course01_w01_m06.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZxod3FcjTP4",
        "outputId": "2ca83c18-9c0d-4cf5-d8a3-41251e1070d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       sensor_value                      timestamp\n",
            "count     10.000000                             10\n",
            "mean      40.600000  2024-05-22 18:26:37.444411904\n",
            "min       10.000000     2024-05-22 18:26:37.444412\n",
            "25%       20.500000  2024-05-22 18:26:37.444411904\n",
            "50%       31.500000  2024-05-22 18:26:37.444411904\n",
            "75%       47.250000  2024-05-22 18:26:37.444411904\n",
            "max       99.000000     2024-05-22 18:26:37.444412\n",
            "std       29.262035                            NaN\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Simulate data generation (sensor readings)\n",
        "np.random.seed(10)  # Set seed for reproducibility\n",
        "sensor_data = np.random.randint(10, 100, 10)  # Generate random sensor readings\n",
        "df = pd.DataFrame({'sensor_value': sensor_data})\n",
        "df['timestamp'] = pd.Timestamp.now()  # Add timestamp\n",
        "\n",
        "# Simulate data storage (in-memory storage for simplicity)\n",
        "in_memory_storage = df.copy()\n",
        "\n",
        "# Simulate data ingestion\n",
        "ingested_data = in_memory_storage.copy()\n",
        "\n",
        "# Simulate data transformation (cleaning missing values)\n",
        "ingested_data.loc[2, 'sensor_value'] = np.NAN  # Introduce missing value\n",
        "ingested_data.fillna(method='ffill', inplace=True)  # Forward fill missing values\n",
        "\n",
        "# Simulate data serving (data analysis)\n",
        "print(ingested_data.describe())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Step 1: Generation\n",
        "sales_data = [\n",
        "    ['1001', '2023-05-01', 'P001', 'Electronics', 2, 99.99, 'C001'],\n",
        "    ['1002', '2023-05-02', 'P002', 'Clothing', 1, 29.95, 'C002'],\n",
        "    ['1003', '2023-05-03', 'P003', 'Books', 3, 14.99, 'C001'],\n",
        "    ['1004', '2023-05-04', 'P004', 'Electronics', 1, 249.99, 'C003'],\n",
        "    ['1005', '2023-05-05', 'P005', 'Clothing', 2, 39.95, 'C002'],\n",
        "    ['1006', '2023-05-06', 'P001', 'Electronics', 1, 99.99, 'C004'],\n",
        "    ['1007', '2023-05-07', 'P002', 'Clothing', 3, 29.95, 'C001'],\n",
        "    ['1008', '2023-05-08', 'P003', 'Books', 2, 14.99, 'C003'],\n",
        "    ['1009', '2023-05-09', 'P004', 'Electronics', 1, 249.99, 'C002'],\n",
        "    ['1010', '2023-05-10', 'P005', 'Clothing', 1, 39.95, 'C004']\n",
        "]\n",
        "\n",
        "with open('sales_data.csv', 'w', newline='') as file:\n",
        "    writer = csv.writer(file)\n",
        "    writer.writerow(['order_id', 'order_date', 'product_id', 'product_category', 'quantity', 'unit_price', 'customer_id'])\n",
        "    writer.writerows(sales_data)\n",
        "\n",
        "# Step 2: Storage\n",
        "# Data is stored in the 'sales_data.csv' file\n",
        "\n",
        "# Step 3: Ingestion\n",
        "df = pd.read_csv('sales_data.csv')\n",
        "print(df.head())\n",
        "print(df.info())\n",
        "\n",
        "# Step 4: Transformation\n",
        "# Data cleaning\n",
        "df = df.dropna()\n",
        "df = df.drop_duplicates()\n",
        "\n",
        "# Formatting\n",
        "df['order_date'] = pd.to_datetime(df['order_date'])\n",
        "\n",
        "# Feature engineering\n",
        "df['total_revenue'] = df['quantity'] * df['unit_price']\n",
        "\n",
        "# Data manipulation\n",
        "grouped = df.groupby('product_category')[['total_revenue', 'quantity']].agg({'total_revenue': 'sum', 'quantity': 'mean'}).reset_index()\n",
        "print(grouped)\n",
        "\n",
        "# Step 5: Serving Data\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='product_category', y='total_revenue', data=grouped, palette='viridis')\n",
        "plt.xlabel('Product Category')\n",
        "plt.ylabel('Total Revenue')\n",
        "plt.title('Total Revenue by Product Category')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KIKkx1B9j1LM",
