[
https://issues.apache.org/jira/browse/SPARK-7178?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14517858#comment-14517858
]
Chris Fregly edited comment on SPARK-7178 at 4/28/15 8:07 PM:
--------------------------------------------------------------
added these to the forums
AND and OR:
https://forums.databricks.com/questions/758/how-do-i-use-and-and-or-within-my-dataframe-operat.html
Nested Map Columns in DataFrames:
https://forums.databricks.com/questions/764/how-do-i-create-a-dataframe-with-nested-map-column.html
Casting columns of DataFrames:
https://forums.databricks.com/questions/767/how-do-i-cast-within-a-dataframe.html
was (Author: cfregly):
added this to the forums to address the AND and OR:
https://forums.databricks.com/questions/758/how-do-i-use-and-and-or-within-my-dataframe-operat.html
> Improve DataFrame documentation and code samples
> ------------------------------------------------
>
> Key: SPARK-7178
> URL: https://issues.apache.org/jira/browse/SPARK-7178
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.3.1
> Reporter: Chris Fregly
> Labels: dataframe
>
> AND and OR are not straightforward when using the new DataFrame API.
> the current convention - accepted by Pandas users - is to use the bitwise &
> and | instead of AND and OR. when using these, however, you need to wrap
> each expression in parenthesis to prevent the bitwise operator from
> dominating.
> also, working with StructTypes is a bit confusing. the following link:
> https://spark.apache.org/docs/latest/sql-programming-guide.html#programmatically-specifying-the-schema
> (Python tab) implies that you can work with tuples directly when creating a
> DataFrame.
> however, the following code errors out unless we explicitly use Row's:
> {code}
> from pyspark.sql import Row
> from pyspark.sql.types import *
> # The schema is encoded in a string.
> schemaString = "a"
> fields = [StructField(field_name, MapType(StringType(),IntegerType())) for
> field_name in schemaString.split()]
> schema = StructType(fields)
> df = sqlContext.createDataFrame([Row(a={'b': 1})], schema)
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
