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        "<a href=\"https://colab.research.google.com/github/Tanu-N-Prabhu/Regina-Gas-Stations/blob/main/Regina_Gas_Station_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **Exploring the Regina Gas Stations Dataset**\n",
        "\n",
        "![Image](https://economicdevelopmentregina.com/wp-content/uploads/2022/05/City-Skyline-Compressed-Keithe-Hershmiller-cropped-1.jpg)\n",
        "\n",
        "---\n",
        "\n",
        "# **Table of contents**\n",
        "1. [Purpose](#purpose)\n",
        "2. [Loading the Dataset](#loading)\n",
        "    1. [Printing the entire dataset](#print)\n",
        "3. [Removing the empty row](#remove)\n",
        "4. [Information about the database](#info)\n",
        "5. [Key Error](#Key)\n",
        "6. [Counting occurrences of a specific value](#count)\n",
        "7. [Grouping Specific Column Data](#group)\n",
        "8. [Plotting the Data](#plot)\n",
        "9. [Conclusion](#conclusion)\n",
        "---\n",
        "\n",
        "## **Purpose** <a name=\"purpose\"></a>\n",
        "I created this dataset to do some experiments and tell stories. I scraped every single data from the Google Maps manually. Although, this is a open source dataset, feel free to edit and update if you have any great suggestions. All the EDA tools and techniques will be applied on this data-set. Get exicited.\n",
        "\n",
        "\n",
        "## **Loading the Dataset** <a name=\"loading\"></a>\n",
        "\n",
        "First you need to upload the dataset to your Google Colab. Then you need to `read` it to your notebook as shown below. Since this is not a big dataset interms of having many rows. I stored them in a Excel Sheet. A simple way to store big data sets is to use CSV files (comma separated files).\n",
        "\n",
        "CSV files contains plain text and is a well know format that can be read by everyone including Pandas. To read a CSV file you might want to use `pd.read_csv('data.csv')`. instead of `pd.read_excel` which is used for excel stored datasets. "
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              "                        Name           Location  Phone Number\n",
              "0                        NaN                NaN           NaN\n",
              "1                      Mobil     Albert Street   306-790-1228\n",
              "2                      Esso      Albert Street   306-584-7185\n",
              "3   Canadian Tire - Gas Plus     Albert Street   306-585-3319\n",
              "4              Co-op Gas Bar     Albert Street   306-791-8890\n",
              "..                       ...                ...           ...\n",
              "64             Cowessess Gas      SK-33 Highway  306-359-6777\n",
              "65             Petro Canada     Gravel Pit Road  306-525-6033\n",
              "66         Petro Pass Regina        529 1 Ave E  306-721-6667\n",
              "67             Co-op Gas Bar  Great Plains Road  306-791-5707\n",
              "68                      Esso  Great Plains Road  306-781-2580\n",
              "\n",
              "[69 rows x 3 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Phone Number</th>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>Mobil</td>\n",
              "      <td>Albert Street</td>\n",
              "      <td>306-790-1228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Esso</td>\n",
              "      <td>Albert Street</td>\n",
              "      <td>306-584-7185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Canadian Tire - Gas Plus</td>\n",
              "      <td>Albert Street</td>\n",
              "      <td>306-585-3319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Co-op Gas Bar</td>\n",
              "      <td>Albert Street</td>\n",
              "      <td>306-791-8890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Cowessess Gas</td>\n",
              "      <td>SK-33 Highway</td>\n",
              "      <td>306-359-6777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Petro Canada</td>\n",
              "      <td>Gravel Pit Road</td>\n",
              "      <td>306-525-6033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Petro Pass Regina</td>\n",
              "      <td>529 1 Ave E</td>\n",
              "      <td>306-721-6667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Co-op Gas Bar</td>\n",
              "      <td>Great Plains Road</td>\n",
              "      <td>306-791-5707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Esso</td>\n",
              "      <td>Great Plains Road</td>\n",
              "      <td>306-781-2580</td>\n",
              "    </tr>\n",
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              "<p>69 rows × 3 columns</p>\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Reading the dataset\n",
        "df = pd.read_excel('/content/Regina Gas Stations.xlsx')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Printing the entire dataset** <a name=\"print\"></a>\n",
        "\n",
        "If you wanted to print the entire dataset, then you can use the `to_string()` method. This will consume a lot of RAM but worth giving a try and know how it works. "
      ],
