Outliers[z_score](Trimming,Capping).ipynb

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      "metadata": {
        "id": "OgJXlS_YdVCl"
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      "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,
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        "outputId": "cc048acd-2fc3-43d2-b54e-c5900e699361"
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      "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/day42-outlier-removal-using-zscore/placement.csv')"
      ],
      "metadata": {
        "id": "YULcL22Gdc7d"
      },
      "execution_count": null,
      "outputs": []
    },
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        "outputId": "d1b50293-0ac1-490d-c411-95e837f275b5"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.shape"
      ]
    },
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            "text/plain": [
              "     cgpa  placement_exam_marks  placed\n",
              "271  7.64                  21.0       1\n",
              "556  6.70                  15.0       1\n",
              "475  7.38                  10.0       0\n",
              "698  7.27                  16.0       0\n",
              "878  6.98                  34.0       0"
            ],
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          "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"
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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",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.distplot(df['placement_exam_marks'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(df['cgpa'],df['placement_exam_marks'],hue=df['placed'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "vpwUSDLFegVY",
        "outputId": "21b67558-9893-4d1b-c334-8b56eb38d08c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. 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 0x7fea246a4370>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_uwOrNlmdVC2"
      },
      "outputs": [],
      "source": [
        "df['placement_exam_marks'].skew()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uOS8pCBdVC3",
        "outputId": "eaedcc1b-c728-4ff5-ad61-bc640b384069"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean value of cgpa 6.96124\n",
            "Std value of cgpa 0.6158978751323896\n",
            "Min value of cgpa 4.89\n",
            "Max value of cgpa 9.12\n"
          ]
        }
      ],
      "source": [
        "print(\"Mean value of cgpa\",df['cgpa'].mean())\n",
        "print(\"Std value of cgpa\",df['cgpa'].std())\n",
        "print(\"Min value of cgpa\",df['cgpa'].min())\n",
        "print(\"Max value of cgpa\",df['cgpa'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghjyA_zNdVC4",
        "outputId": "f77a1e48-5750-4042-e464-c7e306af5d9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highest allowed 8.808933625397168\n",
            "Lowest allowed 5.113546374602832\n"
          ]
        }
      ],
      "source": [
        "# Finding the boundary values\n",
        "print(\"Highest allowed\",df['cgpa'].mean() + 3*df['cgpa'].std())\n",
        "print(\"Lowest allowed\",df['cgpa'].mean() - 3*df['cgpa'].std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P_h1W1dddVC5",
        "outputId": "b645468e-f716-4650-f7d9-8ecbd253795f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed\n",
              "485  4.92                  44.0       1\n",
              "995  8.87                  44.0       1\n",
              "996  9.12                  65.0       1\n",
              "997  4.89                  34.0       0\n",
              "999  4.90                  10.0       1"
            ],
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              "          document.querySelector('#df-f65b5cc1-f907-4029-8075-e0e8b442c864 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-f65b5cc1-f907-4029-8075-e0e8b442c864');\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": 15
        }
      ],
      "source": [
        "# Finding the outliers\n",
        "df[(df['cgpa'] > 8.80) | (df['cgpa'] < 5.11)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtMz5KgcdVC6"
      },
      "source": [
        "## Trimming"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "EqJGsVbBdVC8",
        "outputId": "4151e7c8-7514-4489-9505-686227dc1a08"
      },
      "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\n",
              "..    ...                   ...     ...\n",
              "991  7.04                  57.0       0\n",
              "992  6.26                  12.0       0\n",
              "993  6.73                  21.0       1\n",
              "994  6.48                  63.0       0\n",
              "998  8.62                  46.0       1\n",
              "\n",
              "[995 rows x 3 columns]"
            ],
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              "\n",
              "  <div id=\"df-5ab1402e-a853-4049-a8bb-0e1bff7aa28a\">\n",
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              "      <div>\n",
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              "  <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",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.19</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.42</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>991</th>\n",
              "      <td>7.04</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>992</th>\n",
              "      <td>6.26</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>993</th>\n",
              "      <td>6.73</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>6.48</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>8.62</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>995 rows × 3 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5ab1402e-a853-4049-a8bb-0e1bff7aa28a 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-5ab1402e-a853-4049-a8bb-0e1bff7aa28a');\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": 16
        }
      ],
      "source": [
        "# Trimming\n",
        "\n",
        "new_df = df[(df['cgpa'] < 8.80) & (df['cgpa'] > 5.11)]\n",
        "new_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZKzUYmQwdVC9"
      },
      "outputs": [],
      "source": [
        "# Approach 2\n",
        "\n",
        "# Calculating the Zscore\n",
        "# zscore=(xi-mean)/std\n",
        "\n",
        "df['cgpa_zscore'] = (df['cgpa'] - df['cgpa'].mean())/df['cgpa'].std()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "jPi67yzOdVC_",
        "outputId": "b2cc8d4e-7a32-453c-97b9-86fa7f1f96cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   cgpa  placement_exam_marks  placed  cgpa_zscore\n",
              "0  7.19                  26.0       1     0.371425\n",
              "1  7.46                  38.0       1     0.809810\n",
              "2  7.54                  40.0       1     0.939701\n",
              "3  6.42                   8.0       1    -0.878782\n",
              "4  7.23                  17.0       0     0.436371"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a76731fa-3401-492a-b1b3-0b2cf71600c5\">\n",
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              "      <th>cgpa</th>\n",
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              "      <th>1</th>\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a76731fa-3401-492a-b1b3-0b2cf71600c5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
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          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df.head()"
      ]
    },
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         cgpa  placement_exam_marks  placed  cgpa_zscore\n",
              "995  8.808934                  44.0       1     3.015293\n",
              "996  8.808934                  65.0       1     3.015293"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# zscore value in range of [-3,3] are not outliers\n",
        "# zscore < -3 & zscore > 3  --->> are outliers\n",
        "\n",
