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# Pandas Data Binning (cut / qcut)\n\nData binning (also known as bucketing) is the process of discretizing continuous variables, commonly used in data preprocessing, feature engineering, and data analysis.\n\n* * *\n\n## cut Equal-width Binning\n\n`cut` divides data into equal-width intervals.\n\n## Example\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# Create age data\nages = pd.Series([5,15,25,35,45,55,65,75,85])\n\nprint("Original data:")\nprint(ages.tolist())\nprint()\n\n# Equal-width binning (5 intervals)\nbins = [0,20,40,60,80,100]\nlabels = ["Child","Youth","Middle-aged","Middle-aged & Elderly","Elderly"]\nage_bins = pd.cut(ages, bins=bins, labels=labels)\n\nprint("Equal-width binning result:")\nprint(age_bins)\nprint()\n\n# Include right boundary\nage_bins2 = pd.cut(ages, bins=4)\n\nprint("Auto equal-width binning:")\nprint(age_bins2)\n\n### Return Categories and Boundaries\n\n## Example\n\n```python\nimport pandas as pd\nimport numpy as np\n\nages = pd.Series([5,15,25,35,45])\n\n# Return interval index\nresult = pd.cut(ages, bins=4, labels=False)\n\nprint("Interval index:")\nprint(result)\nprint()