[SPARK-32722][PYTHON][DOCS] Update document type conversion for Pandas UDFs (pyarrow 1.0.1, pandas 1.1.1, Python 3.7)
### What changes were proposed in this pull request?
This PR updates the chart generated at SPARK-25666. We bumped up the minimal PyArrow version. It's better to use PyArrow 0.15.1+
### Why are the changes needed?
To track the changes in type coercion of PySpark <> PyArrow <> pandas.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Use this code to generate the chart:
```python
from pyspark.sql.types import *
from pyspark.sql.functions import pandas_udf
columns = [
('none', 'object(NoneType)'),
('bool', 'bool'),
('int8', 'int8'),
('int16', 'int16'),
('int32', 'int32'),
('int64', 'int64'),
('uint8', 'uint8'),
('uint16', 'uint16'),
('uint32', 'uint32'),
('uint64', 'uint64'),
('float64', 'float16'),
('float64', 'float32'),
('float64', 'float64'),
('date', 'datetime64[ns]'),
('tz_aware_dates', 'datetime64[ns, US/Eastern]'),
('string', 'object(string)'),
('decimal', 'object(Decimal)'),
('array', 'object(array[int32])'),
('float128', 'float128'),
('complex64', 'complex64'),
('complex128', 'complex128'),
('category', 'category'),
('tdeltas', 'timedelta64[ns]'),
]
def create_dataframe():
import pandas as pd
import numpy as np
import decimal
pdf = pd.DataFrame({
'none': [None, None],
'bool': [True, False],
'int8': np.arange(1, 3).astype('int8'),
'int16': np.arange(1, 3).astype('int16'),
'int32': np.arange(1, 3).astype('int32'),
'int64': np.arange(1, 3).astype('int64'),
'uint8': np.arange(1, 3).astype('uint8'),
'uint16': np.arange(1, 3).astype('uint16'),
'uint32': np.arange(1, 3).astype('uint32'),
'uint64': np.arange(1, 3).astype('uint64'),
'float16': np.arange(1, 3).astype('float16'),
'float32': np.arange(1, 3).astype('float32'),
'float64': np.arange(1, 3).astype('float64'),
'float128': np.arange(1, 3).astype('float128'),
'complex64': np.arange(1, 3).astype('complex64'),
'complex128': np.arange(1, 3).astype('complex128'),
'string': list('ab'),
'array': pd.Series([np.array([1, 2, 3], dtype=np.int32), np.array([1, 2, 3], dtype=np.int32)]),
'decimal': pd.Series([decimal.Decimal('1'), decimal.Decimal('2')]),
'date': pd.date_range('19700101', periods=2).values,
'category': pd.Series(list("AB")).astype('category')})
pdf['tdeltas'] = [pdf.date.diff()[1], pdf.date.diff()[0]]
pdf['tz_aware_dates'] = pd.date_range('19700101', periods=2, tz='US/Eastern')
return pdf
types = [
BooleanType(),
ByteType(),
ShortType(),
IntegerType(),
LongType(),
FloatType(),
DoubleType(),
DateType(),
TimestampType(),
StringType(),
DecimalType(10, 0),
ArrayType(IntegerType()),
MapType(StringType(), IntegerType()),
StructType([StructField("_1", IntegerType())]),
BinaryType(),
]
df = spark.range(2).repartition(1)
results = []
count = 0
total = len(types) * len(columns)
values = []
spark.sparkContext.setLogLevel("FATAL")
for t in types:
result = []
for column, pandas_t in columns:
v = create_dataframe()[column][0]
values.append(v)
try:
row = df.select(pandas_udf(lambda _: create_dataframe()[column], t)(df.id)).first()
ret_str = repr(row[0])
except Exception:
ret_str = "X"
result.append(ret_str)
progress = "SQL Type: [%s]\n Pandas Value(Type): %s(%s)]\n Result Python Value: [%s]" % (
t.simpleString(), v, pandas_t, ret_str)
count += 1
print("%s/%s:\n %s" % (count, total, progress))
results.append([t.simpleString()] + list(map(str, result)))
schema = ["SQL Type \\ Pandas Value(Type)"] + list(map(lambda values_column: "%s(%s)" % (values_column[0], values_column[1][1]), zip(values, columns)))
strings = spark.createDataFrame(results, schema=schema)._jdf.showString(20, 20, False)
print("\n".join(map(lambda line: " # %s # noqa" % line, strings.strip().split("\n"))))
```
Closes #29569 from HyukjinKwon/SPARK-32722.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
5775073a01