diff --git a/exercicios/para-casa/exercicio.ipynb b/exercicios/para-casa/exercicio.ipynb
new file mode 100644
index 0000000..14cc2dc
--- /dev/null
+++ b/exercicios/para-casa/exercicio.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic_df = pd.read_csv('titanic.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "titanic_df = processar_dados(titanic_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.hist(titanic_df['Age'].dropna(), bins=20, color='skyblue')\n",
+    "plt.xlabel('Idade')\n",
+    "plt.ylabel('Número de Passageiros')\n",
+    "plt.title('Distribuição de Idades dos Passageiros')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "survival_by_class = titanic_df.groupby('Pclass')['Survived'].mean()\n",
+    "survival_by_class.plot(kind='bar', color='lightcoral')\n",
+    "plt.xlabel('Classe')\n",
+    "plt.ylabel('Taxa de Sobrevivência')\n",
+    "plt.title('Taxa de Sobrevivência por Classe')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "survival_by_sex = titanic_df.groupby(['Sex', 'Survived']).size().unstack()\n",
+    "survival_by_sex.plot(kind='bar', stacked=True, color=['lightcoral', 'lightblue'])\n",
+    "plt.xlabel('Sexo')\n",
+    "plt.ylabel('Contagem de Passageiros')\n",
+    "plt.title('Distribuição de Sobreviventes e Não Sobreviventes por Sexo')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.legend(['Não Sobreviveu', 'Sobreviveu'])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/material/titanic.csv b/exercicios/para-casa/titanic.csv
similarity index 100%
rename from material/titanic.csv
rename to exercicios/para-casa/titanic.csv
diff --git a/material/aula_revisao.ipynb b/material/aula_revisao.ipynb
index 916e3c7..c414395 100644
--- a/material/aula_revisao.ipynb
+++ b/material/aula_revisao.ipynb
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -285,7 +285,7 @@
        "[891 rows x 12 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1970,7 +1970,7 @@
        "99          100         0       2                                  Kantor, Mr. Sinai    male  34.00      1      0            244367   26.00          NaN        S"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2018,7 +2018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -2046,7 +2046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -2105,7 +2105,7 @@
        "Age  714.0  29.7  14.53  0.42   0.8  14.0  28.0  41.0  70.72  80.0"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2117,7 +2117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -2180,7 +2180,7 @@
        "803          804         1       3  Thomas, Master. Assad Alexander  male  0.42      0      1   2625  8.52   NaN        C"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2191,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -2254,7 +2254,7 @@
        "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson  male  80.0      0      0  27042  30.0   A23        S"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2265,7 +2265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -2274,7 +2274,7 @@
        "Text(0.5, 1.0, 'Distribuição de Classes')"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2305,7 +2305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -2384,7 +2384,7 @@
        "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1      0   PC 17599  71.28   C85        C"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2395,7 +2395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -2417,7 +2417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -2430,7 +2430,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2441,7 +2441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2463,7 +2463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2472,7 +2472,7 @@
        "array([3, 1, 2], dtype=int64)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2483,7 +2483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2496,7 +2496,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2507,7 +2507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2529,7 +2529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2551,7 +2551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2571,7 +2571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2698,7 +2698,7 @@
