EmKa_Kurs_BitirmeProjesi.ipynb

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        "# EmKa Akademi Bitime Projesi\n",
        "## Ön tanıtım\n",
        "\n",
        "İnternetten bir film izlemek istediğinizi hayal edin. İnternetteki film puanlamalarına ne kadar güvenebilirsiniz ? *Özellikle* reytingi gösteren aynı şirket sinema bileti satarak da para kazanıyorsa. Sizce de derecelendirme konusunda insanları yanlış yönlendirmesi söz konusu olabilir mi ?\n",
        "\n",
        "### Hedef:\n",
        "\n",
        "**Amacınız aşağıdaki görevleri tamamlayarak *Fandango* websitesinin ratingler konusunda bir yanlış yapıp yapmadığını emin bir şekilde ispatlamak**\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "** Bu da bir sorulu analizdir. Aşağıda sorulan sorulara cevap vermeye ve çıktılara yakın bir çıktı elde etmeye çalışmanız gerekmektedir. **\n",
        "\n",
        "---\n",
        "----\n",
        "\n",
        "## Part One: Datayı Anlamak"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWPY1J7YETNf"
      },
      "source": [
        "### The Data\n",
        "\n",
        "Elimizde biri şüphelenilen *Fandango* sitsine ait yıldızlar ve film derecelendirmeleri içeriyor. Diğeri ise diğer film sitelerindeki film derecelendirmeleri için veriler içeren toplam 2 csv dosyası vardır.\n",
        "\n",
        "#### all_sites_scores.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IaXws8OsETNf"
      },
      "source": [
        "-----\n",
        "\n",
        "`all_sites_scores.csv` Bütün benzeri sitelerin açıklama kullanıcı puanlamaları bilgilerini ve özellikle 'Rotten Tomatoes(Çürük Elma) adlı bir filme ait başka sitelerdeki puanları içermektedir.'"
      ]
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      "source": [
        "Column | Definition\n",
        "--- | -----------\n",
        "FILM | The film in question\n",
        "RottenTomatoes | The Rotten Tomatoes Tomatometer score  for the film\n",
        "RottenTomatoes_User | The Rotten Tomatoes user score for the film\n",
        "Metacritic | The Metacritic critic score for the film\n",
        "Metacritic_User | The Metacritic user score for the film\n",
        "IMDB | The IMDb user score for the film\n",
        "Metacritic_user_vote_count | The number of user votes the film had on Metacritic\n",
        "IMDB_user_vote_count | The number of user votes the film had on IMDb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRQB-hfsETNg"
      },
      "source": [
        "----\n",
        "----\n",
        "\n",
        "#### fandango_scape.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1AULywqETNg"
      },
      "source": [
        "`fandango_scrape.csv` Fandango sitesine ait 538 film içermektedir\n",
        "\n",
        "Column | Definiton\n",
        "--- | ---------\n",
        "FILM | The movie\n",
        "STARS | Number of stars presented on Fandango.com\n",
        "RATING |  The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained.\n",
        "VOTES | number of people who had reviewed the film at the time we pulled it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSYhCAX-ETNg"
      },
      "source": [
        "----\n",
        "\n",
        "**TASK: Analizinde işinize yarayacağını düşündüğünüz kütüphanaleri import edin:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DYh-V05TETNg"
      },
      "outputs": [],
      "source": [
        "# Buraya import ediyorsunuz!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NR70KiBIETNh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BpZ5lhImETNh"
      },
      "source": [
        "## Part Two: Fandango sitesini keşfetmeye çalışalım. Gerçek kullanıcı derecelendirmelerini ve site puanlarını yavaştan karşılaştıralım\n",
        "\n",
        "Fandangonun filmleri puanlama ve derecelendirme konusunda taraflı davranıp davranmadığını analiz etmeye başlayalım !\n",
        "\n",
        "**TASK: Aşağıdaki fandango_scrape.csv dosyasını çalıştırın**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2wIjysEMETNh"
      },
      "outputs": [],
      "source": [
        "fandango = pd.read_csv(\"fandango_scrape.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etHPCiWfETNh"
      },
      "source": [
        "**TASK: Fandango verilerini keşfetmeye çalışın. Aşağıdakine benzer çıktılar elde edin.**\n",
        "\n",
        "# Uyarı ! çıktının tam üstünü kullanmayın. Bir üst hücre satırında çalışmalar yapın."
      ]
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              "                          FILM  STARS  RATING  VOTES\n",
              "0  Fifty Shades of Grey (2015)    4.0     3.9  34846\n",
              "1        Jurassic World (2015)    4.5     4.5  34390\n",
              "2       American Sniper (2015)    5.0     4.8  34085\n",
              "3             Furious 7 (2015)    5.0     4.8  33538\n",
              "4            Inside Out (2015)    4.5     4.5  15749"
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        "fandango.head()"
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              "<div>\n",
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              "      <th>FILM</th>\n",
              "      <th>STARS</th>\n",
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              "      <th>VOTES</th>\n",
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              "      <td>Fifty Shades of Grey (2015)</td>\n",
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              "      <td>3.9</td>\n",
              "      <td>34846</td>\n",
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              "      <th>1</th>\n",
              "      <td>Jurassic World (2015)</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>34390</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>American Sniper (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>34085</td>\n",
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              "      <td>Inside Out (2015)</td>\n",
              "      <td>4.5</td>\n",
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              "                          FILM  STARS  RATING  VOTES\n",
              "0  Fifty Shades of Grey (2015)    4.0     3.9  34846\n",
              "1        Jurassic World (2015)    4.5     4.5  34390\n",
              "2       American Sniper (2015)    5.0     4.8  34085\n",
              "3             Furious 7 (2015)    5.0     4.8  33538\n",
              "4            Inside Out (2015)    4.5     4.5  15749"
            ]
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          "execution_count": 176,
          "metadata": {},
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      ],
      "source": [
        "# Buraya kod yazmayın !"
