[jira] [Commented] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15976778#comment-15976778
]
Len Frodgers commented on SPARK-19732:
--
Thanks. Have updated
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>Priority: Minor
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
> Additionally there's another anomaly:
> Spark (and pyspark) supports filling of bools if you specify the args as a
> map:
> {code}
> fillna({"a": False})
> {code}
> , but not if you specify it as
> {code}
> fillna(False)
> {code}
> This is because (scala-)Spark has no
> {code}
> def fill(value: Boolean): DataFrame = fill(value, df.columns)
> {code}
> method. I find that strange/buggy
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[jira] [Issue Comment Deleted] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Len Frodgers updated SPARK-19732:
-
Comment: was deleted
(was: Actually there's another anomaly:
Spark (and pyspark) supports filling of bools if you specify the args as a map:
{code}
fillna({"a": False})
{code}
, but not if you specify it as
{code}
fillna(False)
{code}
This is because (scala-)Spark has no
{code}
def fill(value: Boolean): DataFrame = fill(value, df.columns)
{code}
method. I find that strange/buggy)
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>Priority: Minor
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
> Additionally there's another anomaly:
> Spark (and pyspark) supports filling of bools if you specify the args as a
> map:
> {code}
> fillna({"a": False})
> {code}
> , but not if you specify it as
> {code}
> fillna(False)
> {code}
> This is because (scala-)Spark has no
> {code}
> def fill(value: Boolean): DataFrame = fill(value, df.columns)
> {code}
> method. I find that strange/buggy
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[jira] [Updated] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Len Frodgers updated SPARK-19732:
-
Description:
In PySpark, the fillna function of DataFrame inadvertently casts bools to ints,
so fillna cannot be used to fill True/False.
e.g. `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
yields
`[Row(a=True), Row(a=None)]`
It should be a=True for the second Row
The cause is this bit of code:
{code}
if isinstance(value, (int, long)):
value = float(value)
{code}
There needs to be a separate check for isinstance(bool), since in python, bools
are ints too
Additionally there's another anomaly:
Spark (and pyspark) supports filling of bools if you specify the args as a map:
{code}
fillna({"a": False})
{code}
, but not if you specify it as
{code}
fillna(False)
{code}
This is because (scala-)Spark has no
{code}
def fill(value: Boolean): DataFrame = fill(value, df.columns)
{code}
method. I find that strange/buggy
was:
In PySpark, the fillna function of DataFrame inadvertently casts bools to ints,
so fillna cannot be used to fill True/False.
e.g. `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
yields
`[Row(a=True), Row(a=None)]`
It should be a=True for the second Row
The cause is this bit of code:
{code}
if isinstance(value, (int, long)):
value = float(value)
{code}
There needs to be a separate check for isinstance(bool), since in python, bools
are ints too
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>Priority: Minor
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
> Additionally there's another anomaly:
> Spark (and pyspark) supports filling of bools if you specify the args as a
> map:
> {code}
> fillna({"a": False})
> {code}
> , but not if you specify it as
> {code}
> fillna(False)
> {code}
> This is because (scala-)Spark has no
> {code}
> def fill(value: Boolean): DataFrame = fill(value, df.columns)
> {code}
> method. I find that strange/buggy
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[jira] [Updated] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Len Frodgers updated SPARK-19732:
-
Issue Type: Improvement (was: Bug)
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
--
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[jira] [Updated] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Len Frodgers updated SPARK-19732:
-
Priority: Minor (was: Major)
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>Priority: Minor
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
--
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[jira] [Commented] (SPARK-19871) Improve error message in verify_type to indicate which field the error is for
[ https://issues.apache.org/jira/browse/SPARK-19871?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15901668#comment-15901668 ] Len Frodgers commented on SPARK-19871: -- https://github.com/apache/spark/pull/17213 > Improve error message in verify_type to indicate which field the error is for > - > > Key: SPARK-19871 > URL: https://issues.apache.org/jira/browse/SPARK-19871 > Project: Spark > Issue Type: Bug > Components: PySpark, SQL >Affects Versions: 2.0.2, 2.1.0 >Reporter: Len Frodgers > > The error message for _verify_type is too vague. It should specify for which > field the type verification failed. > e.g. error: "This field is not nullable, but got None" – but what is the > field?! > In a dataframe with many non-nullable fields, it is a nightmare trying to > hunt down which field is null, since one has to resort to basic (and very > slow) trial and error. > I have happily created a PR to fix this. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-13740) add null check for _verify_type in types.py
[ https://issues.apache.org/jira/browse/SPARK-13740?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15901667#comment-15901667 ] Len Frodgers commented on SPARK-13740: -- https://github.com/apache/spark/pull/17213 > add null check for _verify_type in types.py > --- > > Key: SPARK-13740 > URL: https://issues.apache.org/jira/browse/SPARK-13740 > Project: Spark > Issue Type: Improvement > Components: SQL >Reporter: Wenchen Fan >Assignee: Wenchen Fan > Fix For: 2.0.0 > > -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-19871) Improve error message in verify_type to indicate which field the error is for
