1{
2  "nbformat": 4,
3  "nbformat_minor": 5,
4  "metadata": {
5    "kernelspec": {
6      "display_name": "Python 3",
7      "language": "python",
8      "name": "python3"
9    },
10    "language_info": {
11      "name": "python",
12      "version": "3.12.0"
13    }
14  },
15  "cells": [
16    {
17      "cell_type": "markdown",
18      "metadata": {},
19      "source": [
20        "# Data Analysis Demo\n",
21        "\n",
22        "Demonstrates pandas-style HTML table output and multiple output types."
23      ]
24    },
25    {
26      "cell_type": "code",
27      "metadata": {},
28      "execution_count": 1,
29      "source": [
30        "import pandas as pd\n",
31        "import numpy as np\n",
32        "\n",
33        "np.random.seed(42)"
34      ],
35      "outputs": []
36    },
37    {
38      "cell_type": "markdown",
39      "metadata": {},
40      "source": [
41        "## Generate Sample Data\n",
42        "\n",
43        "Create a DataFrame with sales data across regions."
44      ]
45    },
46    {
47      "cell_type": "code",
48      "metadata": {},
49      "execution_count": 2,
50      "source": [
51        "df = pd.DataFrame({\n",
52        "    'region': ['Europe', 'Europe', 'Asia', 'Asia', 'Americas', 'Americas'],\n",
53        "    'product': ['Widget A', 'Widget B', 'Widget A', 'Widget B', 'Widget A', 'Widget B'],\n",
54        "    'revenue': [12500, 8900, 15200, 11300, 9800, 7600],\n",
55        "    'units': [250, 178, 304, 226, 196, 152],\n",
56        "    'growth': [0.12, -0.03, 0.28, 0.15, 0.05, -0.08]\n",
57        "})\n",
58        "df"
59      ],
60      "outputs": [
61        {
62          "output_type": "execute_result",
63          "execution_count": 2,
64          "data": {
65            "text/html": [
66              "<div>\n",
67              "<table border=\"1\" class=\"dataframe\">\n",
68              "  <thead>\n",
69              "    <tr style=\"text-align: right;\">\n",
70              "      <th></th>\n",
71              "      <th>region</th>\n",
72              "      <th>product</th>\n",
73              "      <th>revenue</th>\n",
74              "      <th>units</th>\n",
75              "      <th>growth</th>\n",
76              "    </tr>\n",
77              "  </thead>\n",
78              "  <tbody>\n",
79              "    <tr><th>0</th><td>Europe</td><td>Widget A</td><td>12500</td><td>250</td><td>0.12</td></tr>\n",
80              "    <tr><th>1</th><td>Europe</td><td>Widget B</td><td>8900</td><td>178</td><td>-0.03</td></tr>\n",
81              "    <tr><th>2</th><td>Asia</td><td>Widget A</td><td>15200</td><td>304</td><td>0.28</td></tr>\n",
82              "    <tr><th>3</th><td>Asia</td><td>Widget B</td><td>11300</td><td>226</td><td>0.15</td></tr>\n",
83              "    <tr><th>4</th><td>Americas</td><td>Widget A</td><td>9800</td><td>196</td><td>0.05</td></tr>\n",
84              "    <tr><th>5</th><td>Americas</td><td>Widget B</td><td>7600</td><td>152</td><td>-0.08</td></tr>\n",
85              "  </tbody>\n",
86              "</table>\n",
87              "</div>"
88            ],
89            "text/plain": "   region   product  revenue  units  growth\n0  Europe  Widget A    12500    250    0.12\n1  Europe  Widget B     8900    178   -0.03\n2    Asia  Widget A    15200    304    0.28\n3    Asia  Widget B    11300    226    0.15\n4 Americas  Widget A     9800    196    0.05\n5 Americas  Widget B     7600    152   -0.08"
90          },
91          "metadata": {}
92        }
93      ]
94    },
95    {
96      "cell_type": "markdown",
97      "metadata": {},
98      "source": [
99        "## Summary Statistics"
100      ]
101    },
102    {
103      "cell_type": "code",
104      "metadata": {},
105      "execution_count": 3,
106      "source": [
107        "summary = df.groupby('region').agg({\n",
108        "    'revenue': 'sum',\n",
109        "    'units': 'sum',\n",
110        "    'growth': 'mean'\n",
111        "}).round(2)\n",
112        "summary"
113      ],
114      "outputs": [
115        {
116          "output_type": "execute_result",
117          "execution_count": 3,
118          "data": {
119            "text/html": [
120              "<div>\n",
121              "<table border=\"1\" class=\"dataframe\">\n",
122              "  <thead>\n",
123              "    <tr style=\"text-align: right;\">\n",
124              "      <th></th>\n",
125              "      <th>revenue</th>\n",
126              "      <th>units</th>\n",
127              "      <th>growth</th>\n",
128              "    </tr>\n",
129              "    <tr>\n",
130              "      <th>region</th>\n",
131              "      <th></th>\n",
132              "      <th></th>\n",
133              "      <th></th>\n",
134              "    </tr>\n",
135              "  </thead>\n",
136              "  <tbody>\n",
137              "    <tr><th>Americas</th><td>17400</td><td>348</td><td>-0.02</td></tr>\n",
138              "    <tr><th>Asia</th><td>26500</td><td>530</td><td>0.22</td></tr>\n",
139              "    <tr><th>Europe</th><td>21400</td><td>428</td><td>0.04</td></tr>\n",
140              "  </tbody>\n",
141              "</table>\n",
142              "</div>"
143            ],
144            "text/plain": "          revenue  units  growth\nregion                          \nAmericas    17400    348   -0.02\nAsia        26500    530    0.22\nEurope      21400    428    0.04"
145          },
146          "metadata": {}
147        }
148      ]
149    },
150    {
151      "cell_type": "markdown",
152      "metadata": {},
153      "source": [
154        "## Key Findings\n",
155        "\n",
156        "- Asia has the highest total revenue ($26,500) and strongest growth (22%)\n",
157        "- Americas shows slight decline (-2% average growth)\n",
158        "- Widget A consistently outperforms Widget B across all regions"
159      ]
160    },
161    {
162      "cell_type": "code",
163      "metadata": {},
164      "execution_count": 4,
165      "source": [
166        "total_revenue = df['revenue'].sum()\n",
167        "top_region = summary['revenue'].idxmax()\n",
168        "print(f\"Total revenue: ${total_revenue:,}\")\n",
169        "print(f\"Top region: {top_region}\")\n",
170        "print(f\"Total units sold: {df['units'].sum():,}\")"
171      ],
172      "outputs": [
173        {
174          "output_type": "stream",
175          "name": "stdout",
176          "text": [
177            "Total revenue: $65,300\n",
178            "Top region: Asia\n",
179            "Total units sold: 1,306\n"
180          ]
181        }
182      ]
183    }
184  ]
185}