        "outputId": "2005eae9-bab8-4c03-e0bc-5b9b0de1104d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   order_id  order_date product_id product_category  quantity  unit_price  \\\n",
            "0      1001  2023-05-01       P001      Electronics         2       99.99   \n",
            "1      1002  2023-05-02       P002         Clothing         1       29.95   \n",
            "2      1003  2023-05-03       P003            Books         3       14.99   \n",
            "3      1004  2023-05-04       P004      Electronics         1      249.99   \n",
            "4      1005  2023-05-05       P005         Clothing         2       39.95   \n",
            "\n",
            "  customer_id  \n",
            "0        C001  \n",
            "1        C002  \n",
            "2        C001  \n",
            "3        C003  \n",
            "4        C002  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10 entries, 0 to 9\n",
            "Data columns (total 7 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   order_id          10 non-null     int64  \n",
            " 1   order_date        10 non-null     object \n",
            " 2   product_id        10 non-null     object \n",
            " 3   product_category  10 non-null     object \n",
            " 4   quantity          10 non-null     int64  \n",
            " 5   unit_price        10 non-null     float64\n",
            " 6   customer_id       10 non-null     object \n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 688.0+ bytes\n",
            "None\n",
            "  product_category  total_revenue  quantity\n",
            "0            Books          74.95      2.50\n",
            "1         Clothing         239.65      1.75\n",
            "2      Electronics         799.95      1.25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-396d26dc50bd>:51: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='product_category', y='total_revenue', data=grouped, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Step 1: Generation\n",
        "sales_data = np.array([\n",
        "    ['1001', '2023-05-01', 'P001', 'Electronics', 2, 99.99, 'C001'],\n",
        "    ['1002', '2023-05-02', 'P002', 'Clothing', 1, 29.95, 'C002'],\n",
        "    ['1003', '2023-05-03', 'P003', 'Books', 3, 14.99, 'C001'],\n",
        "    ['1004', '2023-05-04', 'P004', 'Electronics', 1, 249.99, 'C003'],\n",
        "    ['1005', '2023-05-05', 'P005', 'Clothing', 2, 39.95, 'C002'],\n",
        "    ['1006', '2023-05-06', 'P001', 'Electronics', 1, 99.99, 'C004'],\n",
        "    ['1007', '2023-05-07', 'P002', 'Clothing', 3, 29.95, 'C001'],\n",
        "    ['1008', '2023-05-08', 'P003', 'Books', 2, 14.99, 'C003'],\n",
        "    ['1009', '2023-05-09', 'P004', 'Electronics', 1, 249.99, 'C002'],\n",
        "    ['1010', '2023-05-10', 'P005', 'Clothing', 1, 39.95, 'C004']\n",
        "])\n",
        "\n",
        "# Step 2: Storage\n",
        "np.save('sales_data.npy', sales_data)\n",
        "\n",
        "# Step 3: Ingestion\n",
        "data = np.load('sales_data.npy')\n",
        "\n",
        "# Step 4: Transformation\n",
        "total_revenue = data[:, 4].astype(int) * data[:, 5].astype(float)\n",
        "avg_quantity = np.mean(data[:, 4].astype(int))\n",
        "\n",
        "# Step 5: Serving Data\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(data[:, 3], total_revenue)\n",
        "plt.xlabel('Product Category')\n",
        "plt.ylabel('Total Revenue')\n",
        "plt.title('Total Revenue by Product Category')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n",
        "\n",
        "print(f\"Average Quantity per Order: {avg_quantity:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "xWYnumcjqqAv",
        "outputId": "3b3a3587-3a58-4daf-e193-9aea06b03709"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Quantity per Order: 1.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Step 1: Generation\n",
        "sales_data = [\n",
        "    ['1001', '2023-05-01', 'P001', 'Electronics', 2, 99.99, 'C001'],\n",
        "    ['1002', '2023-05-02', 'P002', 'Clothing', 1, 29.95, 'C002'],\n",
        "    ['1003', '2023-05-03', 'P003', 'Books', 3, 14.99, 'C001'],\n",
        "    ['1004', '2023-05-04', 'P004', 'Electronics', 1, 249.99, 'C003'],\n",
        "    ['1005', '2023-05-05', 'P005', 'Clothing', 2, 39.95, 'C002'],\n",