      ]
    },
    {
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      "source": [
        "fandango.info()"
      ],
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 504 entries, 0 to 503\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   FILM    504 non-null    object \n",
            " 1   STARS   504 non-null    float64\n",
            " 2   RATING  504 non-null    float64\n",
            " 3   VOTES   504 non-null    int64  \n",
            "dtypes: float64(2), int64(1), object(1)\n",
            "memory usage: 15.9+ KB\n"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 504 entries, 0 to 503\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   FILM    504 non-null    object \n",
            " 1   STARS   504 non-null    float64\n",
            " 2   RATING  504 non-null    float64\n",
            " 3   VOTES   504 non-null    int64  \n",
            "dtypes: float64(2), int64(1), object(1)\n",
            "memory usage: 15.9+ KB\n"
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      "source": [
        "# Buraya kod yazmayın, üstte yeni bir kod hücresi ekleyip oraya kodlar yazıp aşağıdaki çıktıyı almaya çalışın."
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              "            STARS      RATING         VOTES\n",
              "count  504.000000  504.000000    504.000000\n",
              "mean     3.558532    3.375794   1147.863095\n",
              "std      1.563133    1.491223   3830.583136\n",
              "min      0.000000    0.000000      0.000000\n",
              "25%      3.500000    3.100000      3.000000\n",
              "50%      4.000000    3.800000     18.500000\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>18.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>189.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>34846.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98256e6f-50b1-450d-a80c-c10875b54c20')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-98256e6f-50b1-450d-a80c-c10875b54c20 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98256e6f-50b1-450d-a80c-c10875b54c20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yy-yCLnsETNi",
        "outputId": "6934b43b-488b-4c24-cd64-21818e7ff038"
      },
      "outputs": [
        {
          "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>STARS</th>\n",
              "      <th>RATING</th>\n",
              "      <th>VOTES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>504.000000</td>\n",
              "      <td>504.000000</td>\n",
              "      <td>504.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.558532</td>\n",
              "      <td>3.375794</td>\n",
              "      <td>1147.863095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.563133</td>\n",
              "      <td>1.491223</td>\n",
              "      <td>3830.583136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.500000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>18.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>189.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>34846.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            STARS      RATING         VOTES\n",
              "count  504.000000  504.000000    504.000000\n",
              "mean     3.558532    3.375794   1147.863095\n",
              "std      1.563133    1.491223   3830.583136\n",
              "min      0.000000    0.000000      0.000000\n",
              "25%      3.500000    3.100000      3.000000\n",
              "50%      4.000000    3.800000     18.500000\n",
              "75%      4.500000    4.300000    189.750000\n",
              "max      5.000000    5.000000  34846.000000"
            ]
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Buraya kod yazmayın, üstte yeni bir kod hücresi ekleyip oraya kodlar yazıp aşağıdaki çıktıyı almaya çalışın."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-h9cln8ETNi"
      },
      "source": [
        "**TASK: Verimizdeki filmlere ait votes ile rating arasındaki ilişkiyi keşfedelim. Derecelendirme ve oylar arasındaki ilişkiyi gösteren bir scatterplot grafiği oluşturun. Görsel stil eklemeler tamamen size kalmış**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "PTI7vcUGETNj",
        "outputId": "fec421e7-e617-46e3-c3a1-e6cd386ee160"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "plt.figure(figsize = (15,5))\n",
        "sns.scatterplot(fandango,\n",
        "                x = \"RATING\",\n",
        "                y = \"VOTES\")\n",
        "plt.title(\"Ratinglere göre oylar\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZ1gh2-fETNj"
      },
      "source": [
        "**TASK: Bütün columnlar arasındaki ilişkiye bakın**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Gr017qKUETNj",
        "outputId": "bfca5d6d-54df-49cf-c6b4-2f25fd96c86a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           STARS    RATING     VOTES\n",
              "STARS   1.000000  0.994696  0.164218\n",
              "RATING  0.994696  1.000000  0.163764\n",
              "VOTES   0.164218  0.163764  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-518d3900-b61e-46f6-a255-44142ce4ca14\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>STARS</th>\n",
              "      <th>RATING</th>\n",
              "      <th>VOTES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>STARS</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994696</td>\n",
              "      <td>0.164218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RATING</th>\n",
              "      <td>0.994696</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.163764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VOTES</th>\n",
              "      <td>0.164218</td>\n",
              "      <td>0.163764</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-518d3900-b61e-46f6-a255-44142ce4ca14')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-518d3900-b61e-46f6-a255-44142ce4ca14 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-518d3900-b61e-46f6-a255-44142ce4ca14');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Buraya kod yazın.\n",
        "fandango.corr(numeric_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W3UYH9A0ETNj",
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              "      <th>STARS</th>\n",
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              "           STARS    RATING     VOTES\n",
              "STARS   1.000000  0.994696  0.164218\n",
              "RATING  0.994696  1.000000  0.163764\n",
              "VOTES   0.164218  0.163764  1.000000"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjYUmk1iETNj"
      },
      "source": [
        "**TASK: Bir şekilde 'Yıl' sütunu ekleyin**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
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        "id": "rV3SneqKETNj",
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              "                                     FILM  STARS  RATING  VOTES  YEAR\n",
              "0             Fifty Shades of Grey (2015)    4.0     3.9  34846  2015\n",
              "1                   Jurassic World (2015)    4.5     4.5  34390  2015\n",
              "2                  American Sniper (2015)    5.0     4.8  34085  2015\n",
              "3                        Furious 7 (2015)    5.0     4.8  33538  2015\n",
              "4                       Inside Out (2015)    4.5     4.5  15749  2015\n",
              "..                                    ...    ...     ...    ...   ...\n",
              "499                      Valiyavan (2015)    0.0     0.0      0  2015\n",
