DemoGraphc.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of DemoGraphc.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNPXb3ep8Xe9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzoNFM50D2w_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1=pd.read_csv('./data/tmdb_5000_credits.csv')\n",
        "df2=pd.read_csv('./data/tmdb_5000_movies.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrXsZMRfBrTl",
        "colab_type": "code",
        "outputId": "9e0d1fd7-929f-4219-e1de-8dfa612e7fc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1QsrVkaPDCd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1.columns = ['id','title','cast','crew']\n",
        "df2= df2.merge(df1,on='id')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_cp-qEuac9X",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# calculate C\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cC3mWr85X_t7",
        "colab_type": "code",
        "outputId": "fbb45e56-353a-49a3-c4aa-656496d04bec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "C= df2['vote_average'].mean()\n",
        "C"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.092171559442011"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2-we-Ehahlg",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# calculate m\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOFvWKaAYEh7",
        "colab_type": "code",
        "outputId": "94d9d3b2-d927-49a5-af56-ed7982ac179c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m= df2['vote_count'].quantile(0.9)\n",
        "m"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1838.4000000000015"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOg36WRoYKC2",
        "colab_type": "code",
        "outputId": "c531f950-8c0f-4a7e-d1f2-4f5d954645e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "q_movies = df2.copy().loc[df2['vote_count'] >= m]\n",
        "q_movies.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(481, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dd9I0LQOY2_m",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "```\n",
        "#Calculate weighted rating\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUB1HKZ7YObZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def weighted_rating(x, m=m, C=C):\n",
        "    v = x['vote_count']\n",
        "    R = x['vote_average']\n",
        "    # Calculation based on the IMDB formula\n",
        "    return (v/(v+m) * R) + (m/(m+v) * C)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x90wYCOGYSsR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q_movies['score'] = q_movies.apply(weighted_rating, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2suDvBfYkUR",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "#sorting according to calculated score\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpoIitBwYZcf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q_movies = q_movies.sort_values('score', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKKamMfCYhAd",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "#top ten movies\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGGQqJzRYdqt",
        "colab_type": "code",
        "outputId": "8fabe7c7-13ad-4478-c674-5826dbbe351e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "q_movies[['original_title', 'vote_count', 'vote_average', 'score']].head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>original_title</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1881</th>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>8205</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.059258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>Fight Club</td>\n",
              "      <td>9413</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.939256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>12002</td>\n",
              "      <td>8.2</td>\n",
              "      <td>7.920020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3232</th>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>8428</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.904645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Inception</td>\n",
              "      <td>13752</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.863239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3337</th>\n",
              "      <td>The Godfather</td>\n",
              "      <td>5893</td>\n",
              "      <td>8.4</td>\n",
              "      <td>7.851236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Interstellar</td>\n",
              "      <td>10867</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.809479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>809</th>\n",
              "      <td>Forrest Gump</td>\n",
              "      <td>7927</td>\n",
              "      <td>8.2</td>\n",
              "      <td>7.803188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>329</th>\n",
              "      <td>The Lord of the Rings: The Return of the King</td>\n",
              "      <td>8064</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.727243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>The Empire Strikes Back</td>\n",
              "      <td>5879</td>\n",
              "      <td>8.2</td>\n",
              "      <td>7.697884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     original_title  ...     score\n",
              "1881                       The Shawshank Redemption  ...  8.059258\n",
              "662                                      Fight Club  ...  7.939256\n",
              "65                                  The Dark Knight  ...  7.920020\n",
              "3232                                   Pulp Fiction  ...  7.904645\n",
              "96                                        Inception  ...  7.863239\n",
              "3337                                  The Godfather  ...  7.851236\n",
              "95                                     Interstellar  ...  7.809479\n",
              "809                                    Forrest Gump  ...  7.803188\n",
              "329   The Lord of the Rings: The Return of the King  ...  7.727243\n",
              "1990                        The Empire Strikes Back  ...  7.697884\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ag5NwxCSA1bu",
        "colab_type": "code",
        "outputId": "d1c1a30d-f981-44ca-f3fd-19fd94cc7175",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "pop= q_movies.sort_values('score', ascending=False)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.barh(pop['original_title'].head(15),pop['score'].head(15), align='center',\n",
        "        color='skyblue')\n",
        "plt.gca().invert_yaxis()\n",
        "plt.xlabel(\"Popularity\")\n",
        "plt.title(\"Popular Movies\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Popular Movies')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EvlB4IBJwGlCXHpq0gOyBSU35IfAI2T2xj7fD3MzMzMzMNipF+DYzM4O6urqo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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}