Merge pull request #7 from limebit/func_cat_refactoring

Func cat refactoring

Author: philippgrosser

README.md

@@ -90,12 +90,11 @@ Total time: 0.0247s
 | Category | Description | Examples |
 |----------|-------------|----------|
 | **col_** | Column operations | `col_rename`, `col_drop`, `col_cast`, `col_add`, `col_select` |
-| **math_** | Arithmetic operations | `math_add`, `math_multiply`, `math_clamp`, `math_round`, `math_abs` |
+| **math_** | Arithmetic & scaling | `math_add`, `math_multiply`, `math_standardize`, `math_minmax`, `math_clamp` |
 | **rows_** | Row filtering & reshaping | `rows_filter`, `rows_drop_nulls`, `rows_sort`, `rows_unique`, `rows_pivot` |
 | **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` |
+| **map_** | Value mapping & encoding | `map_values`, `map_discretize`, `map_onehot`, `map_ordinal` |
 
 ## Installation
 

docs/api/index.md

@@ -82,6 +82,13 @@ All TransformPlan operations at a glance. Click method names for detailed docume
 | [`math_percent_of`](ops/math.md) | Calculate percentage of one column relative to another |
 | [`math_cumsum`](ops/math.md) | Calculate cumulative sum (optionally grouped) |
 | [`math_rank`](ops/math.md) | Calculate rank of values |
+| [`math_standardize`](ops/math.md) | Z-score standardization (mean=0, std=1) |
+| [`math_minmax`](ops/math.md) | Min-max normalization to a range |
+| [`math_robust_scale`](ops/math.md) | Robust scaling using median and IQR |
+| [`math_log`](ops/math.md) | Logarithmic transform |
+| [`math_sqrt`](ops/math.md) | Square root transform |
+| [`math_power`](ops/math.md) | Power transform |
+| [`math_winsorize`](ops/math.md) | Clip values to percentiles or bounds |
 
 ### Row Operations
 

docs/api/ops/encoding.md

@@ -1,10 +1,10 @@
 # Map Operations
 
-Value mapping, discretization, and transformation operations.
+Value mapping, discretization, encoding, and transformation operations.
 
 ## Overview
 
-Map operations transform column values using dictionaries, bins, or other columns. They're useful for categorization, value replacement, and data normalization.
+Map operations transform column values using dictionaries, bins, or encoding schemes. They're useful for categorization, value replacement, data normalization, and ML feature preparation.
 
 ```python
 from transformplan import TransformPlan
@@ -13,6 +13,7 @@ plan = (
     TransformPlan()
     .map_values("status", {"A": "Active", "I": "Inactive"})
     .map_discretize("age", bins=[18, 35, 55], labels=["Young", "Adult", "Senior"])
+    .map_onehot("color", categories=["red", "green", "blue"], drop="first")
 )
 ```
 
@@ -29,6 +30,9 @@ plan = (
         - map_null_to_value
         - map_value_to_null
         - map_from_column
+        - map_onehot
+        - map_ordinal
+        - map_label
 
 ## Examples
 
@@ -155,3 +159,52 @@ plan = TransformPlan().map_value_to_null("score", -999)
 # Replace null with default
 plan = TransformPlan().map_null_to_value("category", "Uncategorized")
 ```
+
+### One-Hot Encoding
+
+```python
+# Basic one-hot encoding
+plan = TransformPlan().map_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().map_onehot(
+    column="color",
+    categories=["red", "green", "blue"],
+    drop="first"
+)
+# Creates columns: color_green, color_blue (drops color_red)
+```
+
+### Ordinal Encoding
+
+```python
+# Ordinal encoding with meaningful order
+plan = TransformPlan().map_ordinal(
+    column="size",
+    categories=["small", "medium", "large"]
+)
+# Maps: small -> 0, medium -> 1, large -> 2
+```
+
+### Label Encoding
+
+```python
+# Label encoding (alphabetically sorted by default)
+plan = TransformPlan().map_label(column="department")
+# Maps alphabetically: Engineering -> 0, HR -> 1, Sales -> 2
+```
+
+### ML Feature Preparation
+
+```python
+# One-hot encode categorical features, dropping first to avoid multicollinearity
+plan = (
+    TransformPlan()
+    .map_onehot("color", categories=["red", "green", "blue"], drop="first")
+    .map_ordinal("quality", categories=["low", "medium", "high"])
+)
+```

docs/api/ops/map.md

@@ -39,6 +39,13 @@ plan = (
         - math_percent_of
         - math_cumsum
         - math_rank
+        - math_standardize
+        - math_minmax
+        - math_robust_scale
+        - math_log
+        - math_sqrt
+        - math_power
+        - math_winsorize
 
 ## Examples
 
@@ -128,3 +135,52 @@ plan = TransformPlan().math_rank(
     group_by="category"
 )
 ```
+
+### Scaling Operations
+
+```python
+# Z-score standardization (explicit params for reproducibility)
+plan = TransformPlan().math_standardize("income", mean=50000, std=25000)
+
+# Derive from data
+plan = TransformPlan().math_standardize("income")
+
+# Min-max normalization to [0, 1]
+plan = TransformPlan().math_minmax("age", min_val=0, max_val=100)
+
+# Custom range
+plan = TransformPlan().math_minmax("score", min_val=0, max_val=100, feature_range=(0, 10))
+
+# Robust scaling (resistant to outliers)
+plan = TransformPlan().math_robust_scale("salary", median=60000, iqr=30000)
+```
+
+### Transform Operations
+
+```python
+# Natural log
+plan = TransformPlan().math_log("price")
+
+# Log base 10
+plan = TransformPlan().math_log("price", base=10)
+
+# Log with offset for zeros
+plan = TransformPlan().math_log("count", offset=1)  # log(x + 1)
+
+# Square root
+plan = TransformPlan().math_sqrt("variance")
+
+# Power transform
+plan = TransformPlan().math_power("value", exponent=2)  # square
+plan = TransformPlan().math_power("value", exponent=0.5)  # sqrt
+```
+
+### Outlier Handling
+
+```python
+# Winsorize by percentiles
+plan = TransformPlan().math_winsorize("salary", lower=0.05, upper=0.95)
+
+# Winsorize by explicit values
+plan = TransformPlan().math_winsorize("salary", lower_value=20000, upper_value=200000)
+```

docs/api/ops/math.md

@@ -93,8 +93,7 @@ Total time: 0.0247s
 | **rows_** | Row filtering & reshaping | `rows_filter`, `rows_drop_nulls`, `rows_sort`, `rows_unique`, `rows_pivot` |
 | **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` |
+| **map_** | Value mapping & encoding | `map_values`, `map_discretize`, `map_onehot`, `map_ordinal` |
 
 
 ## Getting Started

docs/index.md

@@ -91,4 +91,3 @@ 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