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   "source": "# Data Interpolation and Imputation\n\nThis tutorial demonstrates data-wrangler's capabilities for handling missing data through interpolation and imputation techniques. These are essential for cleaning real-world datasets.\n\n## Overview\n\nData-wrangler provides several approaches for handling missing data:\n\n- **Interpolation**: Fill missing values using mathematical interpolation methods\n- **Model-based imputation**: Use machine learning models to predict missing values  \n- **Statistical imputation**: Fill with statistical measures (mean, median, mode)\n- **Custom imputation**: Define your own missing data handling strategies\n\nLet's explore these techniques with practical examples.",
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   "source": "import datawrangler as dw\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom datawrangler.decorate import funnel, interpolate",
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