Outlier_Detection_Process.ipynb

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        "# Outlier Detection Process\n",
        "\n",
        "* Create Importtant Functions for Outliers\n",
        "  1. Create Function to detect outliers\n",
        "  2. Are there outliers in the desired columns?\n",
        "  3. Create function to seperate categroical and numerical variables\n",
        "  4. Are there outliers in the dataframe?\n",
        "  5. Create outlier detection function\n",
        "\n",
        "* Univariate Outlier Analysis\n",
        "  1. Remove outliers\n",
        "  2. Fill with average outliers\n",
        "  3. Fill with median outliers\n",
        "  4. Replace with Threshold\n",
        "\n",
        "* Multivariate Outlier Analysis\n",
        "  1. Remove outliers\n",
        "  2. Replace with threshold"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "# Import Necessary Libraries"
      ],
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      }
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.max_rows\", None)\n",
        "pd.set_option(\"display.width\", 500)\n",
        "pd.set_option(\"display.float_format\", lambda x: \"%.3f\" % x)\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Dataset"
      ],
      "metadata": {
        "id": "pPj0J_Ukiu-r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = sns.load_dataset(\"titanic\")\n",
        "df = titanic.copy()\n",
        "df.head()"
      ],
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        "id": "v4qBTUbTisil",
        "outputId": "b4d37463-7cc7-41cd-e893-ca5ff2e8af6e"
      },
      "execution_count": 2,
      "outputs": [
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            "text/plain": [
              "   survived  pclass     sex    age  sibsp  parch   fare embarked  class    who  adult_male deck  embark_town alive  alone\n",
              "0         0       3    male 22.000      1      0  7.250        S  Third    man        True  NaN  Southampton    no  False\n",
              "1         1       1  female 38.000      1      0 71.283        C  First  woman       False    C    Cherbourg   yes  False\n",
              "2         1       3  female 26.000      0      0  7.925        S  Third  woman       False  NaN  Southampton   yes   True\n",
              "3         1       1  female 35.000      1      0 53.100        S  First  woman       False    C  Southampton   yes  False\n",
              "4         0       3    male 35.000      0      0  8.050        S  Third    man        True  NaN  Southampton    no   True"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 2
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    {
      "cell_type": "markdown",
      "source": [
        "# Create Function to detect outliers"
      ],
      "metadata": {
        "id": "p3uNBm60jrag"
      }
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      "cell_type": "code",
      "source": [
        "def outlier_threshold(dataframe, col_name, q1=0.25, q3=0.75):\n",
        "  quartile1 = dataframe[col_name].quantile(q1)\n",
        "  quartile3 = dataframe[col_name].quantile(q3)\n",
        "  interquartile_range = quartile3 - quartile1\n",
        "  low_limit = quartile1 - 1.5 * interquartile_range\n",
        "  up_limit = quartile3 + 1.5 * interquartile_range\n",
        "  return low_limit, up_limit"
      ],
      "metadata": {
        "id": "I10HwxHXjZK-"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_threshold(df, \"age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBmL4Fsikm-k",
        "outputId": "952534ce-58fe-45a1-ae78-06dad113b42a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-6.6875, 64.8125)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Are there outliers in the desired columns?"
      ],
      "metadata": {
        "id": "YysrMdQjlnjP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_outlier(dataframe, col_name):\n",
        "  low_limit, up_limit = outlier_threshold(dataframe, col_name)\n",
        "  if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):\n",
        "    return True\n",
        "  else:\n",
        "    return False"
      ],
      "metadata": {
        "id": "3olCwzBUlbEx"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "check_outlier(df, \"age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ByrUsYBmwVu",
        "outputId": "b73b6c56-be6e-424a-a687-e359aff08f30"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "check_outlier(df, \"fare\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzZd16Jnmyic",
        "outputId": "786070c7-61a6-494a-81c3-fade3c662361"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create function to seperate categroical and numerical variables"
      ],
      "metadata": {
        "id": "sNY_1k20ntBw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
        "    cat_cols = [col for col in dataframe.columns if str(dataframe[col].dtypes) in [\"category\", \"object\", \"bool\"]]\n",
        "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes in [\"uint8\", \"int64\", \"float64\"]]\n",
        "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and str(dataframe[col].dtypes) in [\"category\", \"object\"]]\n",
        "    cat_cols = cat_cols + num_but_cat\n",
        "    cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
        "\n",
        "    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes in [\"uint8\", \"int64\", \"float64\"]]\n",
        "    num_cols = [col for col in num_cols if col not in cat_cols]\n",
        "\n",
        "    return cat_cols, num_cols, cat_but_car, num_but_cat"
      ],
      "metadata": {
        "id": "jy76DyyBnL_7"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)"
      ],
      "metadata": {
        "id": "zFGq6xOon-Is"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"observations: {df.shape[0]}\")\n",
        "print(f\"variables: {df.shape[1]}\")\n",
        "print(f\"cat_cols: {len(cat_cols)}\")\n",
        "print(f\"num_cols: {len(num_cols)}\")\n",
        "print(f\"cat_but_car: {len(cat_but_car)}\")\n",
        "print(f\"num_but_cat: {len(num_but_cat)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXUbrBktn_Tr",
        "outputId": "181e86c4-41d6-48e9-a23d-0649d5bf90b0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "observations: 891\n",
            "variables: 15\n",
            "cat_cols: 13\n",
            "num_cols: 2\n",
            "cat_but_car: 0\n",
            "num_but_cat: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Are there outliers in the dataframe?"
