dr. Daniel Soranz (PSD/RJ)",
+          "Orlando Silva (PCdoB/SP)",
+          "Antônia Lúcia (REPUBLICANOS/AC)",
+          "Marcos Soares (UNIÃO/RJ)",
+          "Dandara (PT/MG)",
+          "Delegado da Cunha (PP/SP)",
+          "Tabata Amaral (PSB/SP)",
+          "Rosangela Gomes (REPUBLICANOS/RJ)",
+          "Bia Kicis (PL/DF)"
          ]
         }
        ],
@@ -2169,9 +2207,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,817,425.00",
+          "text": "1,000,000.00",
           "textangle": 0,
-          "x": 34817425,
+          "x": 1000000,
           "xanchor": "right",
           "y": 0
          },
@@ -2181,9 +2219,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,817,425.00",
+          "text": "1,000,000.00",
           "textangle": 0,
-          "x": 34817425,
+          "x": 1000000,
           "xanchor": "right",
           "y": 1
          },
@@ -2193,9 +2231,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,817,425.00",
+          "text": "1,000,000.00",
           "textangle": 0,
-          "x": 34817425,
+          "x": 1000000,
           "xanchor": "right",
           "y": 2
          },
@@ -2205,9 +2243,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,815,000.00",
+          "text": "1,000,000.00",
           "textangle": 0,
-          "x": 34815000,
+          "x": 1000000,
           "xanchor": "right",
           "y": 3
          },
@@ -2217,9 +2255,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,634,850.00",
+          "text": "740,000.00",
           "textangle": 0,
-          "x": 34634850,
+          "x": 740000,
           "xanchor": "right",
           "y": 4
          },
@@ -2229,9 +2267,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,634,850.00",
+          "text": "550,000.00",
           "textangle": 0,
-          "x": 34634850,
+          "x": 550000,
           "xanchor": "right",
           "y": 5
          },
@@ -2241,9 +2279,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,500,000.00",
+          "text": "500,000.00",
           "textangle": 0,
-          "x": 34500000,
+          "x": 500000,
           "xanchor": "right",
           "y": 6
          },
@@ -2253,9 +2291,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,317,425.00",
+          "text": "400,000.00",
           "textangle": 0,
-          "x": 34317425,
+          "x": 400000,
           "xanchor": "right",
           "y": 7
          },
@@ -2265,9 +2303,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,317,425.00",
+          "text": "380,000.00",
           "textangle": 0,
-          "x": 34317425,
+          "x": 380000,
           "xanchor": "right",
           "y": 8
          },
@@ -2277,11 +2315,71 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "34,134,850.00",
+          "text": "300,000.00",
           "textangle": 0,
-          "x": 34134850,
+          "x": 300000,
           "xanchor": "right",
           "y": 9
+         },
+         {
+          "align": "left",
+          "font": {
+           "size": 10
+          },
+          "showarrow": false,
+          "text": "215,000.00",
+          "textangle": 0,
+          "x": 215000,
+          "xanchor": "right",
+          "y": 10
+         },
+         {
+          "align": "left",
+          "font": {
+           "size": 10
+          },
+          "showarrow": false,
+          "text": "200,000.00",
+          "textangle": 0,
+          "x": 200000,
+          "xanchor": "right",
+          "y": 11
+         },
+         {
+          "align": "left",
+          "font": {
+           "size": 10
+          },
+          "showarrow": false,
+          "text": "200,000.00",
+          "textangle": 0,
+          "x": 200000,
+          "xanchor": "right",
+          "y": 12
+         },
+         {
+          "align": "left",
+          "font": {
+           "size": 10
+          },
+          "showarrow": false,
+          "text": "200,000.00",
+          "textangle": 0,
+          "x": 200000,
+          "xanchor": "right",
+          "y": 13
+         },
+         {
