[jira] [Commented] (SPARK-18178) Importing Pandas Tables with Missing Values

["Bryan Cutler (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-18178?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15799122#comment-15799122 ] 

Bryan Cutler commented on SPARK-18178:
--------------------------------------

The error I get is
{noformat} TypeError: Can not merge type <class 'pyspark.sql.types.StringType'> and <class 'pyspark.sql.types.DoubleType'> {noformat}
which is because PySpark doesn't do a good job inferring the schema from Pandas, already reported at SPARK-17804

You could specify the schema yourself, like this
{code}
test_df = pd.read_csv(StringIO(',Scan Options\n15,SAT2\n16,\n'))
schema = StructType([StructField("data", IntegerType(), True), StructField("Scan Options", StringType(), True)])
sqlContext.createDataFrame(test_df, schema=schema).registerTempTable('Test')
{code}

> Importing Pandas Tables with Missing Values
> -------------------------------------------
>
>                 Key: SPARK-18178
>                 URL: https://issues.apache.org/jira/browse/SPARK-18178
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.0.0
>            Reporter: Kevin Mader
>
> If you import a table with missing values (like below) and create a dataframe from it, everything works fine until the command is actually execute (.first(), or .toPandas(), etc). The problem came up with a much larger table with values that were not NAN, just empty.
> ```
> import pandas as pd
> from io import StringIO
> test_df = pd.read_csv(StringIO(',Scan Options\n15,SAT2\n16,\n'))
> sqlContext.createDataFrame(test_df).registerTempTable('Test')
> o_qry = sqlContext.sql("SELECT * FROM Test LIMIT 1")
> o_qry.first()
> ```



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