Outliers(IQR).ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vbZ2advKhByt"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDkptd1shByw",
        "outputId": "781d9684-5b2a-4e59-bc9e-fc1a9e399506"
      },
      "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/day43-outlier-removal-using-iqr-method/placement.csv')"
      ],
      "metadata": {
        "id": "rSUdm36VhRIU"
      },
      "execution_count": null,
      "outputs": []
    },
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          "height": 206
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        "outputId": "825151eb-8784-4deb-fd4e-c5e766046e66"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   cgpa  placement_exam_marks  placed\n",
              "0  7.19                  26.0       1\n",
              "1  7.46                  38.0       1\n",
              "2  7.54                  40.0       1\n",
              "3  6.42                   8.0       1\n",
              "4  7.23                  17.0       0"
            ],
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              "      <th>cgpa</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>7.19</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.46</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.54</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
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              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.23</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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        "df.head()"
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      "cell_type": "code",
      "source": [
        "df.shape"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 5
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          "height": 424
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      "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",
            "/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": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(16,5))\n",
        "plt.subplot(1,2,1)\n",
        "sns.distplot(df['cgpa'])\n",
        "plt.subplot(1,2,2)\n",
        "sns.distplot(df['placement_exam_marks'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['placement_exam_marks'].skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkDek8g8iAxh",
        "outputId": "3e236388-a3b2-41df-8152-58fba2d4fd08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8356419499466834"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cgpa'].skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GR3BAm4RiLkO",
        "outputId": "d04e0951-c8f7-4fd4-a9f8-f2fb381a8c56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.014529938929314918"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73o-La9ahByy",
        "outputId": "448c3160-95f3-4500-c6de-8fa0c5a03441"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1000.000000\n",
              "mean       32.225000\n",
              "std        19.130822\n",
              "min         0.000000\n",
              "25%        17.000000\n",
              "50%        28.000000\n",
              "75%        44.000000\n",
              "max       100.000000\n",
              "Name: placement_exam_marks, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df['placement_exam_marks'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "3YaOerGNhByz",
        "outputId": "ff08fbdf-ddfd-42e7-b068-7b1a8bf1ee3e"
      },
      "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 0x7ff22f8aaa60>"
            ]
          },
          "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": [
        "sns.boxplot(df['placement_exam_marks'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QVAoLPsthByz"
      },
      "outputs": [],
      "source": [
        "# Finding the IQR\n",
        "percentile25 = df['placement_exam_marks'].quantile(0.25)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "percentile25\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60vv76UxipkA",
        "outputId": "0182810b-0642-4a90-b7aa-799158c7c3ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17.0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "percentile75 = df['placement_exam_marks'].quantile(0.75)"
      ],
      "metadata": {
        "id": "2DzkxSm-ilWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeQX9vBYhByz",
        "outputId": "d246bdd7-b970-4ba4-e5ed-119ec899d90a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44.0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "percentile75"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ew0dHnhshBy0"
      },
      "outputs": [],
      "source": [
        "iqr = percentile75 - percentile25"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QGLH6_BhBy0",
        "outputId": "0b9bb111-448e-4d0f-910e-c15d87b636b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "iqr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fGU-SvfRhBy1"
      },
      "outputs": [],
      "source": [
        "upper_limit = percentile75 + 1.5 * iqr\n",
        "lower_limit = percentile25 - 1.5 * iqr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXvvzmmXhBy1",
        "outputId": "55250211-2b61-4e1e-fbc1-5d1fd9a76f8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Upper limit 84.5\n",
            "Lower limit -23.5\n"
          ]
        }
      ],
      "source": [
        "print(\"Upper limit\",upper_limit)\n",
        "print(\"Lower limit\",lower_limit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMdIYcfthBy1"
      },
      "source": [
        "## Finding Outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "6KQc8P_XhBy3",
        "outputId": "44a0029a-f7b2-4ad3-db53-e5ff77c130a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed\n",
              "9    7.75                  94.0       1\n",
              "40   6.60                  86.0       1\n",
              "61   7.51                  86.0       0\n",
              "134  6.33                  93.0       0\n",
              "162  7.80                  90.0       0\n",
              "283  7.09                  87.0       0\n",
              "290  8.38                  87.0       0\n",
              "311  6.97                  87.0       1\n",
              "324  6.64                  90.0       0\n",
              "630  6.56                  96.0       1\n",
