{
"cells": [
{
"cell_type": "markdown",
"id": "intro-cell",
"metadata": {},
"source": [
"# Data Analysis Example\n",
"\n",
"This notebook demonstrates basic data analysis operations."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "imports-cell",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "data-section",
"metadata": {},
"source": [
"## Creating Sample Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "create-data",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 1 4 7\n",
"1 2 5 8\n",
"2 3 6 9"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({\n",
" 'a': [1, 2, 3],\n",
" 'b': [4, 5, 6],\n",
" 'c': [7, 8, 9]\n",
"})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "compute-stats",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean values:\n",
"a 2.0\n",
"b 5.0\n",
"c 8.0\n",
"dtype: float64\n"
]
}
],
"source": [
"print(\"Mean values:\")\n",
"print(df.mean())"
]
},
{
"cell_type": "markdown",
"id": "conclusion",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"The analysis shows consistent patterns across all columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "not-executed",
"metadata": {},
"outputs": [],
"source": [
"# This cell hasn't been executed yet\n",
"result = df.sum()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}