stock-missing-values.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Gabriel-Dimitrievski-181225-lab3.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFMjGridvepn"
      },
      "source": [
        "<ul><font size = 5 color='Orange'>\n",
        "Learning Goals</font>\n",
        "<font color = 'Orange'><li>Obtaining the Dataset</li>\n",
        "<li>Handling missing values</li>\n",
        "<li>Encoding the categorical data</li>\n",
        "\n",
        "</ul>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "STS4nUOhw07R"
      },
      "source": [
        "The Dataset for this exercise depends on your index number i.e number_of_data_set = index % 10:\n",
        "\n",
        "\n",
        "0.  https://www.kaggle.com/ruchi798/drug-overdose-deaths\n",
        "1.  https://www.kaggle.com/rishidamarla/employee-data-from-the-city-of-chicago\n",
        "2.  https://www.kaggle.com/vikasukani/loan-eligible-dataset\n",
        "3.  https://www.kaggle.com/tamilsel/healthcare-providers-data\n",
        "4.  https://www.kaggle.com/chirag9073/europe-economic-crisis\n",
        "5.  https://www.kaggle.com/bryanb/cac40-stocks-dataset\n",
        "6.  https://www.kaggle.com/christofel04/cardiovascular-study-dataset-predict-heart-disea\n",
        "7.  https://www.kaggle.com/tsaustin/us-historical-stock-prices-with-earnings-data\n",
        "8.  https://www.kaggle.com/kimjihoo/coronavirusdataset (Dataset: PatientInfo.csv)\n",
        "9.  https://www.kaggle.com/mczielinski/bitcoin-historical-data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaojhkPLyq8I"
      },
      "source": [
        "# <font color = 'Orange'> Read your Dataset (.csv)</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNuwigsVwtaP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d158af2f-80c5-4968-d93d-456a4911b338"
      },
      "source": [
        "#add your code\n",
        "from collections import defaultdict\n",
        "import pandas as pd\n",
        "Stocks=pd.read_csv('/content/CAC40_stocks_2010_2021.csv')\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pB_6nC7UdBaT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqwzn3eGz1rL"
      },
      "source": [
        "# <font color = 'Orange'> Target column/columns </font>\n",
        "\n",
        "Write some info about the target value/values\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXs7BxpseUFJ",
        "outputId": "76588828-57a1-4396-ba07-82e071f3445b"
      },
      "source": [
        "print('Stocks data shape: ', Stocks.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stocks data shape:  (81735, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6YiiLt00Egt"
      },
      "source": [
        "*write here ..*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mqmCV0mB0JEv"
      },
      "source": [
        "Visualize the target column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gChPBA780ApR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "f5b8aec5-ea6e-4f8d-8e45-21570860c27f"
      },
      "source": [
        "CompanyName = Stocks['CompanyName'].value_counts()\n",
        "CompanyName.plot.bar()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f200aef8890>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "fx5TZVw8fI17",
        "outputId": "fdab4ac1-7cdd-4696-b69e-d91115d81815"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.xticks(rotation = 90)\n",
        "Stocks['CompanyName'].hist()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f200a9077d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WpZdCckk0Z8W"
      },
      "source": [
        "# <font color = 'Orange'>Detect the Missing values</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gw4SkFaB1QBr"
      },
      "source": [
        "Count the percentage of missing values in every column of the Dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rPeLHnydfRrA",
        "outputId": "39d92e91-1b40-4fae-a64b-e0b52e870ab9"
      },
      "source": [
        "missing_values_High = Stocks.isnull().sum()\n",
        "percentage_High = 100 * Stocks.isnull().sum() / len(Stocks)\n",
        "missing_values_table_High = pd.concat([missing_values_High, percentage_High], axis=1)\n",
        "missing_values_table_High.columns = ['Missing values counter:', ' Percentage of missing values']\n",
        "missing_values_table_High"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Missing values counter:</th>\n",
              "      <th>Percentage of missing values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CompanyName</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockName</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Open</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Close</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Volume</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Missing values counter:   Percentage of missing values\n",