              "500            WWE SummerSlam 2015 (2015)    0.0     0.0      0  2015\n",
              "501       Yagavarayinum Naa Kaakka (2015)    0.0     0.0      0  2015\n",
              "502  Yesterday, Today and Tomorrow (1964)    0.0     0.0      0  1964\n",
              "503                         Zarafa (2012)    0.0     0.0      0  2012\n",
              "\n",
              "[504 rows x 5 columns]"
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              "      <td>0.0</td>\n",
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              "      <td>2015</td>\n",
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              "      <td>1964</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2012</td>\n",
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              "  </tbody>\n",
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              "<p>504 rows × 5 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "fandango[\"YEAR\"] = fandango[\"FILM\"].astype(str).str[-5:-1]\n",
        "fandango"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7xT9CdEXETNj",
        "outputId": "ab0b35fa-b63e-4d19-d231-16ff716e28cf"
      },
      "outputs": [
        {
          "data": {
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              "      <th>FILM</th>\n",
              "      <th>STARS</th>\n",
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              "      <th>VOTES</th>\n",
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              "      <td>Fifty Shades of Grey (2015)</td>\n",
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              "      <td>3.9</td>\n",
              "      <td>34846</td>\n",
              "      <td>2015)</td>\n",
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              "      <th>1</th>\n",
              "      <td>Jurassic World (2015)</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>34390</td>\n",
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              "      <td>American Sniper (2015)</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>34085</td>\n",
              "      <td>2015)</td>\n",
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              "      <td>Furious 7 (2015)</td>\n",
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              "      <td>4.8</td>\n",
              "      <td>33538</td>\n",
              "      <td>2015)</td>\n",
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              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>15749</td>\n",
              "      <td>2015)</td>\n",
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              "      <td>2015)</td>\n",
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              "      <th>500</th>\n",
              "      <td>WWE SummerSlam 2015 (2015)</td>\n",
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              "      <td>0</td>\n",
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              "      <th>501</th>\n",
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              "      <td>0</td>\n",
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              "      <td>Yesterday, Today and Tomorrow (1964)</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1964)</td>\n",
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              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>Zarafa (2012)</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2012)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>504 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     FILM  STARS  RATING  VOTES   YEAR\n",
              "0             Fifty Shades of Grey (2015)    4.0     3.9  34846  2015)\n",
              "1                   Jurassic World (2015)    4.5     4.5  34390  2015)\n",
              "2                  American Sniper (2015)    5.0     4.8  34085  2015)\n",
              "3                        Furious 7 (2015)    5.0     4.8  33538  2015)\n",
              "4                       Inside Out (2015)    4.5     4.5  15749  2015)\n",
              "..                                    ...    ...     ...    ...    ...\n",
              "499                      Valiyavan (2015)    0.0     0.0      0  2015)\n",
              "500            WWE SummerSlam 2015 (2015)    0.0     0.0      0  2015)\n",
              "501       Yagavarayinum Naa Kaakka (2015)    0.0     0.0      0  2015)\n",
              "502  Yesterday, Today and Tomorrow (1964)    0.0     0.0      0  1964)\n",
              "503                         Zarafa (2012)    0.0     0.0      0  2012)\n",
              "\n",
              "[504 rows x 5 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2ZSr7UrETNj"
      },
      "source": [
        "**TASK: Elimizdeki Fandango verilerinde yıl başına düşen film sayılarını bulun**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63g7qMB_ETNk",
        "outputId": "cfc479ec-7666-45ea-d27c-9b3213f8fc4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YEAR\n",
              "2015    478\n",
              "2014     23\n",
              "1964      1\n",
              "2012      1\n",
              "2016      1\n",
              "Name: FILM, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "fandango.groupby(\"YEAR\")[\"FILM\"].size().sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "srOZ-8sRETNk",
        "outputId": "c98ce407-ebc1-42ed-89f7-73107f85b36a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2015    478\n",
              "2014     23\n",
              "2016      1\n",
              "1964      1\n",
              "2012      1\n",
              "Name: YEAR, dtype: int64"
            ]
          },
          "execution_count": 186,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXH-BaKuETNk"
      },
      "source": [
        "**TASK: Bir plot ile yıllara düşen film sayılarını görselleştirin:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "1FmC3hSYETNk",
        "outputId": "4686e4f4-4130-4bce-d7f9-189c3011c64e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='YEAR', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "film_count = fandango.groupby(\"YEAR\")[\"FILM\"].size().sort_values(ascending = False).reset_index(name = \"count\")\n",
        "\n",
        "sns.barplot(film_count,\n",
        "            x = \"YEAR\",\n",
        "            y = \"count\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vjLL55q0ETNk",
        "outputId": "01c4a2fd-162f-4128-c9f6-22f1339bff6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='YEAR', ylabel='count'>"
            ]
          },
          "execution_count": 188,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pP8U_Yb1ETNk"
      },
      "source": [
        "**TASK: En fazla oyu alan 10 film hangileridir?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Flp-GOHxETNk",
        "outputId": "b4737e89-251f-4b85-b737-9cc52c9fa933"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               FILM  STARS  RATING  VOTES  \\\n",
              "0                       Fifty Shades of Grey (2015)    4.0     3.9  34846   \n",
              "1                             Jurassic World (2015)    4.5     4.5  34390   \n",
              "2                            American Sniper (2015)    5.0     4.8  34085   \n",
              "3                                  Furious 7 (2015)    5.0     4.8  33538   \n",
              "4                                 Inside Out (2015)    4.5     4.5  15749   \n",
              "5  The Hobbit: The Battle of the Five Armies (2014)    4.5     4.3  15337   \n",