        "4            5         0       3                           Allen, Mr. William Henry    male  35.0      0      0            373450   8.05   NaN        S"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2709,7 +2709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2737,7 +2737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2772,7 +2772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2781,7 +2781,7 @@
        "(204, 12)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2792,7 +2792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2803,7 +2803,7 @@
        "Name: PassengerId, dtype: int64"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/material/aula_s12.ipynb b/material/aula_s12.ipynb
index 6a3a013..82cd23b 100644
--- a/material/aula_s12.ipynb
+++ b/material/aula_s12.ipynb
@@ -2531,7 +2531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2549,10 +2549,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x2becd951610>"
+       "<IPython.lib.display.IFrame at 0x1f353fb8b90>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2583,7 +2583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "id": "noSCxvLQS_m6",
     "outputId": "fd434c19-19d3-4395-a595-83703e140d1b"
@@ -2592,10 +2592,10 @@
     {
      "data": {
       "text/plain": [
-       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002BED4E60F50>"
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001F357E8C2D0>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2608,7 +2608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2621,7 +2621,7 @@
        "Name: IdPassageiro, dtype: int64"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2688,7 +2688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2743,7 +2743,7 @@
        "Idade  714.0  29.699118  14.526497  0.42  28.0  65.87  70.7175  80.0"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2754,7 +2754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2895,7 +2895,7 @@
        "851          NaN               S  "
       ]
      },
-     "execution_count": 42,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2906,7 +2906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 27,
    "metadata": {
     "id": "jxk2rE1hS_m7",
     "outputId": "55e2c10a-49b5-4a8a-f979-162b4bc89e1b"
@@ -2945,7 +2945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -3048,7 +3048,7 @@
        "male    0.42  80.0  "
       ]
      },
-     "execution_count": 49,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3060,7 +3060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -3177,7 +3177,7 @@
        "       S                       441  21.711996  10.50000  0.0000  263.0000"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3241,7 +3241,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting matplotlibNote: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: Could not install packages due to an OSError: [WinError 32] O arquivo já está sendo usado por outro processo: 'C:\\\\Users\\\\sofia\\\\AppData\\\\Local\\\\Packages\\\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\\\LocalCache\\\\local-packages\\\\Python311\\\\site-packages\\\\matplotlib\\\\tests\\\\test_quiver.py'\n",
+      "Check the permissions.\n",
+      "\n",
+      "\n",
+      "[notice] A new release of pip is available: 23.1.2 -> 23.3\n",
+      "[notice] To update, run: C:\\Users\\sofia\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  Using cached matplotlib-3.8.0-cp311-cp311-win_amd64.whl (7.6 MB)\n",
+      "Collecting contourpy>=1.0.1 (from matplotlib)\n",
+      "  Using cached contourpy-1.1.1-cp311-cp311-win_amd64.whl (480 kB)\n",
+      "Collecting cycler>=0.10 (from matplotlib)\n",
+      "  Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (4.43.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.4.5)\n",
+      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.26.1)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (23.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (10.1.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (3.1.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "Installing collected packages: cycler, contourpy, matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3251,6 +3301,133 @@
