Analyse_données_imdb.ipynb

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    "colab": {
      "name": "Analyse_données_imdb.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/salimmoulouel/bdle/blob/master/Analyse_donn%C3%A9es_imdb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jwpNz1D-yhY",
        "colab_type": "text"
      },
      "source": [
        "# BDLE TP1 avec colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKJWWE56_nxJ",
        "colab_type": "text"
      },
      "source": [
        "## Initialisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eo2cUzX5Ik20",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4uCc9p9_p3Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "02fe0cc8-4878-422e-ecee-570c21ded3ed"
      },
      "source": [
        "import os\n",
        "\n",
        "# choisir un mode d'execution: colaboratory ou local ou ppti\n",
        "\n",
        "# IL FAUT LANCER JUPYTER NOTEBOOK EN LOCAL ET CHOISIR ENVIRONNEMENT HEBERGE EN HAUT A DROITE\n",
        "EXECUTION='colaboratory'\n",
        "\n",
        "\n",
        "# IL FAUT LANCER JUPYTER NOTEBOOK EN LOCAL ET CHOISIR ENVIRONNEMENT LOCAL EN HAUT A DROITE\n",
        "# \n",
        "# Assurez-vous que l'extension jupyter_http_over_ws Jupyter est activée et à jour sur votre machine.\n",
        "# pip install --upgrade jupyter_http_over_ws>=0.0.1a3 && jupyter serv e port 8888 et qu'il accepte les requêtes du site https://colab.research.google.com.\n",
        "# jupyter notebook   --NotebookApp.allow_origin='https://colab.research.google.com'   --port=8888   --NotebookApp.port_retries=0\n",
        "# voir aussi https://research.google.com/colaboratory/local-runtimes.html\n",
        "# \n",
        "#EXECUTION='local'\n",
        "\n",
        "# EXECUTION EN SALLE PPTI\n",
        "#EXECUTION='ppti'\n",
        "\n",
        "print('EXECUTION', EXECUTION)\n",
        "\n",
        "SPARK_VERSION=\"2.4.4\"\n",
        "\n",
        "if EXECUTION == 'colaboratory':\n",
        "  HOME=\"/content\"\n",
        "  DATASET_DIR=\"/content\"\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "  #os.environ[\"SPARK_HOME\"] = f\"{HOME}/spark-{SPARK_VERSION}-bin-hadoop2.7\"\n",
        "  os.environ[\"SPARK_HOME\"] = \"{}/spark-{}-bin-hadoop2.7\".format(HOME, SPARK_VERSION)\n",
        "\n",
        "elif EXECUTION == 'local':\n",
        "  HOME=os.environ[\"HOME\"]\n",
        "  DATASET_DIR=\"{}/dataset\".format(HOME)\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "  #os.environ[\"SPARK_HOME\"] = f\"{HOME}/anaconda3/bin/spark-{SPARK_VERSION}-bin-hadoop2.7\"\n",
        "  os.environ[\"SPARK_HOME\"] = \"{}/anaconda3/bin/spark-{}-bin-hadoop2.7\".format(HOME, SPARK_VERSION)\n",
        "\n",
        "elif EXECUTION == 'ppti':\n",
        "  HOME=os.environ[\"HOME\"]\n",
        "  DATASET_DIR=\"/Infos/bd/spark/dataset\"\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "  os.environ[\"SPARK_HOME\"] = \"/usr/local/spark-{}-bin-hadoop2.7\".format(SPARK_VERSION)\n",
        "else:\n",
        "    print(\"Valeur incorrecte pour la variable EXECUTION:\", EXECUTION)\n",
        "\n",
        "\n",
        "print('JAVA_HOME:', os.environ[\"JAVA_HOME\"] )\n",
        "print('SPARK_HOME:', os.environ[\"SPARK_HOME\"])\n",
        "print('DATASET_DIR:', DATASET_DIR)\n",
        "print('HOME:', HOME)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "EXECUTION colaboratory\n",
            "JAVA_HOME: /usr/lib/jvm/java-8-openjdk-amd64\n",
            "SPARK_HOME: /content/spark-2.4.4-bin-hadoop2.7\n",
            "DATASET_DIR: /content\n",
            "HOME: /content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fCYt4aw4N3W",
        "colab_type": "text"
      },
      "source": [
        "## Téléchargements (effectuer une fois si nécessaire / ne pas effectuer en salle PPTI)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGnhq33i30KW",
        "colab_type": "text"
      },
      "source": [
        "### Télécharger Spark"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHPKVUL73yKH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "79da585c-cb6e-40cf-f086-7b0d430a5c37"
      },
      "source": [
        "if EXECUTION == 'colaboratory':\n",
        "  CLOSER_LOCATION = \"https://www.apache.org/dyn/closer.lua/spark/spark-{}/spark-{}-bin-hadoop2.7.tgz\".format(SPARK_VERSION, SPARK_VERSION)\n",
        "  # !wget http://mirror.ibcp.fr/pub/apache/spark/spark-{SPARK_VERSION}/spark-{SPARK_VERSION}-bin-hadoop2.7.tgz\n",
        "  !wget {CLOSER_LOCATION} -O suggest.html\n",
        "  !wget `grep \"suggest the following mirror\" -A2 suggest.html |grep -Po 'href=\"\\K[^\"]*'` -O spark-{SPARK_VERSION}-bin-hadoop2.7.tgz\n",
        "  !tar xzf spark-{SPARK_VERSION}-bin-hadoop2.7.tgz\n",
        "  !rm spark-{SPARK_VERSION}-bin-hadoop2.7.tgz\n",
        "  !rm suggest.html\n",
        "  !echo \"spark installé dans spark-{SPARK_VERSION}-bin-hadoop2.7\"\n",
        "else:\n",
        "  print(\"on ne telecharge pas spark\")\n",
        "\n",
        "# Rmq: une autre méthode possible pour télécharger spark: est :  !pip install pyspark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-10 12:24:54--  https://www.apache.org/dyn/closer.lua/spark/spark-2.4.4/spark-2.4.4-bin-hadoop2.7.tgz\n",
            "Resolving www.apache.org (www.apache.org)... 40.79.78.1, 95.216.24.32, 2a01:4f9:2a:185f::2\n",
            "Connecting to www.apache.org (www.apache.org)|40.79.78.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘suggest.html’\n",
            "\n",
            "\rsuggest.html            [<=>                 ]       0  --.-KB/s               \rsuggest.html            [ <=>                ]  29.13K  --.-KB/s    in 0.09s   \n",
            "\n",
            "2019-10-10 12:24:54 (329 KB/s) - ‘suggest.html’ saved [29826]\n",
            "\n",
            "--2019-10-10 12:24:56--  https://www-us.apache.org/dist/spark/spark-2.4.4/spark-2.4.4-bin-hadoop2.7.tgz\n",
            "Resolving www-us.apache.org (www-us.apache.org)... 40.79.78.1\n",
            "Connecting to www-us.apache.org (www-us.apache.org)|40.79.78.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 230091034 (219M) [application/x-gzip]\n",
            "Saving to: ‘spark-2.4.4-bin-hadoop2.7.tgz’\n",
            "\n",
            "spark-2.4.4-bin-had 100%[===================>] 219.43M  31.3MB/s    in 7.6s    \n",
            "\n",
            "2019-10-10 12:25:04 (28.8 MB/s) - ‘spark-2.4.4-bin-hadoop2.7.tgz’ saved [230091034/230091034]\n",
            "\n",
            "spark installé dans spark-2.4.4-bin-hadoop2.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c8lj2dj38Iu",
        "colab_type": "text"
      },
      "source": [
        "### Télécharger findspark"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfohAIQv1UFK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "13cb7540-70cb-43c6-b677-9634e8f45ef2"
      },
      "source": [
        "if EXECUTION == 'ppti' :\n",
        "  !ls -l /Infos/bd/spark/python/findspark.py\n",
        "else :\n",
        "  !pip install findspark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting findspark\n",
            "  Downloading https://files.pythonhosted.org/packages/b1/c8/e6e1f6a303ae5122dc28d131b5a67c5eb87cbf8f7ac5b9f87764ea1b1e1e/findspark-1.3.0-py2.py3-none-any.whl\n",
            "Installing collected packages: findspark\n",
            "Successfully installed findspark-1.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THe8u_Ho4w2z",
        "colab_type": "text"
      },
      "source": [
        "### Télécharger demarrer_spark.py"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_wTd3VO4zzA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2563c89f-2925-4d80-b45b-b58c8c86d0a4"
      },
      "source": [
        "# URL du dossier PUBLIC_DATASET contenant des fichiers\n",
        "PUBLIC_DATASET=\"https://nuage.lip6.fr/s/PQM3RgR4FRnMPQ9/download?path=\"\n",
        "# PUBLIC_DATASET=\"https://newcloud.desir.lip6.fr/s/PQM3RgR4FRnMPQ9/download?path=\"\n",
        "\n",
        "if EXECUTION == 'ppti' :\n",
        "  !ls -l /Infos/bd/spark/python/demarrer_spark.py\n",
        "else :\n",
        "  !wget {PUBLIC_DATASET}/python/demarrer_spark.py -O demarrer_spark.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-10 12:25:37--  https://nuage.lip6.fr/s/PQM3RgR4FRnMPQ9/download?path=/python/demarrer_spark.py\n",
            "Resolving nuage.lip6.fr (nuage.lip6.fr)... 132.227.201.11\n",
            "Connecting to nuage.lip6.fr (nuage.lip6.fr)|132.227.201.11|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1624 (1.6K) [text/x-python]\n",
