diff --git a/README.md b/README.md
index 3bbb5de5fa..10cd7b16fc 100755
--- a/README.md
+++ b/README.md
@@ -146,7 +146,7 @@ in2 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
 output = pairwise_distance(in1, in2, metric="euclidean")
 ```
 
-The `output` array in the above example is of type `raft.common.device_ndarray`, which supports [__cuda_array_interface__](https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html#cuda-array-interface-version-2) making it interoperable with other libraries like CuPy, Numba, and PyTorch that also support it. CuPy supports DLPack, which also enables zero-copy conversion from `raft.common.device_ndarray` to JAX and Tensorflow.
+The `output` array in the above example is of type `raft.common.device_ndarray`, which supports [__cuda_array_interface__](https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html#cuda-array-interface-version-2) making it interoperable with other libraries like CuPy, Numba, PyTorch and RAPIDS cuDF that also support it. CuPy supports DLPack, which also enables zero-copy conversion from `raft.common.device_ndarray` to JAX and Tensorflow.
 
 Below is an example of converting the output `pylibraft.device_ndarray` to a CuPy array:
 ```python
@@ -160,6 +160,11 @@ import torch
 torch_tensor = torch.as_tensor(output, device='cuda')
 ```
 
+Or converting to a RAPIDS cuDF dataframe:
+```python
+cudf_dataframe = cudf.DataFrame(output)
+```
+
 When the corresponding library has been installed and available in your environment, this conversion can also be done automatically by all RAFT compute APIs by setting a global configuration option:
 ```python
 import pylibraft.config