Parte3-PreML.ipynb

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      "cell_type": "markdown",
      "source": [
        "#Análisis pequeño antes del proceso de ML\n",
        "\n",
        "En este cuaderno podras encontrar:\n",
        "- Numero de peliculas que ha calificado cada usuario\n",
        "- Distribución de las calificaciones\n",
        "- Película que más calificaciones tiene\n",
        "- Película que menos calificaciones tiene\n",
        "- Película más vista\n"
      ],
      "metadata": {
        "id": "r9i3T3xaCmAt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pasos básicos"
      ],
      "metadata": {
        "id": "HWTJdo3iE8VJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ZG1uU8XFaWO"
      },
      "outputs": [],
      "source": [
        "#cargamos las librerias \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import gc #garbage collector"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Cargamos nuestros datos \n",
        "df1 = pd.read_parquet('/content/drive/MyDrive/rating.parquet', columns=['userId', 'score', 'id'])\n",
        "df1.info()"
      ],
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      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11024165 entries, 0 to 11024164\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Dtype  \n",
            "---  ------  -----  \n",
            " 0   userId  int64  \n",
            " 1   score   float64\n",
            " 2   id      object \n",
            "dtypes: float64(1), int64(1), object(1)\n",
            "memory usage: 252.3+ MB\n"
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    {
      "cell_type": "code",
      "source": [
        "df1.head(3)"
      ],
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          "data": {
            "text/plain": [
              "   userId  score      id\n",
              "0       1    1.0   as680\n",
              "1       1    4.5  ns2186\n",
              "2       1    5.0  hs2381"
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      "source": [
        "Hacemos algunos cambios para que sean más rápidos los calculos y que consuman los requisitos necesarios.\n",
        "\n",
        "Como se puede observar pasamos de 252.3 MB a solo 147.2 MB solo con el cambio de tipo de variable."
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      "cell_type": "code",
      "source": [
        "df1.userId=df1.userId.astype(np.int32)\n",
        "df1.score=df1.score.astype(np.float16)\n",
        "df1.info()"
      ],
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11024165 entries, 0 to 11024164\n",
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            " #   Column  Dtype  \n",
            "---  ------  -----  \n",
            " 0   userId  int32  \n",
            " 1   score   float16\n",
            " 2   id      object \n",
            "dtypes: float16(1), int32(1), object(1)\n",
            "memory usage: 147.2+ MB\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Utilizamos el label encoder como paso previo al ML debido a que la columna id que tiene el identificador de las películas tiene una combinación de carácteres y números, pero como se mencionó en ML se necesitan usar variables numéricas."
      ],
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        "id": "OMJ4pbV2DXwc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#se carga la libreria \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "columna = df1[\"id\"]\n",
        "\n",
        "\n",
        "# Crear un objeto LabelEncoder y ajustarlo a la columna\n",
        "le = LabelEncoder()\n",
        "le.fit(columna)\n",
        "\n",
        "# Transformar la columna usando LabelEncoder\n",
        "columna_transformada = le.transform(columna)\n",
        "\n",
        "# Reemplazar la columna original con la columna transformada\n",
        "df1[\"id\"] = columna_transformada\n",
        "df1.head(2)"
      ],
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          "height": 112
        },
        "id": "LmBfkVRtROoQ",
        "outputId": "de680eb3-e031-43b0-e67d-9f5d9c091818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  score     id\n",
              "0       1    1.0   6445\n",
              "1       1    4.5  15510"
            ],
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        "df1.score.unique()"
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              "array([1. , 4.5, 5. , 4. , 3.5, 2.5, 0.5, 3. , 2. , 1.5], dtype=float16)"
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        "df1.userId.max()"
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              "270896"
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              "   userId  score     id\n",
              "0       1    1.0   6445\n",
              "1       1    4.5  15510\n",
              "2       1    5.0  12654\n",
              "3       1    5.0  17151\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-cc7716ac-b533-4456-aabe-7aac3e88376b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc7716ac-b533-4456-aabe-7aac3e88376b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmxvwUR6Q4rk",
        "outputId": "00487c68-25b2-44f8-ff03-da017b08dc3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11024165, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n02J1uhZFaWY"
      },
      "source": [
        "**Para saber a qué película corresponde cada calificación, debemos importar el dataset que aporta dicha información.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TXck1gFBFaWZ",
        "outputId": "6164ee99-d13f-41b3-c041-0aa45c2b5c0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    title\n",
              "id                       \n",
              "as1   the grand seduction\n",
              "as2  take care good night\n",
              "as3  secrets of deception\n",
              "as4    pink: staying true\n",
              "as5         monster maker"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
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              "      <td>the grand seduction</td>\n",
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              "      <th>as2</th>\n",
              "      <td>take care good night</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>as3</th>\n",
              "      <td>secrets of deception</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>as4</th>\n",
              "      <td>pink: staying true</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>as5</th>\n",
