DataManipulation.ipynb

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        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/DataManipulation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Axjf2hhWeJdE"
      },
      "source": [
        "![rmotr](https://user-images.githubusercontent.com/7065401/52071918-bda15380-2562-11e9-828c-7f95297e4a82.png)\n",
        "<hr style=\"margin-bottom: 40px;\">\n",
        "\n",
        "<img src=\"https://user-images.githubusercontent.com/7065401/58563302-42466a80-8201-11e9-9948-b3e9f88a5662.jpg\"\n",
        "    style=\"width:400px; float: right; margin: 0 40px 40px 40px;\"></img>\n",
        "\n",
        "# Bike store sales\n",
        "\n",
        "In this class we'll be analyzing sales made on bike stores.\n",
        "\n",
        "[Follow this data in a Google Spreadsheet](https://docs.google.com/spreadsheets/d/1NOe_UrPx6ULF2C5MvHmZ9ODuw8t9M77Q1Y64gP-7JHA/edit?usp=sharing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aYiDOgIeJdM"
      },
      "source": [
        "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
        "\n",
        "## Hands on! "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "6PfX2jCyeJdN"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2-5tgWReJdP"
      },
      "source": [
        "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
        "\n",
        "## Loading our data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "X-57ff1QeJdS"
      },
      "outputs": [],
      "source": [
        "#https://raw.githubusercontent.com/ogut77/DataScience/main/data/sales_data.csv\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the data"
      ],
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      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
              "0      2013-11-26   26  November  2013            19     Youth (<25)   \n",
              "1      2015-11-26   26  November  2015            19     Youth (<25)   \n",
              "2      2014-03-23   23     March  2014            49  Adults (35-64)   \n",
              "3      2016-03-23   23     March  2016            49  Adults (35-64)   \n",
              "4      2014-05-15   15       May  2014            47  Adults (35-64)   \n",
              "...           ...  ...       ...   ...           ...             ...   \n",
              "113031 2016-04-12   12     April  2016            41  Adults (35-64)   \n",
              "113032 2014-04-02    2     April  2014            18     Youth (<25)   \n",
              "113033 2016-04-02    2     April  2016            18     Youth (<25)   \n",
              "113034 2014-03-04    4     March  2014            37  Adults (35-64)   \n",
              "113035 2016-03-04    4     March  2016            37  Adults (35-64)   \n",
              "\n",
              "       Customer_Gender         Country             State Product_Category  \\\n",
              "0                    M          Canada  British Columbia      Accessories   \n",
              "1                    M          Canada  British Columbia      Accessories   \n",
              "2                    M       Australia   New South Wales      Accessories   \n",
              "3                    M       Australia   New South Wales      Accessories   \n",
              "4                    F       Australia   New South Wales      Accessories   \n",
              "...                ...             ...               ...              ...   \n",
              "113031               M  United Kingdom           England         Clothing   \n",
              "113032               M       Australia        Queensland         Clothing   \n",
              "113033               M       Australia        Queensland         Clothing   \n",
              "113034               F          France     Seine (Paris)         Clothing   \n",
              "113035               F          France     Seine (Paris)         Clothing   \n",
              "\n",
              "       Sub_Category              Product  Order_Quantity  Unit_Cost  \\\n",
              "0        Bike Racks  Hitch Rack - 4-Bike               8         45   \n",
              "1        Bike Racks  Hitch Rack - 4-Bike               8         45   \n",
              "2        Bike Racks  Hitch Rack - 4-Bike              23         45   \n",
              "3        Bike Racks  Hitch Rack - 4-Bike              20         45   \n",
              "4        Bike Racks  Hitch Rack - 4-Bike               4         45   \n",
              "...             ...                  ...             ...        ...   \n",
              "113031        Vests      Classic Vest, S               3         24   \n",
              "113032        Vests      Classic Vest, M              22         24   \n",
              "113033        Vests      Classic Vest, M              22         24   \n",
              "113034        Vests      Classic Vest, L              24         24   \n",
              "113035        Vests      Classic Vest, L              23         24   \n",
              "\n",
              "        Unit_Price  Profit  Cost  Revenue  \n",
              "0              120     590   360      950  \n",
              "1              120     590   360      950  \n",
              "2              120    1366  1035     2401  \n",
              "3              120    1188   900     2088  \n",
              "4              120     238   180      418  \n",
              "...            ...     ...   ...      ...  \n",
              "113031          64     112    72      184  \n",
              "113032          64     655   528     1183  \n",
              "113033          64     655   528     1183  \n",
              "113034          64     684   576     1260  \n",
              "113035          64     655   552     1207  \n",
              "\n",
              "[113036 rows x 18 columns]"
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              "      <th>0</th>\n",
              "      <td>2013-11-26</td>\n",
              "      <td>26</td>\n",
              "      <td>November</td>\n",
              "      <td>2013</td>\n",
              "      <td>19</td>\n",
              "      <td>Youth (&lt;25)</td>\n",
              "      <td>M</td>\n",
              "      <td>Canada</td>\n",
              "      <td>British Columbia</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Bike Racks</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>8</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>590</td>\n",
              "      <td>360</td>\n",
              "      <td>950</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-11-26</td>\n",
              "      <td>26</td>\n",
              "      <td>November</td>\n",
              "      <td>2015</td>\n",
              "      <td>19</td>\n",
              "      <td>Youth (&lt;25)</td>\n",
              "      <td>M</td>\n",
              "      <td>Canada</td>\n",
              "      <td>British Columbia</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Bike Racks</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>8</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>590</td>\n",
              "      <td>360</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-03-23</td>\n",
              "      <td>23</td>\n",
              "      <td>March</td>\n",
              "      <td>2014</td>\n",
              "      <td>49</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>Australia</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Bike Racks</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>23</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>1366</td>\n",
              "      <td>1035</td>\n",
              "      <td>2401</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-03-23</td>\n",
              "      <td>23</td>\n",
              "      <td>March</td>\n",
              "      <td>2016</td>\n",
              "      <td>49</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>Australia</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Bike Racks</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>20</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>1188</td>\n",
              "      <td>900</td>\n",
              "      <td>2088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-05-15</td>\n",
              "      <td>15</td>\n",
              "      <td>May</td>\n",
              "      <td>2014</td>\n",
              "      <td>47</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>F</td>\n",
              "      <td>Australia</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Bike Racks</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>238</td>\n",
              "      <td>180</td>\n",
              "      <td>418</td>\n",
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              "      <th>113031</th>\n",
              "      <td>2016-04-12</td>\n",
              "      <td>12</td>\n",
              "      <td>April</td>\n",
              "      <td>2016</td>\n",
              "      <td>41</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>England</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>Vests</td>\n",
              "      <td>Classic Vest, S</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>64</td>\n",
              "      <td>112</td>\n",
              "      <td>72</td>\n",
              "      <td>184</td>\n",
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              "    <tr>\n",
              "      <th>113032</th>\n",
              "      <td>2014-04-02</td>\n",
              "      <td>2</td>\n",
              "      <td>April</td>\n",
              "      <td>2014</td>\n",
              "      <td>18</td>\n",
              "      <td>Youth (&lt;25)</td>\n",
              "      <td>M</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Queensland</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>Vests</td>\n",
              "      <td>Classic Vest, M</td>\n",
              "      <td>22</td>\n",
              "      <td>24</td>\n",
              "      <td>64</td>\n",
              "      <td>655</td>\n",
              "      <td>528</td>\n",
              "      <td>1183</td>\n",
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              "    <tr>\n",
              "      <th>113033</th>\n",
              "      <td>2016-04-02</td>\n",
              "      <td>2</td>\n",
              "      <td>April</td>\n",
              "      <td>2016</td>\n",
              "      <td>18</td>\n",
              "      <td>Youth (&lt;25)</td>\n",
              "      <td>M</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Queensland</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>Vests</td>\n",
              "      <td>Classic Vest, M</td>\n",
              "      <td>22</td>\n",
              "      <td>24</td>\n",
              "      <td>64</td>\n",
              "      <td>655</td>\n",
              "      <td>528</td>\n",
              "      <td>1183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113034</th>\n",
              "      <td>2014-03-04</td>\n",
              "      <td>4</td>\n",
              "      <td>March</td>\n",
              "      <td>2014</td>\n",
              "      <td>37</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>F</td>\n",
              "      <td>France</td>\n",
              "      <td>Seine (Paris)</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>Vests</td>\n",
              "      <td>Classic Vest, L</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>64</td>\n",
              "      <td>684</td>\n",
              "      <td>576</td>\n",
              "      <td>1260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113035</th>\n",
              "      <td>2016-03-04</td>\n",
              "      <td>4</td>\n",
              "      <td>March</td>\n",
              "      <td>2016</td>\n",
              "      <td>37</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>F</td>\n",
              "      <td>France</td>\n",
              "      <td>Seine (Paris)</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>Vests</td>\n",
              "      <td>Classic Vest, L</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>64</td>\n",
              "      <td>655</td>\n",
              "      <td>552</td>\n",
              "      <td>1207</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>113036 rows × 18 columns</p>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Read the data from by uploading it. \n"
      ],
      "metadata": {
        "id": "4DNaRhOddG04"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kd9u0igoeJdT"
      },
      "source": [
        "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
        "\n",
        "## The data at a glance:"
      ]
    },
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            "text/plain": [
              "        Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
              "0 2013-11-26   26  November  2013            19     Youth (<25)   \n",
              "1 2015-11-26   26  November  2015            19     Youth (<25)   \n",
              "2 2014-03-23   23     March  2014            49  Adults (35-64)   \n",
              "3 2016-03-23   23     March  2016            49  Adults (35-64)   \n",
              "4 2014-05-15   15       May  2014            47  Adults (35-64)   \n",
              "\n",
              "  Customer_Gender    Country             State Product_Category Sub_Category  \\\n",
              "0               M     Canada  British Columbia      Accessories   Bike Racks   \n",
              "1               M     Canada  British Columbia      Accessories   Bike Racks   \n",
              "2               M  Australia   New South Wales      Accessories   Bike Racks   \n",
              "3               M  Australia   New South Wales      Accessories   Bike Racks   \n",