+          "align": "left",
+          "font": {
+           "size": 10
+          },
+          "showarrow": false,
+          "text": "150,000.00",
+          "textangle": 0,
+          "x": 150000,
+          "xanchor": "right",
+          "y": 14
          }
         ],
         "bargap": 0.1,
@@ -3106,7 +3204,7 @@
          }
         },
         "title": {
-         "text": "Top 10 Senadores por Valor de Emendas"
+         "text": "15 dos Deputados que menos gastaram emendas"
         },
         "width": 800,
         "xaxis": {
@@ -3116,16 +3214,15 @@
         },
         "yaxis": {
          "title": {
-          "text": "Senadores"
+          "text": "Deputados"
          }
         }
        }
       },
-      "image/png": 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",
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        "                            \n",
-       "var gd = document.getElementById('bd083bc4-01c9-45e6-9095-7337b2bb1f27');\n",
+       "var gd = document.getElementById('9009342a-8de9-4869-b451-516930786ef3');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -3155,24 +3252,5617 @@
     }
    ],
    "source": [
-    "# Criando o gráfico de barras horizontais\n",
-    "fig = go.Figure(go.Bar(\n",
-    "    x=df_emendas_senadores.values,\n",
-    "    y=df_emendas_senadores.index.map(lambda x: f\"{x[0]} ({x[1]}/{x[2]})\"),\n",
-    "    orientation='h',\n",
-    "    marker_color='skyblue'\n",
-    "))\n",
-    "\n",
-    "# Adicionando título e rótulos aos eixos\n",
-    "fig.update_layout(\n",
-    "    title='Top 10 Senadores por Valor de Emendas',\n",
-    "    xaxis_title='Valor Total das Emendas',\n",
-    "    yaxis_title='Senadores',\n",
-    "    font_size=12\n",
-    ")\n",
-    "\n",
-    "# Exibindo os valores nas barras\n",
-    "for i, value in enumerate(df_emendas_senadores.values):\n",
+    "bar_plot(\n",
+    "    df_emendas_deputados.tail(15),\n",
+    "    title=\"15 dos Deputados que menos gastaram emendas\",\n",
+    "    x_label=\"Valor Total das Emendas\",\n",
+    "    y_label=\"Deputados\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ebca9e7-9983-495a-b1d2-9881be64493f",
+   "metadata": {},
+   "source": [
+    "Se dividirmos o valor possível de destinação das emendas em\n",
+    "10 faixas de igual valor, quantos deputados estariam em cada faixa?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6fac90f2-a170-4ca8-b4ed-3b9e6f415f47",
+   "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>Faixa</th>\n",
+       "      <th>Quantidade de Deputados</th>\n",
+       "      <th>Percentual da cota</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R\\$ 150,000.00 - R\\$ 2,028,579.20</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0% - 10%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R\\$ 2,028,579.20 - R\\$ 3,907,158.40</td>\n",
+       "      <td>13</td>\n",
+       "      <td>10% - 20%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R\\$ 3,907,158.40 - R\\$ 5,785,737.60</td>\n",
+       "      <td>13</td>\n",
+       "      <td>20% - 30%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R\\$ 5,785,737.60 - R\\$ 7,664,316.80</td>\n",
+       "      <td>18</td>\n",
+       "      <td>30% - 40%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R\\$ 7,664,316.80 - R\\$ 9,542,896.00</td>\n",
+       "      <td>31</td>\n",
+       "      <td>40% - 50%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>R\\$ 9,542,896.00 - R\\$ 11,421,475.20</td>\n",
+       "      <td>30</td>\n",
+       "      <td>50% - 60%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>R\\$ 11,421,475.20 - R\\$ 13,300,054.40</td>\n",
+       "      <td>45</td>\n",
+       "      <td>60% - 70%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>R\\$ 13,300,054.40 - R\\$ 15,178,633.60</td>\n",
+       "      <td>52</td>\n",
+       "      <td>70% - 80%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>R\\$ 15,178,633.60 - R\\$ 17,057,212.80</td>\n",
+       "      <td>91</td>\n",
