{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9dd73830",
   "metadata": {},
   "source": [
    "# Imprimindo a densidade de probabilidade de uma nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a60e601",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c65f66",
   "metadata": {},
   "source": [
    "## Seja uma lista de notas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b61570fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "listaNotas=[1.2,3.5,4.5,5.0,5.2,4.0,5.0,8.0,7.0,7.0,6.0,4.3,7.1,5.4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c44e704",
   "metadata": {},
   "source": [
    "## Transforma em um DataFrame \n",
    "#### Não vamos usar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67834753",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(listaNotas,columns=['Notas'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0702432f",
   "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>Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Notas\n",
       "0     1.2\n",
       "1     3.5\n",
       "2     4.5\n",
       "3     5.0\n",
       "4     5.2\n",
       "5     4.0\n",
       "6     5.0\n",
       "7     8.0\n",
       "8     7.0\n",
       "9     7.0\n",
       "10    6.0\n",
       "11    4.3\n",
       "12    7.1\n",
       "13    5.4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "faf417a3",
   "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>Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.228571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.760869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Notas\n",
       "count  14.000000\n",
       "mean    5.228571\n",
       "std     1.760869\n",
       "min     1.200000\n",
       "25%     4.350000\n",
       "50%     5.100000\n",
       "75%     6.750000\n",
       "max     8.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4af4d1c",
   "metadata": {},
   "source": [
    "### Calculando desvio média e desvio padrão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "277a725f",
   "metadata": {},
   "outputs": [],
   "source": [
    "media=np.mean(listaNotas)\n",
    "desvPad=np.std(listaNotas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae2a5cd",
   "metadata": {},
   "source": [
    "### Assumindo que essas notas tem uma distribuição Normal ou gaussiana\n",
    "## $$f=\\frac{1}{\\sigma\\sqrt{2\\pi }} e^{-\\frac{1}{2}(\\frac{x-\\mu}{\\sigma})^2}$$\n",
    "\n",
    "###    $\\mu$ : média  \n",
    "### $\\sigma$ : desvio padrão"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294733e5",
   "metadata": {},
   "source": [
    "## Entrando com a nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35c7b197",
   "metadata": {},
   "outputs": [],
   "source": [
    "nota=5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18aafb73",
   "metadata": {},
   "source": [
    "### Calculando a probabilidade da nota obedecendo uma distribuição normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6a9013e",
   "metadata": {},
   "outputs": [],
   "source": [
    "p=norm.pdf(nota,loc=media,scale=desvPad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69be07a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2329888214231506"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d2a608",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8ff88dfd",
   "metadata": {},
   "source": [
    "## Outro modo de calcular\n",
    "\n",
    "### Criar o objeto gaussiana\n",
    "    gaussiana=stats.norm(loc=media,scale=desvPad)\n",
    "    \n",
    "###  Chama o método pdf   \n",
    "    p=gaussiana.pdf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8555743",
   "metadata": {},
   "source": [
    "# Gerando um gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "08251df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(0,10)   #gera 50 números de 0 a 10. Veja também arrange\n",
    "y=norm.pdf(x, loc=media, scale=desvPad)\n",
    "plt.plot(x,y)\n",
    "plt.axvline(nota,color='r',linestyle='--')\n",
    "plt.text(nota,p,f'  Probabilidade: {p:.2f}',color='red')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3de1f43f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}