Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/17435#discussion_r108437512
--- Diff: python/pyspark/sql/types.py ---
@@ -57,7 +57,25 @@ def __ne__(self, other):
@classmethod
def typeName(cls):
- return cls.__name__[:-4].lower()
+ typeTypeNameMap = {"DataType": "data",
+ "NullType": "null",
+ "StringType": "string",
+ "BinaryType": "binary",
+ "BooleanType": "boolean",
+ "DateType": "date",
+ "TimestampType": "timestamp",
+ "DecimalType": "decimal",
+ "DoubleType": "double",
+ "FloatType": "float",
+ "ByteType": "byte",
+ "IntegerType": "integer",
+ "LongType": "long",
+ "ShortType": "short",
+ "ArrayType": "array",
+ "MapType": "map",
+ "StructField": "struct",
--- End diff --
I guess it would not produce correct fields if `struct` is printed from
`StructField`.
```python
>>> from pyspark.sql import Row
>>>
>>> df = spark.createDataFrame([[Row(a=1), 2]])
>>> cols = []
>>> for i in df.schema:
... cols.append("`" + i.name + "`" + "\t" + i.typeName())
...
>>> print ",\n".join(cols)
`_1` struct,
`_2` struct
```
because actual types are as below:
```python
>>> df.schema.simpleString()
'struct<_1:struct<a:bigint>,_2:bigint>'
```
How about the one as below?
```python
from pyspark.sql import Row
df = spark.createDataFrame([[Row(a=1), 2]])
cols = []
for i in df.schema:
cols.append("`" + i.name + "`" + "\t" + i.dataType.simpleString())
print ",\n".join(cols)
```
prints
```
`_1` struct<a:bigint>,
`_2` bigint
```
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