Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/18945
Ah, I had to be clear. I thought something like ...
```python
dtype = {}
for field in self.schema:
pandas_type = _to_corrected_pandas_type(field.dataType)
if pandas_type is not None:
dtype[field.name] = pandas_type
# Columns with int + nullable from schemaa
int_null_cols = [...]
# Columns with int + nullable but with actual None.
int_null_cols_with_none = [...]
# This functions checks if the value is None.
def check_nulls():
for row in rows:
# Check with int_null_cols and set int_null_cols_with_none if there
is None.
yield rows
# Don't check anything if no int + nullable columns.
if len(int_null_cols) > 0:
check_func = check_nulls
else:
check_func = lambda r: r
pdf = pd.DataFrame.from_records(check_null(self.collect()),
columns=self.columns)
# Replace int32 -> float one by checking int_null_cols.
dtype = ...
for f, t in dtype.items():
pdf[f] = pdf[f].astype(t, copy=False)
return pdf
```
So, I was thinking of checking the actual value in the data might be a way
if we can't resolve this only with the schema.
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