              "4               F  Australia   New South Wales      Accessories   Bike Racks   \n",
              "\n",
              "               Product  Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
              "0  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
              "1  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
              "2  Hitch Rack - 4-Bike              23         45         120    1366  1035   \n",
              "3  Hitch Rack - 4-Bike              20         45         120    1188   900   \n",
              "4  Hitch Rack - 4-Bike               4         45         120     238   180   \n",
              "\n",
              "   Revenue  \n",
              "0      950  \n",
              "1      950  \n",
              "2     2401  \n",
              "3     2088  \n",
              "4      418  "
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          },
          "metadata": {},
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      ],
      "source": [
        "#Get the first few rows of dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the first two rows of salaes\n"
      ],
      "metadata": {
        "id": "-KrCgLH0d9iY",
        "outputId": "82434377-29b3-4863-a0b8-863b23f85792",
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      "execution_count": 6,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "        Date  Day     Month  Year  Customer_Age    Age_Group Customer_Gender  \\\n",
              "0 2013-11-26   26  November  2013            19  Youth (<25)               M   \n",
              "1 2015-11-26   26  November  2015            19  Youth (<25)               M   \n",
              "\n",
              "  Country             State Product_Category Sub_Category  \\\n",
              "0  Canada  British Columbia      Accessories   Bike Racks   \n",
              "1  Canada  British Columbia      Accessories   Bike Racks   \n",
              "\n",
              "               Product  Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
              "0  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
              "1  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
              "\n",
              "   Revenue  \n",
              "0      950  \n",
              "1      950  "
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "#Get the last two rows of Sales dataset\n"
      ],
      "metadata": {
        "id": "yMoeyHZwq__q",
        "outputId": "4df6c3c7-2af4-4145-fb11-97abbda92ec5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date  Day  Month  Year  Customer_Age       Age_Group  \\\n",
              "113034 2014-03-04    4  March  2014            37  Adults (35-64)   \n",
              "113035 2016-03-04    4  March  2016            37  Adults (35-64)   \n",
              "\n",
              "       Customer_Gender Country          State Product_Category Sub_Category  \\\n",
              "113034               F  France  Seine (Paris)         Clothing        Vests   \n",
              "113035               F  France  Seine (Paris)         Clothing        Vests   \n",
              "\n",
              "                Product  Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
              "113034  Classic Vest, L              24         24          64     684   576   \n",
              "113035  Classic Vest, L              23         24          64     655   552   \n",
              "\n",
              "        Revenue  \n",
              "113034     1260  \n",
              "113035     1207  "
            ],
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              "\n",
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              "      <th>113034</th>\n",
              "      <td>2014-03-04</td>\n",
              "      <td>4</td>\n",
              "      <td>March</td>\n",
              "      <td>2014</td>\n",
              "      <td>37</td>\n",
              "      <td>Adults (35-64)</td>\n",
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              "      <td>France</td>\n",
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              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>64</td>\n",
              "      <td>655</td>\n",
              "      <td>552</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "KCMFJAyxeJdV",
        "outputId": "d37d1313-20fe-43c0-c193-0e43331bdca1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(113036, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "#Get the number of columns and rows of data\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the number of columns and rows of data seperately\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J62zKnPFKAee",
        "outputId": "dbdee214-6ca7-4341-fb36-048551ef08ca"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "113036\n",
            "18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "peHroU1YeJdW",
        "outputId": "077f4bc2-e103-4546-c8cf-57f91cd1dafb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 113036 entries, 0 to 113035\n",
            "Data columns (total 18 columns):\n",
            " #   Column            Non-Null Count   Dtype         \n",
            "---  ------            --------------   -----         \n",
            " 0   Date              113036 non-null  datetime64[ns]\n",
            " 1   Day               113036 non-null  int64         \n",
            " 2   Month             113036 non-null  object        \n",
            " 3   Year              113036 non-null  int64         \n",
            " 4   Customer_Age      113036 non-null  int64         \n",
            " 5   Age_Group         113036 non-null  object        \n",
            " 6   Customer_Gender   113036 non-null  object        \n",
            " 7   Country           113036 non-null  object        \n",
            " 8   State             113036 non-null  object        \n",
            " 9   Product_Category  113036 non-null  object        \n",
            " 10  Sub_Category      113036 non-null  object        \n",
            " 11  Product           113036 non-null  object        \n",
            " 12  Order_Quantity    113036 non-null  int64         \n",
            " 13  Unit_Cost         113036 non-null  int64         \n",
            " 14  Unit_Price        113036 non-null  int64         \n",
            " 15  Profit            113036 non-null  int64         \n",
            " 16  Cost              113036 non-null  int64         \n",
            " 17  Revenue           113036 non-null  int64         \n",
            "dtypes: datetime64[ns](1), int64(9), object(8)\n",
            "memory usage: 15.5+ MB\n"
          ]
        }
      ],
      "source": [
        "#Get the summary of Slaes data such as coulmn names, cloumn data types, count of non null element in column.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "HY1sSaVJeJdX",
        "outputId": "ed3fec38-09b0-4827-eaca-882fe88161d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Day           Year   Customer_Age  Order_Quantity  \\\n",
              "count  113036.000000  113036.000000  113036.000000   113036.000000   \n",
              "mean       15.665753    2014.401739      35.919212       11.901660   \n",
              "std         8.781567       1.272510      11.021936        9.561857   \n",
              "min         1.000000    2011.000000      17.000000        1.000000   \n",
              "25%         8.000000    2013.000000      28.000000        2.000000   \n",
              "50%        16.000000    2014.000000      35.000000       10.000000   \n",
              "75%        23.000000    2016.000000      43.000000       20.000000   \n",
              "max        31.000000    2016.000000      87.000000       32.000000   \n",
              "\n",
              "           Unit_Cost     Unit_Price         Profit           Cost  \\\n",
              "count  113036.000000  113036.000000  113036.000000  113036.000000   \n",
              "mean      267.296366     452.938427     285.051665     469.318695   \n",
              "std       549.835483     922.071219     453.887443     884.866118   \n",
              "min         1.000000       2.000000     -30.000000       1.000000   \n",
              "25%         2.000000       5.000000      29.000000      28.000000   \n",
              "50%         9.000000      24.000000     101.000000     108.000000   \n",
              "75%        42.000000      70.000000     358.000000     432.000000   \n",
              "max      2171.000000    3578.000000   15096.000000   42978.000000   \n",
              "\n",
              "             Revenue  \n",
              "count  113036.000000  \n",
              "mean      754.370360  \n",
              "std      1309.094674  \n",
              "min         2.000000  \n",
              "25%        63.000000  \n",
              "50%       223.000000  \n",
              "75%       800.000000  \n",
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              "      <td>2.000000</td>\n",
              "      <td>-30.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>2013.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>63.000000</td>\n",
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              "    <tr>\n",
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              "      <td>2014.000000</td>\n",
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              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>223.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>358.000000</td>\n",
              "      <td>432.000000</td>\n",
              "      <td>800.000000</td>\n",
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              "      <td>87.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>2171.000000</td>\n",
              "      <td>3578.000000</td>\n",
              "      <td>15096.000000</td>\n",
              "      <td>42978.000000</td>\n",
              "      <td>58074.000000</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d66b4766-b257-4d6c-ba3b-3af6f4f167d1');\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": 13
        }
      ],
      "source": [
        "#Get the summary statistics of numerical varaible in sales dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get the column names of dataset\n"
      ],
      "metadata": {
        "id": "zR7Ib6rfpYS7",
        "outputId": "4d8f3752-2317-4680-b9af-1c2d48d1f281",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Day', 'Month', 'Year', 'Customer_Age', 'Age_Group',\n",
              "       'Customer_Gender', 'Country', 'State', 'Product_Category',\n",
              "       'Sub_Category', 'Product', 'Order_Quantity', 'Unit_Cost', 'Unit_Price',\n",
              "       'Profit', 'Cost', 'Revenue'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTAj7BE-eJdY"
      },
      "source": [
        "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
        "\n",
        "## Numerical analysis and visualization\n",
        "\n",
        "We'll analyze the `Unit_Cost` column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "HCz0m00ZeJdZ",
        "outputId": "5f729ea9-bcd8-43af-c86e-07a823ac30d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    113036.000000\n",
              "mean        267.296366\n",
              "std         549.835483\n",
              "min           1.000000\n",
              "25%           2.000000\n",
              "50%           9.000000\n",
              "75%          42.000000\n",
              "max        2171.000000\n",
              "Name: Unit_Cost, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#Get the summary statistics of Unit_Cost variables\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "sH-Y7p_ceJda",
        "outputId": "b938b90d-4017-4904-f3e8-c93765b9ee97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "267.296365759581"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#Get the mean of Unit_Cost variables\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "9gnMno4VeJda",
        "outputId": "6c727768-5a71-45a9-ec10-d2707a5fad02",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#Get the median of Unit_Cost variables\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfEroaGteJdb",
        "outputId": "64dd600e-2fbb-4217-9deb-bd808bbaa2fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdccd250730>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": [
        "#Get the box plot of  Unit Cost in Sales data-(kind='box')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bd4H2UsfeJdb",
