7_Chinook_Descriptive_Analysis.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/dBalag/Chinook/blob/main/7_Chinook_Descriptive_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Descriptive Analysis\n",
        "Descriptive analysis is a critical step in data analysis, providing a comprehensive summary of a dataset. This type of analysis focuses on describing the main features of the data quantitatively and visually, without making any inferences or predictions. It includes various techniques such as calculating summary statistics, creating frequency distributions, and generating visualizations to explore and understand the underlying patterns and distributions within the data.\n",
        "\n",
        "Will perform a descriptive analysis on the customer data from the Chinook database.The goal is to gain insights into customer demographics, purchasing behavior, and overall trends."
      ],
      "metadata": {
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        "id": "LGOf-v48nDZJ",
        "outputId": "e322bc93-83d1-4ba6-ebed-579e3ade35a3"
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              "      <script>// Copyright 2017 Google LLC\n",
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              "// you may not use this file except in compliance with the License.\n",
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
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              "// See the License for the specific language governing permissions and\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving customer_cleaned.csv to customer_cleaned.csv\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "# Upload the files\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Load the customer dataset\n",
        "customer_cleaned = pd.read_csv('customer_cleaned.csv')\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count of customers by country\n",
        "customers_by_country = customer_cleaned['country'].value_counts()\n",
        "print(customers_by_country)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XL3quI5OouGC",
        "outputId": "3a41a652-aea5-4a72-8af3-bf30efbde0ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "country\n",
            "USA               13\n",
            "Canada             8\n",
            "Brazil             5\n",
            "France             5\n",
            "Germany            4\n",
            "United Kingdom     3\n",
            "Portugal           2\n",
            "India              2\n",
            "Czech Republic     2\n",
            "Denmark            1\n",
            "Belgium            1\n",
            "Austria            1\n",
            "Finland            1\n",
            "Ireland            1\n",
            "Italy              1\n",
            "Netherlands        1\n",
            "Poland             1\n",
            "Spain              1\n",
            "Sweden             1\n",
            "Norway             1\n",
            "Australia          1\n",
            "Argentina          1\n",
            "Chile              1\n",
            "Hungary            1\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Visualize customers by country\n",
        "plt.figure(figsize=(12, 8))\n",
        "customers_by_country.plot(kind='bar')\n",
        "plt.title('Number of Customers by Country')\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "MpV4QUIIpJKD",
        "outputId": "f1f57078-db90-4ab5-cecc-1f37fe01132c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count of customers by city\n",
        "customers_by_city = customer_cleaned['city'].value_counts()\n",
        "print(customers_by_city)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78GEDzOfpcJ1",
        "outputId": "c441799c-bef8-46fb-f9ae-ddb7da4cf087"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "city\n",
            "Prague                 2\n",
            "Berlin                 2\n",
            "Paris                  2\n",
            "São Paulo              2\n",
            "London                 2\n",
            "Mountain View          2\n",
            "São José dos Campos    1\n",
            "Dublin                 1\n",
            "Lisbon                 1\n",
            "Porto                  1\n",
            "Frankfurt              1\n",
            "Lyon                   1\n",
            "Bordeaux               1\n",
            "Dijon                  1\n",
            "Helsinki               1\n",
            "Budapest               1\n",
            "Amsterdam              1\n",
            "Rome                   1\n",
            "Winnipeg               1\n",
            "Warsaw                 1\n",
            "Madrid                 1\n",
            "Stockholm              1\n",
            "Edinburgh              1\n",
            "Sidney                 1\n",
            "Buenos Aires           1\n",
            "Santiago               1\n",
            "Delhi                  1\n",
            "Yellowknife            1\n",
            "Ottawa                 1\n",
            "Halifax                1\n",
            "Stuttgart              1\n",
            "Montréal               1\n",
            "Oslo                   1\n",
            "Vienne                 1\n",
            "Brussels               1\n",
            "Copenhagen             1\n",
            "Rio de Janeiro         1\n",
            "Brasília               1\n",
            "Edmonton               1\n",
            "Vancouver              1\n",
            "Redmond                1\n",
            "New York               1\n",
            "Cupertino              1\n",
            "Reno                   1\n",
            "Orlando                1\n",
            "Boston                 1\n",
            "Chicago                1\n",
            "Madison                1\n",
            "Fort Worth             1\n",
            "Tucson                 1\n",
            "Salt Lake City         1\n",
            "Toronto                1\n",
            "Bangalore              1\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization Customers by city\n",
        "plt.figure(figsize=(12, 8))\n",
        "customers_by_city.plot(kind='bar')\n",
        "plt.title('Number of Customers by City')\n",
        "plt.xlabel('City')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "Dau1KkIUpgRU",
        "outputId": "5bba5e50-dff1-4365-e8a2-d8528d05cb9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.Merge Data for Further Analysis\n",
        "To analyze customer behavior more deeply, you might want to merge customer data with invoice data."
