{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "markdown",
   "source": "# Data Wrangler I/O Operations\n\nThis tutorial covers the I/O capabilities of data-wrangler, including loading and saving data from various sources and formats.\n\n## Overview\n\nThe `datawrangler.io` module provides seamless loading and saving of data from:\n\n- **Local files**: CSV, JSON, text files, images, and more\n- **URLs**: Load data directly from web sources\n- **Multiple formats**: Automatic format detection based on file extensions\n- **Mixed sources**: Handle lists of files/URLs with different formats\n\nLet's explore these capabilities with practical examples.",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": "import datawrangler as dw\nfrom datawrangler.io import load, save\nimport pandas as pd\nimport numpy as np\nimport os\nfrom pathlib import Path",
   "metadata": {},
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "## Loading Different File Formats\n\nData-wrangler automatically detects file formats and loads them appropriately. Let's demonstrate with different file types:",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": "# Create sample data files for demonstration\nimport tempfile\n\n# Create a temporary directory for our examples\ntemp_dir = tempfile.mkdtemp()\nprint(f\\\"Working in temporary directory: {temp_dir}\\\"\n\n# Create sample CSV file\ncsv_data = pd.DataFrame({\n    'product': ['laptop', 'mouse', 'keyboard', 'monitor'],\n    'price': [999.99, 25.50, 75.00, 300.00],\n    'category': ['electronics', 'accessories', 'accessories', 'electronics']\n})\n\ncsv_file = os.path.join(temp_dir, 'products.csv')\ncsv_data.to_csv(csv_file, index=False)\n\n# Create sample text file\ntext_content = \\\"\\\"\\\"Data science is transforming industries worldwide.\nMachine learning enables computers to learn from data.\nNatural language processing helps computers understand human language.\nData visualization makes complex data insights accessible.\\\"\\\"\\\"\n\ntext_file = os.path.join(temp_dir, 'sample_text.txt')\nwith open(text_file, 'w') as f:\n    f.write(text_content)\n\n# Create sample JSON file\njson_data = {\n    'users': [\n        {'name': 'Alice', 'age': 30, 'city': 'New York'},\n        {'name': 'Bob', 'age': 25, 'city': 'San Francisco'},\n        {'name': 'Charlie', 'age': 35, 'city': 'Chicago'}\n    ]\n}\n\njson_file = os.path.join(temp_dir, 'users.json')\nimport json\nwith open(json_file, 'w') as f:\n    json.dump(json_data, f)\n\nprint(f\\\"Created files:\\\"\nprint(f\\\"- CSV: {csv_file}\\\"\nprint(f\\\"- Text: {text_file}\\\"\nprint(f\\\"- JSON: {json_file}\\\")",
   "metadata": {},
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}