Hm OK, there was only one "explanation" in the excerpt that github selected :-)

How about:
`MinMaxScaler` does not work for sparse matrices when the additive component of the normalization is non-zero. When normalizing to the range of `[0,1]`, this implies that the minimum in every column of `X` must be `0`. Setting `assume_contains_zeros=True` enforces this constraint even if some columns are strictly positive in `X`.