            "Saving to: ‘demarrer_spark.py’\n",
            "\n",
            "demarrer_spark.py   100%[===================>]   1.59K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2019-10-10 12:25:38 (997 KB/s) - ‘demarrer_spark.py’ saved [1624/1624]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy2SS4Y05LlR",
        "colab_type": "text"
      },
      "source": [
        "### Télécharger les datasets IMDB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDq0NIVx1vRJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "65c14cf3-ca5a-45df-949f-8896e3782aa1"
      },
      "source": [
        "if EXECUTION == 'ppti' :\n",
        "  !ls -ld {DATASET_DIR}/imdb/vldb2015\n",
        "  \n",
        "else :\n",
        "  !mkdir -p {DATASET_DIR}/imdb/vldb2015\n",
        "\n",
        "  # les donnees entières (1.18GO)\n",
        "  #!wget {PUBLIC_DATASET}/imdb/vldb2015/csvfiles.tgz -O imdb/vldb2015/csvfiles.tgz\n",
        "  #!cd imdb/vldb2015 && tar zxf csvfiles.tgz && rm csvfiles.tgz\n",
        "  #dir = 'imdb//vldb2015/csvfiles/'\n",
        "\n",
        "  ## Effacer dataset complet\n",
        "  #!cd \n",
        "  #!ls imdb\n",
        "  #!rm -rf imdb/csvfiles\n",
        "\n",
        "  # un sample basé sur 1% des films\n",
        "  !wget {PUBLIC_DATASET}/imdb/vldb2015/csvfiles_sample001.tgz -O {DATASET_DIR}/imdb/vldb2015/csvfiles_sample001.tgz\n",
        "  !cd {DATASET_DIR}/imdb/vldb2015 && tar zxf csvfiles_sample001.tgz && rm csvfiles_sample001.tgz\n",
        "  print(\"dataset téléchargé\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-10 12:25:46--  https://nuage.lip6.fr/s/PQM3RgR4FRnMPQ9/download?path=/imdb/vldb2015/csvfiles_sample001.tgz\n",
            "Resolving nuage.lip6.fr (nuage.lip6.fr)... 132.227.201.11\n",
            "Connecting to nuage.lip6.fr (nuage.lip6.fr)|132.227.201.11|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 73571656 (70M) [application/x-compressed]\n",
            "Saving to: ‘/content/imdb/vldb2015/csvfiles_sample001.tgz’\n",
            "\n",
            "/content/imdb/vldb2 100%[===================>]  70.16M  43.5MB/s    in 1.6s    \n",
            "\n",
            "2019-10-10 12:25:48 (43.5 MB/s) - ‘/content/imdb/vldb2015/csvfiles_sample001.tgz’ saved [73571656/73571656]\n",
            "\n",
            "dataset téléchargé\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTuBkS5z8Qaq",
        "colab_type": "text"
      },
      "source": [
        "### Télécharger la définition des tables imdb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4_PtZSK8VZv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "18663178-3ea1-4216-d1ca-71f77226b69e"
      },
      "source": [
        "if EXECUTION == 'ppti':\n",
        "  !cp {DATASET_DIR}/imdb/vldb2015/tables_imdb.py tables_imdb.py\n",
        "else:\n",
        "  !wget {PUBLIC_DATASET}/imdb/vldb2015/tables_imdb.py -O tables_imdb.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-10 12:25:56--  https://nuage.lip6.fr/s/PQM3RgR4FRnMPQ9/download?path=/imdb/vldb2015/tables_imdb.py\n",
            "Resolving nuage.lip6.fr (nuage.lip6.fr)... 132.227.201.11\n",
            "Connecting to nuage.lip6.fr (nuage.lip6.fr)|132.227.201.11|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4706 (4.6K) [text/x-python]\n",
            "Saving to: ‘tables_imdb.py’\n",
            "\n",
            "\rtables_imdb.py        0%[                    ]       0  --.-KB/s               \rtables_imdb.py      100%[===================>]   4.60K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2019-10-10 12:25:56 (1.89 MB/s) - ‘tables_imdb.py’ saved [4706/4706]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b48RFAkz0fIK",
        "colab_type": "text"
      },
      "source": [
        "## Démarrer la session SPARK"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGN1-AH40fIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "0df733b4-e413-4585-b3ae-d95e8c64e72b"
      },
      "source": [
        "from demarrer_spark import demarrer_spark\n",
        "\n",
        "spark = demarrer_spark()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "les dossiers contenant les packages\n",
            "\n",
            "/env/python\n",
            "/usr/lib/python36.zip\n",
            "/usr/lib/python3.6\n",
            "/usr/lib/python3.6/lib-dynload\n",
            "/usr/local/lib/python3.6/dist-packages\n",
            "/usr/lib/python3/dist-packages\n",
            "/usr/local/lib/python3.6/dist-packages/IPython/extensions\n",
            "/root/.ipython\n",
            "findspark.init() initialise les variables d'environnement pour spark\n",
            "import fait\n",
            "session démarrée, son id est  local-1570710364406\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qn9y6Qmd0fIS",
        "colab_type": "text"
      },
      "source": [
        "### Définir l'accès aux fichiers de données"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZvjUlX4y05zt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "275fc4ff-d011-4b8a-fc2f-180e2137c26c"
      },
      "source": [
        "from tables_imdb import lire_tables\n",
        "dir = \"{}/imdb/vldb2015/csvfiles_sample001/\".format(DATASET_DIR)\n",
        "\n",
        "title, kind_type, movie_info, info_type, cast_info, role_type, name,  \\\n",
        "person_info, movie_companies, company_name,company_type \\\n",
        "= lire_tables(spark, dir)\n",
        "\n",
        "print(\"tables définies\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tables définies\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RX5z18g0fIV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bf235791-96c4-4776-bc2c-41624eeceb42"
      },
      "source": [
        "title.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25084"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QryhkricVNzd",
        "colab_type": "text"
      },
      "source": [
        "## Exemples de requêtes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PF9Uc7sB0fIW",
        "colab_type": "text"
      },
      "source": [
        "### Les films"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkZmO03-0fIX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "8a12ceba-4cb1-4eb6-8134-4efb5a274c52"
      },
      "source": [
        "title.describe(\"kind_id\", \"production_year\").show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+-----------------+------------------+\n",
            "|summary|          kind_id|   production_year|\n",
            "+-------+-----------------+------------------+\n",
            "|  count|            25084|             24368|\n",
            "|   mean|4.934659543932387|1992.7387967826658|\n",
            "| stddev|2.680072651781981|24.353626579442597|\n",
            "|    min|                1|              1895|\n",
            "|    max|                7|              2016|\n",
            "+-------+-----------------+------------------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mi4Iu5fqQr2A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "regarder location à travers les année combien de films ont été fait "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3umGer_Y0fIZ",
        "colab_type": "text"
      },
      "source": [
        "#### Nombre de films"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LRRLbvI0fIZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "aba1a730-701e-4086-8eb3-f2f55e358006"
      },
      "source": [
        "spark.sql(\"SELECT count(*) FROM Title\").show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------+\n",
            "|count(1)|\n",
            "+--------+\n",
            "|   25084|\n",
            "+--------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0h4UoobQ904",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0542f41d-56d1-4b2a-8d2e-cbbb638fd967"
      },
      "source": [
        "spark.sql(\"SELECT distinct title FROM Title \").count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22106"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kkroo9Pne1Ag",
        "colab_type": "text"
      },
      "source": [
        "#### Les types de films"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkXoukeT0fIb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "4cc2adcb-7ae1-4fa8-843b-abaa28e742e7"
      },
      "source": [
        "r = spark.sql(\"\"\"\n",
        "SELECT distinct kind_id \n",
        "FROM Title \n",
        "order by kind_id\n",
        "\"\"\")\n",
        "\n",
        "r.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+\n",
            "|kind_id|\n",
            "+-------+\n",
            "|      1|\n",