              "CompanyName                        0                            0.0\n",
              "StockName                          0                            0.0\n",
              "High                               0                            0.0\n",
              "Low                                0                            0.0\n",
              "Open                               0                            0.0\n",
              "Close                              0                            0.0\n",
              "Volume                             0                            0.0\n",
              "Date                               0                            0.0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SS5AuHms1bnh"
      },
      "source": [
        "Visualize the missing values using Missingno library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehISYUAY1qwz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "outputId": "9c2e9a99-34e5-4ac0-f7cc-425c3c5c7d6d"
      },
      "source": [
        "import missingno as msno\n",
        "msno.bar(Stocks)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ffabbb3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "MRJmP24nf1_B",
        "outputId": "73f69185-f5c6-417d-f143-1cf92f067a39"
      },
      "source": [
        "msno.matrix(Stocks)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ff221ae50>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsZjZCi92J8g"
      },
      "source": [
        "# <font color = 'Orange'> Find reasons for the missing values</font>\n",
        "\n",
        "\n",
        "With the help of visualization matrix, heatmap, dendrogram, show the dependence between the columns with missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JE-uvOiL32-v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "d060ee4d-d212-40e3-f5ac-98d842105c6f"
      },
      "source": [
        "msno.matrix(Stocks.sort_values('Volume'))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ff1b64c10>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXsSC8Ho1y-N"
      },
      "source": [
        "Write the reasons for the missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uBL76jp3-GL"
      },
      "source": [
        "*write here ..*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8TiFswE5TsD"
      },
      "source": [
        "# <font color = 'Orange'>Handle the missing values</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbZn3z4K4B2d"
      },
      "source": [
        "After you observed the data, the main goal is to know how the columns depend one to another. Therefore, the next step is to select one of the techniques of Machine Learning: Linear Regression, KNN Classification or Logistic Regression that you will use for handling the missing values. You don't need to use the whole dataset for detecting the missing values in one column, you can use a subset of columns that are making the prediction of the target missing value. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gbzez457dgX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e9c1b5e-c267-4e1a-be97-970def2e9ff0"
      },
      "source": [
        "Stocks = Stocks.copy()\n",
        "Stocks.dropna(subset=['Volume'],how='any',inplace=True)\n",
        "Stocks['Volume'].isnull().sum()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "WCwvWCBOgS1J",
        "outputId": "1dc7303c-83cc-4716-9c3e-b0bcfed0f801"
      },
      "source": [
        "msno.bar(Stocks)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ff1fd9790>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MqyACVrw7lr-"
      },
      "source": [
        "# <font color = 'Orange'> Encode non numerical values </font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ez34HYgF7iB2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c1b7f261-1c3e-4a51-e69a-14fe8bcd3ba9"
      },
      "source": [
        "Stocks.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>CompanyName</th>\n",
              "      <th>StockName</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>29.855000</td>\n",
              "      <td>29.075001</td>\n",
              "      <td>29.075001</td>\n",
              "      <td>29.615000</td>\n",
              "      <td>1908400.0</td>\n",
              "      <td>2010-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.270000</td>\n",
              "      <td>29.555000</td>\n",
              "      <td>29.610001</td>\n",
              "      <td>30.125000</td>\n",
              "      <td>2206600.0</td>\n",
              "      <td>2010-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.305000</td>\n",
              "      <td>29.434999</td>\n",
              "      <td>30.040001</td>\n",
              "      <td>30.264999</td>\n",
