Encodings

README.md

@@ -95,6 +95,7 @@ Total time: 0.0247s
 | **str_** | String operations | `str_lower`, `str_upper`, `str_strip`, `str_replace`, `str_split` |
 | **dt_** | Datetime operations | `dt_year`, `dt_month`, `dt_parse`, `dt_age_years`, `dt_diff_days` |
 | **map_** | Value mapping | `map_values`, `map_discretize`, `map_case`, `map_from_column` |
+| **enc_** | Categorical encoding | `enc_onehot`, `enc_ordinal`, `enc_label` |
 
 ## Installation
 

docs/api/ops/encoding.md

@@ -0,0 +1,187 @@
+# Encoding Operations
+
+Categorical encoding operations for machine learning preparation.
+
+## Overview
+
+Encoding operations transform categorical columns into numeric representations suitable for machine learning models. They support one-hot encoding, ordinal encoding, and label encoding.
+
+```python
+from transformplan import TransformPlan
+
+plan = (
+    TransformPlan()
+    .enc_onehot("color", categories=["red", "green", "blue"], drop="first")
+    .enc_ordinal("size", categories=["small", "medium", "large"])
+)
+```
+
+## Class Reference
+
+::: transformplan.ops.encoding.EncodingOps
+    options:
+      show_root_heading: true
+      members:
+        - enc_onehot
+        - enc_ordinal
+        - enc_label
+
+## Examples
+
+### One-Hot Encoding
+
+Creates binary indicator columns (0/1) for each category.
+
+```python
+# Basic one-hot encoding
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["red", "green", "blue"]
+)
+# Creates columns: color_red, color_green, color_blue
+
+# Drop first category to avoid multicollinearity (for regression models)
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["red", "green", "blue"],
+    drop="first"
+)
+# Creates columns: color_green, color_blue (drops color_red)
+
+# Drop last category
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["red", "green", "blue"],
+    drop="last"
+)
+# Creates columns: color_red, color_green (drops color_blue)
+
+# Drop specific category
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["red", "green", "blue"],
+    drop="green"
+)
+# Creates columns: color_red, color_blue (drops color_green)
+
+# Custom prefix for new columns
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["red", "green", "blue"],
+    prefix="c"
+)
+# Creates columns: c_red, c_green, c_blue
+
+# Keep original column
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["red", "green", "blue"],
+    drop_original=False
+)
+# Keeps color column alongside color_red, color_green, color_blue
+```
+
+### Ordinal Encoding
+
+Maps categories to integers based on explicit ordering (first=0, second=1, etc.).
+
+```python
+# Ordinal encoding with meaningful order
+plan = TransformPlan().enc_ordinal(
+    column="size",
+    categories=["small", "medium", "large"]
+)
+# Maps: small -> 0, medium -> 1, large -> 2
+
+# Output to new column
+plan = TransformPlan().enc_ordinal(
+    column="size",
+    categories=["small", "medium", "large"],
+    new_column="size_encoded"
+)
+
+# Custom unknown value
+plan = TransformPlan().enc_ordinal(
+    column="size",
+    categories=["small", "medium", "large"],
+    unknown_value=-1  # Default
+)
+# Values not in categories get -1
+```
+
+### Label Encoding
+
+Simple integer encoding, alphabetically sorted by default. Similar to ordinal encoding but without semantic ordering.
+
+```python
+# Label encoding (alphabetically sorted)
+plan = TransformPlan().enc_label(column="department")
+# Maps alphabetically: Engineering -> 0, HR -> 1, Sales -> 2
+
+# With explicit categories
+plan = TransformPlan().enc_label(
+    column="department",
+    categories=["HR", "Engineering", "Sales"]
+)
+# Maps: HR -> 0, Engineering -> 1, Sales -> 2
+```
+
+## Use Cases
+
+### Preparing Data for Machine Learning
+
+```python
+# One-hot encode categorical features, dropping first to avoid multicollinearity
+plan = (
+    TransformPlan()
+    .enc_onehot("color", categories=["red", "green", "blue"], drop="first")
+    .enc_onehot("size", categories=["S", "M", "L", "XL"], drop="first")
+    .enc_ordinal("quality", categories=["low", "medium", "high"])
+)
+```
+
+### Handling Unknown Categories
+
+```python
+# Unknown values get all zeros (one-hot)
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["red", "green", "blue"],
+    unknown_value="all_zero"  # Default
+)
+
+# Unknown values get -1 (ordinal/label)
+plan = TransformPlan().enc_ordinal(
+    column="size",
+    categories=["small", "medium", "large"],
+    unknown_value=-1
+)
+```
+
+### Deriving Categories from Data
+
+When categories are not specified, they are derived from the data (sorted alphabetically):
+
+```python
+# Categories derived from data
+plan = TransformPlan().enc_onehot("color")
+# Uses sorted unique values from the column
+
+# Note: For reproducibility, explicitly specify categories
+plan = TransformPlan().enc_onehot(
+    column="color",
+    categories=["blue", "green", "red"]  # Explicit is better
+)
+```
+
+## Multicollinearity Note
+
+When using one-hot encoding for linear models (regression, logistic regression), you should drop one category to avoid the [dummy variable trap](https://en.wikipedia.org/wiki/Dummy_variable_(statistics)). Use the `drop` parameter:
+
+```python
+# For regression models, drop one category
+plan = TransformPlan().enc_onehot("color", drop="first")
+
+# Tree-based models (random forest, XGBoost) don't require this
+plan = TransformPlan().enc_onehot("color")  # Keep all
+```

docs/index.md

@@ -94,6 +94,7 @@ Total time: 0.0247s
 | **str_** | String operations | `str_lower`, `str_upper`, `str_strip`, `str_replace`, `str_split` |
 | **dt_** | Datetime operations | `dt_year`, `dt_month`, `dt_parse`, `dt_age_years`, `dt_diff_days` |
 | **map_** | Value mapping | `map_values`, `map_discretize`, `map_case`, `map_from_column` |
+| **enc_** | Categorical encoding | `enc_onehot`, `enc_ordinal`, `enc_label` |
 
 
 ## Getting Started

mkdocs.yml

@@ -91,3 +91,4 @@ nav:
           - String Operations: api/ops/string.md
           - Datetime Operations: api/ops/datetime.md
           - Map Operations: api/ops/map.md
+          - Encoding Operations: api/ops/encoding.md