              "      <td>monster maker</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-63b0f3ff-2cc1-4be4-b995-91f0a4323673 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-63b0f3ff-2cc1-4be4-b995-91f0a4323673');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "from pandas.io.parsers.c_parser_wrapper import is_index_col\n",
        "df_title = pd.read_parquet('/content/drive/MyDrive/plataformas.parquet', columns=['id', 'title'])\n",
        "df_title.set_index('id', inplace=True)\n",
        "df_title.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id = 'as680'\n",
        "print(df_title.loc[id].title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPxRAYKSS8AM",
        "outputId": "bdab77c6-7b1e-4476-dd6c-da636d33db18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the english civil war\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_title.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DV1GoaibRuzt",
        "outputId": "b443a0fb-40e3-467d-881e-13235b3c482f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 22998 entries, as1 to ns8807\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   title   22998 non-null  object\n",
            "dtypes: object(1)\n",
            "memory usage: 875.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convertir el índice en una columna\n",
        "df_title.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "53sq_VBPSNx2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_title.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "tJlJBj9xSPdE",
        "outputId": "867cb57b-4414-4d66-f923-d6a30ef1095a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id                 title\n",
              "0  as1   the grand seduction\n",
              "1  as2  take care good night"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <td>as2</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-60b7cfb5-6d22-4b83-95be-c7bae8e564a5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-60b7cfb5-6d22-4b83-95be-c7bae8e564a5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Igual que en pasos anteriores aplicamos LabelEncoder"
      ],
      "metadata": {
        "id": "UiQud9faD69s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "columna = df_title[\"id\"]\n",
        "\n",
        "\n",
        "# Crear un objeto LabelEncoder y ajustarlo a la columna\n",
        "le = LabelEncoder()\n",
        "le.fit(columna)\n",
        "\n",
        "# Transformar la columna usando LabelEncoder\n",
        "columna_transformada = le.transform(columna)\n",
        "\n",
        "# Reemplazar la columna original con la columna transformada\n",
        "df_title[\"id\"] = columna_transformada\n",
        "df_title.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1KzhtRxuRjiH",
        "outputId": "69d834b5-8bc9-47d3-8eb4-a19407acb00c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     id                 title\n",
              "0     0   the grand seduction\n",
              "1  1111  take care good night\n",
              "2  2222  secrets of deception\n",
              "3  3333    pink: staying true\n",
              "4  4444         monster maker"
            ],
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>the grand seduction</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1111</td>\n",
              "      <td>take care good night</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2222</td>\n",
              "      <td>secrets of deception</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3333</td>\n",
              "      <td>pink: staying true</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4444</td>\n",
              "      <td>monster maker</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2993ef1c-1c34-469e-bee3-5dd20bb72cde')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2993ef1c-1c34-469e-bee3-5dd20bb72cde');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_title[df_title.id==6445]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "BVwzqKVKTois",
        "outputId": "2915e319-d1f6-41ad-8111-178975ac9dc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id                  title\n",
              "679  6445  the english civil war"
            ],
            "text/html": [
              "\n",
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              "      <th>679</th>\n",
              "      <td>6445</td>\n",
              "      <td>the english civil war</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "      gap: 12px;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-db711c35-d9cd-401a-a5e8-422f9473758c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-db711c35-d9cd-401a-a5e8-422f9473758c');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_title.set_index('id', inplace=True)"
      ],
      "metadata": {
        "id": "8aXILo7ET6v_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_title.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "lX7J0XXAT9bu",
        "outputId": "df2b1b96-96f0-488a-c5ae-206381ce2d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     title\n",
              "id                        \n",
              "0      the grand seduction\n",
              "1111  take care good night"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc5efd33-013e-40e6-a651-e3cf54795f09\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc5efd33-013e-40e6-a651-e3cf54795f09 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dc5efd33-013e-40e6-a651-e3cf54795f09');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id = 6445\n",
        "print(df_title.loc[id].title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuN_7ZY0UEZ3",
        "outputId": "17f8f296-6760-495a-d239-9e483ccca42f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the english civil war\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_title.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgTMbKPSH_mz",
        "outputId": "fccd001f-b38c-4de1-ccb6-70f870fc1ae4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22998, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "954ZChTTFaWj",
        "outputId": "47cbeb8a-7c6e-4030-96d0-d36abab8c357",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#Fuerza a que se lleve a cabo la recolección de elementos no utilizados.\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#guardo este archivo para gradio\n",
        "df_title.to_parquet('/content/drive/MyDrive/df_titleLabelEncoder.parquet')"
