Analise_Exloratoria.ipynb

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  "metadata": {
    "colab": {
      "name": "Analise Exloratoria.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-X-u_65aDQa",
        "colab_type": "text"
      },
      "source": [
        "## Imports\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3iNXJWjXWj0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "9e29a413-08b6-412d-805c-61dff55e51e6"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpXxOU0waLtV",
        "colab_type": "text"
      },
      "source": [
        "## Importa dados\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ume4-ZSGkMU-",
        "colab_type": "text"
      },
      "source": [
        "#### Dataframe = ratings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jnlMNctVkjeh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a701bbf3-c4b3-49b5-df25-6c1bf10a3ed5"
      },
      "source": [
        "notas_df = pd.read_csv('ml-latest-small/ratings.csv')\n",
        "notas_df"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.649827e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.649812e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.649822e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.649838e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.649829e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43079</th>\n",
              "      <td>288</td>\n",
              "      <td>32587</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.130179e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43080</th>\n",
              "      <td>288</td>\n",
              "      <td>32598</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.145817e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43081</th>\n",
              "      <td>288</td>\n",
              "      <td>33004</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.135790e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43082</th>\n",
              "      <td>288</td>\n",
              "      <td>33164</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.138807e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43083</th>\n",
              "      <td>288</td>\n",
              "      <td>33166</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43084 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       userId  movieId  rating     timestamp\n",
              "0           1        1     4.0  9.649827e+08\n",
              "1           1        3     4.0  9.649812e+08\n",
              "2           1        6     4.0  9.649822e+08\n",
              "3           1       47     5.0  9.649838e+08\n",
              "4           1       50     5.0  9.649829e+08\n",
              "...       ...      ...     ...           ...\n",
              "43079     288    32587     4.5  1.130179e+09\n",
              "43080     288    32598     3.5  1.145817e+09\n",
              "43081     288    33004     3.5  1.135790e+09\n",
              "43082     288    33164     2.5  1.138807e+09\n",
              "43083     288    33166     4.0           NaN\n",
              "\n",
              "[43084 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wp8sE9rSkjef"
      },
      "source": [
        "#### Dataframe = movies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2Jj8_T5Xk10",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "d95031f2-a59a-4c67-99a0-ee9858d0cb20"
      },
      "source": [
        "filmes_df = pd.read_csv('ml-latest-small/movies.csv')\n",
        "filmes_df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9737</th>\n",
              "      <td>193581</td>\n",
              "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
              "      <td>Action|Animation|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9738</th>\n",
              "      <td>193583</td>\n",
              "      <td>No Game No Life: Zero (2017)</td>\n",
              "      <td>Animation|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9739</th>\n",
              "      <td>193585</td>\n",
              "      <td>Flint (2017)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9740</th>\n",
              "      <td>193587</td>\n",
              "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
              "      <td>Action|Animation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9741</th>\n",
              "      <td>193609</td>\n",
              "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9742 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      movieId  ...                                       genres\n",
              "0           1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1           2  ...                   Adventure|Children|Fantasy\n",
              "2           3  ...                               Comedy|Romance\n",
              "3           4  ...                         Comedy|Drama|Romance\n",
              "4           5  ...                                       Comedy\n",
              "...       ...  ...                                          ...\n",
              "9737   193581  ...              Action|Animation|Comedy|Fantasy\n",
              "9738   193583  ...                     Animation|Comedy|Fantasy\n",
              "9739   193585  ...                                        Drama\n",
              "9740   193587  ...                             Action|Animation\n",
              "9741   193609  ...                                       Comedy\n",
              "\n",
              "[9742 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEHCX5itaqzW",
        "colab_type": "text"
      },
      "source": [
        "## Analisando os dados\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vkyW2D5Wk-oj"
      },
      "source": [
        "#### Dataframe = movies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PjjvPSzxh_s0"
      },
      "source": [
        "* Média de nota por filme"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gi2DxM_0h_s5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "a6efb9b8-f53f-417d-b290-0091597bd137"
      },
      "source": [
        "media_por_filme = notas_df.groupby('movieId').mean().rating\n",
        "media_por_filme"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "1         3.897196\n",
              "2         3.366667\n",
              "3         3.538462\n",
              "4         2.600000\n",
              "5         3.480000\n",
              "            ...   \n",
              "193579    3.500000\n",
              "193581    4.000000\n",
              "193583    3.500000\n",
              "193585    3.500000\n",
              "193587    3.500000\n",
              "Name: rating, Length: 6433, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06yvOhb9BFhs",
        "colab_type": "text"
      },
      "source": [
        "* Histograma de média por filme"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cq0tYwEOBI1h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "fce7d87c-199c-4fe9-a1f8-5baeea42ec89"
      },
      "source": [
        "media_por_filme.plot(kind = 'hist')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15ad19320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dy92HO-0CU2s",
        "colab_type": "text"
      },
      "source": [
        "* Detalhes sobre dados do agrupamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-2NwDcsB9Kb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "e3783bb9-7b2e-4817-e41f-824207a09d09"
      },
      "source": [
        "media_por_filme.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    6433.000000\n",
              "mean        3.436251\n",
              "std         0.853154\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQ54WdnLDOZy",
        "colab_type": "text"
      },
      "source": [
        "* Gráfico de Distribuição"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnZOH6ofDgCk",
        "colab_type": "text"
      },
      "source": [
        "A curva no gráfico representa uma aproximação dos dados no agrupamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_ozCxZGCbzV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "6c395007-e8a4-4cc8-d131-8b21a34743b6"
      },
      "source": [
        "sns.distplot(media_por_filme, bins=10)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc158c5a898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DAyYRXIEXb7",
        "colab_type": "text"
      },
      "source": [
        "* Diagrama de Caixa (Boxplot) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gk8svHoAEH5z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "6fc34ec7-3494-4150-e605-4e9467a0a647"
      },
      "source": [
        "plt.figure(figsize = (5,8))\n",
        "sns.boxplot(y = media_por_filme)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc158b06908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2D__mMUEhsY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}