        "outputId": "e9bf16ba-b2f5-4af9-875b-9ac9ce072ae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc44575a410>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAKA8EJgA7CcVcg61S57EDBMAACh9BCYA+8lO04fUDBNNHwAAQGkjMAHYz1tNHw5lhinZMIKSPAAAUOoITAD2MxiOqcqk2uqpfz3Q9AEAAJQLAhOA/QTDMTUE/DKzKZ+DtuIAAKBcEJgA7GcwHDukcjxJqq/xyYzABAAASh+BCcB+BiOxQ2r4IElmpoYa/8h6KAAAgFJFYAKwn2A4fsiBSUo2fmCGCQAAlDoCE4D9BENRNXpd7g5FQ8CvYITABAAAShuBCcB+BsNxNdQc+gxTY8DPDBMAACh5BCYA+wlmoemDRGACAADlgcAEYD/ZaPogJUvyBkIEJgAAUNoITAD2MxjOTmBqDPg1yBomAABQ4ghMAEaEY3FF4y5LTR98GqStOAAAKHEEJgAjUgEnWyV5QdYwAQCAEkdgAjAi1aQhKyV5NX5FYglF44lDPhcAAEChEJgAjEjNCGWjS14qdNEpDwAAlDICE4ARWZ1h8s5BWR4AAChlBCYAI96aYcpG04fUDBONHwAAQOkiMAEYkd2mD8nQxQwTAAAoZQQmACNGSvJqsleSxxomAABQyghMAEZks+lDYy2BCQAAlD4CE4AR2Wz6kJqlGiAwAQCAEpZRYDKze83sEjMjYAFlLBiJqcZXpRr/of9RpyQPAACUg0yvim6S9FFJm8zsH8xsaQ7HBKBABsOxkWYNh4r7MAEAgHKQUWByzj3snPsjSSdLekPSw2b2v2b2p2ZWncsBAsifwXA8K+V4klTjT85UUZIHAABKWcZ1N2Y2XdLHJf25pBckfU/JAPXQOMesNLONZrbZzK4b4/mAmf3ce/4ZM1s46rnrve0bzexCb1utma02s5fMbJ2Z/U2m4wcwsWA4lpWGDymNAT8zTAAAoKRldGVkZvdJWirpNknvdc7t9p76uZmtSXOMT9IPJF0gaYekZ81slXNu/ajdrpa0zzm32MyukPQdSR8xs2WSrpC0XNJcJWe0jpIUlnS+cy7ozWw9aWb/45x7epLvG8AYkiV52Q5M3LgWAACUrkxnmP7DObfMOff3qbBkZgFJcs6tSHPMaZI2O+e2OOciku6UdOkB+1wq6Vbv8T2S3mlm5m2/0zkXds5tlbRZ0mkuKejtX+394zJ8DwAmkO3A1BDwayDEDBMAAChdmQamvxtj21MTHDNP0vZRP+/wto25j3MuJqlP0vTxjjUzn5m9KKlT0kPOuWfGenEzu8bM1pjZmq6urgmGCkBKleRlp+mDJDUGfJTkAQCAkjbur5LNbLaSQaXOzE6SZN5TzZLqczy2MTnn4pJONLNWSfeZ2bHOubVj7PdjST+WpBUrVjALBWRgMBwfuX9SNjQE/OoZjGTtfAAAAPk20ZXRhUo2epgv6cZR2wckfWWCY3dKWjDq5/netrH22WFmfkktkrozOdY512tmj0laKemgwARg8nKxhmlbz1DWzgcAAJBv45bkOedudc6dJ+njzrnzRv3zPufcvROc+1lJS8xskZnVKNnEYdUB+6ySdJX3+DJJjzrnnLf9Cq+L3iJJSyStNrN2b2ZJZlanZEOJVyfxfgGk4ZzTYCT7XfKCrGECAAAlbKKSvI85526XtNDMvnjg8865G8c4LPVczMw+I+lBST5Jtzjn1pnZtyStcc6tknSzpNvMbLOkHiVDlbz97pK0XlJM0rXOubiZzZF0q9eBr0rSXc65+6fwvgEcYDgaV8Ip600fWMMEAABK2URXRg3evxuncnLn3AOSHjhg29dHPQ5JujzNsTdIuuGAbS9LOmkqYwEwvqAXbLLb9MGvwUhciYRTVZVNfAAAAECRGTcwOed+5P2bG8QCZS51v6Rsr2GSpMFITE211Vk7LwAAQL5k1FbczP7RzJrNrNrMHjGzLjP7WK4HByB/UqVz2S7JS56bm9cCAIDSlOl9mN7tnOuX9B5Jb0haLOmvcjUoAPmXusFsU20WZ5i8cwVZxwQAAEpUpoEpdQV1iaS7nXN9ORoPgAJ5aw1TNkvyfPudGwAAoNRkemV0v5m9KmlY0qfMrF1SKHfDApBvgzkITKmb4NIpDwAAlKqMZpicc9dJerukFc65qKRBSZfmcmAA8msgF4EpQEkeAAAobZO5MjpayfsxjT7mp1keD4ACSd1gtjGLa5hS66G4eS0AAChVGV0Zmdltko6U9KKkVLsrJwITUDYGwzFVmVRXnb37MDWMaisOAABQijL9VfIKScuccy6XgwFQOMFwTA0Bv8yyd4PZRkryAABAicu0S95aSbNzORAAhRUMx9SUxfVLkhTwV8lfZTR9AAAAJSvTq6MZktab2WpJ4dRG59z7cjIqAHkXDMWyun5JksxMDQE/a5gAAEDJyvTq6Ju5HASAwkuV5GVbY8CvYDg+8Y4AAABFKKOrI+fcE2Z2uKQlzrmHzaxeUvZWhgMouGA4NtLVLpsaA35K8gAAQMnKaA2TmX1C0j2SfuRtmifpl7kaFID8y1Vgagj4aPoAAABKVqZNH66VdKakfklyzm2SNDNXgwKQf8FQTA01uQhMfgITAAAoWZkGprBzLpL6wbt5LS3GgTIyGM5+0weJkjwAAFDaMg1MT5jZVyTVmdkFku6W9OvcDQtAPiUSTsFI9tuKS6mmDwQmAABQmjINTNdJ6pL0iqT/I+kBSV/L1aAA5NdQNC7nlJMueZTkAQCAUpZpl7yEmf1S0i+dc105HhOAPEuVzOWyJM85JzPL+vkBAAByadwZJkv6ppntlbRR0kYz6zKzr+dneADyYcC7sWxjLkryav1KOCkUTWT93AAAALk2UUneF5Tsjneqc67NOdcm6XRJZ5rZF3I+OgB5kSqZy0VgSpX5DYSjWT83AABArk0UmP5Y0pXOua2pDc65LZI+JulPcjkwAPkzmMPA1Bjwea8Rz/q5AQAAcm2iwFTtnNt74EZvHVN1boYEIN9SJXm5aPrQGEh+VdBaHAAAlKKJAlNkis8BKCGpkrymHDR9aPBmmFKhDAAAoJRMdHV0gpn1j7HdJNXmYDwACiC3JXn+/V4DAACglIx7deSc8+VrIAAKJzXDlJuSPP9+rwEAAFBKMr1xLYAyNhCKqdpnCviz/5WQurfTAIEJAACUIAITAA2GY2oM+HNyY9nm2mTTh4EQbcUBAEDpITABUDAcy0k5niQF/FXyV5mCNH0AAAAliMAEQAOhWE4aPkiSmamp1k+XPAAAUJIITAA0GI7lpKV4SlNtNSV5AACgJBGYAOS0JE9KdsqjSx4AAChFBCYAI00fcqWp1q9+SvIAAEAJIjAB0EBeSvIITAAAoPQQmAAoGIqpoSa3M0zBMGuYAABA6SEwARUunnAajsZHbjCbC3TJAwAApYrABFS4VDOGXK9hGgjF5JzL2WsAAADkAoEJqHD5CEyNgWrFE06haCJnrwEAAJALBCagwg2mAlOOS/IkcS8mAABQcghMQIVLrS3KdUmeJFqLAwCAkkNgAipcvtYwjX4tAACAUkFgAipcMJSPkrxqSZTkAQCA0kNgAipcKsQ0e6EmF0ZmmCjJAwAAJYbABFS4fi8wNeVwhilV7se9mAAAQKnJaWAys5VmttHMNpvZdWM8HzCzn3vPP2NmC0c9d723faOZXehtW2Bmj5nZejNbZ2Z/kcvxA5VgIBRTlUkNNbkvyeunJA8AAJSYnAUmM/NJ+oGkiyQtk3SlmS07YLerJe1zzi2W9F1J3/GOXSbpCknLJa2UdJN3vpikv3TOLZP0NknXjnFOAJPQPxxVY8CvqirL2WukZpho+gAAAEpNLmeYTpO02Tm3xTkXkXSnpEsP2OdSSbd6j++R9E4zM2/7nc65sHNuq6TNkk5zzu12zj0vSc65AUkbJM3L4XsAyt5AKKbmutytX5IkX5WpocZHSR4AACg5uQxM8yRtH/XzDh0cbkb2cc7FJPVJmp7JsV753kmSnhnrxc3sGjNbY2Zrurq6pvwmgHLXH4qOlMzlUlNtNV3yAABAySnJpg9m1ijpF5I+75zrH2sf59yPnXMrnHMr2tvb8ztAoIT0h2JqzmHDh5TGWj8leQAAoOTkMjDtlLRg1M/zvW1j7mNmfkktkrrHO9bMqpUMSz9zzt2bk5EDFaR/OF8zTH5K8gAAQMnJZWB6VtISM1tkZjVKNnFYdcA+qyRd5T2+TNKjzjnnbb/C66K3SNISSau99U03S9rgnLsxh2MHKkZyDVPuZ5iaaqvVP0xJHgAAKC05u0pyzsXM7DOSHpTkk3SLc26dmX1L0hrn3Colw89tZrZZUo+SoUrefndJWq9kZ7xrnXNxM3uHpD+W9IqZvei91Feccw/k6n0A5a4/FM3pTWtTWuqqtb1nKOevAwAAkE05/bWyF2QeOGDb10c9Dkm6PM2xN0i64YBtT0rKXe9joMIkEk7BcH7WMDXX+plhAgAAJackmz4AyI5gJCbnlJc1TC111eobjipZdQsAAFAaCExABUs1YcjHGqbmumrFEk7D0XjOXwsAACBbCExABUuVyOVrhkmS+ijLAwAAJYTABFSwkRmmPASm1Gv0D9NaHAAAlA4CE1DBUjNM+SjJY4YJAACUIgITUMEGwvkryUuFMgITAAAoJQQmoIKlyuPy0VY8NcNEa3EAAFBKCExABRsI5XGGqZaSPAAAUHoITEAF6w/FVFtdpRp/7r8KmlMzTCECEwAAKB0EJqCCDYSieZldkiRflakp4GeGCQAAlBQCE1DB+odjeVm/lNJcV01bcQAAUFIITEAF68/jDJOUDEzMMAEAgFJCYAIqWP9wVK31eQxMtX7WMAEAgJJCYAIqWO9wdKTddz601FXTVhwAAJQUAhNQwXqHomrNY2CiJA8AAJQaAhNQoeIJp/5QVC31NXl7TWaYAABAqSEwARVqIBSVc8rvDFNttQYjcUXjiby9JgAAwKEgMAEVqncoOdOTz6YPLXXJFuYDIVqLAwCA0kBgAipU73ABApP3WqxjAgAApYLABFSo3qGIJKmlLr9rmCQCEwAAKB0EJqBC9RVghqnVazCxzwtrAAAAxY7ABFSokTVMeWz6MC0VmAYJTAAAoDQQmIAKlQpM+bxx7TRvNmvfECV5AACgNBCYgArVOxxRU8Avvy9/XwPNtdWqMmaYAABA6SAwARWqbyiq5jzOLklSVZWptb6GNUwAAKBkEJiACtU3HM1rw4eUafXVI+WAAAAAxY7ABFSo3oIFphr1UJIHAABKBIEJqFC9QxG15vEeTCmU5AEAgFJCYAIqVN9wVC0FmGFqa6AkDwAAlA4CE1CBnHPqHYrm9R5MKdPqa9QzFJFzLu+vDQAAMFkEJqACDUbiiiVcQdYwtdbXKBJLaDgaz/trAwAATBaBCahAvd4aokKsYWprSIY0Gj8AAIBSQGACKtC+weQaokKsYWqtT4Y01jEBAIBSQGACKlD3YFiSNKMx/zNM07zARKc8AABQCghMQAXqDibDyvSGQN5fm5I8AABQSghMQAVKhZW2AswwUZIHAABKCYEJqEDdgxFV+0xNAX/eXzvVypySPAAAUAoITEAF6g6GNb0hIDPL+2v7fVVqrvVrHyV5AACgBBCYgArUMxhRW0P+y/FS2hpq1E1gAgAAJYDABFSgvYMRTS/A+qWU9qaA9gbDBXt9AACATBGYgArUMxjW9ALOMM1oDKhrgMAEAACKH4EJqEA9wYimN+a/pXhKcoaJkjwAAFD8CExAhQlF4xqMxAu6hmlGY0B9w1GFY/GCjQEAACATBCagwqSaLcwo4BqmGd7sVjezTAAAoMjlNDCZ2Uoz22hmm83sujGeD5jZz73nnzGzhaOeu97bvtHMLhy1/RYz6zSztbkcO1Cuur1mC20NhS3Jk0TjBwAAUPRyFpjMzCfpB5IukrRM0pVmtuyA3a6WtM85t1jSdyV9xzt2maQrJC2XtFLSTd75JOk/vW0ApiA1w1TILnmp2S0aPwAAgGKXyxmm0yRtds5tcc5FJN0p6dID9rlU0q3e43skvdOSd9K8VNKdzrmwc26rpM3e+eSc+52knhyOGyhrqTK4QnbJY4YJAACUilwGpnmSto/6eYe3bcx9nHMxSX2Spmd47LjM7BozW2Nma7q6uiY5dKB89QymSvIKv4aJTnkAAKDYlW3TB+fcj51zK5xzK9rb2ws9HLfuei0AAB8qSURBVKBodA9GVOOvUmPAX7Ax1Fb71BTwU5IHAACKXi4D005JC0b9PN/bNuY+ZuaX1CKpO8NjAUxB10BY7Y0BJatfC6e9KaAuSvIAAECRy2VgelbSEjNbZGY1SjZxWHXAPqskXeU9vkzSo845522/wuuit0jSEkmrczhWoGJ09Ic0q7lwHfJSZjQGtJcZJgAAUORyFpi8NUmfkfSgpA2S7nLOrTOzb5nZ+7zdbpY03cw2S/qipOu8Y9dJukvSekm/kXStcy4uSWZ2h6SnJC01sx1mdnWu3gNQjvb0hTSrubbQw2CGCQAAlIScLmJwzj0g6YEDtn191OOQpMvTHHuDpBvG2H5llocJVJTO/rDOWlL4dX0zGmuYYQIAAEWvbJs+ADjYYDimgXCsaGaY+kMxhaLxQg8FAAAgLQITUEE6+kOSpNkthV/DlAptqTEBAAAUIwITUEH2eOFkVlPhZ5jmttZJknb2Dhd4JAAAAOkRmIAKkprNmdVSPIFpdy8zTAAAoHgRmIAK0tGfbLJQDGuY5nihbXcfM0wAAKB4EZiACrKnL6TGgF+NgZw2yMxIbbVPbQ012tXHDBMAACheBCagghTLTWtT5rTUahdrmAAAQBEjMAEVJBmYCl+OlzK3tY41TAAAoKgRmIAK0tEf1uxiCkwttdrFGiYAAFDECExAhUgknDoHQppZRIFpTmudBkIxDYSihR4KAADAmAhMQIXoGAgpGneaP62u0EMZ8VanPMryAABAcSIwARViW/eQJOmwtvoCj+Qt87x7MdH4AQAAFCsCE1AhtvUUX2Cak7p5LTNMAACgSBGYgAqxvWdIVZbsTFcsZjUF5K+ykTAHAABQbAhMQIXY1jOkOS11qvEXzx97v69Kh7XV6429g4UeCgAAwJiK58oJQE5t6xkqqnK8lEUzGrSVwAQAAIoUgQmoENt6hosyMC2c0aA3ugeVSLhCDwUAAOAgBCagAgxFYtobDOuw6cUXmBbNaFAomtCefho/AACA4kNgAirA9p5k2+4FRTjDdMSMBkliHRMAAChKBCagAhRjS/GUhV5g2kJgAgAARYjABFSAN7uTYaQYA9Ps5lrVVlfR+AEAABQlAhNQAV7dM6AZjQG1NdQUeigHqaoyLZzeQEkeAAAoSgQmoAJs3DOgo2c3FXoYaS2a0UBJHgAAKEoEJqDMxRNOmzoHtLSIA9PS2U16o3tQQ5FYoYcCAACwHwITUOa29QwpFE0UdWBaPrdFzkkbdvcXeigAAAD78Rd6AABya+OeZAgp5pK8Y+c1S5LW7uzXKYe3FXg0QHZ09If0w8df14Pr9qhzIKwF0+r0vhPn6ZPnHKH6Gv76BYBSwTc2UOZe3TMgM2nJzOINTLObazW9oUZrd/YVeihAVjz2aqe+eNeLCoZjumDZLC2c3qB1u/r1b49s0qoXd+r7V56s4+a3FHqYAIAMEJiAMrdxz4AWTm9QXY2v0ENJy8y0fF6L1u6iJA+l75cv7NQX7npRR89u1v/30ZN0ZHvjyHNPvd6tL939kj76H0/rP//sNJ1y+LQCjhQAkAnWMAFl7tU9A1o6q3hnl1KOndusTR0DCkXjhR4KMGUPre/QF+96UW9bNF33furt+4UlSTrjyOm6+5NnaHpjja66ZbU2dQwUaKQAgEwRmIAytm8woq17B3XCgtZCD2VCx85rUSzh9BoXkChRW7qC+sLPX9Rx81p088dXpJ3Vndtap5994m2qra7Sn/90jXqHInkeKQBgMghMQBl7Yfs+SdJJhxV/YEqFujVv7CvwSIDJG4rE9Knbn1e1z3TTx06ZsKnDvNY6/eiPT9Gu3mF9+Z6X5ZzL00gBAJNFYALK2PNv9spXZTq+BBaXz2ut06IZDfrD5r2FHgowKc45ffW+tXqtc0Dfu+IkzWuty+i4Uw5v05cvPFq/Xd+hO1Zvz/EoAQBTRWACythTW7p17LyWkmlhfObi6Xp6S7ei8UShhwJk7GfPbNN9L+zU5995lM4+qn1Sx179jkU6a8kMfev+ddrcGczRCAEAh6I0rqIATFowHNNL23t1zdlHFHooGXvH4hm6/elteml7r1YszN/9mAZCUf12XYee2tKt9bv6tac/pGg8IZPUUl+t2c21mtVcqzkttZrbWqc5LXWa21qrOS11amuoka/K8jZWFJeXtvfqW79er3OXtuuz5y+e9PFVVaZ/ufwErfze7/W5O17Qfde+XQF//jpahqJxPb6xS89s7dbmzqCC4Zjqqn2aP61OKw5v0/nHzNSMxkDexgMAxYjABJSpp1/vVizh9PYjZxR6KBk744gZqjLpyc178xKYtvcM6d+feF33PLdD4VhC0+qrdfz8Vp14WKtqfFVyzql3OKo9fSGt3dmn367vUCS2/+xXlUltDQHNaKzR9MYazWgMaHpDQDOaanTM7GadsnCammurc/5ekH/7BiP69M+eV3tTQN/98ImqmmJwntlcq3/80PH685+u0T/+ZqP++j3LsjzSg3UOhPST32/Vfz2zTcFwTLXVVTpqVpNa6qo1FInr4Q2dumvNDvmrTBcun63PvXOJlhbxza8rTUd/SHuDYdX4qrRwRoOqfRQMAblEYALK1MMbOtQY8OvURaVzn5cWL7A8sqFTn3/XUTl7nVA0rn9/4nXd9PjrkpM+ePI8ffjUBTppQavM0l/0OufUMxjR7r6QdvUOa3df8qJlbzCivcGwuoNhvbi9V3sHwhqMJNujm0mnHt6mD50yTxcfN0dNhKeyEIkl9Mnbn1PXQFj3fOoMTWuoOaTzvWvZLF11xuG6+cmtOnPxdJ1/9KwsjXR/kVhCt/7vG/reI5s0HI3rkuPm6MMrFuhtR7TJP+qi2zmnV/cM6N7nd+jO1dv1wNrd+sBJ8/SVi49hxqlAOvpDuu2pN3XfCzu1s3d4ZHuNv0rnHNWuPzr9MJ1zVPu432EApsYqoTPPihUr3Jo1awo9DCBv4gmn07/9iE4/ok0/+OjJhR7OpNzy5FZ96/71evDzZ+fkN9rrdvXps3e8oC1dg3rP8XP0lYuP0dwMF+lPRjAc08vbe/X01h7d//IubekaVHOtX39+1hH6+JkLi2bWKZFw6hgIqW84qlq/T/Om1fHb6gk45/Tle17W3c/t0L9+5ES9/6R5WTlvKBrXB276X3X0h/Q/f3GWZjXXZuW8KZs6BvS5O1/Uht39Om9pu77+3uVaNKNhwuP2DUb0wyde13/+4Q01BHz65vuW630nzOXCPE8isYR++Pjr+uETmxWJJXTu0pl6x+IZmttap+FoTC9t79P9L+/W3mBYpy9q0w0fOE6LZzZOfGIABzGz55xzKw7aTmACys/vXuvSn9yyWjf90cm6+Lg5hR7OpHQHwzr924/oz96xSF+5+Jisndc5p9ueflN/d/8GTWuo1j9ffoLOWjK5BfqH8trPb+vVvz/xuh5a36Fp9dW6/uJjdPkp8wty0RmNJ/SbtXv0qxd36qnXu0dmwySp2mc6YX6rVh47W5edMl+t9Yc2c1JunHP67sOb9G+PbNLn3rlEX7wguzOhr3cF9Z5/e1InLmjVbVeftt+sz1Q55/TTp97Utx/YoMaAX9/+4HG6cPnsSZ9nU8eA/uqel/Xi9l5dfNxs3fD+4w55Zg3je2PvoD57xwt6ZWefLjlujv7vyqN12PT6g/aLxhO6c/U23fjQaxqKxHXdRUfrqjMWTrlMFKhUBCYCEyrIZ+94QU9s7NSzX3tXXheQZ8s1P12j57f16qnrz8/KbEffUFRf/sVLenBdh85b2q5/vvwETS9QWdHanX365qp1WvPmPp26cJpu+MBxOmpWftaGxOIJ/eyZbfr3J17X7r6Q5rXW6dyl7Vo2t1nT6ms0FIlrU+eAnty0V+t29auu2qePnLpAnz7vSM1syu5sRylKJJz+6bcb9cPHX9dlp8zXP112fE4C7z3P7dCX7n5JH3/7Qn3zfcsP6VzdwbD+6p6X9eirnTrnqHb90+XHH9L/y3jC6ce/26IbH9qoafU1+qfLT9A5k+wMiMz88oWd+up9r8jvq9J3PnS8Vh47ccjtHAjp/97zsh7b2KVzl7brXwr4XQeUIgITgQkVYnffsM76zmP6kzMW6uvvzf3i8Vx4ZEOHrr51jW788An64MnzD+lcz725T5+74wV19Id03UVH68/OXFTw37omEk53P7ddf/8/r2owHNOnz12sT593ZE7D7dNbuvXNVev06p4Bnb6oTZ846widd/TMtB3+Nuzu109+v1W/enGnaqt9uva8xfrTMxeqtrr0Ang2dPaH9OVfvKzHN3bpytMO0w3vPzann6O/u3+9fvLkVn3tkmP052dNrdPl7zd16Yt3vaS+oaiuv/hoffztC7MW8Nbu7NMXfv6iNnUGddUZh+u6i45RXU1lfjayLRSN629+vV53rN6mUxdO079O4t5eUnJG8fan39TferPp37/yZJ22KH9dR4FSRmAiMKFCfONXa3X7M9v0+JfO1YK2g0s3SkEi4XTJ95/UYDim337h7CldpEfjCX3/0c36wWObNbe1Vt+/8mSduKA1B6Oduu5gWH97/3r98sVdWjKzUf/woeN1yuHZbdKxu29Y337gVf36pV2a11qnv37PMl24fFbGF85b9w7qhv/eoIc3dGhBW52+evHkji8Gzjlt6xnSht0DerN7UN2DEcUTTn6faWZTrWY1BzS7uVazW2o1s6lWNf7krGY0ntCruwe06qWduv3pbYonnP76vcv0sdMPy/n7jyecPnfHC/rvV3brqxcfo09M4vYAoWhc333oNf3od1u0eGajvn/lSTpmTnPWxxiKxvWPv9moW/6wVUe2N+hfP3KSjiuBm2QXs+09Q/r0z57XKzv79Klzj9RfXnDUlMsy1+7s02f+63lt6xnSFy84Sp8+d3HBf1kEFDsCE4EJFWBz54Au+t7vddkpC/T3Hzyu0MM5JL/f1KU/vnm1Pnv+Yv3lu5dO6tjXu4L64l0v6aXtvfrgSfP0zUuXF02ThbE89mqnvnrfK9rdH9JVZyzUX124VA2BQ2tiOhyJ6+Ynt+imx19XLOH0qXOO1CfPOXLKswC/39Slv71/vV7rCOrMxdP1jfcuz1sp4VQ45/Tcm/t07ws79cTGrv26igX8VfJXmaJxp8gBN0k2k6Y31MjM1DsUUTTu5KsyXXTsbH3p3Uu1MIMmCdkSjSf0F3e+oAde2aMrTl2gb75v+bi/PHDO6bGNnfrGqnXa3jOsj55+mP76kmU5n/n5w+a9+su7XtLeYFiff9cSffKcI7Oy9qrSPLhuj758z8tKOKcbP3yiLlh26J0Sg+GYvnLvK1r10i6dtWSGbvzwiWpvokQPSIfARGBCmQvH4vrwj57Wm92DeugL55TFX4pfuvsl/eL5HfrRx07RuzNYpB4Mx/T9Rzfplie3qr7Gr29/4DhdcnxpNL0IhmP6x9+8qtueflMzGgP6/LuW6MMrFkx6DVci4XTfCzv1z7/dqN19Ib172Sx97ZJlYy4Un6xYPKHbn35TNz70mgYjcf3x2w7XZ89fXFRrJLb3DOne53fq3hd26M3uIdXX+HT2knaduXi6TlwwTYfPqB8Jz8459Q1H1dEf1u6+YXX0h7S7L6SO/rAkp9b6Gh01q1HnHDVTbQVqbhBPON340Eb94LHXNa+1Tl+44Chdctyc/ULQcCSuhzd06P//w1Y9v61Xi2c26m8vPVZnHDk9b+PsG4rqa79aq1+/tEunHD5N/3z5CRl14EOyC+Hf/HqdfvniLi2b06wffuxkHT49e//tnHO689nt+uaqdWquq9a/XH6CzmbdGTAmAhOBCWUsFI3rc3e8oN+u79C/f+xkrTy2NELCRIYiMV35H89o3c4+fenCpfrEWUeMueZmT19Itz39hn72zDb1DkV12Snz9eWVS0uyUcHz2/bp7x/YoGff2Kf50+r00dMP04dXLJjw3jf7BiO674WduvWpN/Rm95COn9+ir158jE4/IvsXzT2DEf3LbzfqjtXbFPD79NHTD9OfnrlQ86cVpgR0IBTV/7yyR/c8v0Ort/bITDrjiOn60MnztfLY2Yc8W1cMRq9Bq62u0tJZTWqqrda+oYg2dQQViSc0r7VOnzznCH3k1MNGygrz7Vcv7tTXfrlWw5G4Ljtlvq49b3HJlgbn2nAkrv/83zf0w8c3aygS12fOX6xPn7s4Z//vNuzu17X/9by2dA3qXcfM1HUXHa3FM4t3lhgohIIEJjNbKel7knySfuKc+4cDng9I+qmkUyR1S/qIc+4N77nrJV0tKS7pc865BzM551gITChnr+7p11fufUUvbO/VN96zTB8/c1Ghh5RVfcNRXfeLl/U/a/doXmudLjl+jg6fXq+ES84mPPfmPj2/bZ8k6d3LZunT5y7WCUW2VmmynHN69NVO/eT3W/XUlm6ZScfPa9Hbjpyuw9saNLslIJOpayCsrd2DWvNGj557c58STjr5sFZd/Y4jdNGxs3O+XmFzZ1A3Pb5Zv3pxlxLO6R2LZ+i9J8zVuUvbcx5WO/pDenxjpx7f2KXHNnYqFE3oiBkN+uDJ8/SBk+dPapF8qUgknFa/0aPfruvQax0DGo7G1RDw65g5TTr3qJk6bVFb2iYe+dTZH9IPHtusO1ZvVzSR0NlL2nXZKfN19lHtaqkrXGmsc057gxHt6QupcyA5k9jRH1LPYEShaFzhWEJx51Tr96m2ukrNddVqbwyovSmgmU0BzW5JrnM7lOYsiYTT+t39uvf5nbrvhR3aNxTV+UfP1JdXLtXRs7O/zuxAoWhct/xhq2567HUFwzGd7d3w9uwl7TTtAFSAwGRmPkmvSbpA0g5Jz0q60jm3ftQ+n5Z0vHPuk2Z2haQPOOc+YmbLJN0h6TRJcyU9LCl1s4txzzkWAhPKhXNOwXBMr3cNau3OPj28oUO/e61LzXXV+rv3H6v3HD+30EPMCeecfrN2j25/5k2t3tqjaDz5vVXjr9Ixs5t07tKZ+uDJ87JaxlIsNnUM6L9f2a0nN+3Vi9t7FUvs/53trzIdPadJ5y2dqQuXz9ax8/K/6H5n77DuXrNdd6/ZMbJW6KhZjVo+t0XL5zZr0YyG5MVmc62aaqsz+g26c06haEL9oah294W0q3dYb3QPat2ufq3b2ac3uockSbOba3XBsln64MnzdOKC1pJqRlHudvcN647V23X3mu3a3ReSr8p0wvwWLZ/boqPnNGlxe6NmNtdqZlPgkGYBnXMajsbVMxgZ+ac7GNHuvmHt7B3Wjn3D2rkv+TgcSxx0fGt9teqqfQr4q1RlpnAsoVA0rv5QdOS7ZrTpDTWa3VKrOS11muOFqLmttWprCKjGV6VAdZVqfFUajsYVDMW0byiiN/YOalNnUKu39qh7MKJqn+ndy2br42cu1KkL89/BrjsY1u1Pb9N/rX5THf1hBfxVOv2I6TpxfouOntOs+dPqNLu5VtMbA0URwoF8KURgOkPSN51zF3o/Xy9Jzrm/H7XPg94+T5mZX9IeSe2Srhu9b2o/77BxzzmWYglM//Tgq/rf17sP2j7e/4K0T41zULpnxn+dcc6X5qmpjHsqn7epjDvbY0v//yE/r+OcNByNjyxCT5nXWqf3nzRXnzjriIq5wWgsnlBXMCxflWlafU1W7tNUKmLxhDoHkr8Vl6S2hhrNa60rmgX2ziV/e/7Yq516fluv1u3q89YD7a/aZ6qr9qm+xj9yMeacU8Il/0yHogkFwzHFEwf/iVjQVqdj57bohAWtOueodh09u4mQVOTiCafnt+3TExu79PSWbr26Z0DBcGy/fap9lpzZqUnO7tT4qsb8/+pcslFHOJoMNaFYQpExQlDKjMbkn5F50+qS/26t05zWOs3ygtqMxkDaAJ9IJNe4dQWTf+b29CXXuO3uC2lP3/DI477h6IT/DapMWtBWr1MOm6a3HTld7zpmVsHWxY0WjSf01Ovdemxjp/6wea82dwY1+o+dmVRX7VNdtU+11T7V1fjkM5OZZGYyb5+q1DZ52/kjiUm4+/+cUTR/j6ULTLks7J4nafuon3dIOj3dPs65mJn1SZrubX/6gGPneY8nOqckycyukXSNJB122GFTewdZVuv3qTHNb9HG+ws/3TPjfSGlP2byrzP+a41zvjRPTeV1bCqvM+5/n8kPLpv/Tafy/6622qfW+hq11ldr4fQGLZ+b/C1gpV0s+n1VmtNSfuVWmfD7qjS3tU5zi7TczMy8WaW3Zrn2BsPa3jM0csE5EIppKBrXcCSuoUhMsYSTyVRl8i64TLXVVWqs9asxUK2mWr9mN9dqrnfRW8iSLkyNr8p06sK2kZkU55x27BvW1r2D2hsMq2sgrN7haDIAeUEoHIun/Z6u8VeptrpKAb9Pgeoq1fqTF/Jt9TWa1lCjNu+f2c21h1RmVlVlmtaQPOd4HSEHwzHt6Q+pdyiisBfgIrGE6mqSf+c311Vr/rS6oryJeLWvSmcf1T7SBCIUjWtTR3CkCUrXQFjD0biGo3ENReIKReOKJ5yckxesko+dpIRLbS//tfHIrlK4jin9lbBpOOd+LOnHUnKGqcDDkSR99p1LCj0EAMirGY2BCRtWoLKYmRa01ZdNM4iGgF9HtjcWehhZUVvt03HzW7ifFnCAXM5/7ZS0YNTP871tY+7jleS1KNn8Id2xmZwTAAAAALIil4HpWUlLzGyRmdVIukLSqgP2WSXpKu/xZZIedckFHqskXWFmATNbJGmJpNUZnhMAAAAAsiJnJXnemqTPSHpQyRbgtzjn1pnZtyStcc6tknSzpNvMbLOkHiUDkLz97pK0XlJM0rXOubgkjXXOXL0HAAAAAJWNG9cCAAAAqHjpuuQVRw8/AAAAAChCBCYAAAAASIPABAAAAABpEJgAAAAAIA0CEwAAAACkQWACAAAAgDQITAAAAACQBoEJAAAAANIgMAEAAABAGuacK/QYcs7MuiS9WehxHKIZkvYWehCA+CyiuPB5RDHh84hiwWdxag53zrUfuLEiAlM5MLM1zrkVhR4HwGcRxYTPI4oJn0cUCz6L2UVJHgAAAACkQWACAAAAgDQITKXjx4UeAODhs4hiwucRxYTPI4oFn8UsYg0TAAAAAKTBDBMAAAAApEFgKgJmdrmZrTOzhJmtOOC5681ss5ltNLMLR21f6W3bbGbXjdq+yMye8bb/3Mxq8vleUN7Sfe6AbDKzW8ys08zWjtrWZmYPmdkm79/TvO1mZv/mfSZfNrOTRx1zlbf/JjO7qhDvBaXNzBaY2WNmtt77e/ovvO18HpF3ZlZrZqvN7CXv8/g33vYxr/3MLOD9vNl7fuGoc415fYmxEZiKw1pJH5T0u9EbzWyZpCskLZe0UtJNZuYzM5+kH0i6SNIySVd6+0rSdyR91zm3WNI+SVfn5y2g3E3wuQOy6T+V/M4b7TpJjzjnlkh6xPtZSn4el3j/XCPph1LyglbSNySdLuk0Sd9IXdQCkxCT9JfOuWWS3ibpWu97j88jCiEs6Xzn3AmSTpS00szepvTXfldL2udt/663X9rry7y+kxJDYCoCzrkNzrmNYzx1qaQ7nXNh59xWSZuV/KI9TdJm59wW51xE0p2SLjUzk3S+pHu842+V9P7cvwNUiDE/dwUeE8qQc+53knoO2Hypkt9p0v7fbZdK+qlLelpSq5nNkXShpIeccz3OuX2SHtLBIQwYl3Nut3Puee/xgKQNkuaJzyMKwPtcBb0fq71/nNJf+43+nN4j6Z3etWK660ukQWAqbvMkbR/18w5vW7rt0yX1OudiB2wHsiHd5w7Ih1nOud3e4z2SZnmPJ/s9CUyJV850kqRnxOcRBeJVGr0oqVPJ4P260l/7jXzuvOf7lLxW5PM4Sf5CD6BSmNnDkmaP8dRXnXO/yvd4AKBUOeecmdHiFXljZo2SfiHp8865/uQv6ZP4PCKfnHNxSSeaWauk+yQdXeAhVQQCU5445941hcN2Slow6uf53jal2d6t5PS/3/tNwuj9gUM13ucRyLUOM5vjnNvtlTh1etvTfS53Sjr3gO2P52GcKDNmVq1kWPqZc+5ebzOfRxSUc67XzB6TdIbSX/ulPo87zMwvqUXJa0X+Pp8kSvKK2ypJV3hdThYpuYh0taRnJS3xuqLUKLlwb5VL3lTrMUmXecdfJYnZK2TLmJ+7Ao8JlWOVkt9p0v7fbask/YnXnextkvq8UqkHJb3bzKZ5i+vf7W0DMuat97hZ0gbn3I2jnuLziLwzs//Xzh27YhRGcRz//pjsDP4Ws5RNSRaDjOzKiMFupmSgd8boX6D0mizKbDSpY3juIHkYKIbvZ77d6fR0fvfc58wMkyWSTAHztHt1vd7vfZ0uA9dDr9jrL9XhhOkfSLIEHAIzwGWS26paqKpxkhFwT9vUszmMYkmyRTtsJ4HjqhoPr9sGzpPsAze0g176sap6/aLupF+T5Iz2NX46yRNtu9gBMEqyATwCK8PjV8Ai7dLyC7AOUFXPSfZoQR9gt6o+LpKQvjMHrAF3w70RgB2sR/2NWeBk2Gg3AYyq6iLJPZ/3fkfAaZIH2iKdVYCv+kt9Li1oSpIkSZI+8pc8SZIkSeowMEmSJElSh4FJkiRJkjoMTJIkSZLUYWCSJEmSpA4DkyRJkiR1GJgkSZIkqcPAJEmSJEkdb/dgIIK/sCeHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Get the density plot of  Unit Cost in Sales data-(kind='density')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OiPCuJhneJdd",
