diff --git a/CHANGELOG.md b/CHANGELOG.md
index aa51635..7909ccb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,12 @@ All notable changes to this project are documented here. The format follows
 [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and the project
 adheres to [Semantic Versioning](https://semver.org/).
 
+## [Unreleased]
+
+### Added
+- Added a runnable PyJanitor interoperability example that demonstrates both
+  tool orderings while keeping PyJanitor optional.
+
 ## [2.0.0] - 2026-07-20
 
 Remediation of the July 2026 v1.2.0 production-readiness audit: the unsafe
diff --git a/docs/examples.md b/docs/examples.md
index fa17310..7294a50 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -23,6 +23,8 @@ directory has narrated Jupyter walkthroughs.
 | `06_large_dataset.py` | Cleaning a large synthetic dataset, with timing |
 | `07_pandas_integration.py` | Dropping freshdata into an existing pandas workflow |
 | `08_csv_automation.py` | Batch CSV cleaning automation with audit logs |
+| `09_pandera_recipe.py` | Validating with pandera before and after freshdata cleaning |
+| `10_pyjanitor_interop.py` | Combining PyJanitor transforms with FreshData quality repair |
 
 ## Missing-value cleaning
 
@@ -72,3 +74,26 @@ for path in Path("inbox").glob("*.csv"):
     out.to_csv(Path("clean") / path.name, index=False)
     print(path.name, "→", cleaner.report_.summary().splitlines()[0])
 ```
+
+## PyJanitor interoperability
+
+FreshData and [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/) solve
+different parts of a pandas workflow. Use PyJanitor for explicit reshaping and
+method-style transformations; use FreshData for evidence-based quality
+detection, conservative repair, and an auditable report.
+
+The runnable [`10_pyjanitor_interop.py`](https://github.com/FreshCode-Org/freshdata/blob/main/examples/10_pyjanitor_interop.py)
+example demonstrates both useful orderings on one small inline DataFrame:
+
+- **PyJanitor then FreshData:** normalize the input shape first, then detect and
+  repair quality issues in the resulting columns.
+- **FreshData then PyJanitor:** clean and record the quality decisions first,
+  then add an explicit presentation transform to the cleaned result.
+
+PyJanitor remains an optional dependency. FreshData 2.0 supports pandas 1.5–2.x;
+install the compatible PyJanitor 0.31 line to run the example:
+
+```bash
+pip install "pyjanitor<0.32"
+python examples/10_pyjanitor_interop.py
+```
diff --git a/examples/10_pyjanitor_interop.py b/examples/10_pyjanitor_interop.py
new file mode 100644
index 0000000..e42541e
--- /dev/null
+++ b/examples/10_pyjanitor_interop.py
@@ -0,0 +1,74 @@
+"""Use FreshData and PyJanitor in the same pandas workflow.
+
+PyJanitor is optional; install it alongside FreshData to run this example:
+
+    pip install freshdata-cleaner "pyjanitor<0.32"
+
+FreshData 2.0 supports pandas 1.5–2.x; PyJanitor 0.31 is the compatible line
+verified for this example.
+
+Use PyJanitor for explicit DataFrame reshaping and method-style transforms.
+Use FreshData for evidence-based quality detection, conservative repair, and
+an audit report. Either tool can go first, depending on which step needs the
+other tool's output.
+"""
+
+import pandas as pd
+from janitor import clean_names, transform_column  # type: ignore[import-untyped]
+
+import freshdata as fd
+
+
+def build_frame() -> pd.DataFrame:
+    """Return one small frame used by both ordering examples."""
+    return pd.DataFrame(
+        {
+            " Customer ID ": ["C-01", "C-02", "C-03", "C-03"],
+            "Order Amount": ["12.50", "n/a", "18.00", "18.00"],
+            "Region Name": [" North ", "south", "NORTH", "NORTH"],
+        }
+    )
+
+
+def pyjanitor_then_freshdata(raw: pd.DataFrame) -> tuple[pd.DataFrame, fd.CleanReport]:
+    """Shape labels explicitly, then detect and repair quality issues."""
+    shaped = clean_names(raw, remove_special=True, strip_underscores="both")
+    return fd.clean(
+        shaped,
+        id_columns=("customer_id",),
+        return_report=True,
+    )
+
+
+def freshdata_then_pyjanitor(raw: pd.DataFrame) -> tuple[pd.DataFrame, fd.CleanReport]:
+    """Repair with an audit trail, then add an explicit presentation column."""
+    cleaned, report = fd.clean(
+        raw,
+        id_columns=("customer_id",),
+        return_report=True,
+    )
+    enriched = transform_column(
+        cleaned,
+        column_name="region_name",
+        function=lambda value: value.strip().title(),
+        dest_column_name="region_label",
+    )
+    return enriched, report
+
+
+def main() -> None:
+    raw = build_frame()
+
+    cleaned, clean_report = pyjanitor_then_freshdata(raw)
+    print("=== PyJanitor then FreshData ===")
+    print(cleaned)
+    print(clean_report.summary())
+
+    enriched, enriched_report = freshdata_then_pyjanitor(raw)
+    print("\n=== FreshData then PyJanitor ===")
+    print(enriched)
+    print(enriched_report.summary())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/README.md b/examples/README.md
index 948591d..4725956 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -20,5 +20,14 @@ python examples/01_missing_values.py
 | [`07_pandas_integration.py`](07_pandas_integration.py) | Dropping freshdata into an existing pandas workflow |
 | [`08_csv_automation.py`](08_csv_automation.py) | Batch CSV cleaning automation with audit logs |
 | [`09_pandera_recipe.py`](09_pandera_recipe.py) | Validating with pandera before and after freshdata cleaning |
+| [`10_pyjanitor_interop.py`](10_pyjanitor_interop.py) | Combining explicit PyJanitor transforms with FreshData quality repair |
+
+The PyJanitor example has one optional dependency. FreshData 2.0 supports
+pandas 1.5–2.x, so install the compatible PyJanitor 0.31 line before running it:
+
+```bash
+pip install "pyjanitor<0.32"
+python examples/10_pyjanitor_interop.py
+```
 
 See the [documentation](https://freshcode-org.github.io/freshdata/) for full guides.