mean_median_mode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UF</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Anos de Estudo</th>\n",
       "      <th>Renda</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>800</td>\n",
       "      <td>1.603808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.739790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UF  Sexo  Idade  Cor  Anos de Estudo  Renda    Altura\n",
       "0  11     0     23    8              12    800  1.603808\n",
       "1  11     1     23    2              12   1150  1.739790"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "FILE_PATH = 'dados.csv'\n",
    "\n",
    "df = pd.read_csv(FILE_PATH)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Subjects</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Beltrano</th>\n",
       "      <th>Sicrano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Math</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portuguese</th>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physics</th>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemistry</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Subjects    Fulano  Beltrano  Sicrano\n",
       "Math             8      10.0      7.5\n",
       "Portuguese      10       2.0      8.0\n",
       "English          4       0.5      7.0\n",
       "Geography        8       1.0      8.0\n",
       "History          6       3.0      8.0\n",
       "Physics         10       9.5      8.5\n",
       "Chemistry        8      10.0      7.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students = pd.DataFrame(data = {'Fulano': [8, 10, 4, 8, 6, 10, 8],\n",
    "                                   'Beltrano': [10, 2, 0.5, 1, 3, 9.5, 10],\n",
    "                                   'Sicrano': [7.5, 8, 7, 8, 8, 8.5, 7]},\n",
    "                           index = ['Math', 'Portuguese', 'English', 'Geography', 'History', 'Physics', 'Chemistry'])\n",
    "df_students.rename_axis('Subjects', axis='columns', inplace=True)\n",
    "df_students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>Idade</th>\n",
       "      <th>Renda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.046554</td>\n",
       "      <td>2192.441596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.127554</td>\n",
       "      <td>1566.847393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Idade        Renda\n",
       "Sexo                        \n",
       "0     44.046554  2192.441596\n",
       "1     44.127554  1566.847393"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Sexo'])[['Idade', 'Renda']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Math           8\n",
       "Portuguese    10\n",
       "English        4\n",
       "Geography      8\n",
       "History        6\n",
       "Physics       10\n",
       "Chemistry      8\n",
       "Name: Fulano, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fulano_grades = df_students['Fulano']\n",
    "fulano_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "English        4\n",
       "History        6\n",
       "Math           8\n",
       "Geography      8\n",
       "Chemistry      8\n",
       "Portuguese    10\n",
       "Physics       10\n",
       "Name: Fulano, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fulano_grades = fulano_grades.sort_values()\n",
    "fulano_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Fulano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>English</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>History</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Math</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Geography</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemistry</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Portuguese</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Physics</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  Fulano\n",
       "0     English       4\n",
       "1     History       6\n",
       "2        Math       8\n",
       "3   Geography       8\n",
       "4   Chemistry       8\n",
       "5  Portuguese      10\n",
       "6     Physics      10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fulano_grades = fulano_grades.reset_index()\n",
    "fulano_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fulano_grades['Fulano'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fulano_grades['Fulano'].quantile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Math          10.0\n",
       "English        0.5\n",
       "Physics        9.5\n",
       "History        3.0\n",
       "Chemistry     10.0\n",
       "Portuguese     2.0\n",
       "Name: Beltrano, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beltrano_grades = df_students['Beltrano'].sample(6, random_state=101)\n",
    "beltrano_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Subjects</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Beltrano</th>\n",
       "      <th>Sicrano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Math</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portuguese</th>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Physics</th>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemistry</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "Subjects    Fulano  Beltrano  Sicrano\n",
       "Math             8      10.0      7.5\n",
       "Portuguese      10       2.0      8.0\n",
       "English          4       0.5      7.0\n",
       "Geography        8       1.0      8.0\n",
       "History          6       3.0      8.0\n",
       "Physics         10       9.5      8.5\n",
       "Chemistry        8      10.0      7.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8\n",
       "Name: Fulano, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students['Fulano'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    788\n",
       "Name: Renda, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Renda'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jeanl\\AppData\\Local\\Temp\\ipykernel_12844\\3148438389.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  ax = sns.distplot(df.query('Renda < 20000')['Renda'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Renda', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "ax = sns.distplot(df.query('Renda < 20000')['Renda'])\n",
    "ax.figure.set_size_inches(12, 6)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(788, 1200.0, 2000.3831988547631)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode   = df['Renda'].mode()[0]\n",
    "median = df['Renda'].median()\n",
    "mean   = df['Renda'].mean()\n",
    "mode, median, mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode < median < mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jeanl\\AppData\\Local\\Temp\\ipykernel_12844\\3545272947.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  ax = sns.distplot(df['Altura'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Altura', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(df['Altura'])\n",
    "ax.figure.set_size_inches(12, 6)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.56812756, 1.6993247325, 1.6995124540575741)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode   = df['Altura'].mode()[0]\n",
    "median = df['Altura'].median()\n",
    "mean   = df['Altura'].mean()\n",
    "mode, median, mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jeanl\\AppData\\Local\\Temp\\ipykernel_12844\\73094806.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  ax = sns.distplot(df['Anos de Estudo'], bins=17)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Anos de Estudo', ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(df['Anos de Estudo'], bins=17)\n",
    "ax.figure.set_size_inches(12, 6)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 11.0, 9.469664237376367)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode   = df['Anos de Estudo'].mode()[0]\n",
    "median = df['Anos de Estudo'].median()\n",
    "mean   = df['Anos de Estudo'].mean()\n",
    "mode, median, mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode > median > mean"
   ]
  }
 ],
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