diff --git a/exercicios/para-casa/BrendaFreire-empregados.ipynb b/exercicios/para-casa/BrendaFreire-empregados.ipynb
new file mode 100644
index 0000000..869449e
--- /dev/null
+++ b/exercicios/para-casa/BrendaFreire-empregados.ipynb
@@ -0,0 +1,595 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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>Education</th>\n",
+       "      <th>JoiningYear</th>\n",
+       "      <th>City</th>\n",
+       "      <th>PaymentTier</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>EverBenched</th>\n",
+       "      <th>ExperienceInCurrentDomain</th>\n",
+       "      <th>LeaveOrNot</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Bangalore</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>Pune</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>New Delhi</td>\n",
+       "      <td>3</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Masters</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>Bangalore</td>\n",
+       "      <td>3</td>\n",
+       "      <td>27</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Masters</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Pune</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Education  JoiningYear       City  PaymentTier  Age  Gender EverBenched  \\\n",
+       "0  Bachelors         2017  Bangalore            3   34    Male          No   \n",
+       "1  Bachelors         2013       Pune            1   28  Female          No   \n",
+       "2  Bachelors         2014  New Delhi            3   38  Female          No   \n",
+       "3    Masters         2016  Bangalore            3   27    Male          No   \n",
+       "4    Masters         2017       Pune            3   24    Male         Yes   \n",
+       "\n",
+       "   ExperienceInCurrentDomain  LeaveOrNot  \n",
+       "0                          0           0  \n",
+       "1                          3           1  \n",
+       "2                          2           0  \n",
+       "3                          5           1  \n",
+       "4                          2           1  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(r'C:\\Users\\brend\\Desktop\\REPROGRAMA\\semana12\\on33-python-s10-pandas-numpy-II\\material\\Employee.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4653 entries, 0 to 4652\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column                     Non-Null Count  Dtype \n",
+      "---  ------                     --------------  ----- \n",
+      " 0   Education                  4653 non-null   object\n",
+      " 1   JoiningYear                4653 non-null   int64 \n",
+      " 2   City                       4653 non-null   object\n",
+      " 3   PaymentTier                4653 non-null   int64 \n",
+      " 4   Age                        4653 non-null   int64 \n",
+      " 5   Gender                     4653 non-null   object\n",
+      " 6   EverBenched                4653 non-null   object\n",
+      " 7   ExperienceInCurrentDomain  4653 non-null   int64 \n",
+      " 8   LeaveOrNot                 4653 non-null   int64 \n",
+      "dtypes: int64(5), object(4)\n",
+      "memory usage: 327.3+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Education                    0\n",
+      "JoiningYear                  0\n",
+      "City                         0\n",
+      "PaymentTier                  0\n",
+      "Age                          0\n",
+      "Gender                       0\n",
+      "EverBenched                  0\n",
+      "ExperienceInCurrentDomain    0\n",
+      "LeaveOrNot                   0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1889\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.duplicated().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.drop_duplicates(inplace=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.duplicated().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 2764 entries, 0 to 4651\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column                     Non-Null Count  Dtype \n",
