Outlier(percentile).ipynb

{
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      "execution_count": null,
      "metadata": {
        "id": "1SbfsH_8kUky"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wzdB3x3UkUlD",
        "outputId": "b51812db-5400-4618-e2be-a4dc0646821c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/100-days-of-machine-learning/day44-outlier-detection-using-percentiles/weight-height.csv')"
      ],
      "metadata": {
        "id": "y5d4xVA7kbyi"
      },
      "execution_count": null,
      "outputs": []
    },
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      "execution_count": null,
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          "height": 206
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        "outputId": "9641108e-d2c7-47bc-e31b-e77dacd8c13c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Gender     Height      Weight\n",
              "0   Male  73.847017  241.893563\n",
              "1   Male  68.781904  162.310473\n",
              "2   Male  74.110105  212.740856\n",
              "3   Male  71.730978  220.042470\n",
              "4   Male  69.881796  206.349801"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>73.847017</td>\n",
              "      <td>241.893563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>68.781904</td>\n",
              "      <td>162.310473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>74.110105</td>\n",
              "      <td>212.740856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>71.730978</td>\n",
              "      <td>220.042470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>69.881796</td>\n",
              "      <td>206.349801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTh5ZJwdkUlN",
        "outputId": "d1be86a3-6e04-429f-b404-1e5e0bd650e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izrw4sNwkUlR",
        "outputId": "959f091c-385f-4c37-91f0-c3ecd4fe59b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    10000.000000\n",
              "mean        66.367560\n",
              "std          3.847528\n",
              "min         54.263133\n",
              "25%         63.505620\n",
              "50%         66.318070\n",
              "75%         69.174262\n",
              "max         78.998742\n",
              "Name: Height, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['Height'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ItqcvFsIkUlV"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "ql7mDyAGkUlc",
        "outputId": "6c62028f-c306-487a-9dbc-5ab745625a8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc85b664b20>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['Height'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "pqbWN4BakUlk",
        "outputId": "cf14d4ea-f790-48e8-d72c-8a4b847dbfea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8594fc3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "sns.boxplot(df['Height'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEB9oL6TkUlq",
        "outputId": "6235331a-f89e-49d4-8888-80623c87b0d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "74.7857900583366"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "upper_limit = df['Height'].quantile(0.99)\n",
        "upper_limit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QPRzEFCkUlu",
        "outputId": "7b2ac36a-e065-490a-eb76-a668440dc8ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58.13441158671655"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "lower_limit = df['Height'].quantile(0.01)\n",
        "lower_limit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xJlpSmoZkUly"
      },
      "outputs": [],
      "source": [
        "new_df = df[(df['Height'] <= 74.78) & (df['Height'] >= 58.13)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-tUuRN3kUl0",
        "outputId": "ff1e4e42-471a-4240-bbba-bd91b6e40575"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    9799.000000\n",
              "mean       66.363507\n",
              "std         3.644267\n",
              "min        58.134496\n",
              "25%        63.577147\n",
              "50%        66.317899\n",
              "75%        69.119859\n",
              "max        74.767447\n",
              "Name: Height, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "new_df['Height'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qkOrdS0tkUl2",
        "outputId": "3baf2f3b-3fdb-41c5-b577-c5338be789a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    10000.000000\n",
              "mean        66.367560\n",
              "std          3.847528\n",
              "min         54.263133\n",
              "25%         63.505620\n",
              "50%         66.318070\n",
              "75%         69.174262\n",
              "max         78.998742\n",
              "Name: Height, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df['Height'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "SEBXPv0SkUl6",
        "outputId": "04db5739-839b-46df-d102-a2b3cddde082"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8590161c0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(new_df['Height'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "J1MTpEzakUl9",
        "outputId": "32dcdca6-8f89-4732-885b-72211a80863b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc858f6ebb0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.boxplot(new_df['Height'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LqTxqtdskUl-"
      },
      "outputs": [],
      "source": [
        "# Capping --> Winsorization\n",
        "df['Height'] = np.where(df['Height'] >= upper_limit,\n",
        "        upper_limit,\n",
        "        np.where(df['Height'] <= lower_limit,\n",
        "        lower_limit,\n",
        "        df['Height']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXXlOcrykUmD",
        "outputId": "3b254afe-30a9-4847-ae35-5260b9ca6c4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njQHW70hkUmF",
        "outputId": "09f58baf-b043-4f93-f980-c4f96c1c1f9d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    10000.000000\n",
              "mean        66.366281\n",
              "std          3.795717\n",
              "min         58.134412\n",
              "25%         63.505620\n",
              "50%         66.318070\n",
              "75%         69.174262\n",
              "max         74.785790\n",
              "Name: Height, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df['Height'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "bnPPNVibkUmG",
        "outputId": "b3cde3d6-677b-4f57-a0b5-967bbe44d408"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc858f60df0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['Height'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Kj-zOws4kUmJ",
        "outputId": "00911729-ad6a-431e-b77c-9c67d7c15b23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc858e72e80>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.boxplot(df['Height'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H9bfHe2skUmM"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}