+       "      <td>80% - 90%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>R\\$ 17,057,212.80 - R\\$ 18,935,792.00</td>\n",
+       "      <td>106</td>\n",
+       "      <td>90% - 100%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   Faixa  Quantidade de Deputados  \\\n",
+       "0      R\\$ 150,000.00 - R\\$ 2,028,579.20                       26   \n",
+       "1    R\\$ 2,028,579.20 - R\\$ 3,907,158.40                       13   \n",
+       "2    R\\$ 3,907,158.40 - R\\$ 5,785,737.60                       13   \n",
+       "3    R\\$ 5,785,737.60 - R\\$ 7,664,316.80                       18   \n",
+       "4    R\\$ 7,664,316.80 - R\\$ 9,542,896.00                       31   \n",
+       "5   R\\$ 9,542,896.00 - R\\$ 11,421,475.20                       30   \n",
+       "6  R\\$ 11,421,475.20 - R\\$ 13,300,054.40                       45   \n",
+       "7  R\\$ 13,300,054.40 - R\\$ 15,178,633.60                       52   \n",
+       "8  R\\$ 15,178,633.60 - R\\$ 17,057,212.80                       91   \n",
+       "9  R\\$ 17,057,212.80 - R\\$ 18,935,792.00                      106   \n",
+       "\n",
+       "  Percentual da cota  \n",
+       "0           0% - 10%  \n",
+       "1          10% - 20%  \n",
+       "2          20% - 30%  \n",
+       "3          30% - 40%  \n",
+       "4          40% - 50%  \n",
+       "5          50% - 60%  \n",
+       "6          60% - 70%  \n",
+       "7          70% - 80%  \n",
+       "8          80% - 90%  \n",
+       "9         90% - 100%  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Encontrando o valor máximo e mínimo das emendas\n",
+    "max_valor = df_emendas_deputados.max()\n",
+    "min_valor = df_emendas_deputados.min()\n",
+    "\n",
+    "# Dividindo o intervalo em 10 faixas iguais\n",
+    "faixa_size = (max_valor - min_valor) / 10\n",
+    "faixas = [min_valor + i * faixa_size for i in range(11)]\n",
+    "\n",
+    "# Contando o número de deputados em cada faixa\n",
+    "faixa_counts = [\n",
+    "    len(df_emendas_deputados[(df_emendas_deputados >= faixas[i]) & (df_emendas_deputados < faixas[i+1])])\n",
+    "    for i in range(10)\n",
+    "]\n",
+    "\n",
+    "# Criando o dataframe\n",
+    "df_distribuicao_emendas_deputados = pd.DataFrame({\n",
+    "    'Faixa': [f'R\\$ {faixas[i]:,.2f} - R\\$ {faixas[i+1]:,.2f}' for i in range(10)],\n",
+    "    'Quantidade de Deputados': faixa_counts,\n",
+    "    'Percentual da cota': [f\"{i * 10}% - {(i+1) * 10}%\" for i in range(10)]\n",
+    "})\n",
+    "df_distribuicao_emendas_deputados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f6dcd30a-1ae0-41ad-b29b-db19a5b29397",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "marker": {
+          "color": "skyblue"
+         },
+         "type": "bar",
+         "x": [
+          "0% - 10%",
+          "10% - 20%",
+          "20% - 30%",
+          "30% - 40%",
+          "40% - 50%",
+          "50% - 60%",
+          "60% - 70%",
+          "70% - 80%",
+          "80% - 90%",
+          "90% - 100%"
+         ],
+         "y": [
+          26,
+          13,
+          13,
+          18,
+          31,
+          30,
+          45,
+          52,
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Criando o gráfico de barras\n",
+    "fig = go.Figure(go.Bar(\n",
+    "    x=df_distribuicao_emendas_deputados[\"Percentual da cota\"],\n",
+    "    y=df_distribuicao_emendas_deputados['Quantidade de Deputados'],\n",
+    "    marker_color='skyblue',\n",
+    "))\n",
+    "\n",
+    "# Adicionando título e rótulos aos eixos\n",
+    "fig.update_layout(\n",
+    "    title='Distribuição dos Deputados por Valor de Emendas',\n",
+    "    xaxis_title='Faixa de Valor das Emendas',\n",
+    "    yaxis_title='Número de Deputados',\n",
+    "    font_size=12\n",
+    ")\n",
+    "\n",
+    "# Exibindo o gráfico\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba1d6d07-2ced-494c-8472-ae6ad692ccba",
+   "metadata": {},
+   "source": [
+    "##### Senado"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62d069c9-d625-4708-9125-2c383e07afb2",