+      "---  ------                     --------------  ----- \n",
+      " 0   Education                  2764 non-null   object\n",
+      " 1   JoiningYear                2764 non-null   int64 \n",
+      " 2   City                       2764 non-null   object\n",
+      " 3   PaymentTier                2764 non-null   int64 \n",
+      " 4   Age                        2764 non-null   int64 \n",
+      " 5   Gender                     2764 non-null   object\n",
+      " 6   EverBenched                2764 non-null   object\n",
+      " 7   ExperienceInCurrentDomain  2764 non-null   int64 \n",
+      " 8   LeaveOrNot                 2764 non-null   int64 \n",
+      "dtypes: int64(5), object(4)\n",
+      "memory usage: 215.9+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       JoiningYear  PaymentTier          Age  ExperienceInCurrentDomain  \\\n",
+      "count  2764.000000  2764.000000  2764.000000                2764.000000   \n",
+      "mean   2015.090449     2.636035    30.952967                   2.644356   \n",
+      "std       1.885943     0.624001     5.108872                   1.610610   \n",
+      "min    2012.000000     1.000000    22.000000                   0.000000   \n",
+      "25%    2013.000000     2.000000    27.000000                   1.000000   \n",
+      "50%    2015.000000     3.000000    30.000000                   2.000000   \n",
+      "75%    2017.000000     3.000000    35.000000                   4.000000   \n",
+      "max    2018.000000     3.000000    41.000000                   7.000000   \n",
+      "\n",
+      "        LeaveOrNot  \n",
+      "count  2764.000000  \n",
+      "mean      0.393632  \n",
+      "std       0.488643  \n",
+      "min       0.000000  \n",
+      "25%       0.000000  \n",
+      "50%       0.000000  \n",
+      "75%       1.000000  \n",
+      "max       1.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime as dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       JoiningYear  PaymentTier          Age  ExperienceInCurrentDomain  \\\n",
+      "count  2764.000000  2764.000000  2764.000000                2764.000000   \n",
+      "mean   2015.090449     2.636035    30.952967                   2.644356   \n",
+      "std       1.885943     0.624001     5.108872                   1.610610   \n",
+      "min    2012.000000     1.000000    22.000000                   0.000000   \n",
+      "25%    2013.000000     2.000000    27.000000                   1.000000   \n",
+      "50%    2015.000000     3.000000    30.000000                   2.000000   \n",
+      "75%    2017.000000     3.000000    35.000000                   4.000000   \n",
+      "max    2018.000000     3.000000    41.000000                   7.000000   \n",
+      "\n",
+      "        LeaveOrNot  \n",
+      "count  2764.000000  \n",
+      "mean      0.393632  \n",
+      "std       0.488643  \n",
+      "min       0.000000  \n",
+      "25%       0.000000  \n",
+      "50%       0.000000  \n",
+      "75%       1.000000  \n",
+      "max       1.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "current_year = dt.date.today().year\n",
+    "five_years_ago = current_year - 5\n",
+    "filtered_df = df[(df[\"JoiningYear\"] <= five_years_ago)]\n",
+    "print(filtered_df.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Age\"].value_counts().sort_index().plot(kind=\"barh\", title=\"Empregados por Idade\", xlabel=\"Quantidade\", ylabel=\"Idades\", color=\"blue\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Gender\"].value_counts().plot(kind=\"pie\", title=\"Empregados por Gênero\", colors=[\"pink\", \"blue\"], autopct=\"%.2f%%\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cidade com maior número de colaboradores: Pune\n"
+     ]
+    }
+   ],
+   "source": [
+    "most_employees_city = df[\"City\"].max()\n",
+    "print(\"Cidade com maior número de colaboradores:\", most_employees_city)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "City\n",
+      "Bangalore    9.076003\n",
+      "New Delhi    8.631313\n",
+      "Pune         8.941323\n",