Len Frodgers created SPARK-19871: Summary: Improve error message in verify_type to indicate which field the error is for Key: SPARK-19871 URL: https://issues.apache.org/jira/browse/SPARK-19871 Project: Spark Issue Type: Bug Components: PySpark, SQL Affects Versions: 2.1.0, 2.0.2 Reporter: Len Frodgers The error message for _verify_type is too vague. It should specify for which field the type verification failed. e.g. error: "This field is not nullable, but got None" – but what is the field?! In a dataframe with many non-nullable fields, it is a nightmare trying to hunt down which field is null, since one has to resort to basic (and very slow) trial and error. I have happily created a PR to fix this. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-13740) add null check for _verify_type in types.py
[ https://issues.apache.org/jira/browse/SPARK-13740?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15901490#comment-15901490 ] Len Frodgers commented on SPARK-13740: -- Thanks for the fix, but IMO the error message is so poor as to render this fix worse than the original behaviour. Error: "This field is not nullable, but got None" -- but what is the field?! In a dataframe with many non-nullable fields, it is a nightmare trying to hunt down which field is null, since one has to resort to basic (and very slow) trial and error. I will happily create a PR to fix this. Let me know if I should create a new issue for this. > add null check for _verify_type in types.py > --- > > Key: SPARK-13740 > URL: https://issues.apache.org/jira/browse/SPARK-13740 > Project: Spark > Issue Type: Improvement > Components: SQL >Reporter: Wenchen Fan >Assignee: Wenchen Fan > Fix For: 2.0.0 > > -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Comment Edited] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15883522#comment-15883522
]
Len Frodgers edited comment on SPARK-19732 at 2/24/17 9:12 PM:
---
Actually there's another anomaly:
Spark (and pyspark) supports filling of bools if you specify the args as a map:
{code}
fillna({"a": False})
{code}
, but not if you specify it as
{code}
fillna(False)
{code}
This is because (scala-)Spark has no
{code}
def fill(value: Boolean): DataFrame = fill(value, df.columns)
{code}
method. I find that strange/buggy
was (Author: lenfrodge):
Actually there's another anomaly:
Spark (and pyspark) supports filling of bools if you specify the args as a map:
fillna({"a": False}) , but not if you specify it as fillna(False)
This is because (scala-)Spark has no `def fill(value: Booloean): DataFrame =
fill(value, df.columns)` method. I find that strange/buggy
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
--
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[jira] [Commented] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15883522#comment-15883522
]
Len Frodgers commented on SPARK-19732:
--
Actually there's another anomaly:
Spark (and pyspark) supports filling of bools if you specify the args as a map:
fillna({"a": False}) , but not if you specify it as fillna(False)
This is because (scala-)Spark has no `def fill(value: Booloean): DataFrame =
fill(value, df.columns)` method. I find that strange/buggy
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
--
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[jira] [Updated] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
[
https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Len Frodgers updated SPARK-19732:
-
Description:
In PySpark, the fillna function of DataFrame inadvertently casts bools to ints,
so fillna cannot be used to fill True/False.
e.g. `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
yields
`[Row(a=True), Row(a=None)]`
It should be a=True for the second Row
The cause is this bit of code:
{code}
if isinstance(value, (int, long)):
value = float(value)
{code}
There needs to be a separate check for isinstance(bool), since in python, bools
are ints too
was:
In PySpark, the fillna function of DataFrame inadvertently casts bools to ints,
so fillna cannot be used to fill True/False.
e.g. `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
yields
`[Row(a=True), Row(a=None)]`
It should be a=True for the second Row
The cause is this bit of code:
if isinstance(value, (int, long)):
value = float(value)
There needs to be a separate check for isinstance(bool), since in python, bools
are ints too
> DataFrame.fillna() does not work for bools in PySpark
> -
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.1.0
>Reporter: Len Frodgers
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
--
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[jira] [Created] (SPARK-19732) DataFrame.fillna() does not work for bools in PySpark
Len Frodgers created SPARK-19732: Summary: DataFrame.fillna() does not work for bools in PySpark Key: SPARK-19732 URL: https://issues.apache.org/jira/browse/SPARK-19732 Project: Spark Issue Type: Bug Components: PySpark Affects Versions: 2.1.0 Reporter: Len Frodgers In PySpark, the fillna function of DataFrame inadvertently casts bools to ints, so fillna cannot be used to fill True/False. e.g. `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()` yields `[Row(a=True), Row(a=None)]` It should be a=True for the second Row The cause is this bit of code: if isinstance(value, (int, long)): value = float(value) There needs to be a separate check for isinstance(bool), since in python, bools are ints too -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