      ],
      "metadata": {
        "id": "f6grj_l3YfX_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdEajzZnFaWm"
      },
      "source": [
        "- - -\n",
        "\n",
        "# Datos importantes que se pueden obtener de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Buscamos por usuario cuantas películas han visto y cuantas han calificado"
      ],
      "metadata": {
        "id": "IO-VCtatEReh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z9-M_Z4DFaWm",
        "outputId": "36e3a25f-d6ea-4901-ed8c-3b4db7f3c363",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(11024165, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  score     id\n",
              "0       1    1.0   6445\n",
              "1       1    4.5  15510\n",
              "2       1    5.0  12654\n",
              "3       1    5.0  17151\n",
              "4       1    5.0   9446"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
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              "    .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>userId</th>\n",
              "      <th>score</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "      <td>15510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9446</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a8ccbc1-a213-4f4a-9c82-ea00f7c30afd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4a8ccbc1-a213-4f4a-9c82-ea00f7c30afd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4a8ccbc1-a213-4f4a-9c82-ea00f7c30afd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "print(df1.shape)\n",
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvILg_W0FaWo",
        "outputId": "641afefb-c840-4194-be0d-486e76ff2a7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId      int32\n",
              "score     float16\n",
              "id          int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df1.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5lXriN1oFaWp",
        "outputId": "88631733-7f29-4b7f-a1d1-1345a94a6540",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "115077\n"
          ]
        }
      ],
      "source": [
        "# Usuarios únicos\n",
        "\n",
        "print(len(df1['userId'].unique()))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.userId.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GsS8x4YKXZQ3",
        "outputId": "a4afae46-d071-4fab-c69d-9aa5a11016f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZ1EiHpndz2z",
        "outputId": "1bdf5aa4-b419-4ae9-8212-b7440463bd2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  score     id\n",
              "0       1    1.0   6445\n",
              "1       1    4.5  15510"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<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",
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              "      <td>15510</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21f9f1c6-a539-4680-9339-33d732ed9c62')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-21f9f1c6-a539-4680-9339-33d732ed9c62 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-21f9f1c6-a539-4680-9339-33d732ed9c62');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1[df1.id==27]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0E7BI6zUd-eA",
        "outputId": "098ffb8f-7d7d-49d4-9461-50c2c1c135d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          userId  score  id\n",
              "31146        354    4.0  27\n",
              "55089        573    3.5  27\n",
              "72476        747    4.0  27\n",
              "84767        850    3.0  27\n",
              "88370        885    5.0  27\n",
              "...          ...    ...  ..\n",
              "10944489  123603    4.0  27\n",
              "10974323  123899    3.5  27\n",
              "10988448  123999    4.0  27\n",
              "11006771  124148    5.0  27\n",
              "11020236  124316    4.5  27\n",
              "\n",
              "[491 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fd3a2dd7-fc57-4aff-b0e8-bcbcd7bc0aa3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fd3a2dd7-fc57-4aff-b0e8-bcbcd7bc0aa3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Número de películas que ha calificado cada usuario"
      ],
      "metadata": {
        "id": "WvtLkyRGFECA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fF-CVXRRFaWp",
        "outputId": "c72d45a0-4181-48e8-9020-bedbf8bb7d58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        score  id\n",
              "userId           \n",
              "1          27  27\n",
              "2          22  22\n",
              "3          10  10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c99d97a5-56f4-4a39-81ea-9ef9fd1a6e86\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>score</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c99d97a5-56f4-4a39-81ea-9ef9fd1a6e86')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c99d97a5-56f4-4a39-81ea-9ef9fd1a6e86 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c99d97a5-56f4-4a39-81ea-9ef9fd1a6e86');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "# Numero de peliculas que ha calificado el usuario\n",
        "df1_by_users = df1.groupby(['userId']).count()\n",
        "df1_by_users.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para poder observar la cantidad de calificaciones que se han dado hacemos un histograma"
      ],
      "metadata": {
        "id": "zRNOcTd9EnIE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BpcS_Rj9FaWq",
        "outputId": "aca3a2ee-3a22-45cd-b2e2-7c9353e55aee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.hist(df1_by_users.score, log= True)\n",
        "plt.title('Calificaciones por usuario')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Distribución de las calificaciones"
      ],
      "metadata": {
        "id": "2spBO6RoFMLS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ADKVvVkNFaWr",
        "outputId": "7f1079bc-937e-48fd-95cc-bb8860624cd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Observamos la distribución de las calificaciones\n",
        "df1.score.hist()\n",
        "plt.title('Distribución de calificaciones')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9W9R4gmFaWs"
      },
      "source": [
        "#Película que más calificaciones tiene\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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              "    userId  score\n",