      ],
      "metadata": {
        "id": "GrZfPZamo_hJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_outlier_dataframe(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)\n",
        "  for col in num_cols:\n",
        "    print(col, check_outlier(dataframe, col))"
      ],
      "metadata": {
        "id": "jrkm5-wAoujO"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "check_outlier_dataframe(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWth_vXppWrV",
        "outputId": "80872cf3-c414-4d85-9750-93a0520e18a2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age True\n",
            "fare True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create outlier detection function"
      ],
      "metadata": {
        "id": "MeZJKAx4pxnw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def grab_outliers(dataframe, col_name, index=False):\n",
        "  low_limit, up_limit = outlier_threshold(dataframe, col_name)\n",
        "  if dataframe[(dataframe[col_name] < low_limit) | (dataframe[col_name] > up_limit)].shape[0] > 10:\n",
        "    print(dataframe[(dataframe[col_name] < low_limit) | (dataframe[col_name] > up_limit)].head())\n",
        "  else:\n",
        "    print(dataframe[(dataframe[col_name] < low_limit) | (dataframe[col_name] > up_limit)])\n",
        "\n",
        "  if index:\n",
        "    outlier_index = dataframe[(dataframe[col_name] < low_limit) | (dataframe[col_name] > up_limit)].index\n",
        "    return outlier_index"
      ],
      "metadata": {
        "id": "h1MPAl7kpYpx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_index = grab_outliers(df, \"age\", index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xkFSN-AcvQYP",
        "outputId": "dacab36b-75f3-4a9b-e9b8-94fca02b46fd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     survived  pclass   sex    age  sibsp  parch   fare embarked   class  who  adult_male deck  embark_town alive  alone\n",
            "33          0       2  male 66.000      0      0 10.500        S  Second  man        True  NaN  Southampton    no   True\n",
            "54          0       1  male 65.000      0      1 61.979        C   First  man        True    B    Cherbourg    no  False\n",
            "96          0       1  male 71.000      0      0 34.654        C   First  man        True    A    Cherbourg    no   True\n",
            "116         0       3  male 70.500      0      0  7.750        Q   Third  man        True  NaN   Queenstown    no   True\n",
            "280         0       3  male 65.000      0      0  7.750        Q   Third  man        True  NaN   Queenstown    no   True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hQjqv7xwbHS",
        "outputId": "7152593a-a73b-4708-9516-4fb5eb23d901"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([33, 54, 96, 116, 280, 456, 493, 630, 672, 745, 851], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Univariate Outlier Analysis"
      ],
      "metadata": {
        "id": "noKN4FRuxWiU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Remove outliers"
      ],
      "metadata": {
        "id": "PQkQi2yJxbiy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_outlier(dataframe, col_name):\n",
        "  low_limit, up_limit = outlier_threshold(dataframe, col_name)\n",
        "  df_without_outliers = dataframe[~((dataframe[col_name] < low_limit) | (dataframe[col_name] > up_limit))]\n",
        "  return df_without_outliers"
      ],
      "metadata": {
        "id": "FOWb6zgEyJvf"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_outliers = remove_outlier(df, \"age\")"
      ],
      "metadata": {
        "id": "BdDjCLlVyCfI"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gk-kXItLyu19",
        "outputId": "d1703d9d-35e2-45e4-f0bf-77f584213ae9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_outliers.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YuIimVgWyqBP",
        "outputId": "f1ac67b8-1974-490b-fea5-1eb4f74bf642"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(880, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_outlier_dataframe(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "  for col in num_cols:\n",
        "    df_without_outliers = remove_outlier(dataframe, col)\n",
        "  return df_without_outliers"
      ],
      "metadata": {
        "id": "0sSxFuXsyrom"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_outliers = remove_outlier_dataframe(df)"
      ],
      "metadata": {
        "id": "N-aJ7bvQzhd8"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_outliers.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjP-TQxxzmLz",
        "outputId": "2252ac9c-9632-432b-8958-6ea2708d8c8e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(775, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fill with average outliers"
      ],
      "metadata": {
        "id": "7xrp9jtp0C-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_with_average_outliers(dataframe, col_name):\n",
