notebooks/01-conhecendo_os_dados.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pandas\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A média de todas as as avaliações é 3.501556983616962:\n",
      "A mediana de todas as as avaliações é 3.5:\n"
     ]
    }
   ],
   "source": [
    "# Base de dados do MovieLens (https://files.grouplens.org/datasets/movielens/ml-25m.zip)\n",
    "# Carregando notas (ratings.csv)\n",
    "data_frame = pandas.read_csv('../dados/ratings.csv')\n",
    "print(f\"A média de todas as as avaliações é {data_frame[\"rating\"].mean()}:\") # Média é a soma de todos os valores dividido pela quantidade de valores\n",
    "print(f\"A mediana de todas as as avaliações é {data_frame[\"rating\"].median()}:\") # Mediana é o valor que separa a metade maior da metade menor de um conjunto de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Foram registradas as seguintes notas: [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]\n"
     ]
    },
    {
     "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>rating</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>26818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>13211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>13136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.5</td>\n",
       "      <td>8551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating  count\n",
       "0     4.0  26818\n",
       "1     3.0  20047\n",
       "2     5.0  13211\n",
       "3     3.5  13136\n",
       "4     4.5   8551\n",
       "5     2.0   7551\n",
       "6     2.5   5550\n",
       "7     1.0   2811\n",
       "8     1.5   1791\n",
       "9     0.5   1370"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contar quantas vezes cada nota foi dada\n",
    "rating_possibilities = data_frame[\"rating\"].unique().tolist()\n",
    "rating_possibilities.sort()\n",
    "print(f\"Foram registradas as seguintes notas: {rating_possibilities}\")\n",
    "\n",
    "data_frame[\"rating\"].value_counts().to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Distribuição de Notas'}, xlabel='Nota', ylabel='Quantidade de Usuários'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criação de Histograma das notas dadas pelos usuários\n",
    "data_frame[\"rating\"].plot(kind='hist', title='Distribuição de Notas', ylabel='Quantidade de Usuários', xlabel='Nota')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100836.000000\n",
       "mean          3.501557\n",
       "std           1.042529\n",
       "min           0.500000\n",
       "25%           3.000000\n",
       "50%           3.500000\n",
       "75%           4.000000\n",
       "max           5.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame[\"rating\"].describe()\n",
    "# Count: quantidade de notas\n",
    "# Mean: média das notas\n",
    "# Std: desvio padrão\n",
    "# Min: menor nota\n",
    "# 25%: 25% das notas estão abaixo desse valor\n",
    "# 50%: 50% das notas estão abaixo desse valor (mediana)\n",
    "# 75%: 75% das notas estão abaixo desse valor\n",
    "# Max: maior nota\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='rating'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot é um gráfico que apresenta a distribuição de dados de forma mais visual\n",
    "# A linha no meio é a mediana\n",
    "# As caixas azuis representas 50% dos dados (25% por cada caixa)\n",
    "sns.boxplot(data_frame[\"rating\"])"
   ]
  }
 ],
 "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
}