              "      <td>2724200.0</td>\n",
              "      <td>2010-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.219999</td>\n",
              "      <td>29.715000</td>\n",
              "      <td>30.155001</td>\n",
              "      <td>29.915001</td>\n",
              "      <td>2581000.0</td>\n",
              "      <td>2010-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>29.625000</td>\n",
              "      <td>29.830000</td>\n",
              "      <td>30.450001</td>\n",
              "      <td>3280400.0</td>\n",
              "      <td>2010-01-08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CompanyName StockName       High  ...      Close     Volume        Date\n",
              "0  TechnipFMC       FTI  29.855000  ...  29.615000  1908400.0  2010-01-04\n",
              "1  TechnipFMC       FTI  30.270000  ...  30.125000  2206600.0  2010-01-05\n",
              "2  TechnipFMC       FTI  30.305000  ...  30.264999  2724200.0  2010-01-06\n",
              "3  TechnipFMC       FTI  30.219999  ...  29.915001  2581000.0  2010-01-07\n",
              "4  TechnipFMC       FTI  30.500000  ...  30.450001  3280400.0  2010-01-08\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XO7aYaozgZnJ",
        "outputId": "9769fc20-12d6-4ad4-ade7-5609bb01d99b"
      },
      "source": [
        "Patient['CompanyName'].value_counts()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unibail-Rodamco-Westfield    2833\n",
              "Engie                        2832\n",
              "Thales Group                 2832\n",
              "Renault                      2832\n",
              "Publicis                     2832\n",
              "Kering                       2832\n",
              "ArcelorMittal                2789\n",
              "Schneider Electric           2789\n",
              "AXA                          2789\n",
              "Atos                         2789\n",
              "Sanofi                       2789\n",
              "Groupe PSA                   2789\n",
              "Vinci                        2789\n",
              "STMicroelectronics           2789\n",
              "TechnipFMC                   2789\n",
              "Airbus                       2789\n",
              "Orange                       2789\n",
              "Essilor                      2789\n",
              "Michelin                     2353\n",
              "Legrand                      2346\n",
              "Carrefour                    2234\n",
              "Saint-Gobain                 2053\n",
              "Société Générale             2053\n",
              "BNP Paribas                  2053\n",
              "Danone                       2053\n",
              "Dassault Systèmes            2034\n",
              "Total                        2033\n",
              "Bouygues                     2031\n",
              "Pernod Ricard                1902\n",
              "LVMH                         1711\n",
              "Hermès                       1591\n",
              "Accor                        1315\n",
              "L’Oréal                      1152\n",
              "Sodexo                        655\n",
              "Safran                        610\n",
              "Crédit Agricole               563\n",
              "Veolia                        335\n",
              "Vivendi                       154\n",
              "Air Liquide                    36\n",
              "Capgemini                       7\n",
              "Name: CompanyName, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Evp_T34fgkah",
        "outputId": "565e153f-7859-4b4e-eb2e-ab8f97d341f8"
      },
      "source": [
        "Stocks['StockName']"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        FTI\n",
              "1        FTI\n",
              "2        FTI\n",
              "3        FTI\n",
              "4        FTI\n",
              "        ... \n",
              "81730     MC\n",
              "81731     MC\n",
              "81732     MC\n",
              "81733     MC\n",
              "81734     MC\n",
              "Name: StockName, Length: 81735, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "C6Z0t7jvg0jq",
        "outputId": "249b0afa-68b9-4ae9-d533-8d41a1ecb3e0"
      },
      "source": [
        "Stocks.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CompanyName</th>\n",
              "      <th>StockName</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>29.855000</td>\n",
              "      <td>29.075001</td>\n",
              "      <td>29.075001</td>\n",
              "      <td>29.615000</td>\n",
              "      <td>1908400.0</td>\n",
              "      <td>2010-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.270000</td>\n",