              "id               \n",
              "0      502    502\n",
              "1      513    513\n",
              "2      430    430\n",
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              "\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "#cuenta cuantos usarios tiene y cuantos raiting tuvo por id de pelicula\n",
        "df1_by_movies = df1.groupby(['id']).count()\n",
        "df1_by_movies.head()"
      ]
    },
    {
      "cell_type": "code",
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        "df1_by_movies.tail()"
      ],
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      "execution_count": null,
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        {
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              "       userId  score\n",
              "id                  \n",
              "22993     447    447\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
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          "metadata": {},
          "execution_count": 40
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KX1p_i0UFaWt",
        "outputId": "72c67e3f-0648-42ab-c0ae-4ea2e4bf5a9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "from other worlds\n"
          ]
        }
      ],
      "source": [
        "#pelicula con mas calificaciones\n",
        "idx_max = df1_by_movies.userId.idxmax()\n",
        "print(df_title.loc[idx_max].title)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Película que menos calificaciones tiene"
      ],
      "metadata": {
        "id": "wEHv0NyzFq4g"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cf3vpgyXFaWu",
        "outputId": "4c52300f-e5fd-4beb-ebd8-4bbb280f544f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mitzi (nbc 1968)\n"
          ]
        }
      ],
      "source": [
        "# Pelicula con menos calificaciones\n",
        "idx_min = df1_by_movies.userId.idxmin()\n",
        "print(df_title.loc[idx_min].title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjGrjFdbFaWv"
      },
      "source": [
        "#Películas por popularidad"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se realiza un conteo mediante la agrupación por identificador de película y creando un nuevo dataframe con estos datos."
      ],
      "metadata": {
        "id": "rGG40KWdF4eI"
      }
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              "       Vistos\n",
              "id           \n",
              "3553      576\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e62a9b11-c63e-4d4b-a0a5-4763ec613f3d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "df1_by_movies = df1.groupby(['id']).count()\n",
        "df1_by_movies.sort_values('userId', ascending = False, inplace = True)\n",
        "df1_by_movies['Vistos'] = df1_by_movies.userId\n",
        "df1_by_movies.drop(columns = ['userId','score'], inplace = True)\n",
        "df1_by_movies.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1_by_movies.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LY9Bg-EKiuxF",
        "outputId": "410c04c0-3956-4890-9f03-08b2cf34cf33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Vistos\n",
              "id           \n",
              "13746     404\n",
              "20342     402\n",
              "11910     401\n",
              "15464     399\n",
              "4379      396"
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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      "cell_type": "code",
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        "id": "hBRuvaAIFaWw",
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        "colab": {
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        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     title\n",
              "id                        \n",
              "0      the grand seduction\n",
              "1111  take care good night\n",
              "2222  secrets of deception"
            ],
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-75954a12-3628-43ce-a1ae-ce6a878bd790');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "df_title.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se \"agrupan\" los titulos con los id"
      ],
      "metadata": {
        "id": "mjfZ1BnAGLdY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ksxK8ZlQFaWx"
      },
      "outputs": [],
      "source": [
        "df1_by_movies['Titulo'] = df_title.loc[df1_by_movies.index].title"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# La película más vista"
      ],
      "metadata": {
        "id": "NBGjmE00GdHe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vt6t_qBxFaWy",
        "outputId": "a4bdfa65-b1b8-4bc0-9d7a-4b0ccc11b6bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Vistos                            Titulo\n",
              "id                                             \n",
              "3553      576                 from other worlds\n",
              "8962      560                  the organization\n",
              "8311      558                         superbook\n",
              "21168     558  leapfrog: sing-along, read-along\n",
              "6664      556                          sideways"
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5cb0441c-3a78-4f8f-8a7f-d616203c08a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "df1_by_movies.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1_by_movies.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jis6x8hioKK1",
        "outputId": "892b7fa7-0e1b-4c8c-dd92-a40dc81b8299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22998, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribución de Vistas "
      ],
      "metadata": {
        "id": "hrs2rhcIG9mA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sAKfd2a9FaWy",
        "outputId": "4a11e7e7-e1e3-4aee-b872-cfa791eaca92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (8,4))\n",
        "\n",
        "df1_by_movies.Vistos[df1_by_movies.Vistos<442].hist(log = True, bins = 20)\n",
        "plt.title('Distribución de la cantidad de vistas')\n",
        "df1_by_movies.Vistos[df1_by_movies.Vistos>=442].hist(log = True, bins = 20)\n",
        "plt.show()"
      ]
    }
  ],
  "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": "3.10.5"
    },
    "vscode": {
      "interpreter": {
        "hash": "9b0261dadc93494a3555537365c322d83416c4a1ed03d5df7c77bc94b07686c8"
      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}