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https://issues.apache.org/jira/browse/ARROW-2135?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16382750#comment-16382750
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ASF GitHub Bot commented on ARROW-2135:
---------------------------------------
cpcloud commented on a change in pull request #1681: ARROW-2135: [Python] Fix
NaN conversion when casting from Numpy array
URL: https://github.com/apache/arrow/pull/1681#discussion_r171711960
##########
File path: cpp/src/arrow/python/numpy_to_arrow.cc
##########
@@ -113,6 +145,55 @@ inline int64_t ValuesToBitmap(PyArrayObject* arr,
uint8_t* bitmap) {
return null_count;
}
+class NumPyNullsConverter {
+ public:
+ /// Convert the given array's null values to a null bitmap.
+ /// The null bitmap is only allocated if null values are ever possible.
+ static Status Convert(MemoryPool* pool, PyArrayObject* arr,
+ bool use_pandas_null_sentinels,
+ std::shared_ptr<ResizableBuffer>* out_null_bitmap_,
+ int64_t* out_null_count) {
+ NumPyNullsConverter converter(pool, arr, use_pandas_null_sentinels);
+ RETURN_NOT_OK(VisitNumpyArrayInline(arr, &converter));
+ *out_null_bitmap_ = converter.null_bitmap_;
+ *out_null_count = converter.null_count_;
+ return Status::OK();
+ }
+
+ template <int TYPE>
+ Status Visit(PyArrayObject* arr) {
+ typedef internal::npy_traits<TYPE> traits;
+
+ const bool null_sentinels_possible =
+ // Always treat Numpy's NaT as null
+ TYPE == NPY_DATETIME ||
Review comment:
AFAIU There's no other way to interpret `NaT` other than `NULL` (unless
there's a standard that defines it in a different way than "missing"). nan is
part of the IEEE floating point specification (as I'm sure you know) and it has
a different meaning than null.
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> [Python] NaN values silently casted to int64 when passing explicit schema for
> conversion in Table.from_pandas
> -------------------------------------------------------------------------------------------------------------
>
> Key: ARROW-2135
> URL: https://issues.apache.org/jira/browse/ARROW-2135
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.8.0
> Reporter: Matthew Gilbert
> Assignee: Antoine Pitrou
> Priority: Major
> Labels: pull-request-available
> Fix For: 0.9.0
>
>
> If you create a {{Table}} from a {{DataFrame}} of ints with a NaN value the
> NaN is improperly cast. Since pandas casts these to floats, when converted to
> a table the NaN is interpreted as an integer. This seems like a bug since a
> known limitation in pandas (the inability to have null valued integers data)
> is taking precedence over arrow's functionality to store these as an IntArray
> with nulls.
>
> {code}
> import pyarrow as pa
> import pandas as pd
> df = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
> schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
> table = pa.Table.from_pandas(df, schema=schema)
> table[0]
> <pyarrow.lib.Column object at 0x7f2151d19c90>
> chunk 0: <pyarrow.lib.Int64Array object at 0x7f213bf356d8>
> [
> 1,
> 2,
> -9223372036854775808
> ]{code}
>
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