              "6               Kingsman: The Secret Service (2015)    4.5     4.2  15205   \n",
              "7                                    Minions (2015)    4.0     4.0  14998   \n",
              "8                    Avengers: Age of Ultron (2015)    5.0     4.5  14846   \n",
              "9                             Into the Woods (2014)    3.5     3.4  13055   \n",
              "\n",
              "   YEAR  \n",
              "0  2015  \n",
              "1  2015  \n",
              "2  2015  \n",
              "3  2015  \n",
              "4  2015  \n",
              "5  2014  \n",
              "6  2015  \n",
              "7  2015  \n",
              "8  2015  \n",
              "9  2014  "
            ],
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "fandango.sort_values(by = \"VOTES\", ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zb3BmuyzETNk",
        "outputId": "943b0934-4c9b-4e19-c0ff-55bf8f3afa6d"
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      "outputs": [
        {
          "data": {
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              "      <th>FILM</th>\n",
              "      <th>STARS</th>\n",
              "      <th>RATING</th>\n",
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              "      <td>3.9</td>\n",
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              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
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              "      <td>4.8</td>\n",
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              "      <td>2015</td>\n",
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              "      <td>The Hobbit: The Battle of the Five Armies (2014)</td>\n",
              "      <td>4.5</td>\n",
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              "      <td>2014</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Kingsman: The Secret Service (2015)</td>\n",
              "      <td>4.5</td>\n",
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              "      <td>2015</td>\n",
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              "      <th>7</th>\n",
              "      <td>Minions (2015)</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>2015</td>\n",
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              "      <th>8</th>\n",
              "      <td>Avengers: Age of Ultron (2015)</td>\n",
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              "      <td>3.4</td>\n",
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              "      <td>2014</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "                                               FILM  STARS  RATING  VOTES  \\\n",
              "0                       Fifty Shades of Grey (2015)    4.0     3.9  34846   \n",
              "1                             Jurassic World (2015)    4.5     4.5  34390   \n",
              "2                            American Sniper (2015)    5.0     4.8  34085   \n",
              "3                                  Furious 7 (2015)    5.0     4.8  33538   \n",
              "4                                 Inside Out (2015)    4.5     4.5  15749   \n",
              "5  The Hobbit: The Battle of the Five Armies (2014)    4.5     4.3  15337   \n",
              "6               Kingsman: The Secret Service (2015)    4.5     4.2  15205   \n",
              "7                                    Minions (2015)    4.0     4.0  14998   \n",
              "8                    Avengers: Age of Ultron (2015)    5.0     4.5  14846   \n",
              "9                             Into the Woods (2014)    3.5     3.4  13055   \n",
              "\n",
              "   YEAR  \n",
              "0  2015  \n",
              "1  2015  \n",
              "2  2015  \n",
              "3  2015  \n",
              "4  2015  \n",
              "5  2014  \n",
              "6  2015  \n",
              "7  2015  \n",
              "8  2015  \n",
              "9  2014  "
            ]
          },
          "execution_count": 190,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzuOXsV1ETNk"
      },
      "source": [
        "**TASK: Kaç tane film 0 vote'u aldı ?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XEDaZ2vKETNk",
        "outputId": "56c34eee-76eb-4e01-8632-c7fb99198499"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "\n",
        "fandango[fandango[\"VOTES\"] == 0].count()[\"FILM\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IpD4alinETNl",
        "outputId": "ebc17452-61c7-4285-e742-1f8a803a77d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "69"
            ]
          },
          "execution_count": 192,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ev20SJw8ETNl"
      },
      "source": [
        "**TASK: Sıfır oyu olan filmleri kaldırarak yalnızca incelenen filmlerin dataframe'ini oluşturun.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "LVWwaz_aETNl"
      },
      "outputs": [],
      "source": [
        "# Buraya kod yazın\n",
        "fandango = fandango[fandango[\"VOTES\"] != 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zSMlF93HETNl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2SoD9w83ETNl"
      },
      "source": [
        "**TASK: Bazı filmlerde usulsüzlük yapıldığı ve kullanıcılara gösterilen STARS değerlerinin gerçek RATING değerlerinden biraz daha fazla çıktığı düşünüyoruz. 'STARS_DIFF' adında yeni bir column ekleyerek STARS ile RATING arasındaki farkları bu column'da tutun.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "Ocnnm4v1ETNl",
        "outputId": "5f7f10eb-824e-40ea-efe3-384dc83f478f"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-903d5104dbe1>:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  fandango = fandango.append(fandango.iloc[:6])\n"
          ]
        },
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          "output_type": "execute_result",
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            "text/plain": [
              "                                                FILM  STARS  RATING  VOTES  \\\n",
              "0                        Fifty Shades of Grey (2015)    4.0     3.9  34846   \n",
              "1                              Jurassic World (2015)    4.5     4.5  34390   \n",
              "2                             American Sniper (2015)    5.0     4.8  34085   \n",
              "3                                   Furious 7 (2015)    5.0     4.8  33538   \n",
              "4                                  Inside Out (2015)    4.5     4.5  15749   \n",
              "..                                               ...    ...     ...    ...   \n",
              "1                              Jurassic World (2015)    4.5     4.5  34390   \n",
              "2                             American Sniper (2015)    5.0     4.8  34085   \n",
              "3                                   Furious 7 (2015)    5.0     4.8  33538   \n",
              "4                                  Inside Out (2015)    4.5     4.5  15749   \n",
              "5   The Hobbit: The Battle of the Five Armies (2014)    4.5     4.3  15337   \n",
              "\n",
              "    YEAR  STARS_DIFF  \n",
              "0   2015         0.1  \n",
              "1   2015         0.0  \n",
              "2   2015         0.2  \n",
              "3   2015         0.2  \n",
              "4   2015         0.0  \n",
              "..   ...         ...  \n",
              "1   2015         0.0  \n",
              "2   2015         0.2  \n",
