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[GitHub] [spark] viirya opened a new pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
viirya opened a new pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025
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When `toPandas` API works on duplicate column names produced from operators like join, we see the error like:
```
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
This patch fixes the error in `toPandas` API.
### Why are the changes needed?
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Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
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To make `toPandas` work on dataframe with duplicate column names.
### Does this PR introduce any user-facing change?
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If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
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Yes. Previously calling `toPandas` API on a dataframe with duplicate column names will fail. After this patch, it will produce correct result.
### How was this patch tested?
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Unit test.
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[GitHub] [spark] HyukjinKwon commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
HyukjinKwon commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398269294
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,36 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index in range(len(dtype)):
+ t = dtype[index]
Review comment:
nit: I think we can do:
```diff
- for index in range(len(dtype)):
- t = dtype[index]
+ for index, t in enumerate(dtype):
```
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[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#issuecomment-604230839
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[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
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[GitHub] [spark] HyukjinKwon commented on issue #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
HyukjinKwon commented on issue #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#issuecomment-604180059
Looks good. a couple of questions.
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[GitHub] [spark] SparkQA commented on issue #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#issuecomment-604769562
**[Test build #120445 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120445/testReport)** for PR 28025 at commit [`4270bcc`](https://github.com/apache/spark/commit/4270bcc35988a99ad0ed7fa0ca28429e8d920d9b).
* This patch **fails Python style tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
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[GitHub] [spark] viirya commented on issue #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
viirya commented on issue #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#issuecomment-604117882
cc @HyukjinKwon
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[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
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[GitHub] [spark] viirya commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
viirya commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398271448
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,36 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index in range(len(dtype)):
+ t = dtype[index]
+ if t is not None:
+ series = pdf.iloc[:, index].astype(t, copy=False)
+ else:
+ series = pdf.iloc[:, index]
+ df.insert(index, self.schema[index].name, series, allow_duplicates=True)
- for f, t in dtype.items():
- pdf[f] = pdf[f].astype(t, copy=False)
Review comment:
Yea, not work, I'm not sure why.
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[GitHub] [spark] viirya commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
viirya commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398951087
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,35 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
Review comment:
ok.
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[GitHub] [spark] viirya commented on issue #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
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Posted by GitBox <gi...@apache.org>.
viirya commented on issue #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#issuecomment-604809085
Thanks @HyukjinKwon @BryanCutler
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[GitHub] [spark] viirya commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
viirya commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398978882
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,35 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index, t in enumerate(dtype):
+ if t is not None:
+ series = pdf.iloc[:, index].astype(t, copy=False)
+ else:
+ series = pdf.iloc[:, index]
+ df.insert(index, self.schema[index].name, series, allow_duplicates=True)
Review comment:
Looks like so. `insert` calls `_sanitize_column` which makes a copy of the data.
But `pdf.iloc[:, index] = pdf.iloc[:, index].astype(t, copy=False)` doesn't work as I replied earlier to @HyukjinKwon. Looks like whether `iloc` returns a view or a copy, may depend on the context.
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[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
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URL: https://github.com/apache/spark/pull/28025#issuecomment-604778937
**[Test build #120446 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120446/testReport)** for PR 28025 at commit [`b8e69e0`](https://github.com/apache/spark/commit/b8e69e0eba86fdf308d708da91fb82107f4f084c).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] [spark] viirya commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
viirya commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398978882
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,35 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index, t in enumerate(dtype):
+ if t is not None:
+ series = pdf.iloc[:, index].astype(t, copy=False)
+ else:
+ series = pdf.iloc[:, index]
+ df.insert(index, self.schema[index].name, series, allow_duplicates=True)
Review comment:
Looks like so. `insert` calls `_sanitize_column` which makes a copy of the data.
But `pdf.iloc[:, index] = pdf.iloc[:, index].astype(t, copy=False)` doesn't work as I replied earlier to @HyukjinKwon. Looks like whethere `iloc` returns a view or a copy, may depend on the context.
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[GitHub] [spark] viirya commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
viirya commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398271547
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,36 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index in range(len(dtype)):
+ t = dtype[index]
Review comment:
ok
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[GitHub] [spark] BryanCutler commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
BryanCutler commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398729811
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,35 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
Review comment:
better to use enumerate here?
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[GitHub] [spark] viirya commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
viirya commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398993343
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,36 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index in range(len(dtype)):
+ t = dtype[index]
+ if t is not None:
+ series = pdf.iloc[:, index].astype(t, copy=False)
+ else:
+ series = pdf.iloc[:, index]
+ df.insert(index, self.schema[index].name, series, allow_duplicates=True)
- for f, t in dtype.items():
- pdf[f] = pdf[f].astype(t, copy=False)
Review comment:
As I read through related doc at Pandas, looks like whether `iloc` returns a view or a copy, depends on the context. That said, `pdf.iloc[:, index] = pdf.iloc[:, index]...` works sometimes but not always.
It explains why when I do simple test in Python shell like creating a Pandas DataFrame and reassigning like `pdf.iloc[:, index] = ...`, it works, but in added unit test it doesn't.
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[GitHub] [spark] BryanCutler commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
BryanCutler commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398731133
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,35 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index, t in enumerate(dtype):
+ if t is not None:
+ series = pdf.iloc[:, index].astype(t, copy=False)
+ else:
+ series = pdf.iloc[:, index]
+ df.insert(index, self.schema[index].name, series, allow_duplicates=True)
Review comment:
Does this make a copy of the data? Seems to go into a `make_block` method, but I can't tell for sure if that is doing an allocation
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[GitHub] [spark] HyukjinKwon commented on a change in pull request #28025:
[SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column
names
Posted by GitBox <gi...@apache.org>.
HyukjinKwon commented on a change in pull request #28025: [SPARK-31186][PySpark][SQL] toPandas should not fail on duplicate column names
URL: https://github.com/apache/spark/pull/28025#discussion_r398270416
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -132,25 +132,36 @@ def toPandas(self):
# Below is toPandas without Arrow optimization.
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
- dtype = {}
- for field in self.schema:
+ dtype = [None] * len(self.schema)
+ for fieldIdx in range(len(self.schema)):
+ field = self.schema[fieldIdx]
+ pandas_col = pdf.iloc[:, fieldIdx]
+
pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
# SPARK-21766: if an integer field is nullable and has null values, it can be
# inferred by pandas as float column. Once we convert the column with NaN back
# to integer type e.g., np.int16, we will hit exception. So we use the inferred
# float type, not the corrected type from the schema in this case.
if pandas_type is not None and \
not(isinstance(field.dataType, IntegralType) and field.nullable and
- pdf[field.name].isnull().any()):
- dtype[field.name] = pandas_type
+ pandas_col.isnull().any()):
+ dtype[fieldIdx] = pandas_type
# Ensure we fall back to nullable numpy types, even when whole column is null:
- if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.float64
- if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
- dtype[field.name] = np.object
+ if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.float64
+ if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
+ dtype[fieldIdx] = np.object
+
+ df = pd.DataFrame()
+ for index in range(len(dtype)):
+ t = dtype[index]
+ if t is not None:
+ series = pdf.iloc[:, index].astype(t, copy=False)
+ else:
+ series = pdf.iloc[:, index]
+ df.insert(index, self.schema[index].name, series, allow_duplicates=True)
- for f, t in dtype.items():
- pdf[f] = pdf[f].astype(t, copy=False)
Review comment:
@viirya, out of curiosity, doesn't it work?
```python
for index, t in enumerate(dtype):
if t is not None
pdf.iloc[:, index] = pdf.iloc[:, index].astype(t, copy=False)
```
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