qcut() should make sure the bins bounderies are unique before passing them to _bins_to_cuts

xref #8309

for example:

pd.qcut([1,1,1,1,1,1,1,1,1,1,1,1,1,5,5,5], [0.00001, 0.5])

will raise “ValueError: Bin edges must be unique: array([ 1., 1.])” exception

Fix suggestion - add one new line:

def qcut(x, q, labels=None, retbins=False, precision=3):
    if com.is_integer(q):
        quantiles = np.linspace(0, 1, q + 1)
    else:
        quantiles = q
    bins = algos.quantile(x, quantiles)
--->bins = np.unique(bins)
    return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,
        precision=precision, include_lowest=True)

[dukebody]

I think we could add a “duplicate_edges” parameter, with the following options:

• ‘raise’ (default). Raise an error if duplicate bin edges are found.
• ‘drop’. Delete duplicate edges, adding the bins = np.unique(bins) line as in https://github.com/pydata/pandas/issues/7751#issue-37814702. This would result in less bins than specified, and some larger (with more elements) than others.
• ‘unique’. Perform the binning based on the unique values present in the input array. This is essentialy the first solution in http://stackoverflow.com/questions/20158597/how-to-qcut-with-non-unique-bin-edges and would imply unevenly sized bins, without altering the total number of bins.

Does it look reasonable? What do you think?

[rainjacket]

dukebody’s suggestion looks good to me.

For my use case, I would prefer for qcut to just return the (non-unique) bin list, and let me handle it.