{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa11f7b5-abd9-4059-be53-55a4f11b01bd",
   "metadata": {},
   "source": [
    "### HOMEWORK 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bccd998b-b60f-40b0-919b-e221e89bab7a",
   "metadata": {},
   "source": [
    "#### Conceptual Questions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2587ec7d-10cb-42a1-851f-5df842f1376a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. A histogram is relevant only for a continuous variable, so it isn’t relevant here. A bar chart is the most appropriate chart for this situation.\n",
    "# 2. The mean will be larger than the median. The large incomes in the right tail pull up the average, but they have no effect on the median.\n",
    "# 3. The standard deviation, like the mean, is highly sensitive to outliers. In the calculation of the standard deviation, the deviations from the mean are squared, so outliers on either side can really increase the standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a844779-71c1-41e5-bb67-2c1fde3d7085",
   "metadata": {},
   "source": [
    "#### CEO Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "109673b3-9b21-42f6-995c-f37c9d035c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "22aab40b-ea7c-4bfe-ab52-bbdb12d16c5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r'/Users/patriciaxufre/Documents/SBE - Disciplinas/2957 | ABA/2024-25/Datasets Examples/CEO.xlsx', sheet_name = 'Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fbb2acc9-0f06-487c-9a5f-0307b62bf66e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Company Name', 'Company Type', 'Executive Name', 'Salary 2008',\n",
      "       'Bonus 2008'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(381, 5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.columns)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2a4a60d2-28b1-4e75-8d6e-80d1daa02fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [s.strip().replace(' ', '_') for s in df.columns] # all columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "314bfa28-f653-4c87-9bde-4fe0b4cbf29d",
   "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>Company_Name</th>\n",
       "      <th>Company_Type</th>\n",
       "      <th>Executive_Name</th>\n",
       "      <th>Salary_2008</th>\n",
       "      <th>Bonus_2008</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3M</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Buckley, George W.</td>\n",
       "      <td>1720000.0</td>\n",
       "      <td>2644100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbott Labs</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>White, Miles D.</td>\n",
       "      <td>1795500.0</td>\n",
       "      <td>4200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Advance Auto Parts</td>\n",
       "      <td>Consumer Services</td>\n",
       "      <td>Jackson, Darren R.</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>1959900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Advanced Micro Devices</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Meyer, Derrick R.</td>\n",
       "      <td>856700.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aecom Technology</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Dionisio, John M.</td>\n",
       "      <td>956500.0</td>\n",
       "      <td>2000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Company_Name       Company_Type      Executive_Name  Salary_2008  \\\n",
       "0                      3M        Industrials  Buckley, George W.    1720000.0   \n",
       "1             Abbott Labs        Health Care     White, Miles D.    1795500.0   \n",
       "2      Advance Auto Parts  Consumer Services  Jackson, Darren R.     800000.0   \n",
       "3  Advanced Micro Devices         Technology   Meyer, Derrick R.     856700.0   \n",
       "4        Aecom Technology        Industrials   Dionisio, John M.     956500.0   \n",
       "\n",
       "   Bonus_2008  \n",
       "0     2644100  \n",
       "1     4200000  \n",
       "2     1959900  \n",
       "3           0  \n",
       "4     2000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4d48b8b9-a49a-4981-81bb-e78171d6c1b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Salary_2008'] = pd.to_numeric(df['Salary_2008'].astype(str).str.replace(r'[\\$,]', '', regex=True), errors='coerce')\n",
    "df['Bonus_2008'] = pd.to_numeric(df['Bonus_2008'].astype(str).str.replace(r'[\\$,]', '', regex=True), errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bb743dcf-1ecc-4a82-b3f8-edf962795faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## boxplot\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(df.Salary_2008)\n",
    "ax.set_xticks([1])\n",
    "ax.set_xticklabels(['Salary'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a0d49b2d-1d18-4c6a-a55b-c0180766d5a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## boxplot\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(df.Bonus_2008)\n",
    "ax.set_xticks([1])\n",
    "ax.set_xticklabels(['Bonus'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76414b1-32a9-4d89-a718-2450ec3ce82a",
   "metadata": {},
   "source": [
    "Both distributions are skewed to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b4168452-8d62-4957-907b-dd4c666a3836",
   "metadata": {},
   "outputs": [],
   "source": [
    "# b.\tFind the annual salary below which 75% of all given CEO salaries fall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cf7c2d61-2bcc-4706-83e8-0ea89ad6777f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below $1253700.0, 75% of the salaries fall.\n"
     ]
    }
   ],
   "source": [
    "print('\\nBelow ${}, 75% of the salaries fall.'.format(df.Salary_2008.quantile(0.75).round(0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5e1f6d26-cca7-41fa-a65b-7e71ca908a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#c.\t    Find the annual bonus above which 55% of all given CEO bonuses fall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c77c9d74-f732-4e25-8dcc-a138c2a03b62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Above $1017300.0, 55% of the salaries fall.\n"
     ]
    }
   ],
   "source": [
    "print('\\nAbove ${}, 55% of the salaries fall.'.format(df.Salary_2008.quantile(1-0.55).round(0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "25557ebd-1ff9-4a94-9bb8-6f6a697471bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#d.\tDetermine the range of the middle 50% of all given total direct compensation figures. \n",
    "#       For the 50% of the executives that do not fall into this middle 50% range, is there more variability in \n",
    "#       total direct compensation to the right than to the left? Explain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "60fbdbd7-0ecb-426e-8bdb-74e09ca79019",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The IQR is $2350300.0\n"
     ]
    }
   ],
   "source": [
    "print('The IQR is ${}'.format(round(df.Bonus_2008.quantile(0.75)-df.Bonus_2008.quantile(0.25),2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e9a8ca-67c5-4d36-bfe3-15fd1961fc0c",
   "metadata": {},
   "source": [
    "There is much more variability on the top end than on the bottom end."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab893f5-dd1d-4640-8923-92128e8bd00a",
   "metadata": {},
   "source": [
    "#### STOCK Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ae10874d-3c3d-4517-8bb0-dd90621783a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r'/Users/patriciaxufre/Documents/SBE - Disciplinas/2957 | ABA/2024-25/Datasets Examples/Stock.xlsx', sheet_name = 'Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "51d0a5ef-78b6-4d5e-ac8a-a9ea36bec3a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Month', 'Adjusted Close'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(208, 2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.columns)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "96f6ba09-7f70-4027-80b4-a972b9b619ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [s.strip().replace(' ', '_') for s in df.columns] # all columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b6b981fa-e933-4228-a287-1e255b6bb718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Month</th>\n",
       "      <th>Adjusted_Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>32.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-02-01</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>28.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-04-01</td>\n",
       "      <td>29.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-05-01</td>\n",
       "      <td>28.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  Adjusted_Close\n",
       "0 2001-01-01           32.14\n",
       "1 2001-02-01           30.00\n",
       "2 2001-03-01           28.23\n",
       "3 2001-04-01           29.01\n",
       "4 2001-05-01           28.86"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9ad3d62b-d0c1-4f89-9435-61b2b2ec311b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a.\tAdd a new variable to the dataset: \"return.\" Each \"return\" represents the percentage change in the adjusted \n",
    "#       closing price from one month to the next. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a0b3b77a-e2a6-44df-8e3d-b29576466fb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Month</th>\n",
       "      <th>Adjusted_Close</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>32.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-02-01</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>28.23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-04-01</td>\n",
       "      <td>29.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-05-01</td>\n",
       "      <td>28.86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  Adjusted_Close  Returns\n",
       "0 2001-01-01           32.14        0\n",
       "1 2001-02-01           30.00        0\n",
       "2 2001-03-01           28.23        0\n",
       "3 2001-04-01           29.01        0\n",
       "4 2001-05-01           28.86        0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Returns'] = int(0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fcfd6220-964a-451f-83fb-d7a36eeb058f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [s.strip().replace(' ', '_') for s in df.columns] # all columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d7536d62-f712-49bb-99c4-12eac5dc4b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Adjusted_Close', 'Returns'], dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "452454e1-cf0f-4909-934f-d8660371b852",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Returns = round((df.Adjusted_Close-df.Adjusted_Close.shift(1))*100/df.Adjusted_Close.shift(1),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f317fb35-5502-4921-a622-ff6656871ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b53b3424-8b0c-4589-b48a-bfa1ed6483ad",
   "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>Month</th>\n",
       "      <th>Adjusted_Close</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-02-01</td>\n",
       "      <td>30.00</td>\n",
       "      <td>-6.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>28.23</td>\n",
       "      <td>-5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-04-01</td>\n",
       "      <td>29.01</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-05-01</td>\n",
       "      <td>28.86</td>\n",
       "      <td>-0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001-06-01</td>\n",
       "      <td>26.59</td>\n",
       "      <td>-7.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  Adjusted_Close  Returns\n",
       "1 2001-02-01           30.00    -6.66\n",
       "2 2001-03-01           28.23    -5.90\n",
       "3 2001-04-01           29.01     2.76\n",
       "4 2001-05-01           28.86    -0.52\n",
       "5 2001-06-01           26.59    -7.87"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5a89d6ed-24ed-42b7-885e-cc874f3cd74e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# b.\tDo monthly stock returns appear to be skewed or symmetric? \n",
    "#       On average, do they tend to be positive, negative, or zero?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cba33595-b089-4fe7-9bdc-dea867fd8081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(df.Returns)\n",
    "ax.set_axisbelow(True)  # Show the grid lines behind the histogram\n",
    "ax.grid(which='major', color='grey', linestyle='--')\n",
    "ax.set_xlabel('Returns'); ax.set_ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e29aec6d-4f4c-4cb1-93ed-2b584e0f9ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of Returns Distribution is 3.22 and the its average is 0.9%\n"
     ]
    }
   ],
   "source": [
    "s = round(df.Returns.skew(),2)\n",
    "a = round(df.Returns.mean(),2)\n",
    "print('Skewness of Returns Distribution is {} and the its average is {}%'.format(s,a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a4d8e46-66f2-49a6-9eeb-a0ccfd53b6ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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