      ],
      "metadata": {
        "id": "wTZdSqb-ptrq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "# Upload the files\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Load the invoice dataset\n",
        "invoice_cleaned = pd.read_csv('invoice_cleaned.csv')\n",
        "invoice_line= pd.read_csv('invoice_line.csv')\n",
        "\n",
        "# Merge customer and invoice data\n",
        "customer_invoice = pd.merge(invoice_cleaned, customer_cleaned, on='customer_id')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "OWJYcph4pogT",
        "outputId": "aef8eded-f95b-46fc-f703-abc861c63d1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bea81041-00ea-47ba-91e5-31a09030dd4b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-bea81041-00ea-47ba-91e5-31a09030dd4b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving invoice_cleaned.csv to invoice_cleaned.csv\n",
            "Saving invoice_line.csv to invoice_line.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge the above result with invoice_line data\n",
        "customer_invoice_line = pd.merge(customer_invoice, invoice_line, on='invoice_id')\n",
        "\n",
        "# Display the first few rows of the merged dataset\n",
        "customer_invoice_line.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "lSBcKchAqc7T",
        "outputId": "bb0c3f3a-a8b8-4a91-ce58-d3ae328433ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0_x  invoice_id  customer_id         invoice_date  \\\n",
              "0           NaN           1            2  2021-01-01 00:00:00   \n",
              "1           NaN           1            2  2021-01-01 00:00:00   \n",
              "2           NaN          12            2  2021-02-11 00:00:00   \n",
              "3           NaN          12            2  2021-02-11 00:00:00   \n",
              "4           NaN          12            2  2021-02-11 00:00:00   \n",
              "\n",
              "           billing_address billing_city billing_state billing_country  \\\n",
              "0  Theodor-Heuss-Straße 34    Stuttgart       Unknown         Germany   \n",
              "1  Theodor-Heuss-Straße 34    Stuttgart       Unknown         Germany   \n",
              "2  Theodor-Heuss-Straße 34    Stuttgart       Unknown         Germany   \n",
              "3  Theodor-Heuss-Straße 34    Stuttgart       Unknown         Germany   \n",
              "4  Theodor-Heuss-Straße 34    Stuttgart       Unknown         Germany   \n",
              "\n",
              "  billing_postal_code  total  ...  postal_code             phone  fax  \\\n",
              "0               70174   1.98  ...        70174  +49 0711 2842222  NaN   \n",
              "1               70174   1.98  ...        70174  +49 0711 2842222  NaN   \n",
              "2               70174  13.86  ...        70174  +49 0711 2842222  NaN   \n",
              "3               70174  13.86  ...        70174  +49 0711 2842222  NaN   \n",
              "4               70174  13.86  ...        70174  +49 0711 2842222  NaN   \n",
              "\n",
              "                   email support_rep_id Unnamed: 0 invoice_line_id track_id  \\\n",
              "0  leonekohler@surfeu.de              5        NaN               1        2   \n",
              "1  leonekohler@surfeu.de              5        NaN               2        4   \n",
              "2  leonekohler@surfeu.de              5        NaN              60      331   \n",
              "3  leonekohler@surfeu.de              5        NaN              61      340   \n",
              "4  leonekohler@surfeu.de              5        NaN              62      349   \n",
              "\n",
              "  unit_price quantity  \n",
              "0       0.99        1  \n",
              "1       0.99        1  \n",
              "2       0.99        1  \n",
              "3       0.99        1  \n",
              "4       0.99        1  \n",
              "\n",
              "[5 rows x 28 columns]"
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              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0_x</th>\n",
              "      <th>invoice_id</th>\n",
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              "      <td>Theodor-Heuss-Straße 34</td>\n",
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              "      <td>Unknown</td>\n",
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              "      <td>Theodor-Heuss-Straße 34</td>\n",
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              "      <td>Germany</td>\n",
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              "      <td>Stuttgart</td>\n",
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              "      <td>Germany</td>\n",
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              "      <td>70174</td>\n",
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              "<p>5 rows × 28 columns</p>\n",
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              "      background-color: #434B5C;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-13dd9ba6-0349-44ff-9409-ba8e017b5bae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "customer_invoice_line"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #Total Amount Spent by Each Customer\n",