        "outputId": "edebb70a-cc79-49d8-bb4d-fd6831ced87d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'dollars')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Get the histogram plot of  Unit Cost in Sales data-(kind='hist')\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "090XbZbneJdd"
      },
      "source": [
        "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
        "\n",
        "## Categorical analysis and visualization\n",
        "\n",
        "We'll analyze the `Age_Group` column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N04L9WtleJde",
        "outputId": "a82443ac-6026-477c-cd84-8f0b80885eed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adults (35-64)          55824\n",
              "Young Adults (25-34)    38654\n",
              "Youth (<25)             17828\n",
              "Seniors (64+)             730\n",
              "Name: Age_Group, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "#get the frequencies  in Age_Group variables.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the percentage counts of each values in Age_Group variables.\n"
      ],
      "metadata": {
        "id": "Z5DW0fc3u4R7",
        "outputId": "3640ea7e-2a81-4b76-940f-d8e6dff54065",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adults (35-64)          0.493860\n",
              "Young Adults (25-34)    0.341962\n",
              "Youth (<25)             0.157720\n",
              "Seniors (64+)           0.006458\n",
              "Name: Age_Group, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WcETYuJ3eJde",
        "outputId": "22d8c303-0e9d-4117-cb8f-8641ac77325d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc42d7baa90>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VqTCVkEpnlwAHhc4hIsFolFIlZRWKmT3DzHLA/cADZnafmdXLscmn0TwjMRHZXL38rqp7bWVudyXwNne/BcDMjgKuApZVKliCnhI6gIgEpRFKlZT7zn1iskwA3P1WYLwykRKn04VFmptGKFVSbqHcZGZfNLNjzewYM7sEuNHMDjOzWm9/FYpIc0ul0tltQ4doBuUe8jo4/vP8KY8fCjhwfGKJkqdCEZFDgV+EDtHoyioUdz+u0kEqSIUiInuGDtAMyioUM/vgdI+7+4eSjZOsVDrbDewSOoeIBKffA1VQ7iGvoZLPu4CTgYeTj5O4vQELHUJEglOhVEG5h7wuLP3azD4F/KQiiZKlw10iAiqUqljoBX89wNIkg1SIrkEREVChVEW5cyg5orO5AFqBHYCanj+JaYQiIqBCqYpy51BOLvl8HFjh7vVwYaMKRURAhVIVZR3ycve/AFsDLwFOBfavZKgE7R46gIjUhE5d3Fh55S4O+W7gGmDH+OMaM3tnJYMlZOvQAUSkZmiUUmHlHvJ6I/Asdx8CMLOPA7cBF1UqWEKWhA4gIjVjF+DB0CEaWblneRkwUfL1BDV+fUcqnW0BtgydQ0RqhkYoFVbuCOUq4A4zuy7++hSiJe1rmUYnIlJKhVJhcxaKmbUAtwM3AkfFD7/e3e+pYK4kqFBEpJRuBVxhcxaKuxfN7GJ3PxS4uwqZEvGSll/3tDFx53q62zfQ3b7BuzuG6O4c8s6uYbo6R+jsmaC13BGaiNS/9tABGl25v1B/YWanAd9zd59z6xpwUccXtgSOmG0bd8aB4SK2sUjLxnFaCuO0jY7RNjZK21jB2ydG6JwYobM47J3FDXQxRDfrvds20G0bvLtlAz2t6727bX1UWu0b6O4coqtz2Ls6h+nsGqarp0hLa3V+ahGZhd5AVli5/4HfCpwDjJvZRqIJeXf3Wj6s1DnXBma0AUta8SWtTNDOBDBWskEyQdwZA0aK2MgELYUJWgvjtI6O0TZWoG2s4B0TI3RMjNBZHPKu4lBcXBu8mw10t6z37tb1cXFteLK4OqLi6uwapqtrmM4ep2WhS+mINAMVSoWVuzjkVpUOUgFzFkq1mNEOtE8W16YnzJFkcY06jDgtIxPYZHGNjdI2Nkr72EbvGB+hozhC58SQd3lcXL7Bu1o20G3rvad1Pd2t671nsrg6NtDdPkxn11A04uoeobNbxSV1SoVSYbP+BzazVqDb3TfEXz8b6Ii/fY+7r69wvsWomUKpFjM6DDqg2BsdY6tYcRXi4to4EX2MjtE6Okr72Cjt4xu9fXzyUGFcXD7kXbaBbltPd8sG72lZT3fbeu9pW093+5B3tw3R1TlEV+eQd3WN0Nk9TGc3WE2fmi51R3MoFTZXY38cWAl8Iv76m8ADRPdEuRt4f+WiLZp+GVWIGZ0GnVCklSIA3QCMxBsk8zrubHQYKdKysUhLYZyW0XHaRkdpGytEI66JjXRMDNNZHI5HXPFhQltPd8v6uLh+2bYNBTfr3mh0jxldBadn1Okac2ufAHPX35UmMNratgb6FvRcM/sA8Fqid2lF4K3ufscC9nMEcKa7v2tBQWberxHd4vgUd19nZlsDVwAHEi3s+wZ3v61k+/cCnwJ2cPfVZey/A/g5cPxs6zjOVSgnAM8o+foJd39JHP6WuUIEVggdQBbHjC6DrhaKQDEeGo+WbFDefr66ZKtff3K7bZ47DAxP+V5L0Sc6xxjuGmWke5SNXaMUego+2lNgrLvAWE+B8S0KTPRs9GJPAe8ehe4C3jXqLV1jtHSO0doxTlv7OG1tE3S0FuloLdLV4nSZ0wVsYQu/TYQk6/dw3ryfZGbPIVog9zB3L5jZ9jx5pGZe3P1O4M55vHZbmQvxvhi4z93XxV9/Dvixu78iLoOekn3uDpwI/HWG17wauNrdbyzJPWpmvwBeTbQM17TmKpSWKT/M++Odu5nV+lXoG0MHkNpw5rr1z7186yX3PtHaesjU7xVbrHWkk61GOtlq7T8fTXbA0jbuha5RRrrG2Ng1ykhPgdHughe2KDDeXWC8p8DYFgUvdhco9hSguwDdozBZWh1jtHVEhdXWNkFnW5GOlmJUWAbdNjlAlLkUF/i8XYDV7l4AKH1Hb2aHA58mWpVjNfA6d3/MzG4E7gCOI1pT8I3ufouZHQuc6+4nm9m2wJeJ7iw7DLzF3e83swzRSul7A381s48QXVzeQfTm5DR3/8OUjGcAX4oz9QLPA14X5x1lk3difAZ4H/D/5vnf4Xrgv1lEoXSY2VaTcyXu/tOSwF3zDFNtGqHIP31x+cotX73rzuOYVX1idrzNOje00blhk0cTLC137xxjpCsaaRW6C2zsGaXQXfCxnniU1bOR8Z6Ce0+BYvco3lPAukadrlFaO8do6RinrWOc9snSainS2eJ0tjg9RKXVCPMPY3NvMq2fAh80s0eIDvtc6+43mVk70XqGL3P3VWb2auCjwBvi57W5+zPN7MXA+cDzp+z3AqK56FPM7Hjgq8Dkm579gaPcfcTMLgI+5+7XxKON6S5DOJLobFyAvYBVwFVmdjBwF/Budx8ys5cB/3D3+2z+U5QPsOkRq83M9Y/rcuBaMzvL3f8KYGZ7ApcSHZ+rZSoU+af9R8ee8uyNhZtu7+46JnSWxJlZoYOeQgc9+S02+UZiL9E64eMlhwYLXaNsLD00uEWBiZ6CT/QUmIhHWd49inWNuv1zlDVGa/sEHW0TtE+OslqcTnuytCp9aHB07k025+4b4pHI0UQjjmvNLE106OpA4GfxL+dW4LGSp34v/vMuIDXNro8CTotf45dmtp2ZTV6K8X13jycluQ34gJktJboWcOroBGDbkpOk2oDDgHe6+x1m9jkgbWb/DfwH0eGuTZjZC4nmzAH2AI4ysw1Awd2fFWecMLPR0kHGVLMWirt/2syGgVvNbPKv6gZgwN0vne25NUCHvGQTn1mx6rAj91y6omi2U+gs9Wai1dqGW1ky3FW6pFGyhwbbx31j1ygjk6XVXaDQPRqVVs9GxrYoUOwp+MQ/Dw2O4t0FWrrG3Dqj+ay29nHa2idoj+ezOqccGhyZO8X03H2CaPmpG+M72PYTFcWD7v6cGZ42+aZ2gvmfsjxU8trfMLM7iM4o+KGZvdXdfzll+3Eza3H3IvB34O8lJw18B0gTHUbbC5gcnSwF7jazZ7r7T4CfwPRzKCU6meV3azlLr1wGXGZmW8Vfb9ZMZtbv7l+Za19VphGKbGJL963evfaJ3Ge23UaFUoPG2qxrrI2u9T1s8+SjiZbWUG4BTzKzpwHFkpHBIcBfgN8DO5jZc9z9tvgQ2L7uXu4S+bcQzX18OJ5bWR2foTX19fcGHnX3z5vZHsAyYGqh/J5ozuWP7r7czP5mZk9z998TnVz1kLvniO5nNbnfQeCIcs7yirffLs4446HDsoeY7r5+lutO3l3ufqpIIxTZzBvy65/bOzFxb+gcEsRCr5vbEviKmT1kZvcTzW9k4snuVwAfN7P7gHuB585jvxng8HifA0Sjnum8CnjAzO4lOsT21Wm2yQLHlnz9TqIbId5PVIAfm0eumRwXv86MLImluczsnnjxyNqR6V0C5EPHkNrzYEfHH07fdae9QkzQS1Bvz/XnLgkdohLMbBfgq+7+ggq+xveAtLs/MtM2SU2C1eKCketZ+Fkd0sAOGB196jM3Fn4VOodU3bq5N6lP7v4YcHnJpH6i4rPLrp+tTCC5Qqm9K40zeSe6yl9kM59dserQFnf9/Wguj4cOUEnu/u2SCxuT3veou093qG0TSRVKrb7be2zuTaQZbeW+5J1r89OdfimN6/9CB2h0ZRWKme1kZlea2Y/ir/c3szdOft/d31GpgIukQpEZvSm/7sglExP3hc4hVfOP0AEaXbkjlKuJzlHeNf76EeDsSgRK2PLQAaS2XbZ8VTfRNQbS2EaJlkaRCiq3ULZ3928Tr4UTr+9VD/8INUKRWR00OrrvERsLt4bOIRW3PNefq8WThxpKuYUyFF/U4vDP+6LUwym5KhSZ0+dWrjrE3FeFziEVpcNdVVBuoZwDfB/Yx8x+RXRhzTsrlio5OuQlc1pS9N63P5H/fegcUlGakK+CsgrF3e8GjiG6CvStwAHufn8lgyVE70qkLG95Yt2RW00U6+HvtCyMCqUKyrpS2MxePuWhfc0sD+S8ts/ln/UiHJFJBnbZipWdZ+yy0wTRra+lsfw5dIBmUO4hrzcSLVd/RvxxOdHNtn5lZv9aoWyLl8nn0TsTKdOywujTDitogr5BlbtgoyxCuYXSBuzn7qe5+2lEi6M58Cxq+77yoL9IMg+fX6EJ+gb1UOgAzaDcQtnd3VeUfL0yfuxxan+9LP1FkrL1Fr333zRB32jyuf7c30OHaAblFsqNZvYDM+s3s36iexHfGN9064nKxUuECkXm5awn1h255URxIbfOkNr0cOgAzaLcQnk7cBXRuvqHEN360t19yN2Pq1S4hKhQZF4M7NIVK9uJ7n4n9U+Hvauk3NOGHXgUGAdOJbrRSr20vgpF5u2QwujTDymMaoK+Meh3QJXMWihmtq+ZnW9mvwMuAv5KdFOu49z9C1VJuFiZ/OPAijm3E5niCytWLTP3NaFzyKJphFIlc41QfgccD5zs7ke5+0XUxxpeU90TOoDUn95iceu3PLFO727rn/79V8lchfJyovWwbjCzy83sBGrxZlpzq9X7tUiNe/sT+aO2KBb1Drd+/SHXn6vli68byqyF4u7Xu/vpwNOBG4iWrN/RzC41sxOrETAhKhRZEAO7ePmqFk3Q1y3926+iciflh9z9G+7+EmAp0RCy1i9oLHUHtX+9jNSowwuF/ZZpgr5eqVCqaN63AHb3te7+JXc/oRKBKiKTH0bHUWURLl6x6iCLLuSV+qJCqaKk7ilfD/QOUxZs62Jxmzfl12kupb48TnRikVSJCkWkTO9Ymz9yi2JRZ33Vj1/rLo3VpUIRKVMLtFy0YhWaoK8bOtxVZc1TKJn8KjT8lUV6xsbC/geOjuoXVX34eegAzaZ5CiXyo9ABpP5dsnzVAea+NnQOmdVjwF2hQzSbZiuU74cOIPVvm2Jx29fn12k14tr2Q82fVF+zFcotRGd+iCzKu9fmj+opFutlgdRm9IPQAZpRcxVKJj8B/DB0DKl/LdDy+RWrikQrcUttKQA/Cx2iGTVXoUR02EsS8ayNhQP21wR9Lbox158bCh2iGTVjofwYGA0dQhrDpctX7WfutX7X0majw12BNF+hZPLriRa6FFm0bYvF7frz6+8PnUM2oUIJpPkKJaLDXpKY96x94qhuTdDXil/n+nODoUM0q2YtlO8S3c5YZNHiCfoJTdDXhK+FDtDMmrNQMvkV6CJHSdCzNxYOfPromCbowxoFrg0dopk1Z6FEvhw6gDSWy5avfLom6IP6Qa4/pxUMAmrmQskCujWoJGa7YnH7f12nCfqAdLgrsOYtlEx+DPh66BjSWM55/Ikju4rF34fO0YTWEL1JlICat1AiOuwliWqF1s+uXD2qCfqquzbXn9NtvgNr7kLJ5B8Efhs6hjSWI0c2HrSvJuir7crQAaTZCyWiUYok7osrVj4N93zoHE3i1lx/7u7QIUSFAvANYH3oENJYtp8o7nDGuvX3hs7RJD4bOoBEVCiZ/DrgS6FjSOP598efOEoT9BU3CFwfOoREVCiRzwCa0JNEtULrp1euLoTO0eC+kOvPTYQOIREVCkAm/w+iQ18iiTp6ZOOyp2iJ+0rZAFwROoQ8SYXypE8BOtVTEvfF5aueivu60Dka0NW5/pxOfKghKpRJmfwDaH0vqYAdJyZ2PH39Bk3QJ6sIfD50CNmUCmVTnwgdQBpTes3aIzuLxT+EztFAvpHrz+m/Z41RoZTK5G8CfhM6hjSeeIJ+OHSOBjEOXBA6hGxOhbK5j4YOII3peSMbD95HV9An4Su5/twfQ4eQzalQpsrkvw/cFjqGNKYvLl/5FNx1Ie3CjQIfDh1CpqdCmd55oQNIY9ppYmKnV63foGVCFu7KXH/uL6FDyPRUKNOJ5lJ+EjqGNKbzNEG/UBvRIemapkKZ2fuJTk0USVQbtH1q1Zqh0Dnq0GW5/tw/QoeQmalQZpLJ3xkc1UAAABETSURBVAd8JXQMaUzHDo8cstfo2K9D56gjj6O5k5qnQpndBwC9k5SK+NLylXtrgr5s5+f6c4+HDiGzU6HMJpN/DPhk6BjSmHaemNj5tPVDmqCf2wPApaFDyNxUKHP7BPCn0CGkMX1gzePP7Si6/n7N7t1aUbg+qFDmksmPAGeFjiGNqR3aP7FqtRaOnNk3c/25X4YOIeVRoZQjk/85mqCXCjlheOTQPcc0QT+NdcB7Q4eQ8qlQyvdeYFXoENKYLn9s5V64bwido8acn+vPPTafJ1jkVjM7qeSxV5rZj+exj63N7G0lXx9rZj8o87mfNbPnlbntOWb2kJndb2a/MLM9S743YWb3xh/fL3n8W2b21HJ/lmpToZQrk18DvCd0DGlMu0xM7HLqhqE7Q+eoIb9iAcvTu7sTHaL+tJl1mdmWwMeAt89jN1sDb5tzqynMbDvg2e5+8xzbbRN/eg9whLsvA77Dpqudj7j7IfHHS0sevxR433yzVYsKZT4y+WuAst/piMzHf61+/MgO90dD56gBQ0B/rj+3oAuL3f0B4H+JLk7+IPB14MJ4JHC7mS0DMLOMmZ07+Twze8DMUsAAsE88Opg8y3NLM/uOmf3OzK4xM5vmpU9jht8PZrajmZ1rZg8Ar45z3uDukytQ3w4sLePHuwV4vpm1lbFt1alQ5u/f0LUpUgHt0D6wcvUToXPUgPfl+nOLPfPtAuC1wEnAzsA98UjgP4CvzvHcNPCneHTw7/FjhwJnA/sDewNHTvO8I4G7Jr8wsxYze5GZfQe4EegCXuTul03z3Dey6Q3+uszszrgAT5l80N2LwB+Bg+f4GYJQocxXJj+IFo+UCnnB8Mhhe4yNNfNq1z8jgWtO3H0IuBb4GvDs+E/c/ZfAdma2ZJ67/I27/z3+hX4vkJpmm13YdJ71eqJ73l8BHODuH3H3v099kpn9C3AEm17ztqe7H0FUip81s31KvrcS2HWe+atChbIQmfxFRENqkcRdvnxliugXYrPJA2/I9ec8of0VmX09vnE2/R3YNcu2hZLPJ4DpDjmNTNnHeURzIxcBF5vZM6Y+wcyeT7Qix0vd/Z+v4e7/iP98lGh0c+iUnCOzZA1GhbJwrwe0UJ0kbtfxiV1eumHot6FzBPDuXH9us3fwCbgFOAOiM7aA1e6+DhgEDosfPwzYK95+PbDVAl7nYeApk1+4+4PufjZwAHAT8NF4HufE+DUPBb5IVCYrJ59nZtuYWWf8+fZEh9IeKnmdfYlWD6g5KpSFis76ei3RuxWRRJ2/+vHntrv/OXSOKvperj9XqWu9MsDhZnY/0YR7f/z4d4FtzexB4B3AIwDuvgb4VTxJP5+ll7LAsVMfdPdRd7/W3U8EXgKsib/1SWBL4H+mnB68H3Cnmd0H3AAMuPtDAGa2E9EZYMvnkatqLDrLThYs05sBzg8dQxrPj7bouet9O25/eOgcVfAH4Ihcf67uVwwws1uBk929IidXmNl7gHXufmUl9r9YGqEs3oeJhrMiiTppaPjwpWNjt4fOUWHDwGmNUCax9wJ7VHD/T1DDq3ZohJKETO9uwH3AdqGjSGP5e1vr/520dNetMesJnaVC/jXXn/t66BCSDI1QkpDJ/wP4V3SHR0nY0vGJXU8eGv5N6BwVconKpLGoUJKSyf8I+Pc5txOZpwtWrXluu/tg6BwJuwMtZdRwVChJyuQ/DVweOoY0lg7o+MiqNatD50jQauCVuf7caOggkiwVSvLeDuj+DZKoFw8NH7Hb2HgjTNCPAC/N9ef+FjqIJE+FkrRMfgx4BfE57SJJuXz5yqW41+QV0mUqAmfk+nPNvLRMQ1OhVEImvxY4GXg8dBRpHLuPjy998dDwHaFzLMLZuf7cdaFDSOXotOFKyvQeC/wUaA+cRBrEKBSeldp9+XjJzZjqxIW5/ty5c28m9UwjlErK5G8kWuZBpxNLIjqg84LVa1bOvWVNuRadAdkUVCiVlsl/E3gToKGgJOKlG4afscv4eL1cm3Iz0c2y9Pe/CahQqiGTv4po8TmRRFzx2Mpd62CC/g7gJbn+XGHOLaUhqFCqJZO/hGidH5FF22N8fOmLanuC/jfACxtojS4pgyblqy3T+59EC0qKLErB2PjsPXdfOW5WycUIF+I3wIm5/lw+dBCpLo1Qqi2T/wjwsdAxpP51Ol3nr3681u6L8VtUJk1LhRJCJv8Bohv9iCzKKRuGnrnz+Hit3N3xTlQmTU2FEkomfx5wDjr7SxbpisdW7oz7xsAxfgO8INefq8iNpaQ+qFBCyuQ/Q7Ts/VjoKFK/9hwf3/0FwyMh1/nKAserTEST8rUg0/tCovtbbxE6itSneIJ+1bjZ7lV+6SuAs3L9uYkqv67UII1QakEm/xPgeKJlvUXmrdPp+q/Vjz9W5Zc9P9efe7PKRCZphFJLMr37Eq39VW/rNEmNOGH3XX+7sq3tGRV+mXHgLbn+3FUVfh2pMxqh1JJM/hHgOUAj3PdCArhi+codca/klelDRFe/q0xkMyqUWpPJPwYcA3wxdBSpP3uNje95wvBIpe438kfgObn+3I8rtH+pczrkVcsyvW8ELgY6Q0eR+rHRbOTZey5dM2G2NMHdfh84U9eYyGw0QqllmfyVwNGAbpcqZety7/7Amsf/kdDuisAHgFNUJjIXjVDqQaZ3B+DbwLGBk0gdOX73Xe9c1dZ2xCJ2sQZ4Ta4/97OkMklj0wilHmTyq4AXABeiK+ulTFcsX7nDIibo7wQOU5nIfKhQ6kUmP04mfy5RsegQmMxp77HxPY+b/wT9BNE6c0fm+nN/rUAsaWA65FWPMr29wEVEy7aIzGjEbPg5ey5dO2G2Wxmb/5Fo4r1SZ4lJg9MIpR5l8nky+TOB09DV9TKLbvee89asnWtE68AlwCEqE1kMjVDqXaZ3J+BLwEtDR5Haddzuu921uq318Gm+9Q/gDbn+3E+rnUkajwqlUWR6Xw98Gtg6dBSpPX9sb//zqbvtvBtmHSUPfwU4W6sES1JUKI0k07sj8Ck0tyLTePtOO9x0c0/3McBDwNty/bmbQmeSxqJCaUSZ3ucRHRM/IHQUqR3DZiuP2WO3T2xsafl8rj+ne/BI4lQojSrT2wa8DcgA24QNIzXgG8D7yOSTuoJeZDMqlEaX6d0O+DDwFqA1cBqpvnuAd5HJ3xo6iDQ+FUqzyPQeQDRaOQ2wsGGkCh4i+v/9HTJ5/SOXqlChNJtM7zLgfOBUVCyN6BHgQ8A3yeSLocNIc1GhNKtM7yFE72BfFjiJJONRokObXyOT1y15JQgVSrPL9B5OVCwnB04iC/MX4KPAVWTy46HDSHNToUgkGrG8A3gt0B04jcztZqL13K7TiERqxZxreVnkVjM7qeSxV5pZ1W8DamZnm9lGM+udZZtBM9t+jv1cbWavKNlnzwJynBl//kkz+52Z3W9m15nZ1vHjKTMbMbN744/LZtjXM0u2uc/MTp3y/VYzu8fMflDy2LfM7KnzyTynTP5eMvk3AUuB9wF/TnT/koQR4ErgEDL5Y8jkv6MykVpS1gjFzA4E/gc4FGgjOhXxRe7+p8rG2yzHHcAo8GV3v2qGbQaBI9x9xkUTzexq4Afu/p1ytp/y3DbgbuAwdx83sxOBX8affxzA3d9vZqn4NQ6cY389wGj8/F2A+4Bd3X08/v45wBHAEnc/OX7sGOBf3P3N5WRekExvC9FhsHcAz0cT+CH9jehC1cvJ5NeEDiMyk7JWG3b3B4D/Bd4PfBD4OnBh/K78djNbBmBmGTM7d/J5ZvZA/E49ZWYPm9nlZvagmf3UzLrjbZ4R7+fe+N3+A9NlMLN9gC2B/wReU/L4dvH+HjSzK4h/8cWv+UDJdueaWWbKPt8F7ArcYGY3xKOBq+PcOTN7zzRRjgfunvyF7+4/nfwcuJ3oHX7Z3H245PldlNxAy6J7gvcBV0x52i3A8+Nyq4xMvkgm/30y+ROB/YDPAI9V7PVkqgLwPaKz8fYikx9QmUitm8/y9RcQHV8/CdgZuMfdlwH/AXy1jOc/FbjY3Q8AniC6HgLgKuCt7n4I0c19ZnI68C2iX6ZPM7Od4sfPB26N93sdsEe5P5C7fx74P+A4dz8OOATYzd0PdPeD4mxTHQncNcMu3wD8qOTrveLDVTeZ2dEz5TCzZ5nZg0AOOKukYD5LdPhpk9M/3b1IdO+Kg+f8IZOQyf+eTP4corI8HrgceLwqr91cJoCfEf092olM/jQy+et1WEvqRdnvcN19yMyuBTYQjRBOix//ZTxKWDLHLv7s7vfGn98FpOL5hq3cffIeDN9g5rONXgOc6u5FM/su8ErgC8DzgJfHWbJmtrbcn2kajwJ7m9lFQBaYbknvXYCHpz5oZh8AxoFr4oceA/Zw9zVmdjhwvZkd4O7rpj7X3e8ADjCz/YCvmNmPiA4zrXT3u8zs2GlyrCQaXc1UbsmLrmu4AbiBTO/bgROJ/r+8jGj0KAtzB9Hf/WvJ5FeEDiOyUPM9ZFJkyrvlKcbZdNTTVfJ56b2tJ5jHmURmdhDRCOdnZgbQQTRp/IUFZpmWu681s4OBFwJnAa8ierdYamTqvszsdURFeILHk1Ie3cu7EH9+l5n9CdjXzHYnGlUBvMnd7yx5/YfNbANwINFI6KVm9uL49ZaY2dfd/V9Kfp6RuX6misnkx4hKN0umt4fo0NxJRLcontdhvyY0BNwI/ATIksk/GjaOSDIWegz+FuAM4MPxu+fV7r4unuCenDg+DNhrtp24+xNmtt7MnhW/Sz99hk1fA2Tc/b8nHzCzP5vZnkSnT74W+Eh8JtrkQogrgB3NbDuiUdXJwHRnpq0HtgJWx2eHjbr7d83s90RzRVM9DDylJMeLiA5LHePuwyWP7wA87u4TZrY3USE+GhfIdSXb7QX8LZ6U3xN4OjDo7ucB58XbHAucW1ImAPsC0843VV0mP0x00sb/RF/37k80ejkROAaY11l0Deo+ogL5CXArmfxo4DwiiVtooWSAL5vZ/cAw0B8//l3gzHg+4A6iZSDm8kbgcjMrAjcB+Wm2OR148ZTHrosfvwD4Zvyavwb+CuDuY2b2IeA3RHel+90Mr/8l4Mdm9n/A2cBVZjY5sjlvmu1/BHyt5OsvAJ08OXq63d3PIjoU9yEzGyMa1Z3l7tPNOxwFpEu2e9tcZ5zF80cj7r58tu2CyeQfIlpL6rNkejuIRlsnAkcTnSnY6AVTJHrj8VuikchPyeR1QoM0vOAXNprZlu6+If48Dezi7u8OGmoOZnYd8D53/0Og138PsM7drwzx+ouS6W0lOmvsiJKPgynjkGQNe5SoPCY/7iaT3xA2kkj11UKhvJpoJNBGtIzE69x9VdBQczCzpwE7ufvNgV7/9cDXSs4Gq2/RvVsOAA4nOjS4D7B3/FEr93IpEM3b/ank43fAXTqdVyQSvFBEZpXp3Zony2VvotPCt5nysXX8Z8cMe5mJA+uAtUSnQZd+rAEGebI8/q7Ve0Vmp0KRxhGdbbYN0RxNK9FZfpMfE0Rn/k3+uQFYq2s8RJKjQhERkUTM50p5ERGRGalQREQkESoUERFJhApFREQSoUIREZFEqFBERCQRKhQREUmECkVERBKhQhERkUSoUEREJBEqFBERSYQKRUREEqFCERGRRKhQREQkESoUERFJhApFREQSoUIREZFEqFBERCQRKhQREUmECkVERBKhQhERkUSoUEREJBEqFBERSYQKRUREEqFCERGRRKhQREQkESoUERFJhApFREQSoUIREZFEqFBERCQRKhQREUmECkVERBKhQhERkUSoUEREJBEqFBERSYQKRUREEqFCERGRRKhQREQkEf8fi2DZoISCj5EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#get the pie chart of number of values in Age_Group variables.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c8MTQ2MyeJdf",
