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   "source": "# Data Wrangler Utilities\n\nThis tutorial covers the utility functions in `datawrangler.util` that help with data type detection, validation, and manipulation. These utilities are the building blocks that power data-wrangler's automatic data type detection.\n\n## Overview\n\nThe `datawrangler.util` module provides essential helper functions:\n\n- **`dataframe_like()`**: Check if an object behaves like a DataFrame\n- **`array_like()`**: Detect array-like objects\n- **`depth()`**: Determine nesting depth of data structures\n- **`btwn()`**: Check if values fall within a range\n\nThese utilities are particularly useful when building custom data processing pipelines or extending data-wrangler's functionality.",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": "import datawrangler as dw\nfrom datawrangler.util import dataframe_like, array_like, depth, btwn\nimport pandas as pd\nimport numpy as np",
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   "execution_count": null
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  {
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   "source": "## Data Type Detection\n\nUnderstanding how data-wrangler detects different data types is crucial for building robust data processing pipelines. Let's explore the detection utilities:",
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   "source": "# Test different data types with detection utilities\ntest_objects = [\n    pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}),  # True DataFrame\n    {'A': [1, 2, 3], 'B': [4, 5, 6]},  # Dict (not DataFrame-like)\n    np.array([[1, 2, 3], [4, 5, 6]]),  # NumPy array\n    [[1, 2, 3], [4, 5, 6]],  # Nested list\n    [1, 2, 3, 4, 5],  # Simple list\n    \\\"Hello World\\\",  # String\n    42  # Number\n]\n\nobject_names = [\n    \\\"pandas DataFrame\\\",\n    \\\"Dictionary\\\", \n    \\\"NumPy Array\\\",\n    \\\"Nested List\\\",\n    \\\"Simple List\\\",\n    \\\"String\\\",\n    \\\"Number\\\"\n]\n\nprint(\\\"=== Data Type Detection Results ===\\\")\nprint(f\\\"{'Object Type':<20} {'DataFrame-like':<15} {'Array-like':<12} {'Depth':<8}\\\")\nprint(\\\"-\\\" * 60)\n\nfor obj, name in zip(test_objects, object_names):\n    is_df_like = dataframe_like(obj)\n    is_array_like = array_like(obj)\n    obj_depth = depth(obj)\n    \n    print(f\\\"{name:<20} {str(is_df_like):<15} {str(is_array_like):<12} {obj_depth:<8}\\\")",
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   "outputs": [],
   "execution_count": null
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