            "|      2|\n",
            "|      3|\n",
            "|      4|\n",
            "|      6|\n",
            "|      7|\n",
            "+-------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZQrfo8se7e3",
        "colab_type": "text"
      },
      "source": [
        "#### Nombre de films par type de film"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PO85qKU0fId",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9f80cfd1-8295-4d39-dbcf-6fab671486a6"
      },
      "source": [
        "r = spark.sql(\"\"\"\n",
        "SELECT kind_id, count(*) as nb \n",
        "FROM Title \n",
        "group by kind_id \n",
        "order by nb desc\n",
        "\"\"\")\n",
        "r.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+-----+\n",
            "|kind_id|   nb|\n",
            "+-------+-----+\n",
            "|      7|15274|\n",
            "|      1| 6602|\n",
            "|      4| 1175|\n",
            "|      3|  979|\n",
            "|      2|  925|\n",
            "|      6|  129|\n",
            "+-------+-----+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12J8FZWb0fIf",
        "colab_type": "text"
      },
      "source": [
        "#### Nombre de film par années"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uloUHFAj0fIg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "40449a59-6c61-4012-b965-5b1237a65cf0"
      },
      "source": [
        "r = spark.sql(\"\"\"\n",
        "select production_year, count(*) as nb\n",
        "from Title\n",
        "where production_year is not null\n",
        "group by production_year\n",
        "order by nb desc\n",
        "\"\"\")\n",
        "\n",
        "r.show(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+---------------+----+\n",
            "|production_year|  nb|\n",
            "+---------------+----+\n",
            "|           2012|1605|\n",
            "|           2011|1517|\n",
            "|           2010|1467|\n",
            "|           2009|1301|\n",
            "|           2008|1192|\n",
            "+---------------+----+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OQm--1C0fIi",
        "colab_type": "text"
      },
      "source": [
        "#### La distribution du nombre de films est biaisée"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrZuXv1S0fIi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "be41419a-e18c-465f-fe54-3d7bbaa1b097"
      },
      "source": [
        "desc = r.describe('nb')\n",
        "desc.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+------------------+\n",
            "|summary|                nb|\n",
            "+-------+------------------+\n",
            "|  count|               122|\n",
            "|   mean| 199.7377049180328|\n",
            "| stddev|331.00849663956006|\n",
            "|    min|                 1|\n",
            "|    max|              1605|\n",
            "+-------+------------------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlL9RyXD0fIk",
        "colab_type": "text"
      },
      "source": [
        "#### Visualiser le nombre de films par années"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HFMW6Rb0ks6Y",
        "colab_type": "text"
      },
      "source": [
        "[Documentation MatplotLib](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Ubj--M3z0fIl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "d315530e-c1e2-4d14-bf00-8e970e7a62a1"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "t = r.collect()\n",
        "\n",
        "tabYear = [x.production_year for x in t]\n",
        "\n",
        "tabNb = [x.nb for x in t]\n",
        "\n",
        "# plt.plot(tabYear, tabNb)\n",
        "plt.bar(tabYear, tabNb)\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('nb films')\n",
        "plt.title('films per year')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x/d6xHhGPAQfWcoeShpO1LsYB64B/BSbVYLvTgGkA++yzz9ZuzszMqlDNs7Nq7V3A8ojo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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MyWtzdd0fIm",
        "colab_type": "text"
      },
      "source": [
        "### Les types de films : fichier kind_type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4RdHz7o0fIn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "be2ce746-2542-4e62-905a-2accc557e0d4"
      },
      "source": [
        "kind_type.printSchema()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- kind: string (nullable = true)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZzQ0TYHA9LI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "4cd9e3fd-00c5-4558-cdd9-e6d7fdaf30a3"
      },
      "source": [
        "spark.sql(\"SELECT * FROM Kind_type\").show()\n",
        "kind_type.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+---+--------------+\n",
            "| id|          kind|\n",
            "+---+--------------+\n",
            "|  1|         movie|\n",
            "|  2|     tv series|\n",
            "|  3|      tv movie|\n",
            "|  4|   video movie|\n",
            "|  5|tv mini series|\n",
            "|  6|    video game|\n",
            "|  7|       episode|\n",
            "+---+--------------+\n",
            "\n",
            "+---+--------------+\n",
            "| id|          kind|\n",
            "+---+--------------+\n",
            "|  1|         movie|\n",
            "|  2|     tv series|\n",
            "|  3|      tv movie|\n",
            "|  4|   video movie|\n",
            "|  5|tv mini series|\n",
            "|  6|    video game|\n",
            "|  7|       episode|\n",
            "+---+--------------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPt1vX1xVxrX",
        "colab_type": "text"
      },
      "source": [
        "#### Jointure entre Title et Kind_Type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMvtvFV-f2dd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "b3e1e220-78ae-444c-e423-211bc2a61c4e"
      },
      "source": [
        "title.printSchema()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- imdb_index: string (nullable = true)\n",
            " |-- kind_id: integer (nullable = true)\n",
            " |-- production_year: integer (nullable = true)\n",
            " |-- imdb_id: integer (nullable = true)\n",
            " |-- phonetic_code: string (nullable = true)\n",
            " |-- episode_id: string (nullable = true)\n",
            " |-- season_nr: integer (nullable = true)\n",
            " |-- episode_nr: integer (nullable = true)\n",
            " |-- series_years: string (nullable = true)\n",
            " |-- md5sum: string (nullable = true)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EKv-l0f0fIq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "3fd78228-1a96-44eb-e796-bc8bdb964a6c"
      },
      "source": [
        "\n",
        "r= spark.sql(\"\"\"\n",
        "\n",
        "SELECT k.kind, t.title, t.production_year \n",
        "FROM Title t, Kind_Type k \n",
        "WHERE t.kind_id = k.id\n",
        "ORDER BY t.production_year DESC\n",
        "\"\"\")\n",
        "r.show(10, False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+---------+-----------------------------------------+---------------+\n",
            "|kind     |title                                    |production_year|\n",
            "+---------+-----------------------------------------+---------------+\n",
            "|movie    |Star Trek 3                              |2016           |\n",
            "|movie    |Hundred Degrees in the Shade             |2015           |\n",
            "|movie    |Arclight                                 |2015           |\n",
            "|movie    |The Bonus Army                           |2015           |\n",
            "|movie    |The Smoker                               |2015           |\n",
            "|movie    |3 Solitude                               |2014           |\n",
            "|movie    |Brothers Day                             |2014           |\n",
            "|tv series|Witness Protection: Return of the Prophet|2014           |\n",
            "|movie    |Dead by Dawn                             |2014           |\n",
            "|movie    |Cockroaches                              |2014           |\n",
            "+---------+-----------------------------------------+---------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diQjEV60iQ6u",
        "colab_type": "text"
      },
      "source": [
        "#### Nombre de films par type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAiRgS6hgYNb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "12396880-4740-44a8-9d74-971dea147029"
      },
