cleaningdatain_imdb_dataset.ipynb

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      "name": "cleaningdatain_imdb_dataset.ipynb",
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path='/content/imdb_top_1000.csv'\n",
        "df=pd.read_csv('imdb_top_1000.csv')"
      ],
      "metadata": {
        "id": "9uFkJ0l-VmY0"
      },
      "execution_count": 20,
      "outputs": []
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      "source": [
        "df.shape"
      ],
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            "text/plain": [
              "(1000, 16)"
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      "source": [
        "df.head()"
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              "                                         Poster_Link  \\\n",
              "0  https://m.media-amazon.com/images/M/MV5BMDFkYT...   \n",
              "1  https://m.media-amazon.com/images/M/MV5BM2MyNj...   \n",
              "2  https://m.media-amazon.com/images/M/MV5BMTMxNT...   \n",
              "3  https://m.media-amazon.com/images/M/MV5BMWMwMG...   \n",
              "4  https://m.media-amazon.com/images/M/MV5BMWU4N2...   \n",
              "\n",
              "               Series_Title Released_Year Certificate  Runtime  \\\n",
              "0  The Shawshank Redemption          1994           A  142 min   \n",
              "1             The Godfather          1972           A  175 min   \n",
              "2           The Dark Knight          2008          UA  152 min   \n",
              "3    The Godfather: Part II          1974           A  202 min   \n",
              "4              12 Angry Men          1957           U   96 min   \n",
              "\n",
              "                  Genre  IMDB_Rating  \\\n",
              "0                 Drama          9.3   \n",
              "1          Crime, Drama          9.2   \n",
              "2  Action, Crime, Drama          9.0   \n",
              "3          Crime, Drama          9.0   \n",
              "4          Crime, Drama          9.0   \n",
              "\n",
              "                                            Overview  Meta_score  \\\n",
              "0  Two imprisoned men bond over a number of years...        80.0   \n",
              "1  An organized crime dynasty's aging patriarch t...       100.0   \n",
              "2  When the menace known as the Joker wreaks havo...        84.0   \n",
              "3  The early life and career of Vito Corleone in ...        90.0   \n",
              "4  A jury holdout attempts to prevent a miscarria...        96.0   \n",
              "\n",
              "               Director           Star1           Star2          Star3  \\\n",
              "0        Frank Darabont     Tim Robbins  Morgan Freeman     Bob Gunton   \n",
              "1  Francis Ford Coppola   Marlon Brando       Al Pacino     James Caan   \n",
              "2     Christopher Nolan  Christian Bale    Heath Ledger  Aaron Eckhart   \n",
              "3  Francis Ford Coppola       Al Pacino  Robert De Niro  Robert Duvall   \n",
              "4          Sidney Lumet     Henry Fonda     Lee J. Cobb  Martin Balsam   \n",
              "\n",
              "            Star4  No_of_Votes        Gross  \n",
              "0  William Sadler      2343110   28,341,469  \n",
              "1    Diane Keaton      1620367  134,966,411  \n",
              "2   Michael Caine      2303232  534,858,444  \n",
              "3    Diane Keaton      1129952   57,300,000  \n",
              "4    John Fiedler       689845    4,360,000  "
            ],
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              "      <th>0</th>\n",
              "      <td>https://m.media-amazon.com/images/M/MV5BMDFkYT...</td>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>1994</td>\n",
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              "      <td>142 min</td>\n",
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              "      <td>The Godfather</td>\n",
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              "      <td>Aaron Eckhart</td>\n",
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              "      <td>2303232</td>\n",
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              "      <th>3</th>\n",
              "      <td>https://m.media-amazon.com/images/M/MV5BMWMwMG...</td>\n",
              "      <td>The Godfather: Part II</td>\n",
              "      <td>1974</td>\n",
              "      <td>A</td>\n",
              "      <td>202 min</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>9.0</td>\n",
              "      <td>The early life and career of Vito Corleone in ...</td>\n",
              "      <td>90.0</td>\n",
              "      <td>Francis Ford Coppola</td>\n",
              "      <td>Al Pacino</td>\n",
              "      <td>Robert De Niro</td>\n",
              "      <td>Robert Duvall</td>\n",
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              "      <td>https://m.media-amazon.com/images/M/MV5BMWU4N2...</td>\n",
              "      <td>12 Angry Men</td>\n",
              "      <td>1957</td>\n",
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              "      <td>96 min</td>\n",