        "  low_limit, up_limit = outlier_threshold(dataframe, col_name)\n",
        "  dataframe.loc[((dataframe[col_name] < low_limit) | (dataframe[col_name] > up_limit)), col_name] = dataframe[col_name].mean()\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "PaZnzrvszn06"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yeni_df = fill_with_average_outliers(df, \"age\")"
      ],
      "metadata": {
        "id": "Wa30ncgA1CQP"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "low_limit, up_limit = outlier_threshold(yeni_df, \"age\")\n",
        "print((low_limit, up_limit))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yc2OrvsX1XlP",
        "outputId": "ecb9d7dc-36b2-403f-e7a6-6ca295b9dbd0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(-5.1875, 62.3125)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_with_average_outliers_dataframe(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "  for col in num_cols:\n",
        "    fill_with_average_outliers(dataframe, col)\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "CNYElQTy1jJ8"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yeni_df = fill_with_average_outliers_dataframe(df)"
      ],
      "metadata": {
        "id": "XdfpOicu2Lxr"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "low_limit, up_limit = outlier_threshold(yeni_df, \"fare\")\n",
        "print((low_limit, up_limit))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lO3whaIw2OG3",
        "outputId": "55da300c-ce07-4f98-d38e-9e7d81fe6556"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(-26.724, 65.6344)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fill with median outliers"
      ],
      "metadata": {
        "id": "5KfTEkZ722sX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_with_median_outliers(dataframe, col_name):\n",
        "  low_limit, up_limit = outlier_threshold(dataframe, col_name)\n",
        "  dataframe.loc[((dataframe[col_name] < low_limit) | (dataframe[col_name] > up_limit)), col_name] = dataframe[col_name].median()\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "n23tuFuq2P6H"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yeni_df = fill_with_median_outliers(df, \"age\")"
      ],
      "metadata": {
        "id": "ovDVE4mE2ewG"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "low_limit, up_limit = outlier_threshold(yeni_df, \"age\")\n",
        "print((low_limit, up_limit))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVNdn68H2orI",
        "outputId": "11b23a47-65d6-4174-d648-02c3e49c4df6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(-5.0, 62.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_with_median_outliers_dataframe(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "  for col in num_cols:\n",
        "    fill_with_median_outliers(dataframe, col)\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "nSZIfAL22rnc"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yeni_df = fill_with_median_outliers_dataframe(df)"
      ],
      "metadata": {
        "id": "q9MLbN8r3RuD"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "low_limit, up_limit = outlier_threshold(yeni_df, \"fare\")\n",
        "print((low_limit, up_limit))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUjJ5RKv3Xtj",
        "outputId": "f0bf63c8-037d-46b2-9d3a-8118fdbe10f4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(-26.724, 65.6344)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Replace with Threshold"
      ],
      "metadata": {
        "id": "jqYZLxBs3n95"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_with_thresholds(dataframe, col_name):\n",
        "  low_limit, up_limit = outlier_threshold(dataframe, col_name)\n",
        "  dataframe.loc[(dataframe[col_name] < low_limit), col_name] = low_limit\n",
        "  dataframe.loc[(dataframe[col_name] > up_limit), col_name] = up_limit"
      ],
      "metadata": {
        "id": "2emisSZe3cCm"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "replace_with_thresholds(df, \"age\")"
      ],
      "metadata": {
        "id": "BHSvvhP24wnG"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_with_thresholds_dataframe(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "  for col in num_cols:\n",
        "    replace_with_thresholds(dataframe, col)"
      ],
      "metadata": {
        "id": "o4hUvJqs45vx"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "replace_with_thresholds_dataframe(df)"
      ],
      "metadata": {
        "id": "Tf7xvz1J5f6c"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multivariate Outlier Analysis"
      ],
      "metadata": {
        "id": "SgwHf0yQ6z8e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Remove Outliers"
      ],
      "metadata": {
        "id": "asBC0Wpa657E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def multivariate_remove_outliers(dataframe, n_neighbors=20, contamination=0.1, threshold_number=10):\n",
        "  dataframe = dataframe.dropna() # remove missing values\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "  dataframe = dataframe[num_cols]\n",