      "source": [
        "r= spark.sql(\"\"\"\n",
        "\n",
        "SELECT k.kind, count(*) as nb\n",
        "FROM Title t, Kind_Type k \n",
        "WHERE t.kind_id = k.id\n",
        "GROUP BY k.kind\n",
        "ORDER BY count(*) DESC\n",
        "\"\"\")\n",
        "r.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----------+-----+\n",
            "|       kind|   nb|\n",
            "+-----------+-----+\n",
            "|    episode|15274|\n",
            "|      movie| 6602|\n",
            "|video movie| 1175|\n",
            "|   tv movie|  979|\n",
            "|  tv series|  925|\n",
            "| video game|  129|\n",
            "+-----------+-----+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHFZ49560fIs",
        "colab_type": "text"
      },
      "source": [
        "### Les roles : fichier cast_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQVxtd5chboS",
        "colab_type": "text"
      },
      "source": [
        " #### Une personne a un ou plusieurs roles dans un film "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrLy7uxM0fIs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "ff187693-5f26-4868-cb7a-b28b2c3fe929"
      },
      "source": [
        "cast_info.printSchema()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- person_id: integer (nullable = true)\n",
            " |-- movie_id: integer (nullable = true)\n",
            " |-- person_role_id: integer (nullable = true)\n",
            " |-- note: string (nullable = true)\n",
            " |-- nr_order: integer (nullable = true)\n",
            " |-- role_id: integer (nullable = true)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIK034w0dOmH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a3db639f-69d2-48c6-bb9e-79cf00697522"
      },
      "source": [
        "cast_info.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "360035"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gyqb-U9J0fIv",
        "colab_type": "text"
      },
      "source": [
        "#### Nombre de roles par acteur"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wu0xfpZ0fIw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "354f8ec7-bfee-4cd1-af1a-1a995ebe91c3"
      },
      "source": [
        "res2 = spark.sql(\"\"\"\n",
        "\n",
        "select person_id, count(*) as nb\n",
        "from Cast_Info\n",
        "group by person_id\n",
        "order by nb desc\n",
        "\n",
        "\"\"\")\n",
        "res2.persist()\n",
        "res2.show(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+---------+---+\n",
            "|person_id| nb|\n",
            "+---------+---+\n",
            "|   175411|120|\n",
            "|    95397|118|\n",
            "|   515315|108|\n",
            "|  1495180|105|\n",
            "|   677696| 90|\n",
            "+---------+---+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzubBYSV0fIz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "9f28caa7-83b7-4852-de32-aee93fce697b"
      },
      "source": [
        "d = res2.describe('nb')\n",
        "d.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+------------------+\n",
            "|summary|                nb|\n",
            "+-------+------------------+\n",
            "|  count|            214749|\n",
            "|   mean|1.6765386567574239|\n",
            "| stddev|1.9565121381308785|\n",
            "|    min|                 1|\n",
            "|    max|               120|\n",
            "+-------+------------------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVpOB8Ct0fI1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "outputId": "54c0b541-237c-4df5-8c9a-ffdd041b8655"
      },
      "source": [
        "#t2 = res2.sample(0.01).collect()\n",
        "t2 = res2.collect()\n",
        "\n",
        "tabNb = [x.nb for x in t2]\n",
        "\n",
        "plt.yscale('log')\n",
        "plt.plot(tabNb) \n",
        "plt.xlabel('actor')\n",
        "plt.ylabel('nb roles')\n",
        "\n",
        "#plt.hist(tabNb)\n",
        "plt.show()\n",
        "\n",
        "plt.yscale('linear')\n",
        "plt.plot(tabNb) \n",
        "plt.xlabel('actor')\n",
        "plt.ylabel('nb roles')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zywOP8dDuZ/tQI0laGDuph5RTdkiqmgExpFotHC4rqVIGxNByTidJ1TIghlQrIHkKIalC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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VRUwfmsb8uR6klqQyfQ+IzPwJ47d0e7Fj+lXH/gvncvevnujX6iRpymnsmdSzZwzxwKNP\nk95VTpI6amxAjAfDo09tq7kSSRpMjQ2IV+77MgC2PPpUzZVI0mBqbEAsKA5QP/Do0zVXIkmDqbEB\nse/eswG475Ff11yJJA2mxgbE0vlzAPjF/Y/VXIkkDabGBsSCvVpdTL9+dnvNlUjSYGpsQEybFix6\n2Sy+ff0v6y5FkgZSYwMC4BX7zOWxp7ax/TnPhZCkHTU6IA5++d4A3LDp4ZorkaTB0+iAePshiwH4\n0j/cVW8hkjSAGh0Q/3px62S5dXc/5CU3JGkHjQ6I2TOGeOehS9j88K/5yrX31F2OJA2URgcEwJ+8\n+ZUAnHX5L/jlw540J0njGh8Qi/eew2v3m8djT29jzTV31V2OJA2MxgcEwCV/9FssmDuTi0fv5T3n\nreUpT56TJAMCYGhacNobD2DZgr348YatXHP7Vu7a+oRBIanR6rjl6EA69cgVvHrp3vzeF/6RU748\nCsCRByzk//3hb9ZcmSTVw4Bo87r95nPue17Hk89s4ytr7+HmXz7C567e+IJ5jjxgIYcsm1dThZLU\nPwZEm2nTgjf/xr4AbH3sGX521618+or1L5jnJxu2cuGqw+soT5L6KqbyCWIjIyM5Ojpa2fs/s+25\nFzz/wAXr+PGGraxYOHfC5Y49aBEfe9MrK6tLknZFRKzLzJFu87kHMYGZ0194DP/3f3M/pkVMuMxN\nmx/h0p9v5vjXLNnl9S+YO/P5O99JUr+5B7Gb/c/v3MJ5P7lzt7zXXjOH+Pl/P5bZM4Z2y/tJErgH\nUZsPvvEAXrNsHrsau/94+6+48Np7OPvK29hr5tQMiOMO3pdX7fuyusuQNEkDFxARcRxwDjAEfDEz\nz6q5pJ0yf+5M3n7Iy3f5fZbMm8PX121i9Y/u2A1V1WPjlsf57O8fWncZkiZpoLqYImIIuA04FtgE\n/Ax4d2be0mn+Qexi2p0G6d9mZ73z89ew/v7HWDJvTt2lSHukd71+GX/42/tPatmp2sV0GLAxM+8A\niIivAscDHQNiTxddDogPslOOWMF3b7qv7jKkPdbCl8yqfB2DFhBLgHvbnm8CPJV5Cnr7IS/fLV1t\nkuoz5a7FFBGrImI0IkbHxsbqLkeS9liDFhCbgWVtz5cWbc/LzNWZOZKZI8PDw30tTpKaZNAC4mfA\ngRGxIiJmAicD36q5JklqpIE6BpGZ2yLig8AVtIa5np+ZN9dcliQ10kAFBEBmXg5cXncdktR0g9bF\nJEkaEAaEJKkjA0KS1NFAXWpjZ0XEGHD3JBdfCGzdjeXsSdw25dw25dw25QZt27wiM7ueJzClA2JX\nRMRoL9ciaSK3TTm3TTm3Tbmpum3sYpIkdWRASJI6anJArK67gAHmtinntinntik3JbdNY49BSJIm\n1uQ9CEnSBBoZEBFxXESsj4iNEXF63fVUJSLuiogbI+K6iBgt2hZExJURsaH4Ob9oj4j4TLFNboiI\nQ9veZ2Ux/4aIWNnW/rri/TcWyw7sHY4i4vyI2BIRN7W1Vb4tytYxSEq2zScjYnPx3bkuIt7a9toZ\nxedcHxFvbmvv+HtVXHxzbdF+UXEhTiJiVvF8Y/H68v584t5FxLKIuDoibomImyPiI0V7M747mdmo\nB62LAN4O7A/MBK4HDqq7roo+613Awh3a/jdwejF9OvCpYvqtwHeBAA4H1hbtC4A7ip/zi+n5xWvX\nFvNGsexb6v7ME2yLo4BDgZv6uS3K1jFIj5Jt80ngv3aY96Did2YWsKL4XRqa6PcKuBg4uZj+AvCf\ni+kPAF8opk8GLqp7W3T4vIuBQ4vpl9K6JfJBTfnu1P4PUMM/+BuAK9qenwGcUXddFX3Wu3hxQKwH\nFhfTi4H1xfS5tO7//YL5gHcD57a1n1u0LQZ+0db+gvkG8QEs3+GPYOXbomwdg/bosG0+SeeAeMHv\nC60rL7+h7Peq+KO3FZhetD8/3/iyxfT0Yr6oe1t02U6XAcc25bvTxC6mTrc1XVJTLVVL4HsRsS4i\nVhVtizJz/GbR9wOLiumy7TJR+6YO7VNJP7ZF2Tqmgg8W3STnt3Vv7Oy22Qd4ODO37dD+gvcqXn+k\nmH8gFV1grwXW0pDvThMDokmOzMxDgbcAp0XEUe0vZuu/Jg5joz/bYopt788D/xJ4DXAf8Jf1llOv\niHgJ8HXgjzPz0fbX9uTvThMDouttTfcUmbm5+LkF+AZwGPBARCwGKH5uKWYv2y4TtS/t0D6V9GNb\nlK1joGXmA5m5PTOfA/6a1ncHdn7b/AqYFxHTd2h/wXsVr+9dzD9QImIGrXC4IDMvLZob8d1pYkA0\n4ramETE3Il46Pg28CbiJ1mcdH0GxklafKkX7e4tRGIcDjxS7t1cAb4qI+UU3w5to9SHfBzwaEYcX\noy7e2/ZeU0U/tkXZOgba+B+mwr+j9d2B1uc5uRiBtAI4kNZB1o6/V8X/fK8GTiyW33E7j2+bE4Ef\nFPMPjOLf8zzg1sw8u+2lZnx36j7oU8eD1kiD22iNuvhE3fVU9Bn3pzWS5Hrg5vHPSauP9ypgA/B9\nYEHRHsDnim1yIzDS9l6nABuLx/vb2kdo/eG4HfgsA3yAEbiQVlfJs7T6eU/tx7YoW8cgPUq2zd8W\nn/0GWn+oFrfN/4nic66nbeRa2e9V8V28tthmXwNmFe2zi+cbi9f3r3tbdNg2R9Lq2rkBuK54vLUp\n3x3PpJYkddTELiZJUg8MCElSRwaEJKkjA0KS1JEBIUnqyICQdrOIODoifqvuOqRdZUBIu9/RwE4F\nRNuZxtLA8DwIqUcR8U1al0uYDZyTmasj4jjgf9G63PVWWieZ/RTYDowBH6J1kbbzgYVF2/sz856I\n+DLwFK0LwP1DZn60v59ImpgBIfUoIhZk5oMRMYfWpSWOAUaBozLzzrbXPwk8npl/USz3beCSzFwT\nEacA78jME4qAWAgcn5nba/lQ0gTsYpJ69+GIuJ7WHsIyYBXwo8y8EyAzHyxZ7g3AV4rpv6V1+YZx\nXzMcNKgMCKkHEXE08Lu0bnBzCPBPtK7Ls6ue2A3vIVXCgJB6szfwUGY+GRGvonWLyNnAUcVVTYmI\nBcW8j9G6PeW4a2hd3RTgD4Af96dkadd4DELqQUTMAr5J69ac64F5tG7LOYfWQeppwJbMPDYi/hVw\nCfAcrYPU9wBfovNB6u9k5iV9/TBSjwwISVJHdjFJkjoyICRJHRkQkqSODAhJUkcGhCSpIwNCktSR\nASFJ6siAkCR19P8BH0PROMlMfdQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08ewqxfYngxV",
        "colab_type": "text"
      },
      "source": [
        "#### Le nombre de rôles par type de film"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54Le4cXgnitT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e81cecc3-7120-42f1-cc3c-7c748474ec49"
      },
      "source": [
        "res3 = spark.sql(\"\"\"\n",
        "SELECT g.kind, count(*) as nb_roles\n",
        "FROM Cast_Info c, Title f, Kind_type g\n",
        "WHERE c.movie_id = f.id AND f.kind_id = g.id\n",
        "AND f.production_year > 2010\n",
        "GROUP BY g.kind\n",
        "ORDER BY nb_roles desc\n",
        "\"\"\")\n",
        "res3.show(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----------+--------+\n",
            "|       kind|nb_roles|\n",
            "+-----------+--------+\n",