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        "id": "EYh5NC6spzb_"
      }
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    {
      "cell_type": "code",
      "source": [
        "print(df.to_string())"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "rqMPEW7RqHTC",
        "outputId": "b5579949-0fea-4ef5-9d97-809bdc5fceb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        Name               Location  Phone Number\n",
            "0                        NaN                    NaN           NaN\n",
            "1                      Mobil         Albert Street   306-790-1228\n",
            "2                      Esso          Albert Street   306-584-7185\n",
            "3   Canadian Tire - Gas Plus         Albert Street   306-585-3319\n",
            "4              Co-op Gas Bar         Albert Street   306-791-8890\n",
            "5                      Shell         Albert Street   306-586-4333\n",
            "6              Petro Canada          Albert Street   306-585-1849\n",
            "7                     Mobil               Broadway   306-569-0255\n",
            "8             Co-op Gas Bar            Gordon Road   306-791-9388\n",
            "9              Petro Canada          Albert Street   306-522-9112\n",
            "10                     Esso          Albert Street   306-525-0414\n",
            "11             Petro Canada        Victoria Avenue   306-352-4252\n",
            "12            Race Trac Gas            Hill Avenue   306-586-5777\n",
            "13              Fas Gas Plus            7th Avenue   306-569-3399\n",
            "14                      Esso            7th Avenue            TBD\n",
            "15       Regina Discount Gas    Saskatchewan Drive   306-924-8733\n",
            "16              Checkers Gas         Pasqua Street   306-791-6362\n",
            "17                      Esso         Arcola Avenue   306-525-9121\n",
            "18                     Shell         Arcola Avenue   306-525-1717\n",
            "19             Petro Canada          Quance Street   306-791-6934\n",
            "20         Regina Messenger         Halifax Street            TBD\n",
            "21  Regina Cabs Gas Station     Saskatchewan Drive   306-543-3333\n",
            "22             Co-op Gas Bar         Dewdney Avenue  306-791-9308\n",
            "23                     Husky            Park Street  306-522-0160\n",
            "24                     Shell       Victoria Avenue   306-789-9495\n",
            "25                     Shell       Victoria Avenue   306-525-1218\n",
            "26                     Domo          Albert Street            TBD\n",
            "27                      Esso         Albert Street   306-543-1155\n",
            "28                    Shell          Albert Street   306-543-2900\n",
            "29             Petro Canada            Fleet Street  306-789-7077\n",
            "30                     Shell             4400 4 Ave  306-337-1252\n",
            "31                    Shell         Truesdale Drive  306-789-6150\n",
            "32       Regina Discount Gas        Winnipeg Street           TBD\n",
            "33  Canadian Tire - Gas Plus         Albert Street   306-924-5535\n",
            "34                      Esso        Winnipeg Street           TBD\n",
            "35             Petro Canada          Albert Street   306-545-3533\n",
            "36                      Esso    180 Albert Street N  306-949-5800\n",
            "37             Petro Canada          Sherwood Drive  306-545-3533\n",
            "38             Petro Canada       Bucking Ham Drive  306-501-9288\n",
            "39                    Mobil   Prince of Wales Drive  306-546-6526\n",
            "40             Co-op Gas Bar        Winnipeg Street  306-791-9303\n",
            "41            Regina Gas Bar         Albert Street   306-791-8890\n",
            "42                     Shell          McCarthy Bvld  306-949-6155\n",
            "43             Co-op Gas Bar  Prince of Wales Drive  306-791-9334\n",
            "44                     Husky  Prince of Wales Drive  306-789-3477\n",
            "45              Fas Gas Plus          Edward Street  306-585-6657\n",
            "46                  Creeland           Angus Street  306-525-8066\n",
            "47                     Shell    310 McCarthy Bvld N  306-949-2550\n",
            "48     Petro Pass Truck Stop            529 1 Ave E  306-924-1919\n",
            "49             Co-op Gas Bar             Chuka Blvd  306-791-1922\n",
            "50           Esso - Cardlock  Prince of Wales Drive           TBD\n",
            "51                     Mobil          Rochdale Blvd  306-546-6626\n",
            "52          Kleins Food Mart         Pasqua Street   306-791-6362\n",
            "53                      Esso          Rochdale Blvd  306-949-5677\n",
            "54             Co-op Gas Bar          Rochdale Blvd  306-791-9333\n",
            "55                  Flying J            Ross Avenue  306-721-0070\n",
            "56                      Esso            Pinkie Road  306-347-0608\n",
            "57             Co-op Gas Bar    8501 Dewdney Avenue  306-791-1932\n",
