src/main/scala/com/amazon/deequ/analyzers/runners/AnalysisRunner.scala (2 lines):
	- line 169: // TODO this can be further improved, we can get the number of rows from other metrics as well
	- line 170: // TODO we could also insert an extra Size() computation if we have to scan the data anyways


src/main/scala/com/amazon/deequ/suggestions/rules/FractionalCategoricalRangeRule.scala (2 lines):
	- line 52: // TODO find a principled way to define these thresholds...
	- line 81: // TODO this needs to be more robust for p's close to 0 or 1


src/main/scala/com/amazon/deequ/analyzers/runners/KLLRunner.scala (1 line):
	- line 142: // TODO at the moment, we will throw exceptions for Decimals


src/main/scala/com/amazon/deequ/repository/AnalysisResultSerde.scala (1 line):
	- line 581: // TODO is it ok to have null here?


src/main/scala/com/amazon/deequ/profiles/ColumnProfiler.scala (1 line):
	- line 429: case TimestampType => String // TODO We should have support for dates in deequ...


src/main/scala/com/amazon/deequ/suggestions/rules/RetainCompletenessRule.scala (1 line):
	- line 40: // TODO this needs to be more robust for p's close to 0 or 1


src/main/scala/com/amazon/deequ/metrics/KLLMetric.scala (1 line):
	- line 92: // TODO not sure if thats correct...


src/main/scala/com/amazon/deequ/suggestions/rules/CategoricalRangeRule.scala (1 line):
	- line 46: // TODO find a principled way to define this threshold...


src/main/scala/com/amazon/deequ/analyzers/DataType.scala (1 line):
	- line 92: // TODO avoid allocation


src/main/scala/com/amazon/deequ/suggestions/rules/UniqueIfApproximatelyUniqueRule.scala (1 line):
	- line 34: // TODO This bound depends on the error guarantees of the HLL sketch


src/main/scala/com/amazon/deequ/analyzers/Histogram.scala (1 line):
	- line 58: // TODO figure out a way to pass this in if its known before hand