        "outputId": "3bf01b3d-b31e-4476-b4fe-58cbd5e85b90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Sales')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Get the bar chart for Age_Group\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATo4tgkSeJdf"
      },
      "source": [
        "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
        "\n",
        "## Relationship between the columns?\n",
        "\n",
        "Can we find any significant relationship?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5VjDudg6eJdg",
        "outputId": "4e9a1037-6eb1-47b5-fa28-396ec316a3a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Day      Year  Customer_Age  Order_Quantity  Unit_Cost  \\\n",
              "Day             1.000000 -0.007635     -0.014296       -0.002412   0.003133   \n",
              "Year           -0.007635  1.000000      0.040994        0.123169  -0.217575   \n",
              "Customer_Age   -0.014296  0.040994      1.000000        0.026887  -0.021374   \n",
              "Order_Quantity -0.002412  0.123169      0.026887        1.000000  -0.515835   \n",
              "Unit_Cost       0.003133 -0.217575     -0.021374       -0.515835   1.000000   \n",
              "Unit_Price      0.003207 -0.213673     -0.020262       -0.515925   0.997894   \n",
              "Profit          0.004623 -0.181525      0.004319       -0.238863   0.741020   \n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#Get the correlation matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RAeDTDRVeJdg",
        "outputId": "09b69fc9-45b9-4b01-959c-edf36b075235",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(8,8))\n",
        "plt.matshow(corr, cmap='RdBu', fignum=fig.number)\n",
        "plt.xticks(range(len(corr.columns)), corr.columns, rotation='vertical');\n",
        "plt.yticks(range(len(corr.columns)), corr.columns);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5PR_5vTkeJdh",
        "outputId": "b673ed0e-8850-462b-9180-444d10d9a8ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade7c34f0>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Scatter plot of 'Customer_Age' and 'Revenue'(kind=Scatter)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9_OsrYajeJdh",
        "outputId": "9f964229-c4e4-4ccf-8acd-c1baec13934a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade7a70a0>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Scatter plot of  'Revenue' and 'Profit'(kind=Scatter)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0s7Ey4BeJdh",
        "outputId": "2a321a96-b6f0-43d6-d252-e78e396a19a0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Profit')"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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fwcbGRjKTc889l4ULF+Jrpc3R3t5undGAWX80GNafgRnOFsGXAi8GOlsDJwK/iIgDgfuBPWqWnViW3Q/M6FbeXpZP7GF5DUDngJC5c+fS0dHB5MmTaWlpcaCIJEnbuGG7fUxm/jozX5iZkzJzEsXl3AMy80FgCfDuKLwGeDwz/wBcC7wpInYuB4m8Cbi2nPdERLymHHH8buA7w3Us26Lm5maWL1/O9ddfz/Lly00CJUmqgCFLBCOiDfhv4OURsTIiZvey+DXAXcAKYDHwAYDMfAT4FPDz8vHJsoxymS+V6/wv8P2hOA5JkqRt1ZBdGs7MXpuUylbBzucJnLiJ5S4GLu6h/BZg6sZrSJIkqT/8ZRFJkqSKMhGUJEmqKBNBSZKkijIRlCRJqigTQUmSpIoyEZQkSaooE0FJkqSKMhGUJEmqKBNBSZKkijIRlCRJqigTQUmSpIoyEZQkSaooE0FJkqSKMhGUJEmqKBNBSZKkijIRlCRJqigTQUmSpIoyEZQkSaooE0FJkqSKMhGUJEmqKBNBSZKkijIRlCRJqigTQUmSpIoyEZQkSaooE0FJkqSKMhGUJEmqKBNBSZKkijIRlCRJqigTQUmSpIoyEZQkSaqoIUsEI+LiiHgoIpbXlH0uIn4TEb+KiG9HxLiaeR+PiBUR8duImFVTfmhZtiIiTq4pf3FE3FyWXxER2w/VsUiSJG2LhrJF8BLg0G5lS4GpmflK4H+AjwNExBTgHcC+5TrnR8ToiBgN/AdwGDAFaC6XBTgD+Hxm7g08CswewmORJEna5gxZIpiZPwYe6VZ2XWauLSdvAiaWz48ELs/M1Zl5N7ACOLB8rMjMuzLzWeBy4MiICOBg4Mpy/UuBo4bqWCRJkrZFDXXc93uBK8rnu1Mkhp1WlmUA93UrfzXwfOCxmqSydvmNRMTxwPEAEyZMoL29fbCxb7NWrVrl66MBs/5oMKw/Ggzrz8DUJRGMiAXAWuCrw7G/zLwIuAhg2rRpOWPGjOHY7YjU3t6Or48GyvqjwbD+aDCsPwMz7IlgRLwHeAswMzOzLL4f2KNmsYllGZsofxgYFxENZatg7fKSJEnqh2G9fUxEHAqcBLw1M/9SM2sJ8I6IGBMRLwb2AX4G/BzYpxwhvD3FgJIlZQK5DHhbuf6xwHeG6zgkSZK2BUN5+5g24L+Bl0fEyoiYDZwH7AgsjYjbI2IRQGbeAXwduBP4AXBiZq4rW/s+CFwLdABfL5cFmA/Mi4gVFH0GW4fqWCRJkrZFQ3ZpODObeyjeZLKWmS1ASw/l1wDX9FB+F8WoYkmSJA2AvywiSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVNWSJYERcHBEPRcTymrLxEbE0In5X/t25LI+I+GJErIiIX0XEATXrHFsu/7uIOLam/FUR8etynS9GRAzVsUiSJG2LhrJF8BLg0G5lJwPXZ+Y+wPXlNMBhwD7l43jgAigSR+BU4NXAgcCpncljucxxNet135ckSZJ6MWSJYGb+GHikW/GRwKXl80uBo2rKL8vCTcC4iHgRMAtYmpmPZOajwFLg0HLe8zLzpsxM4LKabUmSJKkfGoZ5fxMy8w/l8weBCeXz3YH7apZbWZb1Vr6yh/IeRcTxFC2NTJgwgfb29oEfwTZu1apVvj4aMOuPBsP6o8Gw/gzMcCeCG2RmRkQO074uAi4CmDZtWs6YMWM4djsitbe34+ujgbL+aDCsPxoM68/ADPeo4T+Wl3Up/z5Ult8P7FGz3MSyrLfyiT2US5IkqZ+GOxFcAnSO/D0W+E5N+bvL0cOvAR4vLyFfC7wpInYuB4m8Cbi2nPdERLymHC387pptSZIkqR+G7NJwRLQBM4BdImIlxejfzwBfj4jZwL3A28vFrwEOB1YAfwH+GSAzH4mITwE/L5f7ZGZ2DkD5AMXI5OcA3y8fkiRJ6qchSwQzs3kTs2b2sGwCJ25iOxcDF/dQfgswdTAxSpIkVZm/LCJJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVVl0QwIv5fRNwREcsjoi0iGiPixRFxc0SsiIgrImL7ctkx5fSKcv6kmu18vCz/bUTMqsexSJIkjVT9SgQj4qD+lPVzW7sD/wJMy8ypwGjgHcAZwOczc2/gUWB2ucps4NGy/PPlckTElHK9fYFDgfMjYvRAYpIkSaqi/rYIntvPsv5qAJ4TEQ3Ac4E/AAcDV5bzLwWOKp8fWU5Tzp8ZEVGWX56ZqzPzbmAFcOAgYpIkSaqUht5mRsRrgdcBL4iIeTWznkfRkrfZMvP+iDgT+D3wNHAdcCvwWGauLRdbCexePt8duK9cd21EPA48vyy/qWbTtet0P47jgeMBJkyYQHt7+0BCr4RVq1b5+mjArD8aDOuPBsP6MzC9JoLA9sDYcrkda8qfAN42kB1GxM4UrXkvBh4DvkFxaXfIZOZFwEUA06ZNyxkzZgzl7ka09vZ2fH00UNYfDYb1R4Nh/RmYXhPBzPwR8KOIuCQz791C+3wjcHdm/gkgIr4FHASMi4iGslVwInB/ufz9wB7AyvJS8k7AwzXlnWrXkSRJUh967SMYEV8on54XEUu6Pwa4z98Dr4mI55Z9/WYCdwLL+Gsr47HAd8rnS8ppyvk3ZGaW5e8oRxW/GNgH+NkAY5IkSaqcvi4NX1b+PXNL7TAzb46IK4FfAGuB2ygu214NXB4Rny7LWstVWoEvR8QK4BGKkcJk5h0R8XWKJHItcGJmrttScUqSJG3r+koEP0fRYnd4Zs7fUjvNzFOBU7sV30UPo34z8xngHzexnRagZUvFJUmSVCV9JYIviojXAW+NiMuBqJ2Zmb8YssgkSZI0pPpKBP8N+FeKgRhnd5uXFPf+kyRJ0gjU16jhK4ErI+JfM/NTwxSTJEmShkFfLYIAZOanIuKtwBvKovbM/N7QhSVJkqSh1t/fGj4d+BDFCN07gQ9FxMKhDEySJElDq18tgsCbgf0ycz1ARFxKcYuXU4YqMEmSJA2tfrUIlsbVPN9pSwciSZKk4dXfFsHTgdsiYhnFLWTeAJw8ZFFJkiRpyPWZCJY/A3cj8Brg78ri+Zn54FAGJkmSpKHVZyKYmRkR12Tm31D8vq8kSZK2Af3tI/iLiPi7vheTJEnSSNHfPoKvBv4pIu4BnqLoJ5iZ+cqhCkySJElDq7+J4KwhjUKSJEnDrtdEMCIagTnA3sCvgdbMXDscgUmSJGlo9dVH8FJgGkUSeBhw1pBHJEmSpGHR16XhKeVoYSKiFfjZ0IckSZKk4dBXi+CazideEpYkSdq29NUi+LcR8UT5PIDnlNOdo4afN6TRSZIkacj0mghm5ujhCkSSJEnDq783lJYkSdI2xkRQkiSpokwEJUmSKspEUJIkqaJMBCVJkirKRFCSJKmiTAQlSZIqykRQkiSpokwEJUmSKspEUJIkqaJMBCVJkiqqLolgRIyLiCsj4jcR0RERr42I8RGxNCJ+V/7duVw2IuKLEbEiIn4VEQfUbOfYcvnfRcSx9TgWSZKkkapeLYLnAD/IzFcAfwt0ACcD12fmPsD15TTAYcA+5eN44AKAiBgPnAq8GjgQOLUzeZQkSVLfhj0RjIidgDcArQCZ+WxmPgYcCVxaLnYpcFT5/EjgsizcBIyLiBcBs4ClmflIZj4KLAUOHcZDkSRJGtEa6rDPFwN/Av4zIv4WuBX4EDAhM/9QLvMgMKF8vjtwX836K8uyTZVvJCKOp2hNZMKECbS3t2+RA9kWrVq1ytdHA2b90WBYfzQY1p+BqUci2AAcAMzNzJsj4hz+ehkYgMzMiMgttcPMvAi4CGDatGk5Y8aMLbXpbU57ezu+Phoo648Gw/qjwbD+DEw9+giuBFZm5s3l9JUUieEfy0u+lH8fKuffD+xRs/7EsmxT5ZIkSeqHYU8EM/NB4L6IeHlZNBO4E1gCdI78PRb4Tvl8CfDucvTwa4DHy0vI1wJvioidy0EibyrLJEmS1A/1GjU8F/hqRPwK2A9YCHwGOCQifge8sZwGuAa4C1gBLAY+AJCZjwCfAn5ePj5ZlkkaZm1tbUydOpWZM2cydepU2tra6h2SpIrw/DM49egjSGbeDkzrYdbMHpZN4MRNbOdi4OItG52kzdHW1saCBQtobW1l3bp1jB49mtmzZwPQ3Nxc5+gkbcs8/wyevywiaVBaWlpobW2lqamJhoYGmpqaaG1tpaWlpd6hSdrGef4ZPBNBATB37lwaGxtpamqisbGRuXPn1jskjRAdHR1Mnz69S9n06dPp6OioU0QaaWbNmsWoUaNoampi1KhRzJo1q94haYTo6OjgG9/4RpfPr2984xuefzZDXS4Na+syd+5cFi1axBlnnMGUKVO48847mT9/PgDnnntunaPT1m7y5MnceOONNDU1bSi78cYbmTx5ch2j0kgxa9YsrrvuOk444QQOP/xwrrnmGi644AJmzZrFtdc6/k+9GzduHBdeeCGf+9znNnx+fexjH2PcuHH1Dm3EsEVQLF68mDPOOIN58+bR2NjIvHnzOOOMM1i8eHG9Q9MIsGDBAmbPns2yZctYu3Yty5YtY/bs2SxYsKDeoWkEWLp0KSeccALnn38+Y8eO5fzzz+eEE05g6dKl9Q5NI8ATTzzBuHHj2H///WloaGD//fdn3LhxPPHEE/UObcSIYixGdUybNi1vueWWeoexVYkIRo0axfr16zeUdU5XrX5oYNra2mhpaaGjo4PJkyezYMECO2qrXyKCcePG8dhjj20o65z2/KO+RAQHH3wwy5YtIzOJCJqamrjhhhusP91ExK2ZudFAXVsEBcD69esZO3YsF1xwAWPHju2SFEp9aW5uZvny5Vx//fUsX77cJFCb5bHHHmPfffelra2Nfffdt0tSKPVm9OjRLFu2jDPPPJPvf//7nHnmmSxbtozRo0fXO7QRw0RQG5x22mlMmjSJ0047rd6hSKqYN7zhDYwdO5Y3vOEN9Q5FI0hnK2CtiLA1cDM4WEQAHH300ZxyyimsXr2aMWPGcPTRR3PFFVfUOyxJFXDQQQexaNEiLrjgAiKCgw46iJ/+9Kf1DksjwPr163n/+9/f5fPruOOO48ILL6x3aCOGLYIC4Oqrr+aZZ55h2bJlPPPMM1x99dX1DklSRTz22GOsX7+eZcuWsX79ei8Nq9/GjBnDy172si6fXy972csYM2ZMvUMbMWwRFKNGjWLVqlUbNa+PGuX3BElDa/z48dxxxx0bnX/Gjx9fp4g0khx33HF89KMfZf78+axdu5aGhgbWrVvHiSf2+INk6oGf9OIrX/nKZpVL0pbyzne+c7PKpZ509gm0b+DmMxEUJ510Ervuuis33HADS5cu5YYbbmDXXXflpJNOqndokrZxixcv5qyzziIzN9wC5KyzzvI+puqXxYsXc+aZZ264h+natWs588wzrT+bwURQrFy5kssuu6zLbzVedtllrFy5st6hSdrGrV69mjlz5nQpmzNnDqtXr65TRBpJrD+DZyIoSaqbMWPGsGjRoi5lixYtsrO/+sX6M3gOFhETJ07k2GOP5atf/Srr1q1j2bJlHHvssUycOLHeoUnaxh133HEbftt8ypQpnH322cyfP3+jVh6pJ9afwTMRFJ/97Gf50Ic+xHvf+15+//vfs+eee7J27VrOOuuseocmaRt37rnnAnS5D9ycOaGOH1kAAB8dSURBVHM2lEu9sf4MnpeGRXNzM+eccw477LADADvssAPnnHOOPxMmaVice+65Xe4D54e4Nof1Z3BsERRQJIPNzc20t7czY8aMeocjSZKGgS2CAqCtrY2pU6cyc+ZMpk6dSltbW71DklQRnn+k+rFFULS1tbFgwQJaW1tZt24do0ePZvbs2QBeHpY0pDz/SPVli6BoaWmhtbW1y30EW1tbaWlpqXdokrZxnn+k+jIRFB0dHUyfPr1L2fTp0+no6KhTRJKqwvOPVF8mgmLy5MnceOONXcpuvPFGJk+eXKeIJFWF5x+pvkwExYIFC5g9e/aG32lctmwZs2fPZsGCBfUOTdI2zvOPVF8OFtGGDtlz586lo6ODyZMn09LSYkdtSUPO849UXyaCkqS68j6mUv2YCMrbN0iSVFEmgqKlpYW7776bgw8+eKNyE0FJQ2277bZj7dq1G6YbGhpYs2ZNHSPSSDJr1iyWLl1KZhIRHHLIIVx77bX1DmvEcLCIuOOOOzarXJK2lO5JIMDatWvZbrvt6hSRRpJZs2Zx3XXXMWfOHL773e8yZ84crrvuOmbNmlXv0EYMWwQlSXXTPQnsq1yqtXTpUk444QTOP/982tvbOf/88wFYtGhRnSMbOerWIhgRoyPitoj4Xjn94oi4OSJWRMQVEbF9WT6mnF5Rzp9Us42Pl+W/jQjTf0mSKiQzWbNmDY2NjTQ1NdHY2MiaNWvIzHqHNmLU89Lwh4DaW8efAXw+M/cGHgVml+WzgUfL8s+XyxERU4B3APsChwLnR8ToYYpdkrSFfeADH6h3CBqBWltbWbhwId///vdZuHAhra2t9Q5pRKlLIhgRE4E3A18qpwM4GLiyXORS4Kjy+ZHlNOX8meXyRwKXZ+bqzLwbWAEcODxHsG1qbGzkvPPOo7Gxsd6hSKqYnXfemf3335+dd9653qFoBIkIMpMVK1awdu1aVqxYsWHQiPqnXn0EvwCcBOxYTj8feCwzOzuFrAR2L5/vDtwHkJlrI+LxcvndgZtqtlm7ThcRcTxwPMCECRNob2/fYgeyLXnmmWf44Ac/2KXM10qbY9WqVdYZDcijjz7Kcccd16XMuqS+ZCZ77bUXF1xwARdccAEAe+21F/fee6/1p5+GPRGMiLcAD2XmrRExYzj2mZkXARcBTJs2Lb1haf/5WmlzeENgbUnWJfWloaGBe++9t0vZvffeS0NDg/Wnn+pxafgg4K0RcQ9wOcUl4XOAcRHRmZhOBO4vn98P7AFQzt8JeLi2vId1JEnSNq5zdPno0aM5++yzGT16dJdy9W3YE8HM/HhmTszMSRSDPW7IzGOAZcDbysWOBb5TPl9STlPOvyGL4UBLgHeUo4pfDOwD/GyYDkOSJG0FRo8eTUNDA/PmzaOhoWFDMqj+2ZpuKD0fmBcRKyj6AHYO+2kFnl+WzwNOBsjMO4CvA3cCPwBOzMx1wx71NuKII45g3333ZdSoUey7774cccQR9Q5JUkW84hWv6HL+ecUrXlHvkDSCHHPMMey9996MGjWKvffem2OOOabeIY0oUbV77UybNi1vueWWeoexVeltdFXV6ocGxz6C2lyefzQYnfVnwoQJPPTQQ7zwhS/kj3/8I2D96S4ibs3Mad3L/WURSZI0onUmf51/1X9b06VhSZIkDSMTQW0wadIkvvzlLzNp0qR6hyKpYsaOHdvlr7Q53vrWt/Ltb3+bt771rfUOZcQxEdQG99xzD+9617u455576h2KpIpZvXp1l79Sf2233XZ8+MMfZuzYsXz4wx9mu+22q3dII4p9BCVJdTd69GjWrFmz4a/UX6NGjWLWrFmsWbOG7bbbjlGjbOPaHL5akqS62mGHHbq0CO6www51jkgjyerVqxk/fjwA48ePt1V5M5kIaoPOm3B6M05Jw+mpp57iiCOO4Nvf/jZHHHEETz31VL1D0ggxZswYgA11pvNvZ7n65qVhbbBu3boufyVpqI0ZM4bVq1ezZMkSlixZ0qVc6suaNWuYOHEiK1euBGDVqlVMnDiRBx54oM6RjRy2CEqS6mZTXzz9Qqr+2G233TZK+h544AF22223OkU08pgISpLqZu3atZtVLtV64IEHWL9+fZey9evX2yK4GUwEJUnSiNQ9CeyrXBszEZQkSaooE0FJkqSKMhGUJEmqKBNBSYM2d+5cGhsbaWpqorGxkblz59Y7JElSP3gfQUmDMnfuXBYtWsQZZ5zBlClTuPPOO5k/fz4A5557bp2jkyT1xhZBSYOyePFizjjjDObNm0djYyPz5s3jjDPOYPHixfUOTZLUBxNBSYOyevVq5syZ06Vszpw5/t6nJI0AJoKSBmXMmDEsWrSoS9miRYv8iTBJGgHsIyhpUI477rgNfQKnTJnC2Wefzfz58zdqJZQkbX1MBCUNSueAkFNOOYXVq1czZswY5syZ40ARSRoBIjPrHcOwmjZtWt5yyy31DmOrEhGbnFe1+qHBaW9vZ8aMGfUOQyOI5x8NhvWn/yLi1syc1r3cPoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJkka07bffvstf9Z+JoCRJGtGeffbZLn/VfyaCkgatra2NqVOnMnPmTKZOnUpbW1u9Q5Ik9cOw/7JIROwBXAZMABK4KDPPiYjxwBXAJOAe4O2Z+WgUd4s8Bzgc+Avwnsz8RbmtY4FPlJv+dGZeOpzHIqlIAhcsWEBrayvr1q1j9OjRzJ49G4Dm5uY6RydJ6k09WgTXAh/JzCnAa4ATI2IKcDJwfWbuA1xfTgMcBuxTPo4HLgAoE8dTgVcDBwKnRsTOw3kgkqClpYXW1laamppoaGigqamJ1tZWWlpa6h2aJKkPw54IZuYfOlv0MvNJoAPYHTgS6GzRuxQ4qnx+JHBZFm4CxkXEi4BZwNLMfCQzHwWWAocO46FIAjo6Opg+fXqXsunTp9PR0VGniCRJ/TXsl4ZrRcQkYH/gZmBCZv6hnPUgxaVjKJLE+2pWW1mWbaq8p/0cT9GayIQJE2hvb98i8VeBr5X6sueee3Leeeex//77s2rVKtrb27ntttvYc889rT8aFOuPBsP60z91SwQjYizwTeDDmflE7Q9HZ2ZGxBb7tejMvAi4CGDatGk5Y8aMLbXpbZ6vlfqycOHCDX0EGxsbyUzOPfdcFi5caP3RoFh/NBjWn/6pSyIYEdtRJIFfzcxvlcV/jIgXZeYfyku/D5Xl9wN71Kw+sSy7H5jRrbx9KOOWtLHOASFz586lo6ODyZMn09LS4kARSRoBhr2PYDkKuBXoyMyza2YtAY4tnx8LfKem/N1ReA3weHkJ+VrgTRGxczlI5E1lmaRh1tzczPLly7n++utZvny5SaAkjRD1GDV8EPAu4OCIuL18HA58BjgkIn4HvLGcBrgGuAtYASwGPgCQmY8AnwJ+Xj4+WZZJGmbeR1CSRqbI3GJd8UaEadOm5S233FLvMLYqtf0zu6ta/dDma2tr4/3vfz/PPPMMa9asYbvttqOxsZELL7zQlkH1yfOPBsP6038RcWtmTtuovGovlIngxvxH0mA8//nP5/HHH+ezn/0sU6ZM4c477+Skk05ip5124uGHH653eNrKef7RYFh/+m9TiaA/MSdpUB555BFOP/105s2bR2NjI/PmzeP000/nkUfsqSFJWzsTQUmDNnXq1F6nJUlbJxNBSYPS0NDAMcccw7Jly1i7di3Lli3jmGOOoaGhrverlyT1g2dqSYMyZ84c/uM//oNDDjmEdevWMXr0aNavX8+JJ55Y79AkSX2wRVDSoLzuda9jxx13ZNSo4nQyatQodtxxR173utfVOTJJUl9MBCUNSktLC1dddRXPPvssy5Yt49lnn+Wqq66ipaWl3qFJkvpgIihpUDo6Opg+fXqXsunTp9PR0VGniCRJ/WUfQUmDMnnyZN7+9rfz/e9/n9WrVzNmzBgOO+wwJk+eXO/QJEl9sEVQ0qDsvvvuXHXVVbz3ve/lu9/9Lu9973u56qqr2H333esdmiSpD/6yiLwzuwalsbGR1atXb1Q+ZswYnnnmmTpEpJHE848Gw/rTf/6yiKQh0VMS2Fu5JGnrYSIoaYvYeeedu/yVJG39TAQlbRGPPvpol7+SpK2fiaAkSVJFefuYbUxvHWeHa3t20JUkaWQwEdzGDCQJc9SVpC3BL6LSyGMiKEnaIvwiKo089hHUJk+2noQlDbWvfe1rm1UuacsyERRQJH2ZyV7zv7fhuSQNtebmZr72ta+x7777Qoxi33335Wtf+xrNzc31Dk3DLCI2+zGc29tWeWlYklRXzc3NNDc3M+nkq1n+mTfXOxzViV0L6sNEUNIGdvaXpGrx0rCkDTq7BWzOo9Ouu+4KMar4uwW2J0l9sY/74JkIShqU8ePHA/Dggw9Cri/+1pRL0lCyj/vgmAhKGpSHH354o6Rv/PjxPPzww3WKSJLUX/YR3Ir87WnX8fjTa+odBpNOvrpu+97pOdvxy1PfVLf9a2A6k75JJ1/NPXb2H5E8/3j+UTWZCG5FHn96Td0/RNvb25kxY0bd9l/PDwGpyjz/eP5RNZkISpKkQbFFeeS2KJsIStsIT8Qj90QsjXS2KI/cFuURnwhGxKHAOcBo4EuZ+Zk6hyTVhSfikXsilqR6GdGJYESMBv4DOARYCfw8IpZk5p31jWxgdpx8Mn9z6cn1DgMurd+ud5wM4GADabh5/vH8o2oa0YkgcCCwIjPvAoiIy4EjgRGZCD7Z8RlbdGzRGTA/yP0gHwzPP55/BsPzz8g9/4z0RHB34L6a6ZXAq+sUyxaxVZyIflDfPl4amCc77BVh/Rkczz/Wn4Hy/DNy60+M5DtwR8TbgEMz833l9LuAV2fmB7stdzxwPMCECRNedfnllw97rMOlqamp3iGwbNmyeoegAbL+aDCsPxoM68/QampqujUzp3UvH+ktgvcDe9RMTyzLusjMi4CLAKZNm5b1vPQw1Aab2Nf70ozqy/qjwbD+aDCsP/Ux0n9i7ufAPhHx4ojYHngHsKTOMUmSJI0II7pFMDPXRsQHgWspbh9zcWbeUeewJEmSRoQRnQgCZOY1wDX1jkOSJGmkGemXhiVJkjRAJoKSJEkVZSIoSZJUUSaCkiRJFWUiKEmSVFEmgpIkSRVlIihJklRRJoKSJEkVZSIoSZJUUSaCkiRJFRWZWe8YhlVE/Am4t95xbMV2Af5c7yA0Yll/NBjWHw2G9ad3e2XmC7oXVi4RVO8i4pbMnFbvODQyWX80GNYfDYb1Z2C8NCxJklRRJoKSJEkVZSKo7i6qdwAa0aw/GgzrjwbD+jMA9hGUJEmqKFsEJUmSKspEUJIkqaJMBLcSEXFURGREvKKXZS6JiLf1sZ33RMR5Nducsplx7B8RreXzIyPiVxFxe0TcEhHTa5ZbV5bfHhFLetnenhFxXUR0RMSdETGp2/wvRsSqmukPRsR7NydmQUQsiIg7at6vVw9wO9Mi4otDEF9ExA0R8bxyelxEXBkRvynrxmu7Lf+R8v9hl35uf/uI+HFENGzp2LdW5Wt6Y0QcVlP2jxHxgzrFMyEi1kbE+3pZ5n0R8YU+trN3RNxePj8gIg7dzDh2iIj2iBgVEa+KiJsiYnn5v/G2muW+EhF315zH/mYT27s0In4ZEb+OiK9HxA7d5h9d1tX9yun9Os+hVbQl62VEvDcidq2ZXhkR4/qx3rSIuLCf+9jsOlJ+tv7b5h7PViszfWwFD+AK4CfAab0scwnwtj628x7gvP4u38P63wD+tnw+lr/2I30l8Jua5Vb1c3vtwCE123tuzbxpwJdrtwU8F7it3u/HSHoArwX+GxhTTu8C7DZM+27o53JvBj5fM30p8L7y+fbAuJp5ewDXUtz4fZcetnUJMKOH8lOBY+r9fgzzez8V6AAay/+v3wEvrVMsc8tz2PW9LPM+4At9bGdv4Pb+Lt/D+h8CTiyfv7zz9QAmAg8CO5bTXwGO6sf2nlfz/IvAR2vnAT8Cfg7sV1O+DNi93vWjXo8tVS+BG7u9ritrzxW9rPdtYN8+lhk/0DoCBHA70Fjv13pLPGwR3ApExFhgOjAbeEdNeUTEeRHx24j4IfDCmnn3dLaWlN9+2rtt83XAW4HPld9kXhoR/1K2yv0qIi7vIY4dgVdm5i8BMnNVlrUe2AHYrJFFUbRGNmTm0prt/aWcNxr4HHBS7Trl/Hsi4sDN2VfFvQj4c2auBsjMP2fmA7Dh2+6PIuLWiLg2Il5UlrdHxBkR8bOI+J+IeH1ZPiMivlc+Hx8RV5X15aaIeGVZ/u8R8eWI+Cnw5YjYt9zO7eWy+/QQ4zHAd8r1dwLeALSW8T6bmY/VLPt5inqxuSPZrir3UxmZuRz4LjAf+Dfgssz834g4qWzhWB4Rc6FrS1s5fXJEfKJ8fmNEfKZ8H39bnj86W9e+WZ43roziysB+mwinGfgw8JLOelZu431lHfsZ8Jqa8q9ExFE106tqNxYRzymP6Ziybr0tIg4uW+duj4hfdG+dK22oa5n528z83/L5SuBhii9K/ZaZT5TxjKJIbGrr5cLysbrbat8Djt6c/WxLtkS9jIijgf2AK8r3e/tykQ9HxG3lueZl3fddnl9enpl39DCvoWzN+x5wZRnrZteR8nPxJ8Dhm/nSbJVMBLcORwI/yMz/AR6OiFeV5X9P8W1lCvBu4HX93WBm/hewBPhYZu5XVvSTgf0z85XAnB5WmwYsry2IiL+PiN8AVwO1l2wbyw+Fm2pP5t28DHgsIr5V/uN+rkwAAT4ILMnMP/Sw3i3A6/t7rOI6YI/yw/b8iPg/ABGxHXAuRavwq4CLgZaa9Roy80CKD+9Te9juaRSts68ETgEuq5k3BXhjZjZT1KVzMnM/ijq0sodtHQTcWj5/MfAn4D/LevGlzg/0iDgSuL/zy8hmWg783QDWG+lOA94JHAZ8NopuAcdQvBavBT4Qm7js2U2U9eFjFB/eULTyPZiZU4BPAfv3uGLR5WN8Zt5KcVXh7WX5ROBfyzimU7QU9UtmPg18EvhqeQ67sozt+LKuvQF4plscjcDE8gO9e4yd5897aoo/UyYUZ9YkGj0d32UULUUvAc4vy/4OeGFmXtvDKp7DBlkvM/MKila3o8v3/9ly1h8zc3/gS8C8HlY9EPh1bUFE7BMRn6FopTwSOCMzD+6+4mbWkW3mPTYR3Do0A50tdJeX01Cc6Noyc13ZwnPDIPfzK+CrEfFPwNoe5r+I4gN6g8z8dma+AjiK4oOg015Z/JTPO4EvRMRLe9heA8U/ykcp/vlfArwnInYD/pEiSenJQ8Bu/T6qisvMVcCrgOMp3r8rIuI9FF8ipgJLy2/cn6C49NHpW+XfW4FJPWx6OsWlezLzBuD5Ufbxo0jiny6f/zdwSkTMp6gXT2+8KcZn5pPl8wbgAOCC8oT+FHByRDyXIuHcqO9NRMwqWwVup2jp/lI5fXPN67AOeDaKlu3KyMynKLqWfLlsFZ4OfDMzny5f86vo3wdWT/VhOuW5qUzON2plKb2jjAG6nsNeQ3Gp+OHyg/zr/T2uTfgpcE7ZmvS88j2v9ULgke4rRcTuFF0K3lNzleMkYDLFuWlXivNUjzLz3RTnx/8F3la2Dp4FfGQTq1T+HLYF62V3fZ23unyOlS2LHRRfGvbPzH/OzJ90X2kAdWSbeY9NBOssIsYDB1N8sN1D8Y337RERfay6lr++f4393N2bgf+g+BD+eWzcsf7pTW0rM39Mcclnl3L6/vLvXRT9APePiFfHXzvVvpWiZej2zLwrM9dS/OMfQNGqsDewojzm50bEiprdNZaxqJ/KLwvtmXkqRWvrP1D0Y7mj/Da9X2b+TWa+qWa1zstZ6yiSs83xVM2+v0aRnD0NXBMRG33TBtaWH55Q1IuVmdmZxF1JUS9eStFa+MuyXkwEfhERu2bmtZ3HQdHS/b5yuvugmDF0ayWqiPXloze15wzY+H99MPWhGXhf+b59CzggIl7S33jKKwV97jMzP03xhWcscFNs3A1ho3NYeanwamB+Zv68ZlsPZGE1RQJwYLn8D8tz2KJu+15Hkdj8AzCOolX8J+UxT6Oo+50tpp7DCluiXnbXVz3tXgd+QHHV4wjgmxHxjrLleIPNrSM1cW4T77GJYP29jeIb016ZOSkz9wDupvim9GPg6IgYXfa5aapZ7x6KViAoTkw9eRLYETb0b9kjM5dR9NvYieJkWquDIkGjXGfvzoQ0Ig6g+JB9OCJ2jogxZfkuFJf97szMm2uSjiUUHajHRcQLyk0eXC53dWbuWh7vJOAvmblhvxSXlLtcotamRcTLu30g7kcx0OK3wAuiHJEbEdtFxL6bsemfUPa5i4gZFP0Qn+hh/y8B7srML1L0zXplD9v6LUWLMJn5IHBfRLy8nDeTol78OjNfWFMvVgIHlMv3KSKeX8a4ZjOOcVv0E+DvI+I5UfQ/PrIsexDYrfz/baT4YtiXn/LXy7x/Q5H8dBF/7Qu8e8179zmKVsKbgIOj6G+6PcX5rtM9/PUc9vfAaDa24RxW7uulmfmrzDwd+AVFq/cGmfkn4Dmdl/DK89R3gC9l5re7xd3ZXzYoXqPl5TbeWJ7D5kQx8vglNcu9lWLQ3COZuUvN8d4CHJ6Zt5Wb9xy2sYHUyy7vfz91+RzLzMcz87yye8wpFFfaOiLidBhYHSltM+9xZW61sBVrBs7oVvbNsvwDlMkT8HuKS3CdTgNaI+JTFC1yPbkcWBwR/0JxUm4tv/kE8MXs2kGfzPxNROwUETuWTff/ALw7ItZQfPM5OjMzIiYDF0bEeoovE5/JzDu77zwz10XER4Hry3+kW4HF/XhNDgL+vR/LqTAWODeK2yqsBVZQ9KN6NopbIXyxfN8bgC+w6ct73f07cHFE/Ar4C3DsJpZ7O/Cusp48SNF5vrurgRllbFD0Pftq+YF9F/DP/YypN03lfiotM38WEW0UX8SguAT/a4CIWEiRtNxPcV7py7nAZRFxZ7n8ncDj3ZZpphilWeubwKWZuTAiPk2RED5K175bFwLfiYi3UAyu6D7gAoruMB+LiNso+rfOjGJg03qKri7X9bDODyn6U7eXsb2O4gtp521t3lW+HpdHxM4U57BbKfpQdzca+EpNd4PbgBN7WK67JsoBKyoMsF7+J8XVsqfp2hrXmzsovgDvUF6ero3hVuDWKAYizSiLB1pHmoD/18+Ytmr+xJy6iIj/BzyZmV+q0/73B+Zl5rvqsX8NjfKb9WWZecgQ7uNbwMlZDLrSFlB2H2nIzGfKVufrgH3Krh5bpSgGcXwgM7fEl4uB7P85FLePOaiHPowaBhHxMeBPmXnJEG1/N+CSbl1tRiwvDau7C+j5m/lw2YVilKG2IVmMDl8cfx1sskWVLYtXmQRucWOBn0bELyla+d6/NSeBAGUfrxtr+qQOtz2Bk0wC6+o8YCi7iOxBL4OLRhpbBCVJkirKFkFJkqSKMhGUJEmqKBNBSZKkijIRlCRJqigTQUmVEhH/v737CbGyisM4/n0qxHBAKcJN4Q2FhJxa1KLxTwRBLY10MwsXEkFu2xZhuKigBBelK01sJyLCEA0SJChlf9SUIomYWURU1KK4oEj6tDjn1fHOv4vMOKPv84Fh7j33fd9z3ln95pxz36cj6ZJuDrp/SZIlrZ2nPl+X9JOkC5K+l7RbJQt63khaXRMyuvPZT0Tc2VIIRkQb/VLj6hrDwEluZOTOGUmvAS8Az9gepOSW/gncP8WxU6Vr3BLbvfcYETFJCsGIaLUad7UReIWSwNO03yPpozqTd1zSpzWpBUlPSToh6TtJo00U1TTeAHY0ST62r9h+t4nrk9SV9EF9Vt+QpOclna2zh/snxDmO10hHJD0t6Yv6eqekQ5K+lPSzpFfn/I8UEXetFIIR0Xabgc/qw6j/ltTk374MdCj5utuA65nNlOi1rTW/dD8l/myS+gDtAdtjM/S/DDht+0lKzNbHlDjHQUos4I4+7uEJShzlEPBWTT6IiJhVCsGIaLthSi439XezPLwROGz7mu3fKbFhAI8B64DjdZ/hm8DD/XQk6cW6b29c0vrafJWS2tFce2xCQspB4Nk+Ln3M9iXbf9Vx9pvLGhEtd99CDyAiYqFIeoAykzYoycC9gGtW6bSnAT/YHprt+rb/rUu/j9oesz0KjEoaAZbUwy73GUf2Hzf+eV/a29Us7yMippQZwYhos63AIdurbHdsPwKMAZuAU8CWuldwJfBcPeci8JCk60vFkh6foY93gL2SVtTjxeRCrnER6EhaU99vA07U1+NAs2y9pee8zZKWSnqwjvObmW87IqJIIRgRbTYMHO1pO1LbjwC/Aj8CnwBngH9sX6EUkO/VL3icA9Yzvb3A58BpSecpBebZ+nMT25eB7cBhSReAa8C++vHbwB5J31KWkyc6T1kS/grYZfu32W89IgJkZwUhItpDUgcYsb2uj2MHbHfrTNvXwIa6X3DRkLQT6Np+f5rPu7YHbu+oIuJOkT2CEdE2V4Hlks718Zy9kbqku4Qy07aoisCZSFpNmdX8Y6HHEhGLV2YEIyLmgKQPgQ09zXtsH1iI8URE9COFYERERERL5csiERERES2VQjAiIiKipVIIRkRERLRUCsGIiIiIlvofZajhy7vooToAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sales[['Profit', 'Age_Group']].boxplot(by='Age_Group', figsize=(10,6))\n",
        "ax.set_ylabel('Profit')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P69Iw_QHeJdh",
        "outputId": "57174d9f-af08-4ce6-bc59-2ea905adddb0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Year                 AxesSubplot(0.125,0.536818;0.227941x0.343182)\n",
              "Customer_Age      AxesSubplot(0.398529,0.536818;0.227941x0.343182)\n",
              "Order_Quantity    AxesSubplot(0.672059,0.536818;0.227941x0.343182)\n",
              "Unit_Cost               AxesSubplot(0.125,0.125;0.227941x0.343182)\n",
              "Unit_Price           AxesSubplot(0.398529,0.125;0.227941x0.343182)\n",
              "Profit               AxesSubplot(0.672059,0.125;0.227941x0.343182)\n",
              "dtype: object"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "boxplot_cols = ['Year', 'Customer_Age', 'Order_Quantity', 'Unit_Cost', 'Unit_Price', 'Profit']\n",
        "\n",
        "sales[boxplot_cols].plot(kind='box', subplots=True, layout=(2,3), figsize=(14,8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kZ8c5HBeJdi"
      },
      "source": [
        "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
        "\n",
        "## Column wrangling\n",
        "\n",
        "We can also create new columns or modify existing ones.\n",
        "\n",
        "### Add and calculate a new `Revenue_per_Age` column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DPzUXawbeJdi",
        "outputId": "23cb8265-96e6-4ee4-82d4-41d700265722",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    50.000000\n",
              "1    50.000000\n",
              "2    49.000000\n",
              "3    42.612245\n",
              "4     8.893617\n",
              "Name: Revenue_per_Age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "#Revenue_per_Age=Revenue/Customer_Age\n",
        "\n",
        "sales['Revenue_per_Age'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jZgnz1qXeJdi",
        "outputId": "f26c4264-ec03-4922-bf8f-e13bfb431260",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc42c61b890>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plot density graph of Revenue_per_Age"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tmSQxN0leJdj",
        "outputId": "75c8f433-7d1e-45bc-94f0-2361661b91b1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade3f9100>"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Plot histogram plot of Revenue_per_Age"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OboVFK-feJdj"
      },
      "source": [
        "### Add and calculate a new `Calculated_Cost` column\n",
        "\n",
        "Use this formula\n",
        "\n",
        "$$ Calculated\\_Cost = Order\\_Quantity * Unit\\_Cost $$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RRCAXV_WeJdj",
        "outputId": "a2e4a216-4ed7-4a8a-91b3-a4eeb106cd78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     360\n",
              "1     360\n",
              "2    1035\n",
              "3     900\n",
              "4     180\n",
              "Name: Calculated_Cost, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "#Create new Calculated_Cost column\n",
        "sales['Calculated_Cost'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dshrujHreJdk",
        "outputId": "489afe55-e6fd-4fd3-9314-96a7c3c6fc32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "(sales['Calculated_Cost'] != sales['Cost']).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "st5OSm1PeJdk"
      },
      "source": [
        "We can see the relationship between `Cost` and `Profit` using a scatter plot:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o4dYeYOFeJdk",
        "outputId": "c85917ee-e454-4e5a-fb43-545bba03d828",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc42c5ed250>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96j99WWweJdk"
      },
      "source": [
        "### Add and calculate a new `Calculated_Revenue` column\n",
        "\n",
        "Use this formula\n",
        "\n",
        "$$ Calculated\\_Revenue = Cost + Profit $$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iWQ2Bby6eJdk",
        "outputId": "ee349087-91c4-495c-b426-46d3e1b826ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     950\n",
              "1     950\n",
              "2    2401\n",
              "3    2088\n",
              "4     418\n",
              "Name: Calculated_Revenue, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "#calculate a new Calculated_Revenue column based on above \n",
        "\n",
        "sales['Calculated_Revenue'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "suEyuvSjeJdl",
        "outputId": "3bdae195-7ad9-448c-be2b-6a7b60258557",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "113036"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "(sales['Calculated_Revenue'] == sales['Revenue']).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dp6G5TmgeJdl",
        "outputId": "ec9d85e3-a449-407b-a603-922d06c2a08d"
      },
      "outputs": [
        {
          "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>Date</th>\n",
              "      <th>Day</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Age_Group</th>\n",
              "      <th>Customer_Gender</th>\n",
              "      <th>Country</th>\n",
              "      <th>State</th>\n",
              "      <th>Product_Category</th>\n",
              "      <th>...</th>\n",
              "      <th>Product</th>\n",
              "      <th>Order_Quantity</th>\n",
              "      <th>Unit_Cost</th>\n",
              "      <th>Unit_Price</th>\n",
              "      <th>Profit</th>\n",
              "      <th>Cost</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Revenue_per_Age</th>\n",
              "      <th>Calculated_Cost</th>\n",
              "      <th>Calculated_Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-11-26</td>\n",
              "      <td>26</td>\n",
              "      <td>November</td>\n",
              "      <td>2013</td>\n",
              "      <td>19</td>\n",
              "      <td>Youth (&lt;25)</td>\n",
              "      <td>M</td>\n",
              "      <td>Canada</td>\n",
              "      <td>British Columbia</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>...</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>8</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>590</td>\n",
              "      <td>360</td>\n",
              "      <td>950</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>360</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-11-26</td>\n",
              "      <td>26</td>\n",
              "      <td>November</td>\n",
              "      <td>2015</td>\n",
              "      <td>19</td>\n",
              "      <td>Youth (&lt;25)</td>\n",
              "      <td>M</td>\n",
              "      <td>Canada</td>\n",