              "      <td>29.555000</td>\n",
              "      <td>29.610001</td>\n",
              "      <td>30.125000</td>\n",
              "      <td>2206600.0</td>\n",
              "      <td>2010-01-05</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.305000</td>\n",
              "      <td>29.434999</td>\n",
              "      <td>30.040001</td>\n",
              "      <td>30.264999</td>\n",
              "      <td>2724200.0</td>\n",
              "      <td>2010-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.219999</td>\n",
              "      <td>29.715000</td>\n",
              "      <td>30.155001</td>\n",
              "      <td>29.915001</td>\n",
              "      <td>2581000.0</td>\n",
              "      <td>2010-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>29.625000</td>\n",
              "      <td>29.830000</td>\n",
              "      <td>30.450001</td>\n",
              "      <td>3280400.0</td>\n",
              "      <td>2010-01-08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CompanyName StockName       High  ...      Close     Volume        Date\n",
              "0  TechnipFMC       FTI  29.855000  ...  29.615000  1908400.0  2010-01-04\n",
              "1  TechnipFMC       FTI  30.270000  ...  30.125000  2206600.0  2010-01-05\n",
              "2  TechnipFMC       FTI  30.305000  ...  30.264999  2724200.0  2010-01-06\n",
              "3  TechnipFMC       FTI  30.219999  ...  29.915001  2581000.0  2010-01-07\n",
              "4  TechnipFMC       FTI  30.500000  ...  30.450001  3280400.0  2010-01-08\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oeF4911Hg2r7",
        "outputId": "bacf39d8-8294-4d0a-ab90-de748d8801b4"
      },
      "source": [
        "Stocks"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>CompanyName</th>\n",
              "      <th>StockName</th>\n",
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              "      <th>Low</th>\n",
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              "      <th>Date</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>29.855000</td>\n",
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              "      <td>1908400.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>TechnipFMC</td>\n",
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              "      <td>30.270000</td>\n",
              "      <td>29.555000</td>\n",
              "      <td>29.610001</td>\n",
              "      <td>30.125000</td>\n",
              "      <td>2206600.0</td>\n",
              "      <td>2010-01-05</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.305000</td>\n",
              "      <td>29.434999</td>\n",
              "      <td>30.040001</td>\n",
              "      <td>30.264999</td>\n",
              "      <td>2724200.0</td>\n",
              "      <td>2010-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.219999</td>\n",
              "      <td>29.715000</td>\n",
              "      <td>30.155001</td>\n",
              "      <td>29.915001</td>\n",
              "      <td>2581000.0</td>\n",
              "      <td>2010-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>29.625000</td>\n",
              "      <td>29.830000</td>\n",
              "      <td>30.450001</td>\n",
              "      <td>3280400.0</td>\n",
              "      <td>2010-01-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81730</th>\n",
              "      <td>LVMH</td>\n",
              "      <td>MC</td>\n",
              "      <td>53.889999</td>\n",
              "      <td>51.279999</td>\n",
              "      <td>53.689999</td>\n",
              "      <td>51.450001</td>\n",
              "      <td>404700.0</td>\n",
              "      <td>2021-01-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81731</th>\n",
              "      <td>LVMH</td>\n",
              "      <td>MC</td>\n",
              "      <td>50.779999</td>\n",
              "      <td>48.759998</td>\n",
              "      <td>50.419998</td>\n",
              "      <td>49.369999</td>\n",
              "      <td>688200.0</td>\n",
              "      <td>2021-01-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81732</th>\n",
              "      <td>LVMH</td>\n",
              "      <td>MC</td>\n",
              "      <td>51.820000</td>\n",
              "      <td>49.639999</td>\n",
              "      <td>49.930000</td>\n",
              "      <td>51.189999</td>\n",
              "      <td>480400.0</td>\n",
              "      <td>2021-01-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81733</th>\n",
              "      <td>LVMH</td>\n",
              "      <td>MC</td>\n",
              "      <td>51.130001</td>\n",
              "      <td>48.990002</td>\n",
              "      <td>51.099998</td>\n",