+   "metadata": {},
+   "source": [
+    "Os senadores que mais gastaram emendas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "670d5ac6-a81a-48f7-8258-f657bc54b0ca",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 429
+    },
+    "id": "KuO38U6Aewyl",
+    "outputId": "075c698e-6cc2-4ec1-fb4c-d54e33c9f9b0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nome             partido  uf\n",
+       "Marcos Rogério   PL       RO    34817425.0\n",
+       "Jussara Lima     PSD      PI    34817425.0\n",
+       "Jayme Campos     UNIÃO    MT    34817425.0\n",
+       "Davi Alcolumbre  UNIÃO    AP    34815000.0\n",
+       "Otto Alencar     PSD      BA    34634850.0\n",
+       "Ciro Nogueira    PP       PI    34634850.0\n",
+       "Giordano         MDB      SP    34500000.0\n",
+       "Eliziane Gama    PSD      MA    34317425.0\n",
+       "Fernando Farias  MDB      AL    34317425.0\n",
+       "Angelo Coronel   PSD      BA    34134850.0\n",
+       "Name: valor_emenda, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_emendas_senadores = (\n",
+    "    df_emendas[df_emendas['cargo']=='senador']\n",
+    "    .groupby(colunas_agregacao)\n",
+    "    .sum()\n",
+    "    ['valor_emenda']\n",
+    "    .sort_values(ascending=False)\n",
+    ")\n",
+    "df_emendas_senadores.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "901ad9fa-27da-4750-8e9b-c5c2372da6ed",
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+    },
+    "id": "a6stdnTjeyqZ",
+    "outputId": "5579a341-b234-47c0-a98f-b2432012ebbe"
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+   "outputs": [
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+          "Giordano (MDB/SP)",
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+       "\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bar_plot(\n",
+    "    df_emendas_senadores.head(10),\n",
+    "    title=\"10 Senadores que mais gastaram Emendas\",\n",
+    "    x_label=\"Valor Total das Emendas\",\n",
+    "    y_label=\"Senadores\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e77bdcf-4f5b-4abe-9172-d869de2dcd69",
+   "metadata": {},
+   "source": [
+    "Quantos senadores gastaram o valor máximo?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "76e2314b-3fad-4f02-8ed4-81de51882c80",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valor máximo: R$ 34.817.425,00\n",
+      "Quantidade de senadores que usaram este valor:  3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"Valor máximo: R$ {locale.format_string('%.2f', df_emendas_senadores.max(), grouping=True)}\\n\"\n",
+    "    \"Quantidade de senadores que usaram este valor: \",\n",
+    "    len(df_emendas_senadores[df_emendas_senadores.values == df_emendas_senadores.values.max()])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8089713d-c853-4515-bf44-14566935ffee",
+   "metadata": {},
+   "source": [
+    "Os senadores que menos gastaram emendas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ccc00880-02c0-41ea-acd4-bb4d16eda8c0",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 429
+    },
+    "id": "a6stdnTjeyqZ",
+    "outputId": "5579a341-b234-47c0-a98f-b2432012ebbe"
+   },
+   "outputs": [
+    {
+     "data": {
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+          "Sérgio Petecão (PSD/AC)",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bar_plot(\n",
+    "    df_emendas_senadores.tail(10),\n",
+    "    title=\"10 Senadores que menos gastaram Emendas\",\n",
+    "    x_label=\"Valor Total das Emendas\",\n",
+    "    y_label=\"Senadores\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5c057f46-f352-424a-804b-9d23fd4926ca",
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Faixa</th>\n",
+       "      <th>Quantidade de Senadores</th>\n",