     "outputId": "386b4ea1-0ef0-4e2a-891a-e1ea0dee0ed5"
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Matplotlib is building the font cache; this may take a moment.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting matplotlib\n",
+      "  Downloading matplotlib-3.8.0-cp311-cp311-win_amd64.whl (7.6 MB)\n",
+      "                                              0.0/7.6 MB ? eta -:--:--\n",
+      "                                              0.1/7.6 MB 1.9 MB/s eta 0:00:04\n",
+      "     -                                        0.2/7.6 MB 2.3 MB/s eta 0:00:04\n",
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+      "     -------                                  1.5/7.6 MB 4.5 MB/s eta 0:00:02\n",
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+      "     -------------------                      3.7/7.6 MB 5.2 MB/s eta 0:00:01\n",
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+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
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+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  7.6/7.6 MB 5.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------------- 7.6/7.6 MB 3.8 MB/s eta 0:00:00\n",
+      "Collecting contourpy>=1.0.1 (from matplotlib)\n",
+      "  Downloading contourpy-1.1.1-cp311-cp311-win_amd64.whl (480 kB)\n",
+      "                                              0.0/480.5 kB ? eta -:--:--\n",
+      "     --                                       30.7/480.5 kB ? eta -:--:--\n",
+      "     ----------                             133.1/480.5 kB 1.3 MB/s eta 0:00:01\n",
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+      "     ------------------------------------ 480.5/480.5 kB 734.8 kB/s eta 0:00:00\n",
+      "Collecting cycler>=0.10 (from matplotlib)\n",
+      "  Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
+      "  Downloading fonttools-4.43.1-cp311-cp311-win_amd64.whl (2.1 MB)\n",
+      "                                              0.0/2.1 MB ? eta -:--:--\n",
+      "     ----------                               0.6/2.1 MB 17.0 MB/s eta 0:00:01\n",
+      "     ----------                               0.6/2.1 MB 17.0 MB/s eta 0:00:01\n",
+      "     -------------                            0.7/2.1 MB 4.9 MB/s eta 0:00:01\n",
+      "     ------------------------------           1.6/2.1 MB 8.7 MB/s eta 0:00:01\n",
+      "     -----------------------------------      1.9/2.1 MB 8.7 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.1/2.1 MB 8.0 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.1/2.1 MB 8.0 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.1/2.1 MB 8.0 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.1/2.1 MB 8.0 MB/s eta 0:00:01\n",
+      "     ---------------------------------------- 2.1/2.1 MB 4.4 MB/s eta 0:00:00\n",
+      "Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
+      "  Downloading kiwisolver-1.4.5-cp311-cp311-win_amd64.whl (56 kB)\n",
+      "                                              0.0/56.1 kB ? eta -:--:--\n",
+      "     ------------------------------------     51.2/56.1 kB ? eta -:--:--\n",
+      "     ------------------------------------     51.2/56.1 kB ? eta -:--:--\n",
+      "     ------------------------------------     51.2/56.1 kB ? eta -:--:--\n",
+      "     -------------------------------------- 56.1/56.1 kB 327.7 kB/s eta 0:00:00\n",
+      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.26.1)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (23.2)\n",
+      "Collecting pillow>=6.2.0 (from matplotlib)\n",
+      "  Downloading Pillow-10.1.0-cp311-cp311-win_amd64.whl (2.6 MB)\n",
+      "                                              0.0/2.6 MB ? eta -:--:--\n",
+      "     ---------                                0.6/2.6 MB 19.5 MB/s eta 0:00:01\n",
+      "     -------------                            0.9/2.6 MB 11.3 MB/s eta 0:00:01\n",
+      "     -------------------                      1.3/2.6 MB 10.2 MB/s eta 0:00:01\n",
+      "     -----------------------                  1.6/2.6 MB 9.0 MB/s eta 0:00:01\n",
+      "     -----------------------------            1.9/2.6 MB 8.8 MB/s eta 0:00:01\n",
