itholic commented on a change in pull request #33882:
URL: https://github.com/apache/spark/pull/33882#discussion_r699847004
##########
File path: python/pyspark/pandas/namespace.py
##########
@@ -2814,9 +2824,18 @@ def to_numeric(arg):
1.0
"""
if isinstance(arg, Series):
- return arg._with_new_scol(arg.spark.column.cast("float"))
+ if errors == "coerce":
+ return arg._with_new_scol(arg.spark.column.cast("int"))
Review comment:
I changed it to follow pandas' behavior below:
```python
>>> pd.to_numeric(pd.Series(['1', '2', '3']))
0 1
1 2
2 3
dtype: int64
>>> ps.to_numeric(ps.Series(['1', '2', '3']))
0 1.0
1 2.0
2 3.0
dtype: float32
```
But I'll revert this change since we cannot handle the case below properly
with this change.
```python
>>> pd.to_numeric(pd.Series(['1.0', '2', '-3']))
0 1.0
1 2.0
2 -3.0
dtype: float64
>>> ps.to_numeric(ps.Series(['1.0', '2', '-3']))
0 1
1 2
2 -3
dtype: int32
```
##########
File path: python/pyspark/pandas/namespace.py
##########
@@ -2814,9 +2824,18 @@ def to_numeric(arg):
1.0
"""
if isinstance(arg, Series):
- return arg._with_new_scol(arg.spark.column.cast("float"))
+ if errors == "coerce":
+ return arg._with_new_scol(arg.spark.column.cast("int"))
+ elif errors == "ignore":
+ scol = arg.spark.column
+ casted_scol = scol.cast("int")
+ return arg._with_new_scol(F.when(casted_scol.isNull(),
scol).otherwise(casted_scol))
Review comment:
Yeah I think so.
If the `errors` is "ignore", then the original data is returned as is:
```python
>>> pd.to_numeric(pd.Series(["1", "2", "hello"]), errors="ignore")
0 1
1 2
2 hello
dtype: object
```
##########
File path: python/pyspark/pandas/namespace.py
##########
@@ -2814,9 +2824,18 @@ def to_numeric(arg):
1.0
"""
if isinstance(arg, Series):
- return arg._with_new_scol(arg.spark.column.cast("float"))
+ if errors == "coerce":
+ return arg._with_new_scol(arg.spark.column.cast("int"))
+ elif errors == "ignore":
+ scol = arg.spark.column
+ casted_scol = scol.cast("int")
+ return arg._with_new_scol(F.when(casted_scol.isNull(),
scol).otherwise(casted_scol))
Review comment:
Yeah I think so.
If the `errors` is "ignore", then the original data is returned as is (if
it's unable to cast to numeric):
```python
>>> pd.to_numeric(pd.Series(["1", "2", "hello"]), errors="ignore")
0 1
1 2
2 hello
dtype: object
```
##########
File path: python/pyspark/pandas/namespace.py
##########
@@ -2814,9 +2824,18 @@ def to_numeric(arg):
1.0
"""
if isinstance(arg, Series):
- return arg._with_new_scol(arg.spark.column.cast("float"))
+ if errors == "coerce":
+ return arg._with_new_scol(arg.spark.column.cast("int"))
+ elif errors == "ignore":
+ scol = arg.spark.column
+ casted_scol = scol.cast("int")
+ return arg._with_new_scol(F.when(casted_scol.isNull(),
scol).otherwise(casted_scol))
+ elif errors == "raise":
+ raise NotImplementedError("'raise' is not implemented yet, when
the `arg` is Series.")
Review comment:
Sounds good. Let me try
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