Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18378#discussion_r123326970
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -1750,6 +1751,29 @@ def _to_scala_map(sc, jm):
return sc._jvm.PythonUtils.toScalaMap(jm)
+def _to_numpy_type(dt):
+ """
+ Convert a Spark SQL data type to Pandas data type.
+ """
+ import numpy as np
+ if type(dt) == BooleanType:
+ return 'bool'
+ elif type(dt) == ByteType:
+ return 'int8'
+ elif type(dt) == ShortType:
+ return 'int16'
+ elif type(dt) == IntegerType:
+ return 'int32'
+ elif type(dt) == LongType:
+ return 'int64'
+ elif type(dt) == FloatType:
+ return 'float32'
+ elif type(dt) == DoubleType:
+ return 'float64'
+ else:
+ return 'object'
--- End diff --
I think it might cause problems to have all non-primitive types as object.
Things like timestamps will be inferred form a datetime object, for example:
```python
In [10]: pdf = pd.DataFrame.from_records([(1.0, 1, "a",
datetime.datetime.now())])
In [11]: pdf.dtypes
Out[11]:
0 float64
1 int64
2 object
3 datetime64[ns]
dtype: object
In [12]: pdf.astype({0: "float32", 1: "int32", 2: "object", 3:
"object"}).dtypes
Out[12]:
0 float32
1 int32
2 object
3 object
dtype: object
```
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