Distance analyzer for detecting feature drift with PyDeequ

**Is your feature request related to a problem? Please describe.**
It looks like in PyDeequ 1.1.1 the [Deequ distance analyzer](/awslabs/deequ/blob/master/src/main/scala/com/amazon/deequ/analyzers/Distance.scala) is not available. It means that this type of analyzer cannot be run via PyDeequ.

**Describe the solution you'd like**
I'd like to run the distance analysis on my data, so that I can detect feature drift via the 2 methods currently available in Deequ (L-infinity and chi-squared). I'd like to be able to do this in the same way as with the other analyzers.

Are there any plans to add this analyzer in the future?

**Describe alternatives you've considered**
I've read the [documentation](/awslabs/python-deequ/blob/master/docs), but couldn't find anything related to the distance analyzer.

As a hacky workaround, I experimented with invoking the [numericalDistance()](/awslabs/deequ/blob/master/src/main/scala/com/amazon/deequ/analyzers/Distance.scala#L56) method directly with Py4J, but found that instantiating a Scala object of [QuantileNonSample[Double]](https://github.com/awslabs/deequ/blob/master/src/main/scala/com/amazon/deequ/analyzers/QuantileNonSample.scala#L25) doesn't seem to be a straightforward thing to do in Python.

Beginne mit der Dokumentation des Repositorys und der referenzierten Datei Deequ Distance.scala und vergleiche dann die angeforderten L-infinity- und chi-squared-Methoden damit, wie andere Analyzer in PyDeequ bereitgestellt werden. Überprüfe numericalDistance() und die Abhängigkeit QuantileNonSample[Double]. Fertig ist die Aufgabe, wenn die Distanzanalyse in Python auf dieselbe Weise wie andere Analyzer ausgeführt werden kann und die Dokumentation sie abdeckt.

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