+      "     -----------------------------------      2.3/2.6 MB 8.6 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.6/2.6 MB 8.3 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.6/2.6 MB 8.3 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.6/2.6 MB 8.3 MB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.6/2.6 MB 8.3 MB/s eta 0:00:01\n",
+      "     ---------------------------------------- 2.6/2.6 MB 5.4 MB/s eta 0:00:00\n",
+      "Collecting pyparsing>=2.3.1 (from matplotlib)\n",
+      "  Downloading pyparsing-3.1.1-py3-none-any.whl (103 kB)\n",
+      "                                              0.0/103.1 kB ? eta -:--:--\n",
+      "     ---------------------------------------  102.4/103.1 kB ? eta -:--:--\n",
+      "     -------------------------------------- 103.1/103.1 kB 2.0 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\sofia\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
+      "Successfully installed contourpy-1.1.1 cycler-0.12.1 fonttools-4.43.1 kiwisolver-1.4.5 matplotlib-3.8.0 pillow-10.1.0 pyparsing-3.1.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.1.2 -> 23.3\n",
+      "[notice] To update, run: C:\\Users\\sofia\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
+     ]
+    },
     {
      "data": {
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",
@@ -3283,7 +3460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 35,
    "metadata": {
     "id": "bmZg5LjvS_nJ",
     "outputId": "f3874a08-9ec1-4235-8db1-2250deeb54ea"
@@ -3299,7 +3476,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3320,7 +3497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -3341,7 +3518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -3361,7 +3538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -3370,7 +3547,7 @@
        "<Axes: ylabel='Frequency'>"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3391,7 +3568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 39,
    "metadata": {
     "id": "zpZmI2e9S_nJ",
     "outputId": "3f16a4f4-3039-4426-ac46-d0f8178d87a6"
@@ -3403,7 +3580,7 @@
        "Text(0.5, 1.0, 'Número de Passageiros por Classe')"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3430,7 +3607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 40,
    "metadata": {
     "id": "j7lUqI3BS_nK",
     "outputId": "758058f2-185d-412d-835e-38d8ca773f3e"
@@ -3461,7 +3638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 41,
    "metadata": {
     "id": "RzV9cvoOS_nK",
     "outputId": "eea06c73-5805-427d-f2b1-b82d3481b8a8"
@@ -3473,7 +3650,7 @@
        "Text(0.5, 1.0, 'Distribuição de Idade')"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3509,7 +3686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3520,7 +3697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -3572,7 +3749,7 @@
        "0      1  female         94"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3583,7 +3760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -3595,7 +3772,7 @@
        "           )"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3606,25 +3783,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade',\n",
-       "       'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket',\n",
-       "       'NumeroCabine', 'PortoEmbarcacao'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df[df['PortoEmbarcacao']]"
+    "df_filtrado = df[df['PortoEmbarcacao'] == 'C']\n"
    ]
   },
   {
@@ -3655,7 +3818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 51,
    "metadata": {
     "id": "6qrRb5EIS_nL"
    },
@@ -3684,7 +3847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 52,
    "metadata": {
     "id": "suI74yqZS_nL",
     "outputId": "eb87a166-4d92-4068-e5ad-e7ce283ed244"
@@ -3710,7 +3873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 53,
    "metadata": {
     "id": "DxCmoOkhS_nM",
     "outputId": "2c7699a1-519f-4b81-d4e1-836895767375"
@@ -3741,7 +3904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -3750,7 +3913,7 @@
        "[1, 2, 3]"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3761,7 +3924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -3770,7 +3933,7 @@
        "array([1, 2, 3])"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3781,7 +3944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 56,
    "metadata": {
