When I run df.printSchema() I get:
root
|-- durable_key: string (nullable = true)
|-- code: string (nullable = true)
|-- desc: string (nullable = true)
|-- city: string (nullable = true)
|-- state_code: string (nullable = true)
|-- zip_code: string (nullable = true)
|-- county: string (nullable = true)
|-- country_code: string (nullable = true)
|-- latitude: double (nullable = true)
|-- longitude: double (nullable = true)
|-- market: string (nullable = true)
|-- tim_zone_desc: string (nullable = true)
A series-like statement by itself seems to work:
>>> df.state_code == "NY"
Column<(state_code = NY)>
But all of these return the error below:
>>> df.where(df.state_code == "NY")
>>> df[df.state_code == "NY"]
>>> df.filter(df['state_code'] == "NY")
I'm guessing this is a bug. Is there a workaround in 1.3?
Py4JJavaError Traceback (most recent call last)
<ipython-input-106-b1fcf7654244> in <module>()
----> 1 df.filter(df['state_code'] == "NY")
/opt/cloudera/parcels/CDH/lib/spark/python/pyspark/sql/dataframe.py in
filter(self, condition)
627 jdf = self._jdf.filter(condition)
628 elif isinstance(condition, Column):
--> 629 jdf = self._jdf.filter(condition._jc)
630 else:
631 raise TypeError("condition should be string or Column")
/opt/cloudera/parcels/CDH/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py
in __call__(self, *args)
536 answer = self.gateway_client.send_command(command)
537 return_value = get_return_value(answer, self.gateway_client,
--> 538 self.target_id, self.name)
539
540 for temp_arg in temp_args:
/opt/cloudera/parcels/CDH/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py
in get_return_value(answer, gateway_client, target_id, name)
298 raise Py4JJavaError(
299 'An error occurred while calling {0}{1}{2}.\n'.
--> 300 format(target_id, '.', name), value)
301 else:
302 raise Py4JError(
Py4JJavaError: An error occurred while calling o375.filter.
: org.apache.spark.sql.AnalysisException: resolved attributes state_code
missing from
latitude,country_code,tim_zone_desc,longitude,dma_durable_key,submarket,dma_code,dma_desc,county,city,zip_code,state_code;
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis(CheckAnalysis.scala:37)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3.apply(CheckAnalysis.scala:93)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3.apply(CheckAnalysis.scala:43)
at
org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:88)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis.apply(CheckAnalysis.scala:43)
at
org.apache.spark.sql.SQLContext$QueryExecution.assertAnalyzed(SQLContext.scala:1069)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
at
org.apache.spark.sql.DataFrame.logicalPlanToDataFrame(DataFrame.scala:157)
at org.apache.spark.sql.DataFrame.filter(DataFrame.scala:508)
at sun.reflect.GeneratedMethodAccessor42.invoke(Unknown Source)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
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