            "58                     Domo           Rochdale Blvd           TBD\n",
            "59        Costco Gas Station           Anaquod Road  306-271-0912\n",
            "60                  7-Eleven      Bucking Ham Drive           TBD\n",
            "61                     TEMPO        McDonald Street           TBD\n",
            "62             Petro Canada    4415 Albert Street S  306-585-1849\n",
            "63                    Husky         McDonald Street  306-721-6880\n",
            "64             Cowessess Gas          SK-33 Highway  306-359-6777\n",
            "65             Petro Canada         Gravel Pit Road  306-525-6033\n",
            "66         Petro Pass Regina            529 1 Ave E  306-721-6667\n",
            "67             Co-op Gas Bar      Great Plains Road  306-791-5707\n",
            "68                      Esso      Great Plains Road  306-781-2580\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## **Removing the empty row** <a name=\"remove\"></a>\n",
        "\n",
        "As you can see the first row has `NaN` values which means its not a number value. We need to remove it. \n",
        "\n",
        "```\n",
        "DataFrame.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "YPnMoFeNjDjt"
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    {
      "cell_type": "code",
      "source": [
        "df = df.drop(0)\n",
        "df"
      ],
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              "                        Name           Location  Phone Number\n",
              "1                      Mobil     Albert Street   306-790-1228\n",
              "2                      Esso      Albert Street   306-584-7185\n",
              "3   Canadian Tire - Gas Plus     Albert Street   306-585-3319\n",
              "4              Co-op Gas Bar     Albert Street   306-791-8890\n",
              "5                      Shell     Albert Street   306-586-4333\n",
              "..                       ...                ...           ...\n",
              "64             Cowessess Gas      SK-33 Highway  306-359-6777\n",
              "65             Petro Canada     Gravel Pit Road  306-525-6033\n",
              "66         Petro Pass Regina        529 1 Ave E  306-721-6667\n",
              "67             Co-op Gas Bar  Great Plains Road  306-791-5707\n",
              "68                      Esso  Great Plains Road  306-781-2580\n",
              "\n",
              "[68 rows x 3 columns]"
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              "      <th>1</th>\n",
              "      <td>Mobil</td>\n",
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              "      <td>306-790-1228</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Albert Street</td>\n",
              "      <td>306-584-7185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Canadian Tire - Gas Plus</td>\n",
              "      <td>Albert Street</td>\n",
              "      <td>306-585-3319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Co-op Gas Bar</td>\n",
              "      <td>Albert Street</td>\n",
              "      <td>306-791-8890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Shell</td>\n",
              "      <td>Albert Street</td>\n",
              "      <td>306-586-4333</td>\n",
              "    </tr>\n",
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              "      <th>64</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Petro Canada</td>\n",
              "      <td>Gravel Pit Road</td>\n",
              "      <td>306-525-6033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Petro Pass Regina</td>\n",
              "      <td>529 1 Ave E</td>\n",
              "      <td>306-721-6667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Co-op Gas Bar</td>\n",
              "      <td>Great Plains Road</td>\n",
              "      <td>306-791-5707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Esso</td>\n",
              "      <td>Great Plains Road</td>\n",
              "      <td>306-781-2580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>68 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1cf785e5-3ad8-41c4-a088-5747d29310fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1cf785e5-3ad8-41c4-a088-5747d29310fc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1cf785e5-3ad8-41c4-a088-5747d29310fc');\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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## **Information about the Data Frame** <a name=\"info\"></a>"
      ],
      "metadata": {
        "id": "fXkC9CBXjtWZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAxq-49ekTTM",
        "outputId": "85fa1190-02d3-4be3-8ab3-769aa57cdd6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 68 entries, 1 to 68\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Name          68 non-null     object\n",
            " 1   Location      68 non-null     object\n",
            " 2   Phone Number  68 non-null     object\n",
            "dtypes: object(3)\n",
            "memory usage: 1.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we can understand from this data is that there are **67 Gas Stations in Regina**. This excludes the first row that is the table header."