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              "      <td>Lee J. Cobb</td>\n",
              "      <td>Martin Balsam</td>\n",
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              "      <td>689845</td>\n",
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        "df.isnull().sum()"
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          "data": {
            "text/plain": [
              "Poster_Link        0\n",
              "Series_Title       0\n",
              "Released_Year      0\n",
              "Certificate      101\n",
              "Runtime            0\n",
              "Genre              0\n",
              "IMDB_Rating        0\n",
              "Overview           0\n",
              "Meta_score       157\n",
              "Director           0\n",
              "Star1              0\n",
              "Star2              0\n",
              "Star3              0\n",
              "Star4              0\n",
              "No_of_Votes        0\n",
              "Gross            169\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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        "fig,ax=plt.subplots(figsize=(8,8))\n",
        "sns.distplot(df.Meta_score)"
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2def14350>"
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          "execution_count": 9
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          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Meta_score'].fillna(df['Meta_score'].median(),inplace=True)"
      ],
      "metadata": {
        "id": "7H5bZABsWMwv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyvz8HQsWXxQ",
        "outputId": "9b867210-d266-4516-f8cd-e58e8b8b028c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Poster_Link        0\n",
              "Series_Title       0\n",
              "Released_Year      0\n",
              "Certificate      101\n",
              "Runtime            0\n",
              "Genre              0\n",
              "IMDB_Rating        0\n",
              "Overview           0\n",
              "Meta_score         0\n",
              "Director           0\n",
              "Star1              0\n",
              "Star2              0\n",
              "Star3              0\n",
              "Star4              0\n",
              "No_of_Votes        0\n",
              "Gross            169\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gross'].fillna(df['Gross'].mode(),inplace=True)"
      ],
      "metadata": {
        "id": "3hMHeZlIW818"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRiGijohXCZx",
        "outputId": "dba8657b-54ef-47b3-9e84-8e08a40ad058"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Poster_Link        0\n",
              "Series_Title       0\n",
              "Released_Year      0\n",
              "Certificate      101\n",
              "Runtime            0\n",
              "Genre              0\n",
              "IMDB_Rating        0\n",
              "Overview           0\n",
              "Meta_score         0\n",
              "Director           0\n",
              "Star1              0\n",
              "Star2              0\n",
              "Star3              0\n",
              "Star4              0\n",
              "No_of_Votes        0\n",
              "Gross            169\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Certificate'].replace([np.nan], df['Certificate'].mode(), inplace=True)"
      ],
      "metadata": {
        "id": "sj7-JDxHcZFl"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCh7Biqvc0lE",
        "outputId": "dcd33f79-ee73-4202-df57-bdbc006e1702"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Poster_Link        0\n",
              "Series_Title       0\n",
              "Released_Year      0\n",
              "Certificate        0\n",
              "Runtime            0\n",
              "Genre              0\n",
              "IMDB_Rating        0\n",
              "Overview           0\n",
              "Meta_score         0\n",
              "Director           0\n",
              "Star1              0\n",
              "Star2              0\n",
              "Star3              0\n",
              "Star4              0\n",
              "No_of_Votes        0\n",
              "Gross            169\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gross'].replace([np.nan], df['Gross'].mode(), inplace=True)"
      ],
      "metadata": {
        "id": "3Nji9CWrc44s"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roE9EQuBdLti",
        "outputId": "2153e2a9-d137-4093-9fe8-8e2e596a4f51"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Poster_Link      0\n",
              "Series_Title     0\n",
              "Released_Year    0\n",
              "Certificate      0\n",
              "Runtime          0\n",
              "Genre            0\n",
              "IMDB_Rating      0\n",
              "Overview         0\n",
              "Meta_score       0\n",
              "Director         0\n",
              "Star1            0\n",
              "Star2            0\n",
              "Star3            0\n",
              "Star4            0\n",
              "No_of_Votes      0\n",
              "Gross            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}