              "685  6.05                  87.0       1\n",
              "730  6.14                  90.0       1\n",
              "771  7.31                  86.0       1\n",
              "846  6.99                  97.0       0\n",
              "917  5.95                 100.0       0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-032c04a0-07dc-472a-85a8-438773272bf7\">\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>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7.75</td>\n",
              "      <td>94.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>6.60</td>\n",
              "      <td>86.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>7.51</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.33</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>7.80</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>7.09</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>8.38</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>6.97</td>\n",
              "      <td>87.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>6.64</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>6.56</td>\n",
              "      <td>96.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>685</th>\n",
              "      <td>6.05</td>\n",
              "      <td>87.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730</th>\n",
              "      <td>6.14</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771</th>\n",
              "      <td>7.31</td>\n",
              "      <td>86.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>6.99</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>5.95</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-032c04a0-07dc-472a-85a8-438773272bf7')\"\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",
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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-032c04a0-07dc-472a-85a8-438773272bf7 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-032c04a0-07dc-472a-85a8-438773272bf7');\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": 19
        }
      ],
      "source": [
        "df[df['placement_exam_marks'] > upper_limit]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "riU6EjVchBy3",
        "outputId": "6985336b-d161-405e-c32a-53fbac12f9e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [cgpa, placement_exam_marks, placed]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc5044da-7f03-4a97-86da-f4f5fd62b4ae\">\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>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc5044da-7f03-4a97-86da-f4f5fd62b4ae')\"\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-dc5044da-7f03-4a97-86da-f4f5fd62b4ae 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-dc5044da-7f03-4a97-86da-f4f5fd62b4ae');\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": 20
        }
      ],
      "source": [
        "df[df['placement_exam_marks'] < lower_limit]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzWZNSG0hBy3"
      },
      "source": [
        "## Trimming"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dv9V26NIhBy4"
      },
      "outputs": [],
      "source": [
        "new_df = df[df['placement_exam_marks'] < upper_limit]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyX7GeQnhBy4",
        "outputId": "4a830ac8-db59-4242-d3ca-3c158432a765"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(985, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "new_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "B7vfhpk7hBy4",
        "outputId": "2db54738-8df5-4d7b-e64d-93d54996d219"
      },
      "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",
            "/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",
            "/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",
            "/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": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Comparing\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.subplot(2,2,1)\n",
        "sns.distplot(df['placement_exam_marks'])\n",
        "\n",
        "plt.subplot(2,2,2)\n",
        "sns.boxplot(df['placement_exam_marks'])\n",
        "\n",
        "plt.subplot(2,2,3)\n",
        "sns.distplot(new_df['placement_exam_marks'])\n",
        "\n",
        "plt.subplot(2,2,4)\n",
        "sns.boxplot(new_df['placement_exam_marks'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFqNyug5hBy5"
      },
      "source": [
        "## Capping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vgcZwYxdhBy5"
      },
      "outputs": [],
      "source": [
        "new_df_cap = df.copy()\n",
        "\n",
        "new_df_cap['placement_exam_marks'] = np.where(\n",
        "    new_df_cap['placement_exam_marks'] > upper_limit,\n",
        "    upper_limit,\n",
        "    np.where(\n",
        "        new_df_cap['placement_exam_marks'] < lower_limit,\n",
        "        lower_limit,\n",
        "        new_df_cap['placement_exam_marks']\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eayLukFBhBy5"
      },
      "outputs": [],
      "source": [
        "# np.where(condtion,true,false)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lBTo7r4hBy6",
        "outputId": "003af5ed-2fce-48d1-daf1-e78b9f52055c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "new_df_cap.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "Jd044zBfhBy6",
        "outputId": "bc5fec84-39d4-40cf-806d-2a68b7a7739f"
      },
      "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",
            "/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",
            "/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",
            "/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": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Comparing\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.subplot(2,2,1)\n",
        "sns.distplot(df['placement_exam_marks'])\n",
        "\n",
        "plt.subplot(2,2,2)\n",
        "sns.boxplot(df['placement_exam_marks'])\n",
        "\n",
        "plt.subplot(2,2,3)\n",
        "sns.distplot(new_df_cap['placement_exam_marks'])\n",
        "\n",
        "plt.subplot(2,2,4)\n",
        "sns.boxplot(new_df_cap['placement_exam_marks'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dyFpZOpT-jGW"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "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
}