        " total_spent_by_customer = customer_invoice_line.groupby('customer_id')['total'].sum()\n",
        "print(total_spent_by_customer.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNtRaFr8qrTO",
        "outputId": "adb5d5e1-a4f9-4929-d743-5982c80f05da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count     59.000000\n",
            "mean     353.366441\n",
            "std       37.240932\n",
            "min      331.660000\n",
            "25%      334.620000\n",
            "50%      334.620000\n",
            "75%      352.620000\n",
            "max      502.620000\n",
            "Name: total, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the total amount spent by each customer\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.histplot(total_spent_by_customer, bins=30, kde=True)\n",
        "plt.title('Total Amount Spent by Each Customer')\n",
        "plt.xlabel('Total Amount Spent')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "foiRtJ9mq9OG",
        "outputId": "9c247706-7d14-434d-e686-5916269f7159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The histogram and KDE plot reveal that most customers have a total spending amount of around 334. This suggests that the typical spending behavior of customers is clustered around this value. The right-skewed distribution indicates that while higher spending customers exist, they are fewer in number compared to those who spend around 325. This information can be valuable for identifying typical spending patterns and potentially targeting promotions or marketing efforts towards encouraging higher spending among the majority of customers."
      ],
      "metadata": {
        "id": "cFgXkHyQtM4I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Average amount spent per purchase by each customer\n",
        "avg_spent_per_purchase = customer_invoice_line.groupby('customer_id')['total'].mean()\n",
        "print(avg_spent_per_purchase.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UR7tVdy_rIPc",
        "outputId": "6641a4ca-be6b-4f23-b0f8-0829747d5f42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    59.000000\n",
            "mean      9.307335\n",
            "std       0.977181\n",
            "min       8.805789\n",
            "25%       8.805789\n",
            "50%       8.805789\n",
            "75%       9.279474\n",
            "max      13.226842\n",
            "Name: total, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the average amount spent per purchase by each customer\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.histplot(avg_spent_per_purchase, bins=30, kde=True)\n",
        "plt.title('Average Amount Spent per Purchase by Each Customer')\n",
        "plt.xlabel('Average Amount Spent')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "7oo2_f68rX_D",
        "outputId": "ce2ad9f2-8e1e-41f6-d62b-5c26caceb1fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The histogram and KDE plot reveal that most customers spend an average amount of around 9. This suggests that the typical spending behavior of customers is clustered around this value. The right-skewed distribution indicates that while higher spending customers exist, they are fewer in number compared to those who spend around 9. This information can be valuable for identifying typical spending patterns and potentially targeting promotions or marketing efforts towards encouraging higher spending among the majority of customers."
      ],
      "metadata": {
        "id": "tLs2a0eJtB3Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Total number of purchases by each customer\n",
        "total_purchases_by_customer = customer_invoice_line.groupby('customer_id').size()\n",
        "print(total_purchases_by_customer.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8-xeEUZrj65",
        "outputId": "ee6ba5f2-02e4-4273-87b1-704c9f96095e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    59.000000\n",
            "mean     37.966102\n",
            "std       0.260378\n",
            "min      36.000000\n",
            "25%      38.000000\n",
            "50%      38.000000\n",
            "75%      38.000000\n",
            "max      38.000000\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the total number of purchases by each customer\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.histplot(total_purchases_by_customer, bins=30, kde=True)\n",
        "plt.title('Total Number of Purchases by Each Customer')\n",
        "plt.xlabel('Total Purchases')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "eAEc_NxirtK5",
        "outputId": "81dd4b83-647e-46d6-ed50-3d039ca2ea7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The histogram and KDE plot reveal that the vast majority of customers have made around 38 total purchases. This could suggest a uniform purchasing behavior among the customers, or it might indicate a specific sales strategy or promotion that led to this clustering."
      ],
      "metadata": {
        "id": "7FWVc7vLsYQJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y3VzGOmQsbn_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}