doc update

Author: philippgrosser

docs/index.md

@@ -1,5 +1,5 @@
 <div align="center">
-  <img src="assets/images/logo.png" alt="TransformPlan Logo" width="150">
+  <img src="assets/images/logo_wordmark_white.png" alt="TransformPlan Logo" width="450">
 </div>
 
 # TransformPlan
@@ -13,53 +13,88 @@ TransformPlan tracks transformation history, validates operations against DataFr
 - **Declarative transformations**: Build transformation pipelines using method chaining
 - **Schema validation**: Validate operations before execution with dry-run capability
 - **Audit trails**: Generate complete audit protocols with deterministic DataFrame hashing
-- **Chunked processing**: Process large Parquet files that exceed RAM with partition-aware chunking
 - **Multi-backend support**: Works with both Polars (primary) and Pandas DataFrames
 - **Serializable pipelines**: Save and load transformation plans as JSON
 
 ## Quick Example
 
 ```python
-import polars as pl
 from transformplan import TransformPlan, Col
 
-# Create sample data
-df = pl.DataFrame({
-    "name": ["Alice", "Bob", "Charlie"],
-    "age": [25, 30, 35],
-    "salary": [50000, 60000, 70000]
-})
-
-# Build a transformation plan
+# Build readable pipelines with 70+ chainable operations
 plan = (
     TransformPlan()
-    .col_rename(column="name", new_name="employee_name")
-    .math_multiply(column="salary", value=1.1, new_column="new_salary")
-    .math_round(column="new_salary", decimals=0)
-    .rows_filter(Col("age") >= 30)
+    # Standardize column names
+    .col_rename(column="PatientID", new_name="patient_id")
+    .col_rename(column="DOB", new_name="date_of_birth")
+    .str_strip(column="patient_id")
+
+    # Calculate derived values
+    .dt_age_years(column="date_of_birth", new_column="age")
+    .math_clamp(column="age", min_value=0, max_value=120)
+
+    # Categorize patients age
+    .map_discretize(column="age", bins=[18, 40, 65], labels=["young", "adult", "senior"], new_column="age_group")
+
+    # Filter and clean
+    .rows_filter(Col("age") >= 18)
+    .rows_drop_nulls(columns=["patient_id", "age"])
+    .col_drop(column="date_of_birth")
 )
 
-# Validate the plan
-print(plan.validate(df))
+# Execute with schema validation — catch errors before they hit production
+df_result, protocol = plan.process(df, validate=True)
 
-# Execute and get audit trail
-df_result, protocol = plan.process(df)
-protocol.print()
-```
+# Serialize pipelines to JSON — version control your transformations
+plan.to_json("patient_transform.json")
 
-## Why TransformPlan?
+# Reload and reapply — reproducible results across environments
+plan = TransformPlan.from_json("patient_transform.json")
+df_result, protocol = plan.process(new_data)
+```
 
-### Reproducibility
+### Full Audit Trail — Every Step Tracked and Hashed
 
-Every transformation is tracked with deterministic hashes, ensuring you can verify that the same inputs produce the same outputs.
+```python
+protocol.print(show_params=False)
+```
 
-### Safety
+```
+======================================================================
+TRANSFORM PROTOCOL
+======================================================================
+Input:  1000 rows × 5 cols  [a4f8b2c1]
+Output: 847 rows × 5 cols   [e7d3f9a2]
+Total time: 0.0247s
+----------------------------------------------------------------------
+
+#    Operation            Rows         Cols         Time       Hash
+----------------------------------------------------------------------
+0    input                1000         5            -          a4f8b2c1
+1    col_rename           1000         5            0.0012s    b2e4a7f3
+2    col_rename           1000         5            0.0008s    c9d1e5b8
+3    str_strip            1000         5            0.0013s    c9d1e5b8        ○
+4    dt_age_years         1000         6 (+1)       0.0041s    d4f2c8a1
+5    math_clamp           1000         6            0.0015s    e1b7d3f9
+6    map_discretize       1000         7 (+1)       0.0028s    f8a4c2e6
+7    rows_filter          858 (-142)   7            0.0037s    a2e9f4b7
+8    rows_drop_nulls      847 (-11)    7            0.0019s    b5c1d8e3
+9    col_drop             847          6 (-1)       0.0006s    e7d3f9a2
+======================================================================
+○ = no effect (steps 3 did not change data)
+```
 
-Schema validation catches errors before execution. The dry-run feature lets you preview what a pipeline will do without modifying data.
+## Available Operations
 
-### Auditability
+| 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` |
+| **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` |
 
-Complete audit protocols capture every operation, timing, and data shape change - essential for compliance and debugging.
 
 ## Getting Started