+       "      <th>Percentual da cota</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R\\$ 1,500,000.00 - R\\$ 4,831,742.50</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0% - 10%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R\\$ 4,831,742.50 - R\\$ 8,163,485.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10% - 20%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R\\$ 8,163,485.00 - R\\$ 11,495,227.50</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20% - 30%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R\\$ 11,495,227.50 - R\\$ 14,826,970.00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30% - 40%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R\\$ 14,826,970.00 - R\\$ 18,158,712.50</td>\n",
+       "      <td>3</td>\n",
+       "      <td>40% - 50%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>R\\$ 18,158,712.50 - R\\$ 21,490,455.00</td>\n",
+       "      <td>8</td>\n",
+       "      <td>50% - 60%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>R\\$ 21,490,455.00 - R\\$ 24,822,197.50</td>\n",
+       "      <td>8</td>\n",
+       "      <td>60% - 70%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>R\\$ 24,822,197.50 - R\\$ 28,153,940.00</td>\n",
+       "      <td>12</td>\n",
+       "      <td>70% - 80%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>R\\$ 28,153,940.00 - R\\$ 31,485,682.50</td>\n",
+       "      <td>9</td>\n",
+       "      <td>80% - 90%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>R\\$ 31,485,682.50 - R\\$ 34,817,425.00</td>\n",
+       "      <td>15</td>\n",
+       "      <td>90% - 100%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   Faixa  Quantidade de Senadores  \\\n",
+       "0    R\\$ 1,500,000.00 - R\\$ 4,831,742.50                        4   \n",
+       "1    R\\$ 4,831,742.50 - R\\$ 8,163,485.00                        0   \n",
+       "2   R\\$ 8,163,485.00 - R\\$ 11,495,227.50                        3   \n",
+       "3  R\\$ 11,495,227.50 - R\\$ 14,826,970.00                        2   \n",
+       "4  R\\$ 14,826,970.00 - R\\$ 18,158,712.50                        3   \n",
+       "5  R\\$ 18,158,712.50 - R\\$ 21,490,455.00                        8   \n",
+       "6  R\\$ 21,490,455.00 - R\\$ 24,822,197.50                        8   \n",
+       "7  R\\$ 24,822,197.50 - R\\$ 28,153,940.00                       12   \n",
+       "8  R\\$ 28,153,940.00 - R\\$ 31,485,682.50                        9   \n",
+       "9  R\\$ 31,485,682.50 - R\\$ 34,817,425.00                       15   \n",
+       "\n",
+       "  Percentual da cota  \n",
+       "0           0% - 10%  \n",
+       "1          10% - 20%  \n",
+       "2          20% - 30%  \n",
+       "3          30% - 40%  \n",
+       "4          40% - 50%  \n",
+       "5          50% - 60%  \n",
+       "6          60% - 70%  \n",
+       "7          70% - 80%  \n",
+       "8          80% - 90%  \n",
+       "9         90% - 100%  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Encontrando o valor máximo e mínimo das emendas\n",
+    "max_valor = df_emendas_senadores.max()\n",
+    "min_valor = df_emendas_senadores.min()\n",
+    "\n",
+    "# Dividindo o intervalo em 10 faixas iguais\n",
+    "faixa_size = (max_valor - min_valor) / 10\n",
+    "faixas = [min_valor + i * faixa_size for i in range(11)]\n",
+    "\n",
+    "# Contando o número de deputados em cada faixa\n",
+    "faixa_counts = [\n",
+    "    len(df_emendas_senadores[(df_emendas_senadores >= faixas[i]) & (df_emendas_senadores < faixas[i+1])])\n",
+    "    for i in range(10)\n",
+    "]\n",
+    "\n",
+    "# Criando o dataframe\n",
+    "df_distribuicao_emendas_senadores = pd.DataFrame({\n",
+    "    'Faixa': [f'R\\$ {faixas[i]:,.2f} - R\\$ {faixas[i+1]:,.2f}' for i in range(10)],\n",
+    "    'Quantidade de Senadores': faixa_counts,\n",
+    "    'Percentual da cota': [f\"{i * 10}% - {(i+1) * 10}%\" for i in range(10)]\n",
+    "})\n",
+    "df_distribuicao_emendas_senadores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8760f192-c612-4efa-b6dd-4db2d610a49c",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Criando o gráfico de barras\n",
+    "fig = go.Figure(go.Bar(\n",
+    "    x=df_distribuicao_emendas_senadores[\"Percentual da cota\"],\n",
+    "    y=df_distribuicao_emendas_senadores['Quantidade de Senadores'],\n",