            "|    episode|   36527|\n",
            "|      movie|   15897|\n",
            "|   tv movie|    1842|\n",
            "|video movie|    1421|\n",
            "|  tv series|     943|\n",
            "+-----------+--------+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4fm5_Itebi_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "62d66815-eda5-458d-fb5f-c1445e90e79b"
      },
      "source": [
        "t3 = res3.collect()\n",
        "\n",
        "tabNb = [x.nb_roles for x in t3]\n",
        "tabKind = [x.kind for x in t3]\n",
        "\n",
        "plt.yscale('log')\n",
        "plt.bar(tabKind,tabNb) \n",
        "plt.xlabel('kind')\n",
        "plt.ylabel('nb roles')\n",
        "\n",
        "#plt.hist(tabNb)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L8A3gT5OcTffBDvBk4Pn98F8Av98PHwz81Mj4t/XDz+n/Lutv70X3Itmwne2a64aq2gyQ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\n6FEfz6IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6a0JDtZFjln",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AiqmsCkr0fI4",
        "colab_type": "text"
      },
      "source": [
        "## Autres analyses"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPX8CM5GVwhU",
        "colab_type": "text"
      },
      "source": [
        "etude sur les saison a traver le temp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOLxl6tHoSn9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#on recupere pour chaque serie la derniere saison existante et lannee de production de la serie \n",
        "r = spark.sql(\"\"\" \n",
        "SELECT title,max(production_year)as production_year,max(season_nr) as last_saison,max(episode_nr) as last_episode\n",
        "FROM Title \n",
        "where production_year is not null and episode_nr>1\n",
        "group by title\n",
        "\"\"\")\n",
        "#r.show(5,False)\n",
        "r.count()\n",
        "#on remplace la premiere table par celle sans doublon\n",
        "r.createOrReplaceTempView(\"Title_uniques\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWQddp3yru3e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "71f66718-1442-48e4-b9c7-e00e303e7e0f"
      },
      "source": [
        "r=spark.sql(\"\"\"select * from title_uniques\"\"\")\n",
        "r.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+---------------+-----------+------------+\n",
            "|               title|production_year|last_saison|last_episode|\n",
            "+--------------------+---------------+-----------+------------+\n",
            "|              (#1.6)|           2013|          1|           6|\n",
            "|                 DNA|           2010|          1|          13|\n",
            "|             (#2.40)|           2008|          2|          40|\n",
            "|       Travel Writer|           2004|          3|           5|\n",
            "|             (#1.69)|           2007|          1|          69|\n",
            "|         Nya grannen|           1999|          2|           7|\n",
            "|            Philippe|           2013|          3|          66|\n",
            "|     Orlando: Hour 1|           2008|         12|           4|\n",
            "|             (#1.54)|           2009|          1|          54|\n",
            "|          (#1.13839)|           2010|          1|       13839|\n",
            "|   The Fifth of July|           1982|          1|           9|\n",
            "|       Bighorn Otaku|           2011|          1|           2|\n",
            "|              (#1.7)|           2013|          1|           7|\n",
            "|      Anthony Zuiker|           2011|         17|           4|\n",
            "|The Food of Love:...|           1989|          9|          40|\n",
            "|    American Animals|           2012|          8|          13|\n",
            "|              (#1.2)|           2013|          1|           2|\n",
            "| Teatro en la nevera|           2000|          5|          38|\n",
            "|          Unforgiven|           1992|          1|           8|\n",
            "|Playing with Matches|           2002|          5|           8|\n",
            "+--------------------+---------------+-----------+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLxAszB0WqKl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "77e6a708-93a2-4ee4-f3f8-68e1a617f45a"
      },
      "source": [
        "r = spark.sql(\"\"\" \n",
        "select production_year, Mean(last_saison) as meanSeas,count(*) as nb From Title_uniques\n",
        "where last_episode>1\n",
        "\n",
        "group by production_year\n",
        "order by production_year desc\n",
        "\"\"\")\n",
        "\n",
        "r.show(20,False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+---------------+------------------+---+\n",
            "|production_year|meanSeas          |nb |\n",
            "+---------------+------------------+---+\n",
            "|2013           |4.130081300813008 |246|\n",
            "|2012           |4.074534161490683 |644|\n",
            "|2011           |3.909710391822828 |587|\n",
            "|2010           |3.9367088607594938|553|\n",
            "|2009           |4.320408163265306 |490|\n",
            "|2008           |5.098086124401914 |418|\n",
            "|2007           |4.8094117647058825|425|\n",
            "|2006           |4.1656441717791415|326|\n",
            "|2005           |3.848148148148148 |270|\n",
            "|2004           |4.605150214592275 |233|\n",
            "|2003           |4.3023255813953485|215|\n",
            "|2002           |5.204819277108434 |166|\n",
            "|2001           |5.315476190476191 |168|\n",
            "|2000           |5.46875           |160|\n",
            "|1999           |4.382352941176471 |170|\n",
            "|1998           |5.24113475177305  |141|\n",
            "|1997           |4.854166666666667 |144|\n",
            "|1996           |5.909090909090909 |132|\n",
            "|1995           |3.712871287128713 |101|\n",
            "|1994           |4.339285714285714 |112|\n",
            "+---------------+------------------+---+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Om1GWz6RCe_r",
        "colab_type": "text"
      },
      "source": [
        "Distribution de la moyenne des  saisons par année"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWGLl583DXkC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQO1jIJCQfCF",
        "colab_type": "text"
      },
      "source": [
        "nombre de season qui sortent dans les années"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxwbPCsmI3Bu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "9ec8323e-d041-429c-e30d-78169270cc0a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "t = r.collect()\n",
        "\n",
        "tabYear = [x.production_year for x in t]\n",
        "\n",
        "tabNb = [x.meanSeas for x in t]\n",
        "\n",
        "# plt.plot(tabYear, tabNb)\n",
        "plt.bar(tabYear, tabNb)\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('mean of nb Seasons ')\n",
        "plt.title('mean of nb Seasons per year')\n",
        "plt.savefig(\"/content/gdrive/My Drive/BDLE/meansaisonYear\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B9o6IR1sdj1mV3KRjtSVpEmlc9Rud7K0OXMM3M6sJ1/DNzGrCCd/MrCac8M3M\nasIJ38ysJpzwzcxq4n8ALzxvHzwZFo4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_ILZaQBFr0c",
        "colab_type": "text"
      },
      "source": [
        "la longeur des serie augmente avec les année cela est signe du success des serie de plus en plus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYqh2hYiJx18",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "b0b7bae1-228b-421e-e5cf-3dbb82772fe6"
      },
      "source": [
        "t = r.collect()\n",
        "\n",
        "tabYear = [x.production_year for x in t]\n",
        "\n",
        "tabNb = [x.nb for x in t]\n",
        "\n",
        "# plt.plot(tabYear, tabNb)\n",
        "plt.bar(tabYear, tabNb)\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('nb series ')\n",
        "plt.title('nb serie sortant per year')\n",
        "plt.savefig(\"/content/gdrive/My Drive/BDLE/nbsaisonYear\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZmZWyAnCzMwKOUGYmVkhJwgzMyv0/wEebEz8te7z6gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snOvbxK2F-yN",
        "colab_type": "text"
      },
      "source": [
        "le nombre de serie sortant augmente a travers les année cela est signe du success croissant des series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHsP0o77G67L",
        "colab_type": "text"
      },
      "source": [
