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框并设置一个标题，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 的文档我没有找到如何设置标题和分隔符，或将数据集的每一列的名称作为 pandas。知道如何使用 PySparkSQL 为每一列命名吗？
更新:
从@CafeFeed 我尝试了以下方法:
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，我遇到了这个异常:
---------------------------------------------------------------------------
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
提前谢谢你们。
请您参考如下方法:
与 Spark CSV您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 方法设置模式/名称:
sqlContext.read.schema(schema)
其中 schema 是一个 StructType:
schema = StructType([
StructField("A", StringType(), True), StructField("B", StringType(), True)])
或者调用toDF:
df.toDF(['A','B'])