              "      <td>49.709999</td>\n",
              "      <td>341000.0</td>\n",
              "      <td>2021-01-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81734</th>\n",
              "      <td>LVMH</td>\n",
              "      <td>MC</td>\n",
              "      <td>51.290001</td>\n",
              "      <td>49.630001</td>\n",
              "      <td>49.790001</td>\n",
              "      <td>51.250000</td>\n",
              "      <td>348200.0</td>\n",
              "      <td>2021-02-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81735 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      CompanyName StockName       High  ...      Close     Volume        Date\n",
              "0      TechnipFMC       FTI  29.855000  ...  29.615000  1908400.0  2010-01-04\n",
              "1      TechnipFMC       FTI  30.270000  ...  30.125000  2206600.0  2010-01-05\n",
              "2      TechnipFMC       FTI  30.305000  ...  30.264999  2724200.0  2010-01-06\n",
              "3      TechnipFMC       FTI  30.219999  ...  29.915001  2581000.0  2010-01-07\n",
              "4      TechnipFMC       FTI  30.500000  ...  30.450001  3280400.0  2010-01-08\n",
              "...           ...       ...        ...  ...        ...        ...         ...\n",
              "81730        LVMH        MC  53.889999  ...  51.450001   404700.0  2021-01-26\n",
              "81731        LVMH        MC  50.779999  ...  49.369999   688200.0  2021-01-27\n",
              "81732        LVMH        MC  51.820000  ...  51.189999   480400.0  2021-01-28\n",
              "81733        LVMH        MC  51.130001  ...  49.709999   341000.0  2021-01-29\n",
              "81734        LVMH        MC  51.290001  ...  51.250000   348200.0  2021-02-01\n",
              "\n",
              "[81735 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVqsqVHIhEgv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXjNJf9v8H2Q"
      },
      "source": [
        "# <font color = 'Orange'>Save the new Dataset(.csv) </font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cSczPJg8HGo"
      },
      "source": [
        "#add your code\n",
        "Stocks.to_csv('/content/drive/My Drive/saveToNewFile.csv')"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSKgKX6D8Tip"
      },
      "source": [
        "Print the first rows of your final Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fh2KI6iF8R9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7c154601-f742-4d74-bbc5-af57c4616ffe"
      },
      "source": [
        "#add your code\n",
        "Stocks.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CompanyName</th>\n",
              "      <th>StockName</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>29.855000</td>\n",
              "      <td>29.075001</td>\n",
              "      <td>29.075001</td>\n",
              "      <td>29.615000</td>\n",
              "      <td>1908400.0</td>\n",
              "      <td>2010-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.270000</td>\n",
              "      <td>29.555000</td>\n",
              "      <td>29.610001</td>\n",
              "      <td>30.125000</td>\n",
              "      <td>2206600.0</td>\n",
              "      <td>2010-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.305000</td>\n",
              "      <td>29.434999</td>\n",
              "      <td>30.040001</td>\n",
              "      <td>30.264999</td>\n",
              "      <td>2724200.0</td>\n",
              "      <td>2010-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.219999</td>\n",
              "      <td>29.715000</td>\n",
              "      <td>30.155001</td>\n",
              "      <td>29.915001</td>\n",
              "      <td>2581000.0</td>\n",
              "      <td>2010-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TechnipFMC</td>\n",
              "      <td>FTI</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>29.625000</td>\n",
              "      <td>29.830000</td>\n",
              "      <td>30.450001</td>\n",
              "      <td>3280400.0</td>\n",
              "      <td>2010-01-08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CompanyName StockName       High  ...      Close     Volume        Date\n",
              "0  TechnipFMC       FTI  29.855000  ...  29.615000  1908400.0  2010-01-04\n",
              "1  TechnipFMC       FTI  30.270000  ...  30.125000  2206600.0  2010-01-05\n",
              "2  TechnipFMC       FTI  30.305000  ...  30.264999  2724200.0  2010-01-06\n",
              "3  TechnipFMC       FTI  30.219999  ...  29.915001  2581000.0  2010-01-07\n",
              "4  TechnipFMC       FTI  30.500000  ...  30.450001  3280400.0  2010-01-08\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_9d8UgZg_CW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}