        "    ['1006', '2023-05-06', 'P001', 'Electronics', 1, 99.99, 'C004'],\n",
        "    ['1007', '2023-05-07', 'P002', 'Clothing', 3, 29.95, 'C001'],\n",
        "    ['1008', '2023-05-08', 'P003', 'Books', 2, 14.99, 'C003'],\n",
        "    ['1009', '2023-05-09', 'P004', 'Electronics', 1, 249.99, 'C002'],\n",
        "    ['1010', '2023-05-10', 'P005', 'Clothing', 1, 39.95, 'C004']\n",
        "]\n",
        "\n",
        "# Step 2: Storage\n",
        "df = pd.DataFrame(sales_data, columns=['order_id', 'order_date', 'product_id', 'product_category', 'quantity', 'unit_price', 'customer_id'])\n",
        "df.to_csv('sales_data.csv', index=False)\n",
        "\n",
        "# Step 3: Ingestion\n",
        "df = pd.read_csv('sales_data.csv')\n",
        "\n",
        "# Step 4: Transformation\n",
        "df['order_date'] = pd.to_datetime(df['order_date'])\n",
        "df['total_revenue'] = df['quantity'] * df['unit_price']\n",
        "df['month'] = df['order_date'].dt.strftime('%B')\n",
        "\n",
        "# Step 5: Serving Data\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='product_category', y='total_revenue', data=df, palette='cool')\n",
        "plt.xlabel('Product Category')\n",
        "plt.ylabel('Total Revenue')\n",
        "plt.title('Revenue Distribution by Product Category')\n",
        "plt.show()\n",
        "\n",
        "grouped = df.groupby('month')['total_revenue'].sum().reset_index()\n",
        "sns.barplot(x='month', y='total_revenue', data=grouped, palette='cool')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Total Revenue')\n",
        "plt.title('Total Revenue by Month')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-gMziXSNqyK8",
        "outputId": "b995c6c4-9aab-40ee-dae8-b392df53283e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-a23a31841ccc>:33: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(x='product_category', y='total_revenue', data=df, palette='cool')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-a23a31841ccc>:40: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='month', y='total_revenue', data=grouped, palette='cool')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "# Step 1: Generation\n",
        "sales_data = [\n",
        "    ['1001', '2023-05-01', 'P001', 'Electronics', 2, 99.99, 'C001'],\n",
        "    ['1002', '2023-05-02', 'P002', 'Clothing', 1, 29.95, 'C002'],\n",
        "    ['1003', '2023-05-03', 'P003', 'Books', 3, 14.99, 'C001'],\n",
        "    ['1004', '2023-05-04', 'P004', 'Electronics', 1, 249.99, 'C003'],\n",
        "    ['1005', '2023-05-05', 'P005', 'Clothing', 2, 39.95, 'C002'],\n",
        "    ['1006', '2023-05-06', 'P001', 'Electronics', 1, 99.99, 'C004'],\n",
        "    ['1007', '2023-05-07', 'P002', 'Clothing', 3, 29.95, 'C001'],\n",
        "    ['1008', '2023-05-08', 'P003', 'Books', 2, 14.99, 'C003'],\n",
        "    ['1009', '2023-05-09', 'P004', 'Electronics', 1, 249.99, 'C002'],\n",
        "    ['1010', '2023-05-10', 'P005', 'Clothing', 1, 39.95, 'C004']\n",
        "]\n",
        "\n",
        "# Step 2: Storage\n",
        "df = pd.DataFrame(sales_data, columns=['order_id', 'order_date', 'product_id', 'product_category', 'quantity', 'unit_price', 'customer_id'])\n",
        "df.to_csv('sales_data.csv', index=False)\n",
        "\n",
        "# Step 3: Ingestion\n",
        "df = pd.read_csv('sales_data.csv')\n",
        "\n",
        "# Step 4: Transformation\n",
        "df['order_date'] = pd.to_datetime(df['order_date'])\n",
        "df['total_revenue'] = df['quantity'] * df['unit_price']\n",
        "\n",
        "# Step 5: Serving Data\n",
        "fig = px.scatter(df, x='order_date', y='total_revenue', color='product_category', size='quantity',\n",
        "                 hover_data={'order_id': True, 'customer_id': True, 'unit_price': ':.2f'},\n",
        "                 labels={'order_date': 'Order Date', 'total_revenue': 'Total Revenue', 'product_category': 'Product Category'},\n",
        "                 title='Sales Analysis')\n",
        "fig.update_layout(xaxis_title='Order Date', yaxis_title='Total Revenue')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "jBeunHXOq23V",
        "outputId": "9cd24e83-d9cb-4846-a32a-42eeceef9ade"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
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