              "3   2015         0.2  \n",
              "4   2015         0.0  \n",
              "5   2014         0.2  \n",
              "\n",
              "[454 rows x 6 columns]"
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          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "\n",
        "fandango['STARS_DIFF'] = fandango['STARS'] - fandango['RATING']\n",
        "fandango = fandango.append(fandango.iloc[:6])\n",
        "fandango"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ECBP437JETNl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zv8LfzH9ETNl",
        "outputId": "13af6949-4686-484b-d5c5-7a1b538ad2ee"
      },
      "outputs": [
        {
          "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>FILM</th>\n",
              "      <th>STARS</th>\n",
              "      <th>RATING</th>\n",
              "      <th>VOTES</th>\n",
              "      <th>YEAR</th>\n",
              "      <th>STARS_DIFF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fifty Shades of Grey (2015)</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>34846</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jurassic World (2015)</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>34390</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>American Sniper (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>34085</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Furious 7 (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>33538</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Inside Out (2015)</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>15749</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>430</th>\n",
              "      <td>That Sugar Film (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>431</th>\n",
              "      <td>The Intern (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>432</th>\n",
              "      <td>The Park Bench (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>433</th>\n",
              "      <td>The Wanted 18 (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>434</th>\n",
              "      <td>Z For Zachariah (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>435 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                            FILM  STARS  RATING  VOTES  YEAR  STARS_DIFF\n",
              "0    Fifty Shades of Grey (2015)    4.0     3.9  34846  2015         0.1\n",
              "1          Jurassic World (2015)    4.5     4.5  34390  2015         0.0\n",
              "2         American Sniper (2015)    5.0     4.8  34085  2015         0.2\n",
              "3               Furious 7 (2015)    5.0     4.8  33538  2015         0.2\n",
              "4              Inside Out (2015)    4.5     4.5  15749  2015         0.0\n",
              "..                           ...    ...     ...    ...   ...         ...\n",
              "430       That Sugar Film (2015)    5.0     5.0      1  2015         0.0\n",
              "431            The Intern (2015)    5.0     5.0      1  2015         0.0\n",
              "432        The Park Bench (2015)    5.0     5.0      1  2015         0.0\n",
              "433         The Wanted 18 (2015)    5.0     5.0      1  2015         0.0\n",
              "434       Z For Zachariah (2015)    5.0     5.0      1  2015         0.0\n",
              "\n",
              "[435 rows x 6 columns]"
            ]
          },
          "execution_count": 199,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATyW3aO8ETNl"
      },
      "source": [
        "**TASK: STARS_DIFF sütunundaki verilerin kaçar kez oluştuğunu count edin ve plotlayın**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "fDWxqEaYETNl",
        "outputId": "55105b26-a4a7-46ae-a57c-3351a52bc2b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "data = fandango[\"STARS_DIFF\"].value_counts().reset_index()\n",
        "data = data.rename(columns = {\"index\":\"STARS_DIFF\",\"STARS_DIFF\":\"COUNT\"})\n",
        "\n",
        "data[\"STARS_DIFF\"] = data[\"STARS_DIFF\"].apply(lambda x: round(x, 1))\n",
        "\n",
        "\n",
        "plt.figure(figsize = (14,6))\n",
        "sns.barplot(data,\n",
        "            x = \"STARS_DIFF\",\n",
        "            y = \"COUNT\",\n",
        "            errorbar = None,\n",
        "            palette = \"rocket\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lvRPa5WOETNm",
        "outputId": "88375854-3374-4775-9926-c6b14e272870"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='STARS_DIFF', ylabel='count'>"
            ]
          },
          "execution_count": 201,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU5_UxEnETNm"
      },
      "source": [
        "**TASK Üstteki plot'dan gördüğümüz üzere STARS_DIFF'i 1 olan film bile var. Bu çok yoğun usulsüzlük yapılan filmi getirin ve inceleyelim.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "R8jKGIjfETNm",
        "outputId": "a49786ac-b8d9-4de0-c427-37043e0bbeb9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 FILM  STARS  RATING  VOTES  YEAR  STARS_DIFF\n",
              "381  Turbo Kid (2015)    5.0     4.0      2  2015         1.0"
            ],
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              "      <th>FILM</th>\n",
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              "      <th>381</th>\n",
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              "          const element = document.querySelector('#df-65de7f82-5462-462a-8f0f-a5705f1135f1');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "fandango[fandango[\"STARS_DIFF\"] == 1.0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v3nlcPniETNm",
        "outputId": "d9817058-f73b-4fd1-b6f0-e1b197c42772"
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        {
          "data": {
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              "      <th>FILM</th>\n",
              "      <th>STARS</th>\n",
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              "      <th>VOTES</th>\n",
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              "      <th>STARS_DIFF</th>\n",
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              "      <th>381</th>\n",
              "      <td>Turbo Kid (2015)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2015</td>\n",
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              "                 FILM  STARS  RATING  VOTES  YEAR  STARS_DIFF\n",
              "381  Turbo Kid (2015)    5.0     4.0      2  2015         1.0"
            ]
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          "execution_count": 203,
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      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jiKoTUg4ETNm"
      },
      "source": [
        "## Part Three: Fandango Puanlamalarını Başka Sitelerle Karşılaştıralım\n",
        "\n",
        "**TASK: \"all_sites_scores.csv\" dosyasını okuyun**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "MqgxQAfDETNm"
      },
      "outputs": [],
      "source": [
        "all_sites = pd.read_csv(\"all_sites_scores.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKBRyre5ETNm"
      },
      "source": [
        "**TASK: Bu csv dosyasını da keşfetmek için temel analiz başlangıcı kodlarını yazın.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "xEZCJgwXETNm",
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              "                             FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
              "0  Avengers: Age of Ultron (2015)              74                   86   \n",