     "id": "KZ-JzM6US_nM",
     "outputId": "9c7580bf-627b-4926-ed86-8441bb436861"
@@ -3793,7 +3956,7 @@
        "array([10, 20, 30, 40, 50])"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3811,7 +3974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 57,
    "metadata": {
     "id": "SWy82LKAS_nM",
     "outputId": "ee7e4c2e-5553-405f-cbdf-878534650298"
@@ -3824,7 +3987,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\Debora\\Documents\\GitHub\\on26-python-s12-pandas-numpy-II\\material\\aula_s12.ipynb Cell 70\u001b[0m line \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Debora/Documents/GitHub/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y106sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# se fosse uma lista Python, seria impossível pois retornaria um TypeError\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Debora/Documents/GitHub/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y106sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m lista_precos \u001b[39m=\u001b[39m [\u001b[39m12\u001b[39m,\u001b[39m22\u001b[39m,\u001b[39m32\u001b[39m,\u001b[39m42\u001b[39m,\u001b[39m52\u001b[39m]\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Debora/Documents/GitHub/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y106sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m lista_promocial \u001b[39m=\u001b[39m lista_precos \u001b[39m-\u001b[39;49m \u001b[39m2\u001b[39;49m\n",
+      "\u001b[1;32mc:\\Users\\sofia\\estudos\\semana12\\on26-python-s12-pandas-numpy-II\\material\\aula_s12.ipynb Célula 73\u001b[0m line \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/sofia/estudos/semana12/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y131sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# se fosse uma lista Python, seria impossível pois retornaria um TypeError\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/sofia/estudos/semana12/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y131sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m lista_precos \u001b[39m=\u001b[39m [\u001b[39m12\u001b[39m,\u001b[39m22\u001b[39m,\u001b[39m32\u001b[39m,\u001b[39m42\u001b[39m,\u001b[39m52\u001b[39m]\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/sofia/estudos/semana12/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y131sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m lista_promocial \u001b[39m=\u001b[39m lista_precos \u001b[39m-\u001b[39;49m \u001b[39m2\u001b[39;49m\n",
       "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'list' and 'int'"
      ]
     }
@@ -3846,7 +4009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 58,
    "metadata": {
     "id": "LljyqgRgS_nM",
     "outputId": "17c514e4-0fa0-4d36-c6f8-46293589ce4b"
@@ -3858,7 +4021,7 @@
        "array([100, 200, 300])"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3871,7 +4034,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -3883,7 +4046,7 @@
        "dtype: int32"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3895,7 +4058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 60,
    "metadata": {
     "id": "4m4a1TDIS_nN",
     "outputId": "d60ac614-bd45-430f-d375-bafdf4252478"
@@ -3910,7 +4073,7 @@
        "dtype: int32"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3923,7 +4086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 61,
    "metadata": {
     "id": "ZxBABq3pS_nN",
     "outputId": "d11786b2-ac75-43da-9ce9-5e3c8d3869d4"
@@ -3978,7 +4141,7 @@
        "1  4  5  6"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3993,7 +4156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -4048,7 +4211,7 @@
        "1  4  5  6  5"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4060,7 +4223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 63,
    "metadata": {
     "id": "LHfl6fALS_nN",
     "outputId": "5805c47a-4db3-4a7f-8790-dad45dc7ff76"
@@ -4077,7 +4240,7 @@
        "dtype: int32"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4110,45 +4273,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([10, 23, 13, 32, 25, 11, 20, 49, 26,  6, 40, 14, 42, 17, 11,  6, 43,\n",
-       "       15, 19, 12, 29, 47,  5, 12, 42, 47, 40, 15,  7, 22,  8, 41, 35, 25,\n",
