如何使用 PySparkSQL 设置列的标题

Question

只是一个简短的问题伙计们。使用 pandas，我们可以创建一个数据帧并设置一个标头，如下所示：

import pandas as pd
df = pd.read_csv('/file/path', sep='|', names = ['A','B'])

使用 PySpark：

text_file = sc.textFile('path/file')

另一方面，尽管我已经准备好阅读Spark SQL的文档，但我没有找到如何设置标题和分隔符，或者将数据集的每一列的名称作为pandas。知道如何使用 PySparkSQL 为每列命名吗？

更新：

从@CafeFeed我尝试了以下内容：

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df_2 = sqlContext.read.format('com.databricks.spark.csv').options(header='false', delimiter='|').load('path')
df_2

但是，我得到了这个异常：

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-31-ad726583541b> in <module>()
      2 sqlContext = SQLContext(sc)
      3 
----> 4 df_2 = sqlContext.read.format('com.databricks.spark.csv').options(header='false', delimiter='|').load('/Users/user/GitHub/PySpark-Notes/ml-100k/u.user')
      5 df_2
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/sql/readwriter.pyc in load(self, path, format, schema, **options)
    119         self.options(**options)
    120         if path is not None:
--> 121             return self._df(self._jreader.load(path))
    122         else:
    123             return self._df(self._jreader.load())
/usr/local/Cellar/apache-spark/1.5.1/libexec/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:
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/sql/utils.pyc in deco(*a, **kw)
     34     def deco(*a, **kw):
     35         try:
---> 36             return f(*a, **kw)
     37         except py4j.protocol.Py4JJavaError as e:
     38             s = e.java_exception.toString()
/usr/local/Cellar/apache-spark/1.5.1/libexec/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 o67.load.
: java.lang.ClassNotFoundException: Failed to load class for data source: com.databricks.spark.csv.
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.lookupDataSource(ResolvedDataSource.scala:67)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:87)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:114)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:104)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    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)
Caused by: java.lang.ClassNotFoundException: com.databricks.spark.csv.DefaultSource
    at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$$anonfun$4$$anonfun$apply$1.apply(ResolvedDataSource.scala:60)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$$anonfun$4$$anonfun$apply$1.apply(ResolvedDataSource.scala:60)
    at scala.util.Try$.apply(Try.scala:161)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$$anonfun$4.apply(ResolvedDataSource.scala:60)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$$anonfun$4.apply(ResolvedDataSource.scala:60)
    at scala.util.Try.orElse(Try.scala:82)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.lookupDataSource(ResolvedDataSource.scala:60)
    ... 14 more

提前感谢伙计们。

Answer 1

使用 Spark CSV，您可以读取文本文件并使用delimiter选项设置分隔符：

df = sqlContext.read 
   .format('com.databricks.spark.csv') 
   .options(header='false', delimiter='|') 
   .load(path)

可以使用schema方法设置架构/名称：

sqlContext.read.schema(schema)

其中架构是StructType：

schema = StructType([
    StructField("A", StringType(), True), StructField("B", StringType(), True)])

或致电toDF：

df.toDF(['A','B'])