{"library":"optbinning","title":"OptBinning","description":"OptBinning is a Python library for optimal binning, a data preprocessing technique used in machine learning to transform continuous or categorical features into discrete bins. It supports various binning algorithms, including optimal, isotonic, and tree-based methods, and facilitates scorecard development. The current version is 0.21.0, with a release cadence of typically a new minor version every 1-2 months, often including new features and bug fixes.","language":"python","status":"active","last_verified":"Mon May 18","install":{"commands":["pip install optbinning"],"cli":{"name":"optbinning","version":"sh: 1: optbinning: not found"}},"imports":["from optbinning import OptimalBinning","from optbinning import BinningProcess","from optbinning import Scorecard","from optbinning.optimal_binning import OptimalBinningSklearn"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nimport pandas as pd\nfrom optbinning import OptimalBinning\n\n# Create dummy data\nnp.random.seed(42)\nX = pd.DataFrame({\n    'feature_1': np.random.rand(100) * 100,\n    'feature_2': np.random.randint(0, 5, 100),\n    'feature_3': np.random.normal(50, 10, 100),\n})\ny = np.random.randint(0, 2, 100) # Binary target\n\n# Initialize and fit OptimalBinning for a continuous feature\noptb_num = OptimalBinning(name=\"feature_1\", dtype=\"numerical\", dtype_target=\"binary\")\noptb_num.fit(X[\"feature_1\"], y)\n\n# Transform the feature\nX[\"feature_1_binned\"] = optb_num.transform(X[\"feature_1\"])\n\n# Print binning table\nprint(f\"Binning Table for feature_1:\\n{optb_num.binning_table.build()}\\n\")\n\n# Example with a categorical feature\noptb_cat = OptimalBinning(name=\"feature_2\", dtype=\"categorical\", dtype_target=\"binary\")\noptb_cat.fit(X[\"feature_2\"], y)\nX[\"feature_2_binned\"] = optb_cat.transform(X[\"feature_2\"])\nprint(f\"Binning Table for feature_2:\\n{optb_cat.binning_table.build()}\\n\")\n\n# The transformed data\nprint(\"Transformed DataFrame head:\")\nprint(X.head())","lang":"python","description":"This example demonstrates how to use `OptimalBinning` to discretize both numerical and categorical features for a binary target. It covers initialization, fitting the binning process, and transforming the data, finally printing the generated binning tables.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-18","installed_version":"0.21.0","pypi_latest":"0.21.0","is_stale":false,"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":21.6,"avg_import_s":7.31,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"optbinning","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"optbinning","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":21.6,"import_time_s":5.42,"mem_mb":95.1,"disk_size":"556M"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"optbinning","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"optbinning","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":19.7,"import_time_s":8.15,"mem_mb":112.9,"disk_size":"597M"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"optbinning","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"optbinning","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":20.3,"import_time_s":8.88,"mem_mb":110.2,"disk_size":"584M"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"optbinning","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"optbinning","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":21.5,"import_time_s":8.03,"mem_mb":110.3,"disk_size":"590M"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"optbinning","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"optbinning","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":25.1,"import_time_s":6.05,"mem_mb":92.1,"disk_size":"568M"}]}}