-       "       14,  5, 31, 13, 42, 49, 49, 47, 41, 37,  5, 22, 34, 21, 14, 10, 13,\n",
-       "       10, 37, 27, 24, 31, 34, 31, 23, 32, 23, 47, 22, 30, 14, 40, 31, 42,\n",
-       "       35, 21, 40, 42, 36, 37, 11,  8, 14, 46, 11, 28, 39, 35, 29,  5, 46,\n",
-       "       18, 24, 17, 29, 48, 35, 14, 38, 30, 11, 10, 47, 48, 38,  5, 37,  5,\n",
-       "       26, 10, 10, 29,  6, 49, 15, 48, 13, 30, 24, 19, 15, 40, 43,  5, 35,\n",
-       "       46, 45, 44, 28,  7, 13, 44,  5, 47, 14, 13, 17, 43, 33, 46, 32, 16,\n",
-       "       39, 46, 23, 38, 21, 46, 18, 31,  7, 43,  8, 31, 11, 18, 37, 40, 48,\n",
-       "       41, 13,  6, 33, 13, 11,  6, 15, 23, 30, 16, 31, 34, 16,  6, 14,  5,\n",
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-       "       27, 42, 37, 48, 13, 12, 36, 15, 41,  6, 40, 19, 17, 14, 27, 39, 24,\n",
-       "       17, 29, 14, 24, 27, 21, 12, 44, 12, 39, 18,  7, 34, 20, 17, 19, 27,\n",
-       "       31, 19, 18, 13, 47, 38, 47, 14, 39, 26, 30,  6, 18, 37, 44, 42, 21,\n",
-       "       20,  6, 48,  6, 29,  7, 35, 47, 17, 22, 42, 32, 24, 48, 22, 28, 27,\n",
-       "       34, 21, 48,  7, 39, 26,  8, 43,  7, 19, 22, 49, 29,  6, 22, 30, 32,\n",
-       "       49, 35,  9, 40, 21, 18, 21, 33,  6, 14, 47,  6, 10, 46, 10, 24, 24,\n",
-       "       27, 18, 40, 34, 28, 45, 16, 38, 38, 45, 23, 37, 25, 46, 25, 37, 22,\n",
-       "       38, 30, 18, 44, 11, 39, 32, 26, 12,  7,  8,  7, 18, 23, 41, 37, 39,\n",
-       "       40, 45, 38, 43, 48, 19, 13, 25, 11, 37,  9, 11, 42, 18, 49, 24, 40,\n",
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-       "       35, 12, 25, 40, 16, 42, 40, 42, 15, 25, 33, 30, 40, 30, 20, 22,  7,\n",
-       "       16,  8, 34,  8, 38,  8, 31,  9, 28, 23, 36,  8, 13, 38, 31, 29, 18,\n",
-       "       42, 45, 34, 41, 37, 28, 24, 36, 45, 49, 45, 23, 14, 25, 30, 27, 44,\n",
-       "       45, 19, 18, 24, 35, 34, 42])"
+       "array([48, 33, 41,  5,  9, 47, 42, 17, 10,  9, 47, 34, 35, 13, 37, 18, 49,\n",
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+       "       47,  8, 31, 46, 13,  9,  9, 40, 20, 33,  8, 11, 43, 33,  8, 45, 18,\n",
+       "       36, 35, 44, 25, 36, 13, 16, 13, 39, 47, 49, 23, 35, 33, 41, 36,  9,\n",
+       "        8, 15,  5, 11, 12, 13, 20, 25, 36, 37, 19, 23,  6, 33, 24, 37, 37,\n",
+       "       16, 40,  7, 27, 12,  7, 44, 22, 12, 44, 36, 22,  8, 22, 31, 25, 28,\n",
+       "       23,  8, 48, 41,  7, 46, 46,  9, 47, 44, 19, 35, 23, 26, 29, 14, 25,\n",
+       "       39, 13, 44, 10, 19,  9, 13, 23, 26, 11, 26, 14, 14, 25, 23, 31, 32,\n",
+       "       14, 23, 15, 18, 11, 20, 35, 25, 49, 22, 24, 43, 36, 18, 44, 49, 27,\n",
+       "        5, 15, 29, 25,  8,  9, 29,  7, 35, 46, 10,  8, 23, 44, 29, 47, 41,\n",
+       "       24, 25, 11, 25, 32, 33, 11, 31, 29, 44, 36, 28, 38, 35, 46, 43, 17,\n",
+       "       46, 33, 31, 47,  7, 45, 49, 47, 46, 43,  8, 39, 44, 17, 26, 25, 18,\n",
+       "       32,  9, 10, 34, 11, 39, 45, 17, 27, 28, 26,  9, 14, 41,  6,  7, 25,\n",
+       "       31, 13, 12, 22, 24, 40, 18, 48, 42, 29, 40, 14, 12, 49, 32, 49, 32,\n",
+       "       12, 20, 44, 43, 32, 18, 12, 19, 22, 27,  7, 21, 14, 16, 40, 20, 24,\n",
+       "       43, 25, 33, 20, 45, 32, 18, 17, 33, 18, 19, 20, 31, 46, 49, 14, 35,\n",
+       "       12, 41, 31, 19, 17, 17, 33, 42, 34, 30, 35, 21,  7, 18, 17, 40, 15,\n",
+       "       34, 18, 18, 37, 22, 14, 28, 11, 43,  6, 25, 21, 48, 46,  8, 38, 38,\n",
+       "       14,  5, 30,  6, 32, 25, 36, 36,  9, 18, 24, 49, 18, 12, 43, 22, 20,\n",
+       "       42, 33, 31, 25, 13, 39, 38, 42, 17, 23, 24, 46, 33, 40, 37, 22, 35,\n",
+       "       28, 38,  8, 36, 41, 21, 46, 23, 20, 14,  8, 15,  9,  7, 16, 29, 40,\n",
+       "       29, 49, 29, 38, 34, 16, 32, 16, 27, 27, 34, 25, 19, 21, 17, 36,  7,\n",
+       "       14, 47, 45, 19, 34, 28, 38, 17,  5, 26, 38, 48, 15, 28, 31, 11, 33,\n",
+       "       12, 48, 49, 30,  9, 12, 47, 10, 41, 26, 35, 18, 49, 43, 27, 16, 15,\n",
+       "       10, 17, 27, 30, 43, 31, 46,  5, 25, 42,  6,  5, 29, 10, 45, 46, 41,\n",
+       "       35, 31, 26, 19, 17, 36, 41, 41, 44, 34, 28, 15, 23, 16, 14, 45, 11,\n",
+       "       17, 41, 29, 10, 49, 32, 38])"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }