Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/19459#discussion_r145863796
--- Diff: python/pyspark/sql/session.py ---
@@ -414,6 +415,73 @@ def _createFromLocal(self, data, schema):
data = [schema.toInternal(row) for row in data]
return self._sc.parallelize(data), schema
+ def _createFromPandasWithArrow(self, pdf, schema):
+ """
+ Create a DataFrame from a given pandas.DataFrame by slicing it
into partitions, converting
+ to Arrow data, then sending to the JVM to parallelize. If a schema
is passed in, the
+ data types will be used to coerce the data in Pandas to Arrow
conversion.
+ """
+ from pyspark.serializers import ArrowSerializer
+ from pyspark.sql.types import from_arrow_schema, to_arrow_type,
_cast_pandas_series_type
+ import pyarrow as pa
+
+ # Slice the DataFrame into batches
+ step = -(-len(pdf) // self.sparkContext.defaultParallelism) #
round int up
+ pdf_slices = (pdf[start:start + step] for start in xrange(0,
len(pdf), step))
+
+ if schema is None or isinstance(schema, list):
+ batches = [pa.RecordBatch.from_pandas(pdf_slice,
preserve_index=False)
+ for pdf_slice in pdf_slices]
+
+ # There will be at least 1 batch after slicing the
pandas.DataFrame
+ schema_from_arrow = from_arrow_schema(batches[0].schema)
+
+ # If passed schema as a list of names then rename fields
+ if isinstance(schema, list):
+ fields = []
+ for i, field in enumerate(schema_from_arrow):
+ field.name = schema[i]
+ fields.append(field)
+ schema = StructType(fields)
+ else:
+ schema = schema_from_arrow
+ else:
+ batches = []
+ for i, pdf_slice in enumerate(pdf_slices):
+
+ # convert to series to pyarrow.Arrays to use mask when
creating Arrow batches
+ arrs = []
+ names = []
+ for c, (_, series) in enumerate(pdf_slice.iteritems()):
+ field = schema[c]
+ names.append(field.name)
+ t = to_arrow_type(field.dataType)
+ try:
+ # NOTE: casting is not necessary with Arrow >= 0.7
+
arrs.append(pa.Array.from_pandas(_cast_pandas_series_type(series, t),
+
mask=series.isnull(), type=t))
+ except ValueError as e:
--- End diff --
I think this guard only works to prevent casting like:
```python
>>> s = pd.Series(["abc", "2", "10001"])
>>> s.astype(np.object_)
0 abc
1 2
2 10001
dtype: object
>>> s
0 abc
1 2
2 10001
dtype: object
>>> s.astype(np.int8)
...
ValueError: invalid literal for long() with base 10: 'abc'
```
For the casting that can cause overflow, this seems don't work.
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