Is there a real C4.5 implementation in Python ? (handling missing value)

To my understanding, C4.5 comes with 4 improvements compared to ID3:

1. Handling missing values in both training data and “test” data,
2. Handling continuous data
3. Handling costs on attributes.
4. The pruning

Source

However, not one of all decision tree python modules that I found, even the so-called C4.5, handles missing values.

Do you know of a library that handles them ? And it would be great if it can work with Pandas DataFrames “out-of-the-box”…