+    "    marker_color='skyblue',\n",
+    "))\n",
+    "\n",
+    "# Adicionando título e rótulos aos eixos\n",
+    "fig.update_layout(\n",
+    "    title='Distribuição dos Senadores por Valor de Emendas',\n",
+    "    xaxis_title='Faixa de Valor das Emendas',\n",
+    "    yaxis_title='Quantidade de Senadores',\n",
+    "    font_size=12\n",
+    ")\n",
+    "\n",
+    "# Exibindo o gráfico\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9adce570-7f74-44ac-9445-cbea933647ec",
+   "metadata": {},
+   "source": [
+    "#### Valores totais por beneficiário"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbf47b1f-fc14-4305-ba82-9d27c47a98cf",
+   "metadata": {},
+   "source": [
+    "Quantidade de emendas por beneficiário:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "acce1821-528e-4214-82ca-4fcd4a4a33bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nome_beneficiario_plano_acao\n",
+       "ESTADO DE MINAS GERAIS       29\n",
+       "ESTADO DA BAHIA              22\n",
+       "ESTADO DE SAO PAULO          21\n",
+       "ESTADO DO ACRE               18\n",
+       "MUNICIPIO DE PORTO ALEGRE    17\n",
+       "                             ..\n",
+       "LUTÉCIA                       1\n",
+       "MUNICIPIO DE ITUMIRIM         1\n",
+       "MUNICIPIO DE IBITURUNA        1\n",
+       "MUNICIPIO DE CANELA           1\n",
+       "MUNICIPIO DE ITACOATIARA      1\n",
+       "Name: count, Length: 4458, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_emendas.nome_beneficiario_plano_acao.value_counts()"
+   ]
+  },
+  {
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+   "execution_count": 28,
+   "id": "c7292dc8-267a-4474-89f8-bda2e9a58265",
+   "metadata": {},
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+       "nome_beneficiario_plano_acao\n",
+       "ESTADO DA BAHIA                 106139019.0\n",
+       "ESTADO DE MATO GROSSO DO SUL     67253544.0\n",
+       "ESTADO DE SAO PAULO              51593567.0\n",
+       "MUNICIPIO DE MACAPA              46299542.0\n",
+       "ESTADO DO ACRE                   40565055.0\n",
+       "ESTADO DO PIAUI                  32849182.0\n",
+       "COARI                            31434456.0\n",
+       "MUNICIPIO DE COTIA               30071292.0\n",
+       "ESTADO DE MINAS GERAIS           29689690.0\n",
+       "MUNICIPIO DE CARAPICUIBA         24555054.0\n",
+       "MUNICIPIO DE SAO PAULO           24100000.0\n",
+       "ESTADO DO ESPIRITO SANTO         24090812.0\n",
+       "MUNICIPIO DE BOA VISTA           23935794.0\n",
+       "ESTADO DO MARANHAO               22557377.0\n",
+       "MUNICIPIO DE SAO LUIZ            21935792.0\n",
+       "Name: valor_emenda, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_emendas_beneficiario_camara = (\n",
+    "    df_emendas[df_emendas['cargo']=='deputado']\n",
+    "    .groupby([\"nome_beneficiario_plano_acao\"])\n",
+    "    .sum()\n",
+    "    ['valor_emenda']\n",
+    "    .sort_values(ascending=False)\n",
+    ")\n",
+    "df_emendas_beneficiario_camara.head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "00a12e37-da51-48bd-a777-265ef3104a2a",
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+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Criando o gráfico de barras horizontais\n",
+    "fig = go.Figure(go.Bar(\n",
+    "    x=df_emendas_beneficiario_camara.head(15).values,\n",
+    "    y=df_emendas_beneficiario_camara.head(15).index,\n",
+    "    orientation='h',\n",
+    "    marker_color='skyblue'\n",
+    "))\n",
+    "\n",
+    "# Adicionando título e rótulos aos eixos\n",
+    "fig.update_layout(\n",
+    "    title=\"15 beneficiários que mais receberam emendas\",\n",
+    "    xaxis_title=\"Beneficiário\",\n",
+    "    yaxis_title=\"Total em emendas\",\n",
+    "    font_size=12\n",
+    ")\n",
+    "\n",
+    "# Adicionando os valores nas barras\n",
+    "for i, value in enumerate(df_emendas_beneficiario_camara.head(15).values):\n",
     "    fig.add_annotation(\n",
     "        x=value,\n",
     "        y=i,\n",