              "      <td>British Columbia</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>...</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>8</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>590</td>\n",
              "      <td>360</td>\n",
              "      <td>950</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>360</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-03-23</td>\n",
              "      <td>23</td>\n",
              "      <td>March</td>\n",
              "      <td>2014</td>\n",
              "      <td>49</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>Australia</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>...</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>23</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>1366</td>\n",
              "      <td>1035</td>\n",
              "      <td>2401</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>1035</td>\n",
              "      <td>2401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-03-23</td>\n",
              "      <td>23</td>\n",
              "      <td>March</td>\n",
              "      <td>2016</td>\n",
              "      <td>49</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>Australia</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>...</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>20</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>1188</td>\n",
              "      <td>900</td>\n",
              "      <td>2088</td>\n",
              "      <td>42.612245</td>\n",
              "      <td>900</td>\n",
              "      <td>2088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-05-15</td>\n",
              "      <td>15</td>\n",
              "      <td>May</td>\n",
              "      <td>2014</td>\n",
              "      <td>47</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>F</td>\n",
              "      <td>Australia</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>...</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>238</td>\n",
              "      <td>180</td>\n",
              "      <td>418</td>\n",
              "      <td>8.893617</td>\n",
              "      <td>180</td>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
              "0 2013-11-26   26  November  2013            19     Youth (<25)   \n",
              "1 2015-11-26   26  November  2015            19     Youth (<25)   \n",
              "2 2014-03-23   23     March  2014            49  Adults (35-64)   \n",
              "3 2016-03-23   23     March  2016            49  Adults (35-64)   \n",
              "4 2014-05-15   15       May  2014            47  Adults (35-64)   \n",
              "\n",
              "  Customer_Gender    Country             State Product_Category  ...  \\\n",
              "0               M     Canada  British Columbia      Accessories  ...   \n",
              "1               M     Canada  British Columbia      Accessories  ...   \n",
              "2               M  Australia   New South Wales      Accessories  ...   \n",
              "3               M  Australia   New South Wales      Accessories  ...   \n",
              "4               F  Australia   New South Wales      Accessories  ...   \n",
              "\n",
              "               Product Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
              "0  Hitch Rack - 4-Bike              8         45         120     590   360   \n",
              "1  Hitch Rack - 4-Bike              8         45         120     590   360   \n",
              "2  Hitch Rack - 4-Bike             23         45         120    1366  1035   \n",
              "3  Hitch Rack - 4-Bike             20         45         120    1188   900   \n",
              "4  Hitch Rack - 4-Bike              4         45         120     238   180   \n",
              "\n",
              "   Revenue  Revenue_per_Age  Calculated_Cost  Calculated_Revenue  \n",
              "0      950        50.000000              360                 950  \n",
              "1      950        50.000000              360                 950  \n",
              "2     2401        49.000000             1035                2401  \n",
              "3     2088        42.612245              900                2088  \n",
              "4      418         8.893617              180                 418  \n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sales.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Gw_SPrRleJdl",
        "outputId": "b8f0835e-220f-428e-9199-06d35a549185",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b2199dd30>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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EXx4ELAOeG1t/rtUkSZIkHaFJz5RvBe5I8om2fiGwZZghSZIkSQvLpHdf+c0ktwA/3EqXVNXdww1LkiRJWjgmvXwF4OXAs1X1QWBXktMGGpMkSZK0oEwUypNcCfwi8J5WeinwP4calCRJkrSQTHqm/N8Abwe+BlBVfwO8cqhBSZIkSQvJpKH8uaoqoACSvGK4IUmSJEkLy6Sh/IYkvw8sTnIZ8GngD4YbliRJkrRwvODdV5IE+BjweuBZ4HXAr1bVjoHHJkmSJC0ILxjKq6qS3FxV3w8YxCVJkqSjbNLLVz6X5AcHHYkkSZK0QE36jZ5nAz+d5FFGd2AJo5PoPzDUwCRJkqSF4pChPMlrqur/AufO0ngkSZKkBeeFzpT/ObCqqh5L8qdV9e9mY1CSJEnSQvJC15RnrP3aIQciSZIkLVQvFMrrIG1JkiRJR8kLXb7yxiTPMjpjfnxrw7c/6HnCoKOTJEmSFoBDhvKqOma2BiJJkiQtVJPep1ySJEnSQAzlkiRJUmeGckmSJKkzQ7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnQ0WypMcl+SOJJ9P8kCSX2/105LcnmQ6yceSHNvqL2vr0237irFjvafVH05y7lh9XatNJ9k01GuRJEmShjTkmfJvAG+tqjcCZwDrkqwB3g98oKq+F3gauLT1vxR4utU/0PqR5HTgIuD7gHXA7yY5JskxwIeA84DTgXe1vpIkSdK8Mlgor5GvttWXtqWAtwI3tvoW4MLWXt/WadvPSZJWv76qvlFVXwKmgbPaMl1Vj1TVc8D1ra8kSZI0rwx6TXk7o30PsAfYAXwR+EpVPd+67AJOae1TgMcB2vZngFeP1w/Y52B1SZIkaV4ZNJRX1Ter6gxgOaMz268f8vkOJsnGJFNJpvbu3dtjCJIkSdJBzcrdV6rqK8BngTcDi5MsapuWA7tbezdwKkDb/irgy+P1A/Y5WH2m57+2qlZX1eqlS5celdckSZIkHS1D3n1laZLFrX088DbgIUbh/B2t2wbgptbe1tZp2z9TVdXqF7W7s5wGrATuAO4EVra7uRzL6MOg24Z6PZIkSdJQFr1wl+/YycCWdpeUlwA3VNUnkzwIXJ/kN4C7geta/+uAjyaZBvYxCtlU1QNJbgAeBJ4HrqiqbwIk+VlgO3AMsLmqHhjw9UiSJEmDGCyUV9W9wJtmqD/C6PryA+v/D/jJgxzrN4HfnKF+M3DzEQ9WkiRJ6shv9JQkSZI6M5RLkiRJnRnKJUmSpM4M5ZIkSVJnhnJJkiSpM0O5JEmS1JmhXJIkSerMUC5JkiR1ZiiXJEmSOjOUS5IkSZ0ZyiVJkqTODOWSJElSZ4ZySZIkqTNDuSRJktSZoVySJEnqzFAuSZIkdWYolyRJkjozlEuSJEmdGcolSZKkzgzlkiRJUmeGckmSJKkzQ7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnRnKJUmSpM4M5ZIkSVJnhnJJkiSpM0O5JEmS1JmhXJIkSerMUC5JkiR1NlgoT3Jqks8meTDJA0l+rtVPTLIjyc72uKTVk+TqJNNJ7k2yauxYG1r/nUk2jNXPTHJf2+fqJBnq9UiSJElDGfJM+fPAf62q04E1wBVJTgc2AbdW1Urg1rYOcB6wsi0bgWtgFOKBK4GzgbOAK/cH+dbnsrH91g34eiRJkqRBDBbKq+qJqvpca/8d8BBwCrAe2NK6bQEubO31wNYauQ1YnORk4FxgR1Xtq6qngR3AurbthKq6raoK2Dp2LEmSJGnemJVrypOsAN4E3A4sq6on2qYngWWtfQrw+Nhuu1rtUPVdM9QlSZKkeWXwUJ7ku4A/Bd5dVc+Ob2tnuGsWxrAxyVSSqb179w79dJIkSdJhGTSUJ3kpo0D+R1X1Z638VLv0hPa4p9V3A6eO7b681Q5VXz5D/Z+pqmuranVVrV66dOmRvShJkiTpKBvy7isBrgMeqqrfGtu0Ddh/B5UNwE1j9YvbXVjWAM+0y1y2A2uTLGkf8FwLbG/bnk2ypj3XxWPHkiRJkuaNRQMe+y3AfwDuS3JPq/0ScBVwQ5JLgceAd7ZtNwPnA9PA14FLAKpqX5L3AXe2fu+tqn2tfTnwEeB44Ja2SJIkSfPKYKG8qv43cLD7hp8zQ/8CrjjIsTYDm2eoTwFvOIJhSpIkSd35jZ6SJElSZ4ZySZIkqTNDuSRJktSZoVySJEnqzFAuSZIkdWYolyRJkjozlEuSJEmdGcolSZKkzgzlkiRJUmeDfaOnJrNi06dmrD961QWzPBJJkiT14plySZIkqTNDuSRJktSZoVySJEnqzFAuSZIkdWYolyRJkjozlEuSJEmdGcolSZKkzgzlkiRJUmeGckmSJKkzQ7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnRnKJUmSpM4M5ZIkSVJnhnJJkiSpM0O5JEmS1JmhXJIkSerMUC5JkiR1ZiiXJEmSOjOUS5IkSZ0NFsqTbE6yJ8n9Y7UTk+xIsrM9Lmn1JLk6yXSSe5OsGttnQ+u/M8mGsfqZSe5r+1ydJEO9FkmSJGlIQ54p/wiw7oDaJuDWqloJ3NrWAc4DVrZlI3ANjEI8cCVwNnAWcOX+IN/6XDa234HPJUmSJM0Lg4XyqvprYN8B5fXAltbeAlw4Vt9aI7cBi5OcDJwL7KiqfVX1NLADWNe2nVBVt1VVAVvHjiVJkiTNK7N9TfmyqnqitZ8ElrX2KcDjY/12tdqh6rtmqEuSJEnzTrcPerYz3DUbz5VkY5KpJFN79+6djaeUJEmSJjbbofypdukJ7XFPq+8GTh3rt7zVDlVfPkN9RlV1bVWtrqrVS5cuPeIXIUmSJB1Nsx3KtwH776CyAbhprH5xuwvLGuCZdpnLdmBtkiXtA55rge1t27NJ1rS7rlw8dixJkiRpXlk01IGT/Anwo8BJSXYxuovKVcANSS4FHgPe2brfDJwPTANfBy4BqKp9Sd4H3Nn6vbeq9n949HJGd3g5HrilLZIkSdK8M1gor6p3HWTTOTP0LeCKgxxnM7B5hvoU8IYjGaMkSZI0F/iNnpIkSVJnhnJJkiSpM0O5JEmS1Nlg15RrGCs2fWrG+qNXXTDLI5EkSdLR4plySZIkqTNDuSRJktSZoVySJEnqzFAuSZIkdWYolyRJkjozlEuSJEmdGcolSZKkzgzlkiRJUmeGckmSJKkzQ7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnS3qPQDNbMWmT/UegiRJkmaJZ8olSZKkzgzlkiRJUmeGckmSJKkzQ7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnRnKJUmSpM78Rs8XiYN9A+ijV10wyyORJEnS4TKUv8gdLKyDgV2SJGmuMJQvYJ5dlyRJmhu8plySJEnqbN6fKU+yDvggcAzw4aq6qvOQ5j3PoEuSJM2ueX2mPMkxwIeA84DTgXclOb3vqCRJkqTDM9/PlJ8FTFfVIwBJrgfWAw92HdWL1KE+NDokz9BLkqQXu/keyk8BHh9b3wWc3WksmmVH65eEQ4V+L+WRJEmzYb6H8okk2QhsbKtfTfJwh2GcBPxth+ed9/L+WTv+xHM09Jh0UP47mtucn7nPOZr7nKO5b9I5+p7DOeh8D+W7gVPH1pe32j9RVdcC187WoGaSZKqqVvccgw7NOZr7nKO5zfmZ+5yjuc85mvuGmqN5/UFP4E5gZZLTkhwLXARs6zwmSZIk6bDM6zPlVfV8kp8FtjO6JeLmqnqg87AkSZKkwzKvQzlAVd0M3Nx7HBPoevmMJuIczX3O0dzm/Mx9ztHc5xzNfYPMUapqiONKkiRJmtB8v6ZckiRJmvcM5QNLsi7Jw0mmk2zqPZ4XuySbk+xJcv9Y7cQkO5LsbI9LWj1Jrm5zc2+SVWP7bGj9dybZMFY/M8l9bZ+rk2R2X+H8l+TUJJ9N8mCSB5L8XKs7T3NEkuOS3JHk822Ofr3VT0tye/u5fqx9wJ4kL2vr0237irFjvafVH05y7ljd98YjlOSYJHcn+WRbd37mkCSPtvehe5JMtZrvc3NIksVJbkzyhSQPJXlz1zmqKpeBFkYfPv0i8FrgWODzwOm9x/ViXoAfAVYB94/V/huwqbU3Ae9v7fOBW4AAa4DbW/1E4JH2uKS1l7Rtd7S+afue1/s1z7cFOBlY1dqvBP4PcLrzNHeW9nP7rtZ+KXB7+3neAFzU6r8H/OfWvhz4vda+CPhYa5/e3vdeBpzW3g+P8b3xqM3TzwN/DHyyrTs/c2gBHgVOOqDm+9wcWoAtwH9q7WOBxT3nyDPlwzoLmK6qR6rqOeB6YH3nMb2oVdVfA/sOKK9n9A+P9njhWH1rjdwGLE5yMnAusKOq9lXV08AOYF3bdkJV3Vajf21bx46lCVXVE1X1udb+O+AhRt/O6zzNEe1n/dW2+tK2FPBW4MZWP3CO9s/djcA57YzQeuD6qvpGVX0JmGb0vuh74xFKshy4APhwWw/Oz3zg+9wckeRVjE7kXQdQVc9V1VfoOEeG8mGdAjw+tr6r1TS7llXVE639JLCstQ82P4eq75qhru9Q+zP6mxidiXWe5pB2acQ9wB5G/8l8EfhKVT3fuoz/XL81F237M8CrOfy50+R+G/gF4B/b+qtxfuaaAv4yyV0ZfbM4+D43l5wG7AX+sF0G9uEkr6DjHBnKtaC031a95dAckOS7gD8F3l1Vz45vc576q6pvVtUZjL4p+Szg9Z2HpCbJjwN7ququ3mPRIf1QVa0CzgOuSPIj4xt9n+tuEaPLXa+pqjcBX2N0ucq3zPYcGcqHtRs4dWx9eatpdj3V/oxEe9zT6gebn0PVl89Q12FK8lJGgfyPqurPWtl5moPan3M/C7yZ0Z9r93+/xfjP9Vtz0ba/Cvgyhz93msxbgLcneZTRpSVvBT6I8zOnVNXu9rgH+ASjX259n5s7dgG7qur2tn4jo5DebY4M5cO6E1jZPhF/LKMP2GzrPKaFaBuw/9PQG4CbxuoXt09UrwGeaX+y2g6sTbKkfep6LbC9bXs2yZp2PebFY8fShNrP7jrgoar6rbFNztMckWRpksWtfTzwNkbX/n8WeEfrduAc7Z+7dwCfaWeYtgEXZXT3j9OAlYw++OR74xGoqvdU1fKqWsHoZ/eZqvopnJ85I8krkrxyf5vR+9P9+D43Z1TVk8DjSV7XSucAD9Jzjib9hKrLd/zJ3vMZ3V3ii8Av9x7Pi30B/gR4AvgHRr8FX8ro2slbgZ3Ap4ETW98AH2pzcx+weuw4/5HRh56mgUvG6qsZvbF+EfgftC/gcjmsOfohRn8OvBe4py3nO09zZwF+ALi7zdH9wK+2+msZhbZp4OPAy1r9uLY+3ba/duxYv9zm4WHG7jzge+NRm6sf5dt3X3F+5sjS5uLzbXlg/8/Q97m5tQBnAFPtve7PGd09pdsc+Y2ekiRJUmdeviJJkiR1ZiiXJEmSOjOUS5IkSZ0ZyiVJkqTODOWSJElSZ4ZySZIkqTNDuSRJktSZoVySJEnq7P8Drz4FOxGve3UAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plot histogram graph of Revenue for bins=100\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skHKE4d9eJdl"
      },
      "source": [
        "### Modify all `Unit_Price` values adding 3% tax to them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yym3mm_8eJdm",
        "outputId": "60cba21b-b79f-40dc-c328-79e6aeaf9337"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    120\n",
              "1    120\n",
              "2    120\n",
              "3    120\n",
              "4    120\n",
              "Name: Unit_Price, dtype: int64"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sales['Unit_Price'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "70GsKKvEeJdm"
      },
      "outputs": [],
      "source": [
        "#Modify all Unit_Price values adding 3% tax to them\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "0rjOtc-XeJdm",
        "outputId": "4491b7e2-e1da-42dc-cabd-f54e1ac4d442",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    123.6\n",
              "1    123.6\n",
              "2    123.6\n",
              "3    123.6\n",
              "4    123.6\n",
              "Name: Unit_Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "sales['Unit_Price'].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zK2Sp8hJeJdm"
      },
      "source": [
        "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
        "\n",
        "## Selection & Indexing:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrcPhw13eJdn"
      },
      "source": [
        "### Get all the sales made in the state of `Kentucky`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1wB9zXabeJdn",
        "outputId": "0469dc4b-8e8e-41a5-acff-2150e30f7110",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
              "156   2013-11-04    4  November  2013            40  Adults (35-64)   \n",
              "157   2015-11-04    4  November  2015            40  Adults (35-64)   \n",
              "23826 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
              "23827 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
              "31446 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
              "31447 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
              "79670 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
              "79671 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
              "79672 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
              "79673 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
              "\n",
              "      Customer_Gender        Country     State Product_Category  \\\n",
              "156                 M  United States  Kentucky      Accessories   \n",
              "157                 M  United States  Kentucky      Accessories   \n",
              "23826               M  United States  Kentucky      Accessories   \n",
              "23827               M  United States  Kentucky      Accessories   \n",
              "31446               M  United States  Kentucky      Accessories   \n",
              "31447               M  United States  Kentucky      Accessories   \n",
              "79670               M  United States  Kentucky      Accessories   \n",
              "79671               M  United States  Kentucky      Accessories   \n",
              "79672               M  United States  Kentucky      Accessories   \n",
              "79673               M  United States  Kentucky      Accessories   \n",
              "\n",
              "          Sub_Category                 Product  Order_Quantity  Unit_Cost  \\\n",