+      "Name: LenghtService, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"LenghtService\"] = current_year - df[\"JoiningYear\"]\n",
+    "lenght_service_mean_by_city = df.groupby([\"City\"])[\"LenghtService\"].mean()\n",
+    "print(lenght_service_mean_by_city)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Em 2024 cerca de 60.64% dos empregados ainda trabalham na empresa.\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_employees = len(df)\n",
+    "not_working = df[\"LeaveOrNot\"].value_counts()\n",
+    "not_working_pct = (not_working / total_employees) * 100\n",
+    "print(f\"Em {current_year} cerca de {not_working_pct[0]:.2f}% dos empregados ainda trabalham na empresa.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Atualmente existem 1676 empregados na empresa.\n"
+     ]
+    }
+   ],
+   "source": [
+    "empolyees = len(df[\"PaymentTier\"]) - not_working[1]\n",
+    "print(f\"Atualmente existem {empolyees} empregados na empresa.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0       False\n",
+      "1       False\n",
+      "2       False\n",
+      "3       False\n",
+      "4        True\n",
+      "        ...  \n",
+      "4645    False\n",
+      "4647    False\n",
+      "4649    False\n",
+      "4650    False\n",
+      "4651     True\n",
+      "Name: EverBenched, Length: 2764, dtype: bool\n"
+     ]
+    }
+   ],
+   "source": [
+    "def convertion(value):\n",
+    "    if value == \"Yes\":\n",
+    "        return True\n",
+    "    if value == \"No\":\n",
+    "        return False\n",
+    "    else:\n",
+    "        return \"Não categorizado\"\n",
+    "\n",
+    "df[\"EverBenched\"] = df[\"EverBenched\"].apply(convertion)\n",
+    "print(df[\"EverBenched\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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H+vXrsXnzZsyZMwcLFizA1q1b0a1btwa1GQB69+6NNm3aYM2aNRg/fjw2btwIrVaLsWPHXlf7AGt4icX2B/xmsxlDhgyBWq3G7Nmz0bFjR/j5+SEzMxOTJk2qNrjfVJ+7+lx/zJgx2L17N5555hmkpKRApVLBYrFg6NCh9ZqEcC0N/Qw2ZOyson0zZ85Eampqjftca9p9TfdXVFSEgQMHIiAgAK+88gri4+Mhl8tx8OBBzJ492yHPS11cKhQaqyK5K/8COH36NABUmxVUVcWv9IiICNxyyy217hcbG2u7r5ruv7K1a9dCLpdj8+bNdt0dy5cvr3abFovF9sunQtUvuPDwcCiVyhq/+E6ePAmxWGx3VBUSEoLJkydj8uTJKCsrw4ABA/DSSy/VGQqxsbE1djnVdJ/BwcEoKiqy22YwGJCdnW23LT4+HmVlZXU+rw2xevVqJCQkYNWqVXbbS0tLr3nd+r7Olfd/+umn8fTTT+PMmTNISUnBf/7zH3z11VcAav61WZcxY8Zg6dKlKCkpwerVq9GmTRtbd8n1tK8mx44dw+nTp7Fy5UpMmDDBtr0+M8+aU2FhIX777Te8/PLLmDt3rm17fbs84+Pj8eeff8JoNNoGrquq72ewLrW9xhVduFKp1GHvbcA6U6mgoADr1q3DgAEDbNvPnz/vsPuoi0tMSXWUrKws/PDDD7a/S0pK8MUXXyAlJeWah/ypqakICAjA66+/DqPRWO3yvLw8ANZxi6SkJHzxxRd2fcO///47jh07ZncdiUQCkUhk98v5woULWL9+vd1+w4YNAwC88847dturzsqRSCT417/+hR9//NFuamhOTg6++eYb9OvXz9a9UHVKn0qlQkJCgt3hc01uvfVW7N27F/v27bN77F9//XW1fePj46uNB3z88cfVjhTGjBmDPXv2YPPmzdVuo6ioCCaTqc42VSUSiWCxWOx+Me3evbteK+Tr+zprNBrodDq7y+Lj4+Hv72/3HPr5+VULxrqMHTsWer0eK1euxC+//GLXZ9+Q9tWl4hd65V/kgiBg6dKl9W5nc6ipnUD1931tRo0ahfz8fLz33nvVLqu4zfp+ButSMaOr6uscERGBQYMG4aOPPqr2Qwio32tVk5qeF4PBgPfff/+6bq+hmuVIYdOmTTUOjvTp08duwLSx2rdvjylTpmD//v2IjIzE559/jpycnHr9KggICMAHH3yA++67D927d8fdd9+N8PBwXLp0CT/99BP69u1re/O9/vrrGDFiBPr27YvJkyejsLAQ7733HpKSkuyCYvjw4XjzzTcxdOhQjB8/Hrm5uVi2bBkSEhJw9OhR234pKSkYN24c3n//fRQXF6NPnz747bffajwaefXVV7Flyxb069cPjzzyCHx8fPDRRx9Br9dj8eLFtv0SExMxaNAg9OjRAyEhIThw4AC+//57PPbYY3U+D7NmzcKXX36JoUOH4oknnrBNSY2NjbVrM2AdnHz44YcxatQoDBkyBEeOHMHmzZsRFhZmt98zzzyDDRs24LbbbsOkSZPQo0cPlJeX49ixY/j+++9x4cKFatepy/Dhw/HDDz9g5MiRGD58OM6dO4ePPvoInTt3vubRQn1f59OnT+Pmm2/GmDFjkJiYCB8fH/zwww/IycnB3Xffbbu9Hj164IMPPsCrr76KhIQERERE1DoeAADdu3dHQkICXnjhBej1eruuo4a0ry4dO3ZEfHw8Zs6ciczMTAQEBGDt2rUOGTB3pICAANs0WKPRiOjoaPz666/1/kU8YcIEfPHFF3jqqaewb98+9O/fH+Xl5fjf//6HRx55BCNGjKj3Z7AuCoUCiYmJWL16Ndq3b4+QkBAkJSUhKSkJy5YtQ79+/ZCcnIypU6ciLi4OOTk52LNnDzIyMnDkyJEGPy99+vRBcHAwJk6ciMcffxwikQhffvllo7rLG6QppzbVNSUVgLB8+XJBEP6ZwlZ1+l/FNK7vvvuuxtvdv3+/bVtsbKwwfPhwYfPmzUKXLl0EX19foWPHjvW6btX7TE1NFQIDAwW5XC7Ex8cLkyZNEg4cOGC337fffit07NhR8PX1FZKSkoQNGzYIo0aNEjp27Gi332effSa0a9fO1p7ly5cL8+bNE6o+9VqtVnj88ceF0NBQwc/PT7j99tuFy5cv1zgV7uDBg0JqaqqgUqkEpVIp3HTTTcLu3bvt9nn11VeFnj17CkFBQYJCoRA6duwovPbaa4LBYKjxcVd