        "  clf = LocalOutlierFactor(n_neighbors=n_neighbors, contamination=contamination)\n",
        "  clf.fit_predict(dataframe)\n",
        "  df_scores = clf.negative_outlier_factor_\n",
        "  threshold_value = np.sort(df_scores)[threshold_number]\n",
        "  dataframe = dataframe.loc[df_scores > threshold_value]\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "Qf6cbT7b5iQE"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe = multivariate_remove_outliers(df)"
      ],
      "metadata": {
        "id": "bAQp-g5K-VyM"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
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      "source": [
        "dataframe.head()"
      ],
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        "id": "11TDswfG-ZRX",
        "outputId": "159e011b-8d47-4f96-ca3c-3284e9dbd8ea"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age   fare\n",
              "1  38.000 32.204\n",
              "3  35.000 53.100\n",
              "11 58.000 26.550\n",
              "21 34.000 13.000\n",
              "23 28.000 35.500"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 168,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.283564414466378,\n        \"min\": 11.0,\n        \"max\": 62.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55.0,\n          33.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.212508185654974,\n        \"min\": 0.0,\n        \"max\": 63.3583,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          25.5875,\n          26.3875,\n          25.9292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-VXpJB6AsWJ",
        "outputId": "d1330921-78ab-4c14-a5af-0aa9e6110c36"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(168, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Replace With Thresholds (NOT RECOMMENDED)"
      ],
      "metadata": {
        "id": "Q-m7GT72Cvur"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def multivariate_replace_with_thresholds(dataframe, n_neighbors=20, contamination=0.1, threshold_number=10):\n",
        "  dataframe = dataframe.dropna() # remove missing values\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "  dataframe = dataframe[num_cols]\n",
        "  clf = LocalOutlierFactor(n_neighbors=n_neighbors, contamination=contamination)\n",
        "  clf.fit_predict(dataframe)\n",
        "  df_scores = clf.negative_outlier_factor_\n",
        "  threshold_value = np.sort(df_scores)[threshold_number]\n",
        "  threshold_row = dataframe[df_scores == threshold_value]\n",
        "  no_outlier_df = df_scores > threshold_value\n",
        "  outliers = dataframe[~no_outlier_df]\n",
        "  results = outliers.to_records(index=False)\n",
        "  results[:] = threshold_row.to_records(index=False)\n",
        "  dataframe[~no_outlier_df] = pd.DataFrame(results, index=dataframe[~no_outlier_df].index)\n",
        "  return dataframe[~no_outlier_df]"
      ],
      "metadata": {
        "id": "u88ZpH7qA_tb"
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      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "multivariate_replace_with_thresholds(df)"
      ],
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        "colab": {
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          "height": 394
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        "id": "5KSm6D8oEN6Q",
        "outputId": "47b89355-5906-48a2-e19a-07d7b1606c58"
      },
      "execution_count": 67,
      "outputs": [
        {
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              "      age   fare\n",
              "10  4.000 32.204\n",
              "183 4.000 32.204\n",
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              "205 4.000 32.204\n",
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              "772 4.000 32.204"
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              "      const buttonEl =\n",
              "        document.querySelector('#df-bef9a168-7a28-40e8-9166-3441e66a8f7d 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-bef9a168-7a28-40e8-9166-3441e66a8f7d');\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",
              "\n",
              "\n",
              "<div id=\"df-1f12ba0c-a39e-49b1-96ad-2fd6d36c3313\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1f12ba0c-a39e-49b1-96ad-2fd6d36c3313')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1f12ba0c-a39e-49b1-96ad-2fd6d36c3313 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"multivariate_replace_with_thresholds(df)\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 4.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.452235082682191e-15,\n        \"min\": 32.204207968574636,\n        \"max\": 32.204207968574636,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          32.204207968574636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OM2pQLnyEXeG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}