      ],
      "metadata": {
        "id": "pP3zFTiAnORy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## **Key Error** <a name=\"key\"></a>\n",
        "\n",
        "This can be a nightmare sometimes. It took me several hours to find the root cause of the problem. The column names had a white space appenede at the end. Remove this and your issue will be solved. \n"
      ],
      "metadata": {
        "id": "6UUxCG_Fkdv1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This is to check if any white space is appended to the column names\n",
        "print(df.columns.tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BABXvkDSk98j",
        "outputId": "acb6dc37-8d0f-45d9-8e79-d0924e8476de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Name', 'Location', 'Phone Number']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## **Counting occurrences of a specific value** <a name=\"count\"></a>\n",
        "\n",
        "```\n",
        "data['column_name'].value_counts()[value]\n",
        "```\n",
        "\n",
        "where\n",
        "\n",
        "* data is the input dataframe\n",
        "* value is the string/integer value present in the column to be counted\n",
        "* column_name is the column in the dataframe\n",
        "\n"
      ],
      "metadata": {
        "id": "Snc6lT36lDnW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Location'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kfrdrKAszOk",
        "outputId": "afb0ae1b-6ac8-4f18-985b-8367612b7832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Albert Street            14\n",
            "Rochdale Blvd             4\n",
            "Prince of Wales Drive     4\n",
            "Winnipeg Street           3\n",
            "Victoria Avenue           3\n",
            "McDonald Street           2\n",
            "529 1 Ave E               2\n",
            "Bucking Ham Drive         2\n",
            "Great Plains Road         2\n",
            "7th Avenue                2\n",
            "Arcola Avenue             2\n",
            "Pasqua Street             2\n",
            "Saskatchewan Drive        2\n",
            "Quance Street             1\n",
            "Gravel Pit Road           1\n",
            "SK-33 Highway             1\n",
            "4415 Albert Street S      1\n",
            "Gordon Road               1\n",
            "Anaquod Road              1\n",
            "8501 Dewdney Avenue       1\n",
            "Pinkie Road               1\n",
            "Ross Avenue               1\n",
            "Hill Avenue               1\n",
            "Chuka Blvd                1\n",
            "Angus Street              1\n",
            "310 McCarthy Bvld N       1\n",
            "Halifax Street            1\n",
            "Edward Street             1\n",
            "McCarthy Bvld             1\n",
            "Broadway                  1\n",
            "180 Albert Street N       1\n",
            "Truesdale Drive           1\n",
            "4400 4 Ave                1\n",
            "Fleet Street              1\n",
            "Park Street               1\n",
            "Dewdney Avenue            1\n",
            "Sherwood Drive            1\n",
            "Name: Location, dtype: int64\n",
            "['Name', 'Location', 'Phone Number']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## **Grouping Specific Column Data** <a name=\"group\"></a>\n",
        "\n",
        "Grouping the column `Location`, this gives a count of the occurances. You call `.groupby()` and pass the name of the column that you want to group on, which is \"Location\". Then, you use `[\"Name\"]` to specify the columns on which you want to perform the actual aggregation."
      ],
      "metadata": {
        "id": "PiVm9eE6oVFy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Location_count = df.groupby(\"Location\")[\"Name\"].count()\n",
        "Location_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5JDEyP5oYMz",
        "outputId": "eaacbb92-55d3-49cf-f850-671b04d518a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location\n",
              "180 Albert Street N       1\n",
              "310 McCarthy Bvld N       1\n",
              "4400 4 Ave                1\n",
              "4415 Albert Street S      1\n",
              "529 1 Ave E               2\n",
              "7th Avenue                2\n",
              "8501 Dewdney Avenue       1\n",
              "Albert Street            14\n",
              "Anaquod Road              1\n",
              "Angus Street              1\n",
              "Arcola Avenue             2\n",
              "Broadway                  1\n",
              "Bucking Ham Drive         2\n",
              "Chuka Blvd                1\n",
              "Dewdney Avenue            1\n",
              "Edward Street             1\n",
              "Fleet Street              1\n",
              "Gordon Road               1\n",
              "Gravel Pit Road           1\n",
              "Great Plains Road         2\n",
              "Halifax Street            1\n",
              "Hill Avenue               1\n",
              "McCarthy Bvld             1\n",
              "McDonald Street           2\n",
              "Park Street               1\n",
              "Pasqua Street             2\n",
              "Pinkie Road               1\n",
              "Prince of Wales Drive     4\n",
              "Quance Street             1\n",
              "Rochdale Blvd             4\n",
              "Ross Avenue               1\n",
              "SK-33 Highway             1\n",
              "Saskatchewan Drive        2\n",
              "Sherwood Drive            1\n",
              "Truesdale Drive           1\n",
              "Victoria Avenue           3\n",
              "Winnipeg Street           3\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## **Plotting the Data** <a name=\"plot\"></a>\n",
        "\n",
        "For this we can use the pandas bar plot. A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value.\n",
        "\n"
      ],
      "metadata": {
        "id": "0LYaE0w9ueVq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Location_count.plot.bar(stacked=True, figsize = (15, 4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "yLYMJ1m7t4gI",
        "outputId": "b83c35ee-23ce-4a1a-fd26-b6e21e217694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Location'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Conclusion** <a name= \"conclusion\"></a>\n",
        "\n",
        "Hence there I conclude there are currently 69 Gas Stations in the City of Regina. This will dynamic and hence can change frequently. I will in the future create a scraper tool that will automatically scrape the data so that I don't manually need to add new cells in the Excel Sheet. If you have any intresting EDA tools, feel free to let me know. If will definetly add it in here. My contact info is `tanunprabhu95@gmail.com`. Thanks for reading my article. Stay tuned for more updates. Until then see you next time. "
      ],
      "metadata": {
        "id": "LgpWZ_66UhDA"
      }
    }
  ]
}