fix: teach column and getitem to respect validity#1927
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Mask sets entries of an array to null. I like the analogy to light: the array is a sequence of lights (each value might be a different wavelength). Null is represented by the absence of light. Placing a mask (i.e. a piece of plastic with slits) over the array causes those values where the mask is present (i.e. "on", "true") to be dark. An example in pseudo-code: ```rust a = [1, 2, 3, 4, 5] a_mask = [t, f, f, t, f] mask(a, a_mask) == [null, 2, 3, null, 5] ``` Specializations --------------- I only fallback to Arrow for two of the core arrays: - Sparse. I was skeptical that I could do better than decompressing and applying it. - Constant. If the mask is sparse, SparseArray might be a good choice. I didn't investigate. For the non-core arrays, I'm missing the following. I'm not clear that I can beat decompression for run end. The others are easy enough but some amount of typing and testing. - fastlanes - fsst - roaring - runend - runend-bool - zigzag Naming ------ Pandas also calls this operation [`mask`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mask.html) but accepts an optional second argument which is an array of values to use instead of null (which makes Pandas' mask more like an `if_else`). Arrow-rs calls this [`nullif`](https://arrow.apache.org/rust/arrow/compute/fn.nullif.html). Arrow-cpp has [`if_else(condition, consequent, alternate)`](https://arrow.apache.org/docs/cpp/compute.html#cpp-compute-scalar-selections) and [`replace_with_mask(array, mask, replacements)`](https://arrow.apache.org/docs/cpp/compute.html#replace-functions) both of which can implement our `mask` by passing a `NullArray` as the third argument.
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Will reopen after #1900 merges. |
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