              "1               Cinderella (2015)              85                   80   \n",
              "2                  Ant-Man (2015)              80                   90   \n",
              "3          Do You Believe? (2015)              18                   84   \n",
              "4   Hot Tub Time Machine 2 (2015)              14                   28   \n",
              "\n",
              "   Metacritic  Metacritic_User  IMDB  Metacritic_user_vote_count  \\\n",
              "0          66              7.1   7.8                        1330   \n",
              "1          67              7.5   7.1                         249   \n",
              "2          64              8.1   7.8                         627   \n",
              "3          22              4.7   5.4                          31   \n",
              "4          29              3.4   5.1                          88   \n",
              "\n",
              "   IMDB_user_vote_count  \n",
              "0                271107  \n",
              "1                 65709  \n",
              "2                103660  \n",
              "3                  3136  \n",
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          "metadata": {},
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      ],
      "source": [
        "all_sites.head()"
      ]
    },
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              "                             FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
              "0  Avengers: Age of Ultron (2015)              74                   86   \n",
              "1               Cinderella (2015)              85                   80   \n",
              "2                  Ant-Man (2015)              80                   90   \n",
              "3          Do You Believe? (2015)              18                   84   \n",
              "4   Hot Tub Time Machine 2 (2015)              14                   28   \n",
              "\n",
              "   Metacritic  Metacritic_User  IMDB  Metacritic_user_vote_count  \\\n",
              "0          66              7.1   7.8                        1330   \n",
              "1          67              7.5   7.1                         249   \n",
              "2          64              8.1   7.8                         627   \n",
              "3          22              4.7   5.4                          31   \n",
              "4          29              3.4   5.1                          88   \n",
              "\n",
              "   IMDB_user_vote_count  \n",
              "0                271107  \n",
              "1                 65709  \n",
              "2                103660  \n",
              "3                  3136  \n",
              "4                 19560  "
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 146 entries, 0 to 145\n",
            "Data columns (total 8 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   FILM                        146 non-null    object \n",
            " 1   RottenTomatoes              146 non-null    int64  \n",
            " 2   RottenTomatoes_User         146 non-null    int64  \n",
            " 3   Metacritic                  146 non-null    int64  \n",
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            " 5   IMDB                        146 non-null    float64\n",
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            " 7   IMDB_user_vote_count        146 non-null    int64  \n",
            "dtypes: float64(2), int64(5), object(1)\n",
            "memory usage: 9.2+ KB\n"
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        "all_sites.info()"
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              "       RottenTomatoes  RottenTomatoes_User  Metacritic  Metacritic_User  \\\n",
              "count      146.000000           146.000000  146.000000       146.000000   \n",
              "mean        60.849315            63.876712   58.808219         6.519178   \n",
              "std         30.168799            20.024430   19.517389         1.510712   \n",
              "min          5.000000            20.000000   13.000000         2.400000   \n",
              "25%         31.250000            50.000000   43.500000         5.700000   \n",
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              "             IMDB  Metacritic_user_vote_count  IMDB_user_vote_count  \n",
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              "mean     6.736986                  185.705479          42846.205479  \n",
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              "min      4.000000                    4.000000            243.000000  \n",
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              "50%      6.900000                   72.500000          19103.000000  \n",
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              "      <th></th>\n",
              "      <th>RottenTomatoes</th>\n",
              "      <th>RottenTomatoes_User</th>\n",
              "      <th>Metacritic</th>\n",
              "      <th>Metacritic_User</th>\n",
              "      <th>IMDB</th>\n",
              "      <th>Metacritic_user_vote_count</th>\n",
              "      <th>IMDB_user_vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>146.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>146.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>60.849315</td>\n",
              "      <td>63.876712</td>\n",
              "      <td>58.808219</td>\n",
              "      <td>6.519178</td>\n",
              "      <td>6.736986</td>\n",
              "      <td>185.705479</td>\n",
              "      <td>42846.205479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>30.168799</td>\n",
              "      <td>20.024430</td>\n",
              "      <td>19.517389</td>\n",
              "      <td>1.510712</td>\n",
              "      <td>0.958736</td>\n",
              "      <td>316.606515</td>\n",
              "      <td>67406.509171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>2.400000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>243.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.250000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>43.500000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>6.300000</td>\n",
              "      <td>33.250000</td>\n",
              "      <td>5627.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>63.500000</td>\n",
              "      <td>66.500000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>6.850000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>72.500000</td>\n",
              "      <td>19103.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>89.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>168.500000</td>\n",
              "      <td>45185.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>9.600000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>2375.000000</td>\n",
              "      <td>334164.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41a9e349-3079-46e2-9b62-afdc05d175a7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-41a9e349-3079-46e2-9b62-afdc05d175a7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-41a9e349-3079-46e2-9b62-afdc05d175a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "all_sites.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ulney5bzETNm"
      },
      "source": [
        "### Rotten Tomatoes\n",
        "\n",
