Support dask dataframes as input to lens.summarise

Currently, lens requires a pandas dataframe as input to the `lens.summarise` method. This places an upper limit on the size of the dataset analysed, which must be smaller than the available memory in the machine. Even with efficient optimisation of memory usage during the execution of the dask graph, the initial requirement prevents `lens` from scaling.

Ideally, `lens.summarise` should accept dask dataframes as input, and build the execution graph based on this delayed dataframe. This will require a rework of the functions in `lens.metrics`, given that all of them currently take either `pd.Series` or `pd.Dataframe` as arguments. In most cases we should be able to use the dask dataframe API, but for other metrics it will be necessary to access the individual chunks and reduce the result appropriately.

Adding this support, along with the distributed scheduler #11, will allow lens to analyse datasets significantly larger than the memory of the machine.