              "156         Bike Racks     Hitch Rack - 4-Bike               1         45   \n",
              "157         Bike Racks     Hitch Rack - 4-Bike               1         45   \n",
              "23826          Fenders   Fender Set - Mountain              12          8   \n",
              "23827          Fenders   Fender Set - Mountain              14          8   \n",
              "31446          Helmets  Sport-100 Helmet, Blue              29         13   \n",
              "31447          Helmets  Sport-100 Helmet, Blue              31         13   \n",
              "79670  Tires and Tubes        ML Mountain Tire               2         11   \n",
              "79671  Tires and Tubes        ML Mountain Tire              21         11   \n",
              "79672  Tires and Tubes        ML Mountain Tire               1         11   \n",
              "79673  Tires and Tubes        ML Mountain Tire              18         11   \n",
              "\n",
              "       Unit_Price  Profit  Cost  Revenue  \n",
              "156           120      63    45      108  \n",
              "157           120      63    45      108  \n",
              "23826          22     142    96      238  \n",
              "23827          22     165   112      277  \n",
              "31446          35     537   377      914  \n",
              "31447          35     574   403      977  \n",
              "79670          30      32    22       54  \n",
              "79671          30     336   231      567  \n",
              "79672          30      16    11       27  \n",
              "79673          30     288   198      486  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Day</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Age_Group</th>\n",
              "      <th>Customer_Gender</th>\n",
              "      <th>Country</th>\n",
              "      <th>State</th>\n",
              "      <th>Product_Category</th>\n",
              "      <th>Sub_Category</th>\n",
              "      <th>Product</th>\n",
              "      <th>Order_Quantity</th>\n",
              "      <th>Unit_Cost</th>\n",
              "      <th>Unit_Price</th>\n",
              "      <th>Profit</th>\n",
              "      <th>Cost</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>2013-11-04</td>\n",
              "      <td>4</td>\n",
              "      <td>November</td>\n",
              "      <td>2013</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Bike Racks</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>63</td>\n",
              "      <td>45</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>2015-11-04</td>\n",
              "      <td>4</td>\n",
              "      <td>November</td>\n",
              "      <td>2015</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Bike Racks</td>\n",
              "      <td>Hitch Rack - 4-Bike</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>120</td>\n",
              "      <td>63</td>\n",
              "      <td>45</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23826</th>\n",
              "      <td>2014-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2014</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Fenders</td>\n",
              "      <td>Fender Set - Mountain</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>22</td>\n",
              "      <td>142</td>\n",
              "      <td>96</td>\n",
              "      <td>238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23827</th>\n",
              "      <td>2016-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2016</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Fenders</td>\n",
              "      <td>Fender Set - Mountain</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>22</td>\n",
              "      <td>165</td>\n",
              "      <td>112</td>\n",
              "      <td>277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31446</th>\n",
              "      <td>2014-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2014</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Helmets</td>\n",
              "      <td>Sport-100 Helmet, Blue</td>\n",
              "      <td>29</td>\n",
              "      <td>13</td>\n",
              "      <td>35</td>\n",
              "      <td>537</td>\n",
              "      <td>377</td>\n",
              "      <td>914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31447</th>\n",
              "      <td>2016-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2016</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Helmets</td>\n",
              "      <td>Sport-100 Helmet, Blue</td>\n",
              "      <td>31</td>\n",
              "      <td>13</td>\n",
              "      <td>35</td>\n",
              "      <td>574</td>\n",
              "      <td>403</td>\n",
              "      <td>977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79670</th>\n",
              "      <td>2014-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2014</td>\n",
              "      <td>40</td>\n",
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              "      <td>United States</td>\n",
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              "      <td>Accessories</td>\n",
              "      <td>Tires and Tubes</td>\n",
              "      <td>ML Mountain Tire</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
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              "      <td>22</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79671</th>\n",
              "      <td>2014-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2014</td>\n",
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              "      <td>Adults (35-64)</td>\n",
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              "      <td>United States</td>\n",
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              "      <td>Accessories</td>\n",
              "      <td>Tires and Tubes</td>\n",
              "      <td>ML Mountain Tire</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>336</td>\n",
              "      <td>231</td>\n",
              "      <td>567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79672</th>\n",
              "      <td>2016-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2016</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Tires and Tubes</td>\n",
              "      <td>ML Mountain Tire</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79673</th>\n",
              "      <td>2016-04-16</td>\n",
              "      <td>16</td>\n",
              "      <td>April</td>\n",
              "      <td>2016</td>\n",
              "      <td>40</td>\n",
              "      <td>Adults (35-64)</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Tires and Tubes</td>\n",
              "      <td>ML Mountain Tire</td>\n",
              "      <td>18</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>288</td>\n",
              "      <td>198</td>\n",
              "      <td>486</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e4e8685-2478-43bc-bdc0-2b91fa6168b3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <script>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e4e8685-2478-43bc-bdc0-2b91fa6168b3');\n",
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              "                                                     [key], {});\n",
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              "          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",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rI5-U3FUeJdn"
      },
      "source": [
        "### Get the mean revenue of the `Adults (35-64)` sales group"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "96e8VvO0eJdn",
        "outputId": "f03620fa-dfe1-45a3-ca7a-85803a1c2602",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "762.8287654055604"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXVRmMgneJdo"
      },
      "source": [
        "### How many records belong to Age Group `Youth (<25)` or `Adults (35-64)`?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "oSGoirzDeJdo",
        "outputId": "58c27c07-4de0-4b7e-a000-eca0262e3a06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(73652, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3z2XJF_ceJdo"
      },
      "source": [
        "### Get the mean revenue of the sales group `Adults (35-64)` in `United States`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "fOBaobjGeJdo",
        "outputId": "b4356ca9-99cc-4994-c805-a68e1b57da86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "726.7260473588342"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "sales.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNPZoOqlNO8l",
        "outputId": "a9136276-7818-4ec1-9410-9826fe35a633"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Day', 'Month', 'Year', 'Customer_Age', 'Age_Group',\n",
              "       'Customer_Gender', 'Country', 'State', 'Product_Category',\n",
              "       'Sub_Category', 'Product', 'Order_Quantity', 'Unit_Cost', 'Unit_Price',\n",
              "       'Profit', 'Cost', 'Revenue'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gMH4hSm4eJdp"
      },
      "source": [
        "### Increase the revenue by 10% to every sale made in France"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5hnrCKMaeJdp",
        "outputId": "847692dc-03b1-44f9-8e20-5f3d30a9a2dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50     787\n",
              "51     787\n",
              "52    2957\n",
              "53    2851\n",
              "60     626\n",
              "Name: Revenue, dtype: int64"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sales.loc[sales['Country'] == 'France', 'Revenue'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "DSHtksgSeJdp"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "xa_jrY7GeJdq",
        "outputId": "705106ab-7aa0-4f12-8e4a-5bfba9c2fec5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50     865.7\n",
              "51     865.7\n",
              "52    3252.7\n",
              "53    3136.1\n",
              "60     688.6\n",
              "Name: Revenue, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Convert Categorical Values To Numerical Values"
      ],
      "metadata": {
        "id": "oiTo445YSAPb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales['Age_Group'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-OsRt2MQYAQ",
        "outputId": "ac980155-9e5a-4b88-9b91-f9a066fdd42a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adults (35-64)          55824\n",
              "Young Adults (25-34)    38654\n",
              "Youth (<25)             17828\n",
              "Seniors (64+)             730\n",
              "Name: Age_Group, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I- Using Replace"
      ],
      "metadata": {
        "id": "TTIf-mT8S98O"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNnwm6XGS7Ig",
        "outputId": "6841f4ed-0777-4e3f-c2d7-54f642906243"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    55824\n",
              "2    38654\n",
              "3    17828\n",
              "4      730\n",
              "Name: Age_Group2, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "II-Get Dummies"
      ],
      "metadata": {
        "id": "C-XD7krTY-2f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#pd.get_dummies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "GflmrzNqT2bX",
        "outputId": "c7c3f86d-7271-44ae-8cfc-d4c6990e8030"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Seniors (64+)  Young Adults (25-34)  Youth (<25)\n",
              "0                   0                     0            1\n",
              "1                   0                     0            1\n",
              "2                   0                     0            0\n",
              "3                   0                     0            0\n",
              "4                   0                     0            0\n",
              "...               ...                   ...          ...\n",
              "113031              0                     0            0\n",
              "113032              0                     0            1\n",
              "113033              0                     0            1\n",
              "113034              0                     0            0\n",
              "113035              0                     0            0\n",
              "\n",
              "[113036 rows x 3 columns]"
            ],
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              "\n",
              "  <div id=\"df-98b0afb3-45d4-44f6-bc37-1f6ff7853971\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>113034</th>\n",
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              "    <tr>\n",
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              "<p>113036 rows × 3 columns</p>\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "III-Label Encoder "
      ],
      "metadata": {
        "id": "TMq6MEPAZChC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "sales['Age_Group3']= le.fit_transform(sales['Age_Group'])\n",
        "sales['Age_Group3'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s_t_mwQSXZHT",
        "outputId": "82e7693a-eb0f-447f-9587-f177747a4ae6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    55824\n",
              "2    38654\n",
              "3    17828\n",
              "1      730\n",
              "Name: Age_Group3, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vs3gOXfFeJdq"
      },
      "source": [
        "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
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        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.1"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}