29OhRYeDAgYJcLheio6OF+fPnC5999lm1Kalms1mYPXu2EBYWJiiVSiE1NVU4e/ZstSmpgmCdavncc88JCQkJgkwmE8LCwoQ+ffoIb7zxxjXbVHVKqsViEV599VWhdevWglwuF3r06CFs2rRJmDhxYrUpwbW51uucn58vPProo0LHjh0FPz8/ITAwUOjVq5ewZs0au9u5cuWKMHz4cMHf318AYGtnTVMQK7zwwgsCACEhIeG62ycI1umkfn5+NV7/77//Fm655RZBpVIJYWFhwtSpU4UjR47Yfe7quo2BAwcKnTt3rra94nNWl9o+z4JQfRpzRkaGMHLkSCEoKEgIDAwURo8eLWRlZdVr6qsgWKduvvDCC0Lbtm0FqVQqREVFCf/+97/tpmzX9zMIQHj00UdrvJ/du3cLPXr0EGQyWbW2paenCxMmTBCioqIEqVQqREdHC7fddpvw/fff2/apbUpqTc+xIAjCrl27hN69ewsKhUJo2bKlMGvWLGHz5s21vqccSSQIzRU/TatNmzZISkrCf//7X6e1ISUlBeHh4S7Xd+sIK1aswOTJk3H+/Plrjs84Wv/+/eHr64v//e9/zXq/RN7Io8YUmovRaKzWD759+3YcOXLkugtmUe2ys7Mb1L1ERNfPo2YfNZfMzEzccsstuPfee9GyZUucPHkSH374IaKiovDwww87u3keY/fu3Vi3bh3S09Mxe/ZsZzeHyCswFK5DcHAwevTogU8//RR5eXnw8/PD8OHDsXDhwmr1nOj6ffLJJ9i0aRNmzJhR46pPInI8jxlTICKixuOYAhER2TAUiIjIhqFAREQ2DAUiIrJhKBARkQ1DgYiIbBgKRERkw1AgIiIbhgIREdmwzAURuTSz2VzjCYfInlQqrfUUqA3BUCAilyQIAq5cudKgM9t5u6CgIERFRTX4NLGVMRSIyCVVBEJERASUSmWjvug8nSAI0Gg0yM3NBQC0aNHium+LoUBELsdsNtsCgZWH60ehUAAAcnNzERERcd1dSRxoJiKXUzGGoFQqndwS91LxfDVmDIahQEQui11GDeOI54uhQERENgwFIiInWLduHYKCgjBnzhxs2bIFjz76qLObBIADzUTkZtadym62+7qrQ8Nn8UyaNAkrV67EggUL8Oyzz9q2r1+/HiNHjkTFyS7XrVuHL7/8EuvXr8e0adPwzTffOKzdjcFQICJyMLlcjkWLFuGhhx5CcHBwjft89dVXAIDbb7+9OZt2Tew+IiJysFtuuQVRUVFYsGBBjZcXFBRg3LhxiI6OhlKpRHJyMlatWmW3j16vx+OPP46IiAjI5XL069cP+/fvb/K2MxSIiBxMIpHg9ddfx7vvvouMjIxql+t0OvTo0QM//fQTjh8/jgcffBD33Xcf9u3bZ9tn1qxZWLt2LVauXImDBw8iISEBqampUKvVTdp2hgIRURMYOXIkUlJSMG/evGqXRUdHY+bMmUhJSUFcXBymT5+OoUOHYs2aNQCA8vJyfPDBB1iyZAmGDRuGxMREfPLJJ1AoFPjss8+atN0MBSKiJrJo0SKsXLkSJ06csNtuNpsxf/58JCcnIyQkBCqVCps3b8alS5cAAOnp6TAajejbt6/tOlKpFD179qx2W47GUCAiaiIDBgxAamoqnnvuObvtS5YswdKlSzF79mxs27YNhw8fRmpqKgwGg5Na+g+GAhFRE1q4cCE2btyIPXv22Lbt2rULI0aMwL333ouuXbsiLi4Op0+ftl0eHx8PmUyGXbt22bYZjUbs378fiYmJTdpeTkklImpCycnJuOeee/DOO+/YtrVr1w7ff/89du/ejeDgYLz55pvIycmxfeH7+flh2rRpeOaZZxASEoLWrVtj8eLF0Gg0mDJlSpO2l0cKRERN7JVXXoHFYrH9/eKLL6J79+5ITU3FoEGDEBUVhTvvvNPuOgsXLsSoUaNw3333oXv37jh79iw2b95c67oHRxEJFcvriIhchE6nw/nz59G2bVvI5XJnN8dtOOJ545ECERHZMBSIiMiGoUBERDYMBSIismEoEBGRDUOBiIhsGApERGTDUCAiIhuGAhER2TAUiIiakUgkwvr1653djFqxIB4RuZdNfzTffQ0b0OCr5OXlYe7cufjpp5+Qk5OD4OBgdO3aFXPnzkXfvn2RnZ3d5PWLGoOhQETkQKNGjYLBYMDKlSsRFxeHnJwc/PbbbygoKAAAREVFObmFdWMokEcRBAFGiwCTRYDJYoHJUvG35eo24eq2f/42WwSIRIBELIJEZP0ntv1/zdulYhGUUgnkPhJnP2RyIUVFRdixYwe2b9+OgQMHAgBiY2PRs2dP2z4ikQg//PAD7rzzTly4cAFt27bF6tWr8e677+LAgQNISkrC119/jeLiYkybNg0nT55E//798cUXXyA8PLzJHwNDgdyOzmRGmcGMcqMJGqMZGpPZ+l+jGVqTGZZmrPsrFgFKHwmU0n/+KXzs/18sEjVfg8ipVCoVVCoV1q9fj969e8PX17de15s3bx7efvtttG7dGvfffz/Gjx8Pf39/LF26FEqlEmPGjMHcuXPxwQcfNPEjYCiQC9OazFBrjSg1mFBmMNn+a2zOb/1rsAhAmdGMMqO5xstFAOQ+YiilPgjw9UGQrxRBcikCZD6QiBkWnsbHxwcrVqzA1KlT8eGHH6J79+4YOHAg7r77bnTp0qXW682cOROpqakAgCeeeALjxo3Db7/9ZjtH85QpU7BixYrmeAgMBXINFkFAsd4ItdaIAq0BBVojtKaav2jdiQBAa7JAazKgQPvP+XdFgC0kguVShChkCPT1gYhHFW5v1KhRGD58OHbs2IG9e/di06ZNWLx4MT799FNMmjSpxutUDozIyEgA1jO2Vd6Wm5vbpO2uwFAgp9CbLVBrrV+Uaq0RhTojzF50vicBQLHehGK9CRdLtAAAH5EIwQopQq6GRJhCBqmEs8bdkVwux5AhQzBkyBDMmTMHDzzwAObNm1drKEilUtv/V/wwqLqt8pnbmhJDgZqFyWLBlTI9rpTrodYaau1u8WYmQUCexoA8jQFAOUQAwpQyRPn5ooVKDpWMH1d3lZiY6NJrEyrju4yajN5kRnaZHlllOuRq9M06AOwJBMAWEsfySqGSSdDCT44olS9CFTIOYLuggoICjB49Gvfffz+6dOkCf39/HDhwAIsXL8aIESOc3bx6YSiQQ2mMZmSV6ZBVqkOB1gDmgOOUGcw4YyjHmcJySMUiRF49goj084WM3UwuQaVSoVevXnjrrbeQnp4Oo9GIVq1aYerUqXj++eed3bx6EQmCF3XkUpMo0RttQVCkNzm7OV5HBCBUIUO0vxytAhQeERCOOAG9N3LE88YjBbouepMZF4q1uFis4fiAkwkA8rUG5GsNOJ5Xgmh/BdoEKhGmlDm7aeSGGArUIPkaA84XlSOzTMcxAhdkFoBLJVpcKtHCX+aDNoEKtA5QwtfH/Y8eqHkwFOiaTBYLLpVocb5Ig2J2D7mNUoMJx/JKkZZfipYqOdoEKhGulHEtBNWJoUC1KtEbca5Ig0slWph4WOC2LAKQUapDRqkOflIJ2gQqERuoYN0mqhFDgexYBAFZpTqcK9Igv9IKXPIM5UYz0vJL8Xd+KVoFKNAxVMX1D2SH7wYCAJgtAi4Ua3BKXQadqXlWTpLzCLCOPVwu0bp0ODTXKl5P4Yjny/XeBdSsGAberXI4xFwNB38XCAeZTAaxWIysrCyEh4dDJuNYSF0EQYDBYEBeXh7EYjFksuufecZ1Cl6KYUA1EQGI8ZejY5i/08PBYDAgOzsbGo3Gqe1wJ0qlEi1atGAoUP0JgoCLJVqcyC+FlmFAdYjxl6NTqD/8fZ0XDoIgwGQywWzmWphrkUgk8PFpfKVdhoIXySrT4e+8UpQYOK2U6i/GX45OLnDkQM2DoeAFCjQGHM8vQYHW6OymkJsSAUgI9kPHMBWkYi6E82QMBQ+mN1twLLcEl67W6ydqLLlEjKRwf7QKUHDg10MxFDzUpWINjuaVwmDmuAE5XohcipTIQATJpdfemdwKQ8HDlBlMOJxTjFwNF55R0xIBiA/2Q2KYCj7sUvIYDAUPYREEnFGX42RBKcx8RakZKXzE6BoZiJYqlrj2BAwFD6DWGnAop5jF6sipWqrk6BoZAAVrKrk1hoIbM1osSMsrxbkiLu4h1+AjFqFrRABiA5XObgpdJ4aCm8ou0+FQTjFXI5NLah2gQEpkAMca3BBDwc1YBAFpeaU4U1ju7KYQ1UklleDGlsEI5gwlt8JQcCMaoxn7sgqh1nERGrkHsQhICgtAQoifs5tC9cRQcBM55Xrszy7iugNyS1F+vugRFcTTgroBhoKLEwQBfxeU4VRBmbObQtQoch8xbmwRhHClr7ObQnVgKLgwncmM/dlFyONCNPIgHUJVSAxVsUyGi2IouKg8jR77soqgZ3cReaBQhRQ9WwRDIeWaBlfDUHAxgiDglLoMJ/LLwBeGPJncR4w+0SGsn+RiGAouxGwRsC+7ENllemc3hahZ+IhF6NUyGJF+HGdwFQwFF2EwW7A7Q83ppuR1RAC6RQWiDVdBuwSGggvQGM3YlVGAUgNPOUjeq0OoCp3D/J3dDK/HUHCyYr0RuzLULFdBBKBVgAI9ogIh5swkp2EoOFG+xoA9mWoYLXwJiCqEK2Xo1TIYMgkXujkDQ8FJMkt12J9dCOYBUXX+Mh/0jQmGUurj7KZ4HYaCE5wrKsfhnBJnN4PIpflKxOgTE8KCes2ModDM/s4vxUmWrCCqFx+xCP1iQhCikDm7KV6DodBMBEHAoZwSXCjmCXGIGkIqFqF/q1AucmsmHMlpJgwEoutjtAjYmaFGiZ5reJoDQ6EZHMktZiAQNYLBbMGOy2qUGnge8qbGUGhix/NKkF7IQCBqLL3Zgp2XC1DOYGhSDIUmdCK/FKfVPG0mkaNoTRbsyFBDY+Tq/6bCUGgiZ9XlOMFZRkQOpzGasfNyAbQmBkNTYCg0gcslWhzN4zoEoqZSZjRj52U19AwGh2MoOFhOuR4Hsouc3Qwij1dqMGFnhprnLXcwhoIDqbUG7M0s5MlxiJpJsd6EPZlqmFkvxmEYCg5SajBhd2YhzFwLSNSsCrRGHMopdnYzPAZDwQGMZgv28DCWyGkulWhxihM7HIKh0EiCIODAlSKUcYockVOl5Zciq1Tn7Ga4PYZCI51Sl/GcykQuYn92EYp4SttGYSg0Qk65Hn/n85CVyFWYBQF7MwuhZ1fudWMoXKdyown7swqd3QwiqkJjMmN/ViFYAPr6MBSug9li/TVi4DQ4IpeUqzEgLb/U2c1wSwyF63AopxjFehblInJlp9XlyCzVOrsZboeh0EDnCstxqYRvNCJ38Fd2Mc/D0EAMhQZQaw2saUTkRkyCgP3ZRbBwfKHeGAr1pDOZsTerEBxGIHIvxXoTKxY3AEOhng7lFENn4jQ3Ind0uqAMaq3B2c1wCwyFesgo0XKBGpEbEwD8daWIhfPqgaFwDXqTBUdyOY5A5O5KDWZOU60HhsI1HMkt5upIIg9xtrAc+Roe9deFoVCHrFIdMlhgi8ijHLhSDJOFP/Rqw1CohcFswWHWaCfyOBqjGUdz2Y1UG4ZCLY7mlkDHbiMij3ShWIOccnYj1YShUIMr5TquWibycH9dKeKJsWrAUKjCaLHg0BV2GxF5Op3JgmOsUFANQ6GK47ml0HKRGpFXuFis5Ul5qmAoVFKgMeB8scbZzSCiZsSjBXsMhUr45iDyPnkaA7LLOPW8AkPhqsxSHdQ8jCTySsfySlhJ9SqGAgBBEPB3Po8SiLxVmcGM80XsOgYYCgCAC8ValBrMzm4GETnRiYJSTlGFE0Nh3bp1CAoKwpw5c7BlyxY8+uijTmmHySLgBItkEXk9g1nASZ53wfGhMGnSJIhEIixcuNBu+/r16yESiWx/r1u3Dl9++SWysrIwbdo0TJw40dFNqZezhWVcuUxEAIBzReUoM3j3+ddFguDY0ZVJkyZh9erVkMvlOHfuHIKDgwFYQ2HkyJFw8N01it5kwebzuTCxxjoRXdVSJUfv6GBnN8NpmqT76JZbbkFUVBQWLFhQ4+UFBQUYN24coqOjoVQqkZycjFWrVtnto9fr8fjjjyMiIgJyuRz9+vXD/v37HdrOk+pSBgIR2ckq0yFf471naWuSUJBIJHj99dfx7rvvIiMjo9rlOp0OPXr0wE8//YTjx4/jwQcfxH333Yd9+/bZ9pk1axbWrl2LlStX4uDBg0hISEBqairUarVD2lhuMHG2ARHV6LgXr1lqsoHmkSNHIiUlBfPmzat2WXR0NGbOnImUlBTExcVh+vTpGDp0KNasWQMAKC8vxwcffIAlS5Zg2LBhSExMxCeffAKFQoHPPvvMIe37O78UPEggopqodUYUeOnRQpPOPlq0aBFWrlyJEydO2G03m82YP38+kpOTERISApVKhc2bN+PSpUsAgPT0dBiNRvTt29d2HalUip49e1a7retRrDfiMk+eQ0R1OF3onTORmjQUBgwYgNTUVDz33HN225csWYKlS5di9uzZ2LZtGw4fPozU1FQYDM2TzGfU5c1yP0TkvrLL9Cj1wplITb5OYeHChdi4cSP27Nlj27Zr1y6MGDEC9957L7p27Yq4uDicPn3adnl8fDxkMhl27dpl22Y0GrF//34kJiY2qj1aoxmXea4EIqqHs174A7LJQyE5ORn33HMP3nnnHdu2du3aYcuWLdi9ezdOnDiBhx56CDk5ObbL/fz8MG3aNDzzzDP45Zdf8Pfff2Pq1KnQaDSYMmVKo9pztrAcHEogovq4WKKB3uRd1Q6aZUXzK6+8AkulE2W/+OKL6N69O1JTUzFo0CBERUXhzjvvtLvOwoULMWrUKNx3333o3r07zp49i82bN9vWPVwPo8WCCyyNTUT1ZBGAdC+bpejwxWuu7Iy6DMfyWNKCiOpPJhFhWFwkJGLRtXf2AF5TEE8QBK9LfCJqPINZ8KoeBq8JhSvlemiM3tU3SESOcbaw3KVK9DQlrwmF9ELvSXoicqxyoxlZXnJ2Nq8IhTKDCbkavbObQURuzFvWN3lFKJzjWAIRNZJaZ0Sx3vNP2evxoWCyCLjoRYNERNR0LhV7/sJXjw+FrDIdjKx8R0QOcKlE6/EDzh4fChksaUFEDqI3W5BT7tnjkx4dCkazhQPMRORQlzz8h6ZHh0JWmY7nTCAih8oq08Howed19+hQyOQ5E4jIwSwCPHrNgseGgsEL+v6IyDkyPPgHp8eGQlaZjiWyiahJ5JbrYfDQLiSPDQV2HRFRUxHgud8xHhkKBrMFuew6IqImlFnqmbOQPDIUMkvZdURETStPY/DIs7J5aCh4ZoITkesQAORqDM5uhsN5XCjoTRbkeeALRUSuxxMXx3pcKORq9C7VdWQ2m7Fq6WJMu7kXxnWNwyND/g/fvf9WtfopGelnsGDaRNx3QweM7xaPWf8ehrysjFpv12Q0Ys2yN/HIkP/D3V3a4qkRt+DQjm217r/u43cxqmNLfP76XLvtyxe8hIm9EvHgoB74Y+M6u8t2/7IRrz884ToeNZF38MQfoD7OboCjudqLtP6TZdi8aiWmL1yKVgkdkH78CN57/kkoVf4YPuEBAMCVSxfwwvg7cfO/78bY6TOhVPnj8tlTkPnKa73dVUsX4Y8N6/Dw/CWIjkvA4Z3bsfixKXht1Y+IS0y22/fsscPYsvorxHZItNu+f+uv2PnTD5jz6SpkXzyH9194Gin9BiIgOBTlpSX45q1FmLf8W8c/KUQeQmM0o9xggp/Mc75KPe5IIV/rWodzpw4dwI03p6LHoFsQEdMK/zf0NnTtOxBnjx227fPN2wvRfeBgTHhmDuISkxHVug1uHJyKwNCwWm/39x/X4q6HpqPHwJsR1SoWQ8dNRLcBg7Fx+Ud2+2nLy/H2zMfw8PwlUAUE2l2Wee4MOvf8PyQkd0X/20ZCoVIhN+MyAODLJa8iddwEhLeMcdyTQeSBPG1cwaNCQWcyo8zgWrMBOnS7Acf27ETW+XQAwIWTaTh5cB+6DRgMALBYLPhr+29o2SYOr0wZh8l9kvHsmOH483+b6rxdo8EAqa+v3TZfuRwn/tpnt+3TV55Hj0E3o2ufAdVuI7ZDZ6QfP4qy4iKkHz8Kg06HqNZtcOKvP3Hu72O49b4pjXnoRF4hz8PGFTznmAdAvtb1Envkg49BU16Kx28dALFEAovZjPEznsWA2+8CABQX5EOnKccPn7yHcU/Mxn0zX8ChHduwZPoDeHnl9+jc8/9qvN2UfgOxccXHSLyhN6Jat8GxPTuwd8vPsFRaZbnzp/U49/cxLPr+5xpvo1v/QRhw+12YPfpWyHzlmL5wKXwVSnz80nN4bMHb2LxqJTZ99Tn8g0Pw8CtL0LpdB8c/QURuLk9jgCAIEIlEzm6KQ3hWKLjgYdzuTRuwY+M6zHhjGVoldMD5k2lY/vo8BEdE4qaRYyBYrF/iNw5Oxe2THgQAtO2UhFOHDmDzt1/UGgr3vzAfH8yZiSduHQCIRIhqFYvBd43F1rWrAQD52Zn4/PW5mPv5t3WOTYydPhNjp8+0/b3mvf+gS5/+kEh9sPbDpXhzw1b8tW0L3p39OJas2+yop4XIY+jNFpToTQiUS53dFIdgKDSxL5bMx8ipj6Hf8DsBALEdOiE/KwPrPn4XN40cA//gEEh8fNAqob3d9WLi21XrCqosMCQUzy5bDoNeh9KiQoREROGr/7yGiFatAQDpaUdRXJCPZ+5KtV3HYjbj7wN7senr5fj26AVIJBK728w4dwa/b1yHN9b9iq1rV6HTDb0RGBKKPsPuwLIXnoK2rAwKlcpBzwyR58jV6BkKrsZgtqDEYHJ2M6rRa3UQie2HbsRiCYSrJ3qQymRISOqKzKtjDhWyLpyr1yCvzFeO0MgWMBmN2Pvrz+gz9HYAQJfe/fHWhq12+773/JOIjkvAyAcerRYIgiDgo7mzMWn2PCj8/GCxWGA2WU9SXvFfi8W1xmuIXEWexoB2Ic5uhWN4TCi44lECANxw0xCs/fAdhLeItnYfnTiOjSs+wuBRd9v2GTHlEbz51MNIvKE3knr1waEd23Bg2xa88sX3tn3emf04QiKicO/TzwMATh85CHXOFbTp1BnqnCtY895/YLFYcOcDjwAAFCoVWrfvaNcWuUIJ/6DgatsB4H/ffYOAkFDcOPhfAICO3W/Emvf+g9OH/8LBP7YiJqE9/KrMXiIiq3ytARZBgNgDxhU8JxRccJAZAB548VWsemcxPn7lOZQUFCA4IhJDxt6H0Y88adun15BhePClhVj38Xv4/LU5aNk2Ds+88wk69ehl2yc/KxMi0T9HHEa9HquWLkLO5UuQK5XoPvBmPL7onev64i7Kz8PaD5fi9VUbbNvademG2yc/hNcemoDA0FBMX7j0Op8BIs9nsggo1BkRqpA5uymNJhKqLq11U1sv5KNIb3R2M4jIS3WJCEBCsJ+zm9FoHrFOwWi2oJiBQEROVOIh30EeEQqFOqNL1TsiIu9TrHe9iS7XwyNCodQFZx0RkXcpNZiqFbp0RwwFIiIHMFkEaDzgpDseEQplDAUicgElHtCF5BGhwCMFInIFDAUXYLJYoDVZrr0jEVET84QZSG4fCqUuViqbiLyXK5baaSgPCAX3fxGIyDN4wgwktw8FDjITkauwCHC5E301lNuHAo8UiMiVFBvce1zB/UPBA0b7ichzuHvvhVuHgiAIKDO69wtARJ5F5+azId06FDQmMyzuPaZDRB5Gb2YoOI3O6N5PPhF5HgNDwXncPZGJyPPo2X3kPAwFInI17v695Oah4N7zgYnI8+jNFrdewObmoeDeiUxEnsmdv5vcOhQMbt53R0SeiaHgJAbORyUiF+TOg81uHQpGN05jIvJcPFJwEhOPFIjIBTEUnMRocd8nnog8F7uPnIRHCkTkisyckuocRoYCEbkgrlNwAjMDgYhclPt2HrlxKIhEzm4BEVHN3PhAwX1DQcxUICIXJcB9U8FtQwEAGAtE5Irc+UjBx9kNaAyRyL2ffHIdIgA9ogLh7yt1dlPIA8gk7vuT1a1DQQwRLG58mEauQwCQll+Kga1DoZS69ceCqFHcu/vIfcOYXJDWZMHOy2roTCzJTt7LrX8SiUQigEcK5EBlRjN2ZagxoFUopJIqv5nUReBJwckhpD5AoL+zW1Ejtw4Ftz7MIZdVrDdhd6YafWNC4SOudDiq1QNHTzmvYeQ5ggOA3inObkWN3Pp7ld1H1FQKtEb8mVUIS+WZDNGRQKd45zWKqBm4eSgwFajp5JTrsT+7yL5kQZtoICHWeY0iz+DC311uHQpu3XhyC5mlOhzKKbHf2C7WGg5E14uh0DR4pEDN4UKxBsfzqgRDxzhrdxLR9ZBInN2CWrl1KEjFDAVqHqfV5ThVUPbPBpEISG4PRIY6r1HkvnwYCk1C4cJPLHmetPxSnC/S/LNBJAK6dgJCg5zWJnJTLvzd5dahIPdx6+aTGzqUU4yMEu0/GyRioHtnl51zTi6K3UdNQ+7CaUuea392Ea6U6f7Z4CMBbkgCVErnNYrciwt/d7l1KCh4pEBOIAD4M6sI+RrDPxtlUuDGZEAhd1q7yI34uO66Ybf+VuWRAjmLWRCwJ1ONIp3xn41yX6BnMuArc17DyD248HeXm4eCWzef3JzRImBXhhplBtM/G5UK6xEDK61SXVz4/eHW36qcfUTOpjdbsOOyGhpjpcqq/n5AjyTrIDRRTXx9nd2CWrn1u1YqEcOHC9jIybQmM3ZlFEBvqnS69uAA66wkvj+pJnLX7WJ061AA2IVErqHUYC25bTRXCoawYCClE88bS/ZEcOlxJ7f/RuVgM7mKIr0RezILYa58zoWoMCCpvfMaRa5HJnPpI0gPCAW3fwjkQfK1huolt2OirLWSiACX7joCPCAUlFIeKZBruVKux19VS263jQHiWzmvUeQ6XHiQGfCAUAjwlTq7CUTVXC7V4Uhulcqq7dsCrVs6p0HkOnik0LSCfF13vi95t3NFGqTll9pvTIwHWoQ7p0HkGpQKZ7egTm4fCv4yH0hcd8yGvNypgjKcUVcpud2lIxAe4rxGkXO5eI0stw8FkUjELiRyacfySnGhuFLJbbEI6JYIhAQ6r1HkPH48UmhygQwFcnGHrhQjs7SGktsBKuc1ipqfWOzyRRM9IhQ4rkCuToC15HZuuf6fjVIfa50kF//lSA7kp3DpNQqAp4SCnEcK5PosArAnsxBqbdWS212sFVbJ87nBDwCPCAWOKZC7MAvWyqrF+koltxW+QM8u1oAgz+bn2oPMgIeEgo9YBH8ZF7GRezBaBOy6rEZ55ZLbfldLbrNsi2fz93N2C67JI0IB4GAzuRed2YKdGWpoTZVKbgeorCW3xR7zsaSqAl1/YoHHvPuCGArkZsqNZuy6rIahcmXVkECge6LLD0bSdZBKXX7hGuBBoRDIwWZyQyUGE3ZlqGGyVAqG8BCgawfnNYqaRpDrHyUAHhQKIQopy9aTWyrU1VByu0UE0Llds7bjj2NHcPtLz6LlvXdBdOtArN+9w+7yl75ajo4P3ge/kakIHjMctzz/FP48+fc1b3fZxh/QZtJYyEcMQa8ZD2PfqRPV9tlz4jgGPzsDfiNTETBqGAY8Mx1avXX6rt5owH1LXkXAqGFo/8A9+N+hA3bXXfL9Kkz/4O3rf+DNJdDf2S2oF48JBalYjGAeLZCbytMYsD+70L6yausWQPs2zdaGcp0WXdsmYNkjM2q8vH10DN6b9gSOvb8cO5e8hzYRUfjXizORV1xU622u/n0rnvpkGeaNn4iD736CrnHxSJ0zE7lFhbZ99pw4jqFzZuFf3W/Evrc/xP6lH+Gx20dCLLb+zPt400b8dfY09rz5Ph4cdjvGL55ve57OX8nGJ7/8F69NnOqw56HJuEkoiAS7d6F7+zu/FCcLyq69I5GLah2gQI+oQIgqjymcPAecz2jWdohuHYgfXnwVd/bpX+s+JZpyBP77Vvzv9Tdxc0qPGvfpNeNh3Ni+I967GjQWiwWtJo7G9NvvwrNj7gEA9H5yGoZ0uwHzJ0yp8TYeWfYmApR+WDj5IWj1eihH/gu5q35EeGAQhs55Bg8Nux0j+wxo3ANuDoN7u/QZ1yp4zJECAIQrXf8JJ6rLpRItjuVVqazaMQ5oFeWcBtXCYDTi400bEeinQte28bXu89fZ07ilUmCIxWLcktIDe06mAQByiwrx56m/EREUhD5PP4LI8Xdi4KzHsTPtqO06XdsmYGfaMWj1emz+ax9ahIQiLCAQX2/bArlU5h6BoPB1i0AAAI+qDxGqkEEiEsHsOQc/5IXOFpZDKhahU1il7obO7QCjGbiS57yGAfjvn7tx96JXoNHr0CIkFFteewNhgUE17ptfUgyzxYzI4GC77ZFBwTh5+RIA4NyVLADAS1+vwBtTpiElPgFf/PYrbn7uKRz/YAXaRcfg/n/diqPn05H48ASEBQRizXMvobCsFHO//BzbFy3Fiys/xbd/bEV8i5b4fMZsRIe5YGnykCBnt6DePOpIQSwSIYxHC+QBThSUIb2w/J8NIpF1RlJYcO1XagY3de2Gw+99it3/WYahPXpizIKX7MYHGspydXD9oWG3Y/K/bkW3+PZ468HH0CGmFT7/9WcAgNTHB8sefRLnl6/G/qUfo1/nLnj6k2V4/I5ROJR+Buv37sSRZZ+hd8dEPP7hOw55nA4XGuTsFtSbR4UCAEQqWUOGPMOR3BJcsiu5LbauYQgOcFqb/OQKJLSMQe+OnfHZjNnwkUjw2eafatw3LCAQErEEOYX2oZFTVIioEOv5JFqEhAIAElu3sdunU6tYXMrLqfF2tx05iLRLF/DY7SOx/egh3HpDL/jJFRjT/yZsP3a4cQ+wqTAUnCdKxVAgz/HXlWJklen+2SCRWFc9u0i5BItFgN5orPEymVSKHgnt8duRvyrtb8Fvhw/i/zp2BgC0iYxCy9AwnMq4bHfd05mXERsRWe02dQY9Hn3/bXw0/WlIJBKYLRYYzdZV4UaTCebK6z1chUrpVgUPPS4UVDIfqKSsH0OeQQCwL6sQeZoaSm47eHVsmVaDw+lncDj9DADgfE42DqefwaXcHJTrtHh+xcfYezINF3Ou4K8zp3D/WwuRWZCP0f0H2W7j5ueexHsb19n+fmrkGHzyy09Y+b9fcOLSBUxb9ibK9VpMHjIMgPUkWc+MuhvvbFiL73dux9msDMz54jOczLiEKanDq7Vx/qovcOuNvdEtvj0AoG9iEtbt+gNHz6fjvY0/oG9ikkOfE4dwo6MEwMMGmitEqeQ4W7k/lsiNVZTc7t8qBMEVJ333lQE9k4G9hwGdoc7r19eBM6dw07MzbH8/9ckyAMDEW4biw8eewsmMS1j52mbkFxcjNCAAN7bviB1L3kHn2La266RnZyG/uNj299iBg5FXUoS5X36OK4VqpMQl4JdXliAy+J/Tkc64czR0BgOe/Pg9qEtL0TUuHlte+w/iW0Tbte/4hXNYs2MbDr/3mW3bv/sNwvZjh9H/menoENMK38ya45DnwqHcLBQ8ap1ChdxyPXZmqJ3dDCKHkknEGNAqxL5UfJkG2HsEqKULh5xMBODmPtajOzfhcd1HABCmlMFHzKIX5FkMZgt2ZaihMVYqua1SAjcmWccayPUEB7pVIAAeGgpikQgtVa59HlSi66E1WbDzshq6yiW3A/2BHp1ZctsVRYY5uwUN5rHvotYBrl+iluh6lBnN2JVRpeR2aBCQ0oklt11NZKizW9BgHhsK4UoZlDyLFXmoYr0JuzPUMFWurBoZCiS3d16jyF6AClC4X4+Fx4aCSCRC60AeLZDnUuuM2JtZCEvluSLRkUBizbWIqJm5YdcR4MGhALALiTxfrkaP/dlF9iW3Y6OBdrHOaxRZuWHXEeDhoaCS+SBUwXMskGfLLNXhUE6x/caEWKBNdM1XoKbnp3CZVecN5dGhAACxAUpnN4GoyV0o1uJ4Xon9xo5x1u4kan4tXLBSaz15fChE+8sh4YwM8gKn1eU4VfkkUyKRdeDZTfu23Zobh7HHh4JUIkZLFskjL5GWX4pzRVVLbnd0u1ILbi040OF1qZqTx4cCAMQGsguJvMfhnBJklGj/2SARA907u805gt1ejPseJQBeEgrhShkUPl7xUIkAAPuzi3ClcsltHwlwQ5K1LAY1HYkYiHLf8QTAS0JBJBKhNQecyYsIAP7MKkS+plIFVZnUWnLbDRdUuY2oMGsAuzGvCAUAiAtSgjXyyJuYBWBPphpFukoVVOW+1pLbbnISebcTHeXsFjSa14SCQirhYjbyOkaLgF0ZapQaKlVWVSqsRwxuVr3T5SnlQEigs1vRaF4TCgDQPkQFHiyQt9GbrZVVNcZKlVX9/axjDCy57TitW3pEQUKvCgWVzAfR/uxPJe+jNZmxK6MA+solt4MCgO6JYL+qA0jEQIz7dx0BXhYKgPVogcgblRqsJbeNlUtuhwUDXTuBh9CN1DKySbvjRCIR1q9f32S3X5nXhUKQXIpIPy5mI+9UpDdhT2YhzJVLbkeFAUksud0ojawzlZeXh2nTpqF169bw9fVFVFQUUlNTsWvXLgBAdnY2hg0b5oiWXpNXjjR1DFEhp1zv7GYQOUW+1oA/swrROzoY4oo+8JgowGgCTp5zbuPcUXhIo9d/jBo1CgaDAStXrkRcXBxycnLw22+/oaCgAAAQFdV8XVNed6QAAKFKGUIVnJJH3utKuR5/VS253TYGiG/tvEa5q0YeJRQVFWHHjh1YtGgRbrrpJsTGxqJnz5547rnncMcddwCw7z66cOECRCIR1qxZg/79+0OhUODGG2/E6dOnsX//ftxwww1QqVQYNmwY8vLyGtwerwwFAOgQ6p5lbYkc5XKpDkdyq1RWbd8GiG3plPa4pQCVdVymEVQqFVQqFdavXw+9vv49GPPmzcOLL76IgwcPwsfHB+PHj8esWbOwdOlS7NixA2fPnsXcuXMb3B6vDYUoPzmCfL2y94zI5lyRBml5pfYbO8UDLSOc0yB3k9D4kxn5+PhgxYoVWLlyJYKCgtC3b188//zzOHr0aJ3XmzlzJlJTU9GpUyc88cQT+OuvvzBnzhz07dsX3bp1w5QpU7Bt27YGt8drQwEA2odyJhLRKXUZzqirltzuAESEOK9R7iBA5bCzq40aNQpZWVnYsGEDhg4diu3bt6N79+5YsWJFrdfp0qWL7f8jI61F+JKTk+225ebmNrgtXh0K0So5Anm0QIRjeaW4UKz5Z4NYBKQkesQK3SbjgKOEyuRyOYYMGYI5c+Zg9+7dmDRpEubNm1fr/lLpP2eVFF2dMFB1m8ViqXa9a/HqUBCJROgSEeDsZhC5hENXipFZWqXkdo/O1l/EZM+BRwm1SUxMRHl5+bV3dDCvDgUACFf6cpUzEayVVfdnFyG38nRtHx9rnSQ/Vhm2k+C4WVoFBQUYPHgwvvrqKxw9ehTnz5/Hd999h8WLF2PEiBEOu5/6Yt8JgOTwAFwp08EsXHtfIk9mEYA9mYXo3yoEIRXTtitKbv95GNByfQ/8/YAIxx0lqFQq9OrVC2+99RbS09NhNBrRqlUrTJ06Fc8//7zD7qe+RILdRGXvdSK/FCcqn9+WyItJxSIMaB2KQN9/+qhRrgX2HgYMxlqv5xVuSLIuWPNQXt99VKF9iApKNz85BpGjGC0Cdl5Wo6xyyW2/qyW3fby4gyEs2KMDAWAo2EjEIiRH8By2RBX0Zgt2ZqihrVxZNUAF3NDZOgjtbUQAOsY5uxVNzgtf2dpF+ysQrmT5C6IKGqMZuy6rYahcWTU4EOiW6BHnDmiQmBbW8QQPx1CooktEAKsIE1VSYjBhV4Yapspz3sNDgK4dndeo5uYjAdo5dl2Cq2IoVBHoK0XbIE6/I6qsUGesXnK7RTiQ1M55jWpOca295rzWDIUaJIb5Qybh8QJRZXkaA/ZnF9pXVm3VAmjf1nmNag4KeaMroboThkINZBIxksO50pmoqqwyPf66UmwfDPGtgLhWzmtUU+uc4FUD697zSBsoNlCJFiqeoY2oqkslWhzNq1Jyu0Nb61GDp2kZ4fFTUKtiKNShW2QgfL3oFwJRfaUXanAiv0rJ7c4J1nEGTyH18YopqFXxG68Och8JUiLZjURUkxMFZUgvrFSwTSQCunQAwht30hmX0THOawaXK2MoXEO0vwKtAhTObgaRSzqSW4JLdiW3xdY1DMFu/mMqNMh63movxFCoh64RAVCwBAZRjf66UoysMt0/GyQSoEeS+y70EouBzl4y1bYGDIV6kEnEuLFFEBe1EdVAALAvqxB5mkoVVKVXS24r3fAou30ba50nL8VQqKcwpQwdefpOohpVlNwu1Bn+2egrA3omA3I36pcPDfKqNQk1YSg0QMdQFcIUbvQGJ2pGJouAXRlqlOgrldZWyIEbu1jPyeDqpD7WgXJvq+lUBUOhAUQiEW5oEQSZ2LvfNES1MZgF7MxQo9xYqeS2Smk9B4Grj8sltwfkXJvEUGggpVSC7i2CnN0MIpelM1mw87IausoltwP9ge6drYO4rigmCogMc3YrXIKLvkKuraVKjs5hPPcCUW3KjWbsyqhScjs0COjWyfW6Z5QKoFO8s1vhMhgK16lDqAqtuX6BqFbFehN2Z6hhqlxZNSLU2k3jKsRiIKWj63dtNSOGQiN0jwpEqMINBtCInEStM2JvZiEslQvoRUcCiQnOa1RlnROsXVtkw1BoBLFIhN4tQ6CU8lcGUW1yNXrszy6yr6wa29L5J61p1cJrVy3XhaHQSL4+YvSJDoYPZyQR1SqzVIdDOcX2GxNinbcmIMgfSOQ4Qk0YCg4Q4CtFr5bBXPFMVIcLxVocy61ScrtTPBAT2bwNkUmt9ZlcdSaUk/FZcZBIP190iXDzImBETexMYTlOFZTZb0xq33zTQUUiIKUT1yPUgaHgQPHBfojj+Z2J6pSWX4pzRVVKbqd0tE5ZbWqd4pvnftwYQ8HBukQEIELJUhhEdTmcU4LLJdp/NojF1sVtQU04E6htjHWAm+rEUHAwsUiEXi2DESznVFWiuhzILsKVyiW3fSTWchhNUXI7Ktx6ylC6JoZCE5BKxOgXE8JgIKqDAODPrELkaypVVpVKrcGglDvujoIDWOiuARgKTYTBQHRtZgHYk6lGka5SZVW5r/VcDI44FaafwtotxXOt1xufqSbEYCC6NuPVktulhkqVVZUKazBIfa7/hmVXjzrcoWy3C2EoNDEGA9G16c3WyqoaY6XKqv5+1i91yXVUDHDnM785GUOhGUglYvRlMBDVSWsyY2dGAfSVS24HBQDdE4GGVAzwkVgDIYBnSrweDIVmIrsaDEEMBqJalRmsJbeNlUtuhwUDXTuhXiUDJBLghmQWuWsEhkIzkl3tSmIwENWuSG/CnsxCmCuX3I4Ks658rotEDNzQ2TrbiK4bQ6GZMRiIri1fa0CORm+/MSYK6BhX8xUqFr+FBDV52zwdQ8EJZBIx+seEIELJ+itEVYlFwI0tgtBSVcNahbYxQELrKlcQW8cdwoKbp4EeTiTYFTmn5iQIAo7kluBckcbZTSFyCT4iEXpFByPS7xo/mP4+C1zMso4h9OjMekYOxFBwAemF5TiaWwK+EOTNfCVi9IkJRrC8HovWBAE4kQ60jLDOUCKHYSi4iJxyPf7MKrQ/ny2Rl/CTStA3JgQqWSMWq5FDMBRcSIneiN2ZhfYLeIg8XJSfL25sEQQpS1G4BIaCi9GbzNibVYgCrfHaOxO5ucQwFTqEqCBisTqXwVBwQWaLgEM5xbhUud48kQeRiUW4sWU9BpSp2TEUXNjJglL8nV927R2J3EiQrw96RQfDrzHF7qjJMBRcXFaZDgezi2DgADR5gNgABVIiAyFpSC0jalYMBTegMZpxILsI+VrDtXcmckFikfVUtXFBTXBWNXIohoKbEAQBJwvKcLKgjOsZyK0ofMTo1TIYIQqeu9wdMBTcTIHGgH3ZRdCaOG2VXF8LlS+6RwbC1+c6zolATsFQcENGswVHcks4O4lclkwiRteIALQK4Elu3A1DwY1llmpxKKcEhsq154mcLMZfjq4RATw6cFMMBTenM5lxKKcY2WX6a+9M1ITkEjFSIgPR0r+G6qbkNhgKHuJisQbH80qh51EDOUFsoALJ4QGQsVSF22MoeBCD2YIT+aU4V6ThDCVqFkofCbpFBXJlsgdhKHigEr0RR3JLkKfhugZqOnFBSiSF+8NHzKMDT8JQ8GCZpVocyy2FhtNXyYFC5FIkhwcgVMl1B56IoeDhzBYBp9VlOK0ug5mvNDWCv0yCxLAARHMg2aMxFLyExmjGsdwSZJbpnN0UcjNyHzE6hfqjTaCCJa69AEPBy+Rp9DiaW4JivcnZTSEXJxWL0D5EhYRgPxaw8yIMBS8kCAKyyvQ4pS5DkY4n8yF7YhEQF+SHjqEqTjH1QgwFL5dTrsepgjJWYCUAQOsABRLD/KGUcjWyt2IoEACgQGvAqYIyXCnnymhvIwLQKkCBdiF+CPSVOrs55GQMBbJTpDPitLoMGaUckPZ0UrEIbYOUiA/yg4JHBnQVQ4FqVGYw4ZS6DJdLtOBJ3zyLUipBQrAf2gQquPCMqmEoUJ00RjMuFGtwqUQLjZGL4NxZpJ8v4oKUiPLz5dRSqhVDgepFEATkaw24WKxFZqkOZr5t3IJMLEJsoBJtg5RQyXyc3RxyAwwFajCjxYLMUh0uFmtRwFlLLkcsAiKUvojxlyPaX8E1BtQgDAVqlDKDCRdLtLhUrOUpQp2oIgii/eVoqZJDyvUFdJ0YCuQQgiAgT2PAxRItrpTpYOTodJMTAYjwsx4RtFDJudCMHIKhQA5nEQQUaA24UqZHdrkOZQYeQThKRRBUHBEwCMjRGArU5MoMJlwp1yOnXI98jYGD1A2kkkkQppAhTOmLKD9fBgE1KYYCNSuLIECtNSBXY0BuuR6FOiPPEldFoK/P1RCQIVQhg9yHC8uo+TAUyKmMZguK9EYU6Ywo0ptQrDOi1GDymqAQAQiSS+1CgEcC5EwMBXI5ZouAYr0RRXojinUmFOmNKNEb3f4kQQofMfxlPlDJfOB/9V+IQspVxeRSGArkFgRBQKnBhCK9CSV6I3QmC7QmM7QmM3QmC0wuMttJIhJBJZPYfflb/yvhlz+5BYYCeQSjxWINCqMZOpMZWpPF7r8miwABAiwCIAiABYL1v4Jg93flD4MI1qJxPmIxfMQiSCXW/5eJRfD1kUAmEcP36j+ZRAylVAKFj5glJMitMRSIKhEEAQKswcGVwOSNGApERGTDTk4iIrJhKBARkQ1DgYiIbBgKRERkw1AgIiIbhgIREdkwFIiIyIahQERENgwFIiKyYSgQEZENQ4GIiGwYCkREZMNQICIiG4YCERHZMBSIiMiGoUBERDYMBSIismEoEBGRDUOBiIhsGApERGTDUCAiIhuGAhER2TAUiIjIhqFAREQ2DAUiIrJhKBARkQ1DgYiIbBgKRERkw1AgIiIbhgIREdkwFIiIyIahQERENgwFIiKyYSgQEZENQ4GIiGwYCkREZMNQICIiG4YCERHZMBSIiMiGoUBERDYMBSIismEoEBGRDUOBiIhsGApERGTDUCAiIpv/B837bXYkFqS6AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"EverBenched\"].value_counts().plot(kind=\"pie\", title=\"Empregados que já estiveram na carteira\", labels=[\"Não\", \"Sim\"], colors=[\"lightblue\", \"lightpink\"], autopct=\"%.2f%%\", explode=[0, 0.06])\n",
+    "plt.ylabel(\"\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"LeaveOrNot\"].value_counts().plot(kind=\"pie\", title=\"Colaboradores que não fazem mais parte da equipe\", labels=[\"Permanecem na empresa\", \"Saíram da empresa\"], colors=[\"lightblue\", \"pink\"], autopct=\"%.2f%%\", explode=[0, 0.06])\n",
+    "plt.ylabel(\"\")\n",
+    "plt.legend()\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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}