        "Önce Rotten Tomatoes'i inceleyelim. RottenTomatoes adında gerçek yorumcuların derecelendirmeleri ve RottenTomatoes_User adında kullanıcıların derecelendirmelerini içeren bir sütun bulunuyor. Bu sütunları plot edelim.\n",
        "\n",
        "**TASK: Aşağıdaki gibi bir scatterplot oluşturun.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "qk6pRjdUETNn",
        "outputId": "81759149-8438-43d4-af2d-736f9df3033f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='RottenTomatoes', ylabel='RottenTomatoes_User'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "\n",
        "plt.figure(figsize = (15,5))\n",
        "\n",
        "sns.scatterplot(all_sites,\n",
        "                x = \"RottenTomatoes\",\n",
        "                y = \"RottenTomatoes_User\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "We1CBj43ETNn",
        "outputId": "4cc04b72-5374-4ab6-a784-073dee6bcc19"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.0, 100.0)"
            ]
          },
          "execution_count": 209,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmo-fATQETNn"
      },
      "source": [
        "**TASK: Rotten DIFF adında bir column oluşturun ve bu column RottenTomatoes-RottenTomatoes_User bilgilerinden oluşsun.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "GpxwncjQETNn",
        "outputId": "4450e326-8ef8-4e15-99d8-0271930f2e38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
              "0  Avengers: Age of Ultron (2015)              74                   86   \n",
              "1               Cinderella (2015)              85                   80   \n",
              "2                  Ant-Man (2015)              80                   90   \n",
              "3          Do You Believe? (2015)              18                   84   \n",
              "4   Hot Tub Time Machine 2 (2015)              14                   28   \n",
              "\n",
              "   Metacritic  Metacritic_User  IMDB  Metacritic_user_vote_count  \\\n",
              "0          66              7.1   7.8                        1330   \n",
              "1          67              7.5   7.1                         249   \n",
              "2          64              8.1   7.8                         627   \n",
              "3          22              4.7   5.4                          31   \n",
              "4          29              3.4   5.1                          88   \n",
              "\n",
              "   IMDB_user_vote_count  Rotten DIFF  \n",
              "0                271107          -12  \n",
              "1                 65709            5  \n",
              "2                103660          -10  \n",
              "3                  3136          -66  \n",
              "4                 19560          -14  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FILM</th>\n",
              "      <th>RottenTomatoes</th>\n",
              "      <th>RottenTomatoes_User</th>\n",
              "      <th>Metacritic</th>\n",
              "      <th>Metacritic_User</th>\n",
              "      <th>IMDB</th>\n",
              "      <th>Metacritic_user_vote_count</th>\n",
              "      <th>IMDB_user_vote_count</th>\n",
              "      <th>Rotten DIFF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Avengers: Age of Ultron (2015)</td>\n",
              "      <td>74</td>\n",
              "      <td>86</td>\n",
              "      <td>66</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.8</td>\n",
              "      <td>1330</td>\n",
              "      <td>271107</td>\n",
              "      <td>-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cinderella (2015)</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>67</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.1</td>\n",
              "      <td>249</td>\n",
              "      <td>65709</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ant-Man (2015)</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>64</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.8</td>\n",
              "      <td>627</td>\n",
              "      <td>103660</td>\n",
              "      <td>-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Do You Believe? (2015)</td>\n",
              "      <td>18</td>\n",
              "      <td>84</td>\n",
              "      <td>22</td>\n",
              "      <td>4.7</td>\n",
              "      <td>5.4</td>\n",
              "      <td>31</td>\n",
              "      <td>3136</td>\n",
              "      <td>-66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
              "      <td>14</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.1</td>\n",
              "      <td>88</td>\n",
              "      <td>19560</td>\n",
              "      <td>-14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3380594d-0c54-4e55-a9b9-3d4be162efac button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3380594d-0c54-4e55-a9b9-3d4be162efac');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "all_sites[\"Rotten DIFF\"] = all_sites[\"RottenTomatoes\"] - all_sites[\"RottenTomatoes_User\"]\n",
        "all_sites.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7bjJMSYxETNn"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PlIr8ipTETNn"
      },
      "source": [
        "**TASK: RT Eleştirmen Puanı ile RT Kullanıcı Puanı arasındaki farkların dağılımını çizin. Bu dağılım grafiğinde negatif değerler olmalıdır. Bu dağıtımı görüntülemek için KDE veya Histogramları kullanabilirsiniz**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "c15z6sZJETNn",
        "outputId": "159eeea2-7156-447d-f94d-284619eeee12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Rotten DIFF', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "sns.histplot(all_sites,\n",
        "             x= \"Rotten DIFF\",\n",
        "             kde = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LsvMsjMpETNn",
        "outputId": "1e08f914-ccd0-4565-bcc1-698e31b87213"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "696-hgvaETNn"
      },
      "source": [
        "**Hadi Kullanıcı ile eleştirmen puanlamaları arasındaki farkın(Rotten_Diff) en yüksek ve en düşük olduğu filmlere bi bakalım.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0NvRcfiETNn"
      },
      "source": [
        "**TASK: Ortalama olarak kullanıcıların, eleştirmenlerden daha fazla sevdiği 5 filmi getirin.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "lc0tgmP2ETNn",
        "outputId": "4931b4db-117c-4e48-f3eb-fb14c5ec2fac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Users Love but Critics Hate\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          FILM  Rotten DIFF\n",
              "3       Do You Believe? (2015)          -66\n",
              "85           Little Boy (2015)          -61\n",
              "134    The Longest Ride (2015)          -42\n",
              "105    Hitman: Agent 47 (2015)          -42\n",
              "125  The Wedding Ringer (2015)          -39"
            ],
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              "          const element = document.querySelector('#df-b3f7fc9a-2a2a-40ec-820e-8f7ca54be2e7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "\n",
        "print(\"Users Love but Critics Hate\")\n",
        "all_sites[[\"FILM\", \"Rotten DIFF\"]].sort_values(\"Rotten DIFF\").head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zgYpvAPfETNo",
        "outputId": "8976919c-708f-4a71-c35c-c5711f7a0634"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Users Love but Critics Hate\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>FILM</th>\n",
