show summary output, resort features, align data

Author: MariusMerkleQC

README.md

@@ -53,7 +53,11 @@ right = pl.DataFrame({
 comparison = compare_frames(left, right, primary_key="id")
 
 if not comparison.equal():
-    print(comparison.summary())
+    summary = comparison.summary(
+        top_k_column_changes=1,
+        show_sample_primary_key_per_change=True
+    )
+    print(summary)
 ```
 
 ```

diffly/test.py

@@ -15,5 +15,8 @@
 comparison = compare_frames(left, right, primary_key="id")
 
 if not comparison.equal():
-    print(comparison.summary(
-        top_k_column_changes=1, show_sample_primary_key_per_change=True))
+    summary = comparison.summary(
+        top_k_column_changes=1,
+        show_sample_primary_key_per_change=True
+    )
+    print(summary)

docs/index.md

@@ -22,12 +22,12 @@ Diffly is a utility package for comparing Polars DataFrames and LazyFrames with
 ## Key Features
 
 - **Primary key-based comparison**: Join DataFrames on specified primary keys for row-by-row comparison
-- **Tolerance-based equality**: Configure absolute and relative tolerances for floating point comparisons
-- **Temporal tolerance**: Support for comparing temporal types (dates, datetimes) with configurable tolerances
 - **Rich summaries**: Generate detailed, visually formatted comparison reports
 - **Lazy evaluation**: Uses Polars LazyFrames internally for efficient computation
-- **Method caching**: Automatically caches comparison results to avoid recomputation
+- **Tolerance-based equality**: Configure absolute and relative tolerances for floating point comparisons
+- **Temporal tolerance**: Support for comparing temporal types (dates, datetimes) with configurable tolerances
 - **Per-column tolerances**: Fine-grained control over comparison tolerances for each column
+- **Method caching**: Automatically caches comparison results to avoid recomputation
 - **Testing utilities**: Built-in assertion functions for DataFrame and Collection equality in tests
 
 ## Quick Example
@@ -40,13 +40,11 @@ from diffly import compare_frames
 left = pl.DataFrame({
     "id": ["a", "b", "c"],
     "value": [1.0, 2.0, 3.0],
-    "category": ["x", "y", "z"]
 })
 
 right = pl.DataFrame({
     "id": ["a", "b", "d"],
-    "value": [1.0, 2.1, 4.0],
-    "category": ["x", "y", "w"]
+    "value": [1.0, 2.5, 4.0],
 })
 
 # Compare the DataFrames
@@ -56,8 +54,8 @@ comparison = compare_frames(left, right, primary_key="id")
 if not comparison.equal():
     # Display a detailed summary
     summary = comparison.summary(
-        show_perfect_column_matches=True,
-        top_k_column_changes=5
+        top_k_column_changes=1,
+        show_sample_primary_key_per_change=True
     )
     print(summary)
 ```