        "Utilisation de Cube et Aggregation "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRJc8Nc6IKiK",
        "colab_type": "text"
      },
      "source": [
        "####pour chaque annee j'ai combien d'acteur et d'actrice"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4GkO4ijG6LX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#on recupere les film avec les different roles disponibles\n",
        "role_movie=spark.sql(\"select distinct role_id,movie_id from cast_info\")\n",
        "role_movie.createOrReplaceTempView(\"role_movie\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sob7gkLOHeSQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#on construit un cube pour ventiler sur la dimension temps par rapport au role\n",
        "personnes=spark.sql(\"\"\"select t.production_year,rt.role,count(*) as nb from role_movie c1,role_type rt,title t\n",
        "where t.id=c1.movie_id and c1.role_id=rt.id and t.production_year is not null and (rt.role=\"actress\" or rt.role=\"actor\")\n",
        "group by CUBE(role,t.production_year)\n",
        "order by production_year,role\n",
        "\"\"\")\n",
        "personnes.createOrReplaceTempView(\"personnes\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Zq5usRrIXTl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pyspark.sql import functions as F\n",
        "#on effectue une transformation les lignes deviennent des collonnes (pivot)\n",
        "pivoted=personnes.groupBy(\"production_year\",\"role\").pivot(\"role\").agg(F.first(\"nb\")).na.fill(0)\n",
        "res=pivoted.groupBy(\"production_year\").agg({\"actor\":\"sum\" ,\"actress\":\"sum\"}).withColumnRenamed(\"sum(actor)\",\"actor\").withColumnRenamed(\"sum(actress)\",\"actress\").orderBy(\"production_year\",ascending=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpkkCNYQI3Y7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "936f5ae2-bb18-427b-e5e2-910e9dd94000"
      },
      "source": [
        "t = res.collect()\n",
        "\n",
        "tabYear = [x.production_year for x in t[1:-2]]\n",
        "\n",
        "tabNbH = [x.actor for x in t[1:-2]]\n",
        "tabNbF=[x.actress for x in t[1:-2]]\n",
        "# plt.plot(tabYear, tabNb)\n",
        "#plt.bar(tabYear, tabNb)\n",
        "plt.plot(tabYear,tabNbH,label=\"homme\")\n",
        "plt.plot(tabYear,tabNbF,label=\"femmes\")\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('nb nbActor ')\n",
        "plt.title('nb tournage par genre d\\'acteur per year')\n",
        "plt.legend(loc='upper center', shadow=True, fontsize='x-large')\n",
        "\n",
        "#plt.savefig(\"/content/gdrive/My Drive/BDLE/nbsaisonYear\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2A98DV9h1bgA+sV9/ai9jr19gjDGtGK9SSrWKnfaIql5RwbDqVYjqA8OneDiq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\nHGZmVooLh5mZleLCYWZmpfwHbAfPr7vNGF8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Wh1OxPLI8Ry",
        "colab_type": "text"
      },
      "source": [
        "depuis la derniere decennie les hommes ont connu plus de succes que les femmes dans le cinema ( si l'echantillon est vraiment representatif)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KT__bj__SnQe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XALA2UNQSoSB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "detail_film = spark.sql(\"\"\"\n",
        "\n",
        "select m.info, count(distinct t.id) as nbfilms_sans_doublons, count(t.id) as nbfilms_avec_doublons\n",
        "from \n",
        "Title t, Kind_Type k,  Movie_info m, Info_type i\n",
        "where t.kind_id = k.id and t.id = m.movie_id and m.info_type_id = i.id\n",
        "and k.kind = \"movie\"\n",
        "and i.info = 'genres'\n",
        "group by m.info\n",
        "order by nbfilms_sans_doublons desc\n",
        "\"\"\")\n",
        "\n",
        "detail_film.show(27)\n",
        "detail_film.count()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anhi3Hn_T0O5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#on recupere pour chaque serie la derniere saison existante et lannee de production de la serie \n",
        "r = spark.sql(\"\"\" \n",
        "SELECT title,max(production_year)as production_year,max(season_nr) as last_saison\n",
        "FROM Title \n",
        "where production_year is not null\n",
        "group by title\n",
        "\"\"\")\n",
        "#r.show(5,False)\n",
        "r.count()\n",
        "#on remplace la premiere table par celle sans doublon\n",
        "r.createOrReplaceTempView(\"Title_uniques\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WaoruPa2T4_5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "bd9f9d73-2f22-4182-9272-81395f879cd8"
      },
      "source": [
        "r.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+---------------+-----------+\n",
            "|               title|production_year|last_saison|\n",
            "+--------------------+---------------+-----------+\n",
            "|Beautiful, Marrie...|           2009|       null|\n",
            "|              (#1.6)|           2013|          1|\n",
            "|        (2001-04-26)|           2001|       null|\n",
            "|        (2010-04-09)|           2010|       null|\n",
            "|        (2006-05-15)|           2006|       null|\n",
            "|                 DNA|           2012|          1|\n",
            "|                Baar|           2004|       null|\n",
            "|De moord van Lied...|           1966|       null|\n",
            "|             (#2.40)|           2008|          2|\n",
            "|        (2000-06-30)|           2000|       null|\n",
            "|       Travel Writer|           2004|          3|\n",
            "|             (#1.69)|           2007|          1|\n",
            "|         Nya grannen|           1999|          2|\n",
            "|     Baywatch Nights|           1995|       null|\n",
            "|        (1993-01-15)|           1993|       null|\n",
            "|            Philippe|           2013|          3|\n",
            "|        (2009-06-11)|           2009|       null|\n",
            "|     Orlando: Hour 1|           2008|         12|\n",
            "|             (#1.54)|           2009|          1|\n",
            "|          (#1.13839)|           2010|          1|\n",
            "+--------------------+---------------+-----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NjYtoCyv3hw",
        "colab_type": "text"
      },
      "source": [
        "#### La liste des pays ou un film a été tourné"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVEmayAuv7of",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#sortir les country de chaque film\n",
        "r1 = spark.sql(\"\"\"\n",
        "  select distinct m.movie_id,m.info\n",
        "  from movie_info m,info_type i\n",
        "  where i.id = m.info_type_id and i.info = \"countries\"\n",
        "\"\"\")\n",
        "\n",
        "r1.createOrReplaceTempView(\"r1\")\n",
        "\n",
        "r1.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZyypp1wv-JQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "r2 = spark.sql(\"\"\"\n",
        "  select distinct m.info ,count(*) as nb\n",
        "  from movie_info m, info_type i\n",
        "  where m.info_type_id=i.id and i.info = \"countries\"\n",
        "  group by m.info\n",
        "  order by nb desc\n",
        "\"\"\")\n",
        "#136 = count\n",
        "r2.createOrReplaceTempView(\"r2\")\n",
        "\n",
        "r2.show(10,False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9XEhFWqwGXA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t = r.collect()\n",
        "countries = [x.info for x in t]\n",
        "nbFilm = [x.nb for x in t]\n",
        "import pandas as pd\n",
        "tmp = {\"countrie\":countries,\"nbFilm\":nbFilm}\n",
        "df_NbFilmCountries = pd.DataFrame(tmp)\n",
        "from google.colab import drive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aogYZ2ydwIgU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKUoPzmgwJ-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_NbFilmCountries.to_csv(\"/content/gdrive/My Drive/is/location_films.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8usGB6TwLbg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "t = r.collect()\n",
        "t = t[:10]\n",
        "\n",
        "countries = [x.info for x in t]\n",
        "nbFilm = [x.nb for x in t]\n",
        "\n",
        "\n",
        "\n",
        "plt.bar(countries, nbFilm)\n",
        "plt.xlabel('Countries')\n",
        "plt.ylabel('nb films')\n",
        "plt.title('films per countries')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBILQB9VtAYG",