              "      <th>Rotten_Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Do You Believe? (2015)</td>\n",
              "      <td>-66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Little Boy (2015)</td>\n",
              "      <td>-61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Hitman: Agent 47 (2015)</td>\n",
              "      <td>-42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>The Longest Ride (2015)</td>\n",
              "      <td>-42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>The Wedding Ringer (2015)</td>\n",
              "      <td>-39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          FILM  Rotten_Diff\n",
              "3       Do You Believe? (2015)          -66\n",
              "85           Little Boy (2015)          -61\n",
              "105    Hitman: Agent 47 (2015)          -42\n",
              "134    The Longest Ride (2015)          -42\n",
              "125  The Wedding Ringer (2015)          -39"
            ]
          },
          "execution_count": 219,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mFQ_I9sETNo"
      },
      "source": [
        "**TASK: Şimdi de tam tersi, kullanıcıların eleştirmenlerden bile daha çok sevmediği 5 filmi getirin**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "gJHK9SdeETNo",
        "outputId": "de426c12-95d5-4f85-a2e0-3d1b179c0638"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Critics love, but Users Hate\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   FILM  Rotten DIFF\n",
              "69                    Mr. Turner (2014)           42\n",
              "112                   It Follows (2015)           31\n",
              "115            While We're Young (2015)           31\n",
              "145  Kumiko, The Treasure Hunter (2015)           24\n",
              "37                 Welcome to Me (2015)           24"
            ],
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              "      <td>It Follows (2015)</td>\n",
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              "      <td>While We're Young (2015)</td>\n",
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              "      <td>Welcome to Me (2015)</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# Buraya kod yazın\n",
        "print(\"Critics love, but Users Hate\")\n",
        "all_sites[[\"FILM\", \"Rotten DIFF\"]].sort_values(\"Rotten DIFF\", ascending = False).head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "tj8nGD5CETNo",
        "outputId": "167d7184-fb1e-44d8-c1a5-ffccf0ff1839"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Critics love, but Users Hate\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   FILM  Rotten DIFF\n",
              "69                    Mr. Turner (2014)           42\n",
              "112                   It Follows (2015)           31\n",
              "115            While We're Young (2015)           31\n",
              "145  Kumiko, The Treasure Hunter (2015)           24\n",
              "37                 Welcome to Me (2015)           24"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FILM</th>\n",
              "      <th>Rotten DIFF</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Mr. Turner (2014)</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>It Follows (2015)</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>While We're Young (2015)</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Kumiko, The Treasure Hunter (2015)</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Welcome to Me (2015)</td>\n",
              "      <td>24</td>\n",
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              "</table>\n",
              "</div>\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79616523-31ec-44a9-9117-86b82abac363 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-79616523-31ec-44a9-9117-86b82abac363');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlEUsqNQETNo"
      },
      "source": [
        "## MetaCritic\n",
        "\n",
        "Burdan sonraki analizlerde sizi özgür bırakıyoruz. İstediğiniz her şeye bakabilir her türlü incelemeyi ve plot'u çizdirebilirsiniz. Ve en sonunda ulaşmanızı istediğimiz bir sonuç var. *Fandango* sitesi kullanıcıları film puanlamaları konusunda kayda değer ölçüde kandırıyor çünkü .... şeklinde bir karara varmanız ya da *Fandango* sitesi diğer sitelere benzer bir tutum sergiliyor ve herhangi bir yanlış yönlendirme yapmıyor çünkü .... şeklinde bir karara varmanız. \n",
        "\n",
        "Varacağınız bu kararı maddeler ve kanıtlar halinde notebook'unuzun en altında mantıklı bir şekilde delilleriyle açıklayın. Burada hangi karara varırsanız varın önemli olan ve bizim de EmKa Academy olarak dikkat edeceğimiz şey, ulaştığınız kararı düzgün bir şekilde açıklayabiliyor musunuz ? Asıl odaklandığımız nokta sonuçtan ziyade bu. O yüzden en altta maddeler halinde kararınızı açıklarken dikkatli davranın. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WVOkvFHIETNo"
      },
      "outputs": [],
      "source": [
        "#chatgpt den yardım alarak bu sonuclara varıyorum\n",
        "# Ulaşmamız gereken sonuca aklıma gelen ilk yöntem site ve kullanıcı incelemeleri arasındaki ilişkilere bakmak.\n",
        "# Buradan muhtemel fandango sitesinin puan şişirmesi sonucuna varacağım.\n",
        "# Bana en net bilgiyi vereceğini düşündüğüm yöntem farkların bir ısı haritasını çıkarmak."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
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          "base_uri": "https://localhost:8080/",
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        "id": "gczL2ZpCETNo",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(fandango[[\"STARS\", \"RATING\", \"STARS_DIFF\"]].corr(numeric_only = True), cmap = \"coolwarm\", annot = True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "xnZhhkf7ETNo",
        "outputId": "c7fb790d-7a12-4a31-f82b-3de0aadeae67"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "corr_rotten = all_sites[[\"RottenTomatoes\",\"RottenTomatoes_User\",\"Rotten DIFF\"]].corr()\n",
        "sns.heatmap(corr_rotten, cmap = \"coolwarm\", annot = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "SYFwuwDxETNo",
        "outputId": "f89e0276-cf4d-401a-f76f-db09daa6800d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "all_sites[\"Meta DIFF\"] = all_sites[\"Metacritic\"]*0.1 - all_sites[\"Metacritic_User\"]\n",
        "corr_meta = all_sites[[\"Metacritic\",\"Metacritic_User\",\"Meta DIFF\"]].corr(numeric_only = True)\n",
        "sns.heatmap(corr_meta, cmap = \"coolwarm\", annot = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Korelasyon ilişkisinde 0-1 aralığı söz konusudur ve bu iki ucun birbirine yakınlığı bize yorum yapmamızı sağlar."
      ],
      "metadata": {
        "id": "417f5RabKO4O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sz8CmH20ETNo"
      },
      "outputs": [],
      "source": [
        "# Grafiklerde dikatinizi çekmek istediğim nokta SİTE PUANLAMALARI VE FARKLAR (Grafiklerde sol alta tekabül ediyor)\n",
        "# Metacritic ve RottenTomatoes siteleri elde edilen farklara oldukça yakın oranlar verdi.\n",
        "# Fakat Fandango sitesi elde edilen farka çok uzakta.\n",
        "# Bu da bize Fandango sitesinin puan şişirmesi çıkarımı yaptırıyor."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNaC0cXkETNp"
      },
      "source": [
        "# BAŞARILAR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLR0KutBETNp"
      },
      "source": [
        "----"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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