        "df[df['cgpa_zscore'] > 3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 143
        },
        "id": "KMsN_hyNdVDB",
        "outputId": "e0bc90e6-c7c5-4c80-b398-462ed09f57a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed  cgpa_zscore\n",
              "485  4.92                  44.0       1    -3.314251\n",
              "997  4.89                  34.0       0    -3.362960\n",
              "999  4.90                  10.0       1    -3.346724"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "      <th>cgpa_zscore</th>\n",
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              "      <th>485</th>\n",
              "      <td>4.92</td>\n",
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              "      <td>-3.314251</td>\n",
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              "      <th>999</th>\n",
              "      <td>4.90</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05a966e1-5b1c-4214-9c49-ff1b176047b3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-05a966e1-5b1c-4214-9c49-ff1b176047b3 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df[df['cgpa_zscore'] < -3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MA6033GRdVDC",
        "outputId": "99dd2c0c-038a-46d2-874b-6c527a88a722"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed  cgpa_zscore\n",
              "485  4.92                  44.0       1    -3.314251\n",
              "995  8.87                  44.0       1     3.099150\n",
              "996  9.12                  65.0       1     3.505062\n",
              "997  4.89                  34.0       0    -3.362960\n",
              "999  4.90                  10.0       1    -3.346724"
            ],
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              "\n",
              "  <div id=\"df-4ae79937-ae4d-4738-bcde-68b95668ff8a\">\n",
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              "  <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",
              "      <th>cgpa_zscore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>4.92</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-3.314251</td>\n",
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              "    <tr>\n",
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              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-3.362960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>4.90</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-3.346724</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ae79937-ae4d-4738-bcde-68b95668ff8a')\"\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",
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              "    <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-4ae79937-ae4d-4738-bcde-68b95668ff8a 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-4ae79937-ae4d-4738-bcde-68b95668ff8a');\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": 22
        }
      ],
      "source": [
        "df[(df['cgpa_zscore'] > 3) | (df['cgpa_zscore'] < -3)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LO2D_lcQdVDC"
      },
      "outputs": [],
      "source": [
        "# Trimming\n",
        "new_df = df[(df['cgpa_zscore'] < 3) & (df['cgpa_zscore'] > -3)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "hG81op3jdVDD",
        "outputId": "8ba9f00e-67ae-4ec1-f25a-81be3909dbf6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed  cgpa_zscore\n",
              "0    7.19                  26.0       1     0.371425\n",
              "1    7.46                  38.0       1     0.809810\n",
              "2    7.54                  40.0       1     0.939701\n",
              "3    6.42                   8.0       1    -0.878782\n",
              "4    7.23                  17.0       0     0.436371\n",
              "..    ...                   ...     ...          ...\n",
              "991  7.04                  57.0       0     0.127878\n",
              "992  6.26                  12.0       0    -1.138565\n",
              "993  6.73                  21.0       1    -0.375452\n",
              "994  6.48                  63.0       0    -0.781363\n",
              "998  8.62                  46.0       1     2.693239\n",
              "\n",
              "[995 rows x 4 columns]"
            ],
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              "      <th>cgpa</th>\n",
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              "      <td>7.54</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.939701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.42</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.878782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.23</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.436371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>991</th>\n",
              "      <td>7.04</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.127878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>992</th>\n",
              "      <td>6.26</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.138565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>993</th>\n",
              "      <td>6.73</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.375452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>6.48</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.781363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>8.62</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.693239</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>995 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4e78022-0b18-472f-8717-e11e95f97021')\"\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",
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              "  </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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4e78022-0b18-472f-8717-e11e95f97021 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-f4e78022-0b18-472f-8717-e11e95f97021');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "new_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phV97DUrdVDD"
      },
      "source": [
        "## Capping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wohLNBEUdVDE"
      },
      "outputs": [],
      "source": [
        "upper_limit = df['cgpa'].mean() + 3*df['cgpa'].std()\n",
        "lower_limit = df['cgpa'].mean() - 3*df['cgpa'].std()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuoNPt7wdVDE",
        "outputId": "5a1cca67-56a7-4e84-aa3a-26f9fa06155b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.113546374602832"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "lower_limit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x-jXetX7dVDE"
      },
      "outputs": [],
      "source": [
        "df['cgpa'] = np.where(\n",
        "    df['cgpa']>upper_limit,\n",
        "    upper_limit,\n",
        "    np.where(\n",
        "        df['cgpa']<lower_limit,\n",
        "        lower_limit,\n",
        "        df['cgpa']\n",
        "    )\n",
        ")\n",
        "\n",
        "# np.where(x,y,z)\n",
        "# x-->condition\n",
        "# y-->if x true\n",
        "# z-->if x false"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3W6S5-Q9dVDF",
        "outputId": "7cd21642-64ff-4502-f8f4-19ca0e603e8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ktay-dPJdVDF",
        "outputId": "49e2f1f6-c6cb-469a-fc45-061f6221ad56"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1000.000000\n",
              "mean        6.961499\n",
              "std         0.612688\n",
              "min         5.113546\n",
              "25%         6.550000\n",
              "50%         6.960000\n",
              "75%         7.370000\n",
              "max         8.808934\n",
              "Name: cgpa, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df['cgpa'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C9xLshXadVDG"
      },
      "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": []
    }
  },
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  "nbformat_minor": 0
}