        "colab_type": "text"
      },
      "source": [
        "#### comparaison pays genre decomposition temporelle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSDTxeDkQAWh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "05e53dd3-6867-42e0-fb93-520b35a3a396"
      },
      "source": [
        "#try to add countries\n",
        "#nb movie pr genre\n",
        "\n",
        "movies_infos = spark.sql(\"\"\"\n",
        "\n",
        "  select distinct m.movie_id, m.info as genre, t.production_year, m2.info as country\n",
        "  from \n",
        "  Title t, Kind_Type k,  Movie_info m, Info_type i , Movie_info m2, info_type i2\n",
        "  where t.id = m.movie_id and t.kind_id = k.id and  m.info_type_id = i.id\n",
        "   and m2.movie_id = t.id and m2.info_type_id = i2.id and i2.info=\"countries\"\n",
        "  and i.info = \"genres\"\n",
        "  and k.kind = \"movie\"\n",
        "\"\"\")\n",
        "#les pays ayant \n",
        "\n",
        "#movies_infos.createOrReplaceTempView(\"movies_infos\")\n",
        "\n",
        "\n",
        "\n",
        "movies_infos.show(6)\n",
        "movies_infos.count()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------+-------+---------------+-----------+\n",
            "|movie_id|  genre|production_year|    country|\n",
            "+--------+-------+---------------+-----------+\n",
            "| 1633601|  Drama|           2011|        USA|\n",
            "| 1633601|Fantasy|           2011|        USA|\n",
            "| 1633601|  Short|           2011|        USA|\n",
            "| 1636236|  Drama|           2003|Netherlands|\n",
            "| 1640259| Action|           1998|        USA|\n",
            "| 1640259| Comedy|           1998|        USA|\n",
            "+--------+-------+---------------+-----------+\n",
            "only showing top 6 rows\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11922"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2DDhqLPQ2pk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pyspark.sql.functions import udf \n",
        "from pyspark.sql.types import StringType \n",
        "from pyspark.sql.types import IntegerType \n",
        "# de la merde \n",
        "def get_mill(annee):   \n",
        "  annee=str(annee)   \n",
        "  return annee[0] \n",
        "def get_siecle(annee):   \n",
        "  annee=str(annee)   \n",
        "  return annee[1]\n",
        "def get_decenie(annee):   \n",
        "  annee=str(annee)   \n",
        "  return annee[2]\n",
        "def get_numanne(annee):   \n",
        "  annee=str(annee)   \n",
        "  return annee[3]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzwwWuLVQ_Tr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "func_udf = udf(get_mill, StringType())\n",
        "s = movies_infos.withColumn('millenaire',func_udf(movies_infos.production_year))\n",
        "\n",
        "\n",
        "func_udf2 = udf(get_siecle, StringType())\n",
        "s1 =s.withColumn(\"ciecle\",func_udf2(s.production_year))\n",
        "\n",
        "func_udf3 = udf(get_decenie, StringType())\n",
        "s2 = s1.withColumn('decenie',func_udf3(s1.production_year) ) \n",
        "\n",
        "func_udf4 = udf(get_numanne, StringType())\n",
        "s3 =s2.withColumn(\"annee\",func_udf4(s2.production_year) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlfnWaynRDEj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s3.createOrReplaceTempView(\"movies_infos\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMyzq77AQDWa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "4cb1b791-0fd2-4221-fd00-c2ebbc212d11"
      },
      "source": [
        "#select movie_id, genre , production_year, country ,count(*) as nb\n",
        "  \n",
        "r = spark.sql(\"\"\"\n",
        "  select millenaire,ciecle,decenie,country,genre,count(*) as nb\n",
        "  from movies_infos\n",
        "  group by ROLLUP(millenaire,ciecle,decenie),country,genre\n",
        "  order by country,millenaire,ciecle,decenie,genre\n",
        "  \"\"\")\n",
        "# \n",
        " \n",
        "#order by movie_id, genre, production_year,country\n",
        "\n",
        "r.show(10)\n",
        "r.count()\n",
        "r.createOrReplaceTempView(\"movies_infos\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+----------+------+-------+-------+-----+----+\n",
            "|millenaire|ciecle|decenie|country|genre|  nb|\n",
            "+----------+------+-------+-------+-----+----+\n",
            "|      null|  null|   null|   null| null|   1|\n",
            "|      null|  null|   null|   null| null|3296|\n",
            "|      null|  null|   null|   null| null|   1|\n",
            "|      null|  null|   null|   null| null|   1|\n",
            "|      null|  null|   null|   null| null|   1|\n",
            "|      null|  null|   null|   null| null|   1|\n",
            "|         1|  null|   null|   null| null|   1|\n",
            "|         1|  null|   null|   null| null|   1|\n",
            "|         1|  null|   null|   null| null|   1|\n",
            "|         1|  null|   null|   null| null|1891|\n",
            "+----------+------+-------+-------+-----+----+\n",
            "only showing top 10 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9fnlwygX0f0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "r.createOrReplaceTempView(\"genres_country\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DdkseuMqZJAS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "r = spark.sql(\"\"\"\n",
        "  select * \n",
        "  from movies_infos\n",
        "  where ( country is not null ) and (genre is not null) and millenaire=2\n",
        "  \"\"\") \n",
        " "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oILZFtYZZLl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "r.createOrReplaceTempView(\"resultat_cleaned\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITrLs-QkuCG6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "creLOsc-fHg2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#items = [\"France\", \"Canda\", \"Spain\" , \"UK\"]\n",
        "\n",
        "res_comedy=spark.sql(\"\"\"select * from resultat_cleaned\n",
        "  where genre='Comedy' \"\"\").filter('country in (\"France\", \"Canada\", \"Spain\" , \"UK\", \"Italy\",\"Germany\")')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFBS3nSuhbH-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t = res.collect()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SUto7kAiF06",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "histo_x={}\n",
        "for i in t:\n",
        "  if( i.country not in histo_x.keys()):\n",
        "  #if ( len(histo_x[i.country])==0 ):\n",
        "    histo_x[i.country]=[i.nb]\n",
        "  else:\n",
        "    histo_x[i.country].append(i.nb)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EaPieLEKmW-t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "f9db35fe-b024-4b24-89b7-4eeda28aba71"
      },
      "source": [
        "color=[\"b\",\"r\",\"y\",\"g\",\"k\",\"m\"]\n",
        "decenie=np.array([2000,2010])\n",
        "c=0\n",
        "for i in histo_x.keys():\n",
        "  plt.bar(decenie+c,histo_x[i], width=1, color=color[c], align='center',label=i)\n",
        "  c+=1\n",
        "plt.legend()\n",
        "plt.title(\"Comparaison du genre Comedy par decenie et pays\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Comparaison du genre Comedy par decenie et pays')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zzTfTqlWrvea5/fbbGT16NOeccw6PP/441aol7zpXyV1EEqVdu3bMmjXre+Mn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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KGK9IpBuYRK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "outputId": "93210efa-c914-40ba-ace3-96ab64505f90"
      },
      "source": [
        "genres = [\"Action\",\"Comedy\",\"Documentary\"]\n",
        "\n",
        "for genre in genres:  \n",
        "  res=spark.sql(\"\"\"select * from resultat_cleaned\n",
        "  where genre='\"\"\"+genre+\"\"\"' \"\"\").filter('country in (\"France\", \"Canada\", \"Spain\" , \"UK\", \"Italy\",\"Germany\")')\n",
        "  t = res.collect()\n",
        "  histo_x={}\n",
        "  for i in t:\n",
        "    if( i.country not in histo_x.keys()):\n",
        "    #if ( len(histo_x[i.country])==0 ):\n",
        "      histo_x[i.country]=[i.nb]\n",
        "    else:\n",
        "      histo_x[i.country].append(i.nb)\n",
        "\n",
        "  color=[\"b\",\"r\",\"y\",\"g\",\"k\",\"m\"]\n",
        "  decenie=np.array([2000,2010])\n",
        "  c=0\n",
        "  for i in histo_x.keys():\n",
        "    plt.bar(decenie+c,histo_x[i], width=1, color=color[c], align='center',label=i)\n",
        "    c+=1\n",
        "  plt.legend()\n",
        "  plt.title(\"Comparaison du genre \"+genre+\" par decenie et pays\")\n",
        "  plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TEhcRiTAlcRGRCFMSFxGJMCVxEZEIUxIXEYkwJXERkQhTEhcRibCEk7iZZZnZ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\nSuIiIhGmJC4iEmH/H8DBfIdkKIvnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zzTfTqlWrvea5/fbbGT16NOeccw6PP/441aol7zpXyV1EEqVdu3bMmjXre+Mn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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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evDnjx4+vGtarVy/69OnDkCFD6Nu3L0VFRTz55JMAlJeX061bN773ve/Ro0cPhg8fzief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\nnM8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lz79Vqgro-zl",
        "colab_type": "text"
      },
      "source": [
        "#### Fenetres"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXVZHvDepPjH",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGgpUN1_pltV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "1b7b2080-e213-403e-b4c8-976fd6cd535e"
      },
      "source": [
        "r2 = spark.sql(\"\"\"\n",
        "  select distinct m.info ,count(*) as nb\n",
        "  from movie_info m, info_type i\n",
        "  where m.info_type_id=i.id and i.info = \"countries\"\n",
        "  group by m.info\n",
        "  order by nb desc\n",
        "\"\"\")\n",
        "#136 = count\n",
        "r2.createOrReplaceTempView(\"r2\")\n",
        "\n",
        "r2.show(10,False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+---------+----+\n",
            "|info     |nb  |\n",
            "+---------+----+\n",
            "|USA      |5380|\n",
            "|UK       |1190|\n",
            "|France   |615 |\n",
            "|Japan    |516 |\n",
            "|Germany  |508 |\n",
            "|Spain    |474 |\n",
            "|Canada   |450 |\n",
            "|Australia|350 |\n",
            "|Italy    |339 |\n",
            "|India    |275 |\n",
            "+---------+----+\n",
            "only showing top 10 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zt9TdeeItGQS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "3e691f14-57b5-425e-a762-60c7799a7496"
      },
      "source": [
        "#try to add countries\n",
        "#nb movie pr genre\n",
        "\n",
        "movies_infos = spark.sql(\"\"\"\n",
        "\n",
        "  select distinct m.movie_id, m.info as genre, t.production_year, m2.info as country\n",
        "  from \n",
        "  Title t, Kind_Type k,  Movie_info m, Info_type i , Movie_info m2, info_type i2\n",
        "  where t.id = m.movie_id and t.kind_id = k.id and  m.info_type_id = i.id\n",
        "   and m2.movie_id = t.id and m2.info_type_id = i2.id and i2.info=\"countries\"\n",
        "  and i.info = \"genres\"\n",
        "  and k.kind = \"movie\"\n",
        "\"\"\")\n",
        "#les pays ayant \n",
        "\n",
        "movies_infos.createOrReplaceTempView(\"movies_infos\")\n",
        "\n",
        "\n",
        "\n",
        "movies_infos.show(6)\n",
        "movies_infos.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------+-------+---------------+-----------+\n",
            "|movie_id|  genre|production_year|    country|\n",
            "+--------+-------+---------------+-----------+\n",
            "| 1633601|  Drama|           2011|        USA|\n",
            "| 1633601|Fantasy|           2011|        USA|\n",
            "| 1633601|  Short|           2011|        USA|\n",
            "| 1636236|  Drama|           2003|Netherlands|\n",
            "| 1640259| Action|           1998|        USA|\n",
            "| 1640259| Comedy|           1998|        USA|\n",
            "+--------+-------+---------------+-----------+\n",
            "only showing top 6 rows\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11922"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZZWBYT_pKri",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f2267ae9-ebab-42fd-d91c-dd7c722a0161"
      },
      "source": [
        "nbFilmPerCountry = spark.sql(\"\"\"\n",
        "  select mi.country,mi.production_year,count (*) as nbFilm\n",
        "  from movies_infos mi\n",
        "  group by mi.country,mi.production_year\n",
        "  order by production_year desc\n",
        "\"\"\")\n",
        "\n",
        "nbFilmPerCountry.createOrReplaceTempView(\"nbFilmPerCountry\")\n",
        "nbFilmPerCountry.show(5)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+---------------+------+\n",
            "|country|production_year|nbFilm|\n",
            "+-------+---------------+------+\n",
            "|    USA|           2016|     3|\n",
            "|    USA|           2015|     8|\n",
            "|     UK|           2014|     6|\n",
            "|    USA|           2014|    24|\n",
            "|Finland|           2014|     2|\n",
            "+-------+---------------+------+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SgGpX78vBP3",
        "colab_type": "text"
      },
      "source": [
        "##### Classement des pays pour chaque année selon le nbr de film produits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myy8zJZCpLWO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "13c6b93e-0ca5-4bea-d8ab-fbb7a7252fa9"
      },
      "source": [
        "\n",
        "\n",
        "#classement des pays pour chaque année\n",
        "r = spark.sql(\"\"\"\n",
        "  select mi.* , rank() over (partition by production_year order by nbFilm desc) as classement\n",
        "  from nbFilmPerCountry mi \n",
        "  where mi.production_year = 2001\n",
        "  \n",
        "\"\"\")\n",
        "\n",
        "r.show(10)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-e4caa5e6ba69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m r = spark.sql(\"\"\"\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0mselect\u001b[0m \u001b[0mmi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m*\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mover\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpartition\u001b[0m \u001b[0mby\u001b[0m \u001b[0mproduction_year\u001b[0m \u001b[0morder\u001b[0m \u001b[0mby\u001b[0m \u001b[0mnbFilm\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mclassement\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0;32mfrom\u001b[0m \u001b[0mnbFilmPerCountry\u001b[0m \u001b[0mmi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mwhere\u001b[0m \u001b[0mmi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproduction_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2001\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'spark' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nicy3T6kvC1f",
        "colab_type": "text"
      },
      "source": [
        "#####Cumule du nbr film produit par fenetre pour chaque pays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Byh4_DH4vSno",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "96caa8d7-4baa-4297-d27d-0294d0845a80"
      },
      "source": [
        "#ici on a filtrer sur les USA pour bien observer nos fenetres\n",
        "r = spark.sql(\"\"\"\n",
        "  select mi.* ,\n",
        "  sum(nbFilm) over (partition by country order by production_year range between 1 preceding and 1 following) as nbFilms\n",
        "  from nbFilmPerCountry mi\n",
        "  where mi.production_year > 1950 and mi.country= \"USA\"\n",
        "  \n",
        "\"\"\")\n",
        "\n",
        "r.show(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+---------------+------+-------+\n",
            "|country|production_year|nbFilm|nbFilms|\n",
            "+-------+---------------+------+-------+\n",
            "|    USA|           1951|    12|     21|\n",
            "|    USA|           1952|     9|     30|\n",
            "|    USA|           1953|     9|     29|\n",
            "|    USA|           1954|    11|     47|\n",
            "|    USA|           1955|    27|     41|\n",
            "|    USA|           1956|     3|     36|\n",
            "|    USA|           1957|     6|     15|\n",
            "|    USA|           1958|     6|     24|\n",
            "|    USA|           1959|    12|     31|\n",
            "|    USA|           1960|    13|     36|\n",
            "+-------+---------------+------+-------+\n",
            "only showing top 10 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcYgwrgdtMLO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}