blocks|key|4453216|text|results.show(20,+false)不会截断。检查source|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4453217|20是在不带任何参数的情况下调用show()时显示的默认行数。|4453218|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/apache/spark/blob/d83c2f9f0b08d6d5d369d9fae04cdb15448e7f0d/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala#L395^0|0|N|U|6|0|0|0|2|G|6|0^^$0|@$1|2|3|4|5|6|7|P|8|@$9|Q|A|R|B|C]]|D|@$9|S|A|T|1|U]]|E|$]]|$1|F|3|G|5|6|7|V|8|@$9|W|A|X|B|C]|$9|Y|A|Z|B|C]]|D|@]|E|$]]|$1|H|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|I|$J|$5|K|L|M|E|$N|O]]]]

<code>results.show(20, false)</code> will not truncate. Check the <a href="https://github.com/apache/spark/blob/d83c2f9f0b08d6d5d369d9fae04cdb15448e7f0d/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala#L395" rel="noreferrer">source</a>
<code>20</code> is the default number of rows displayed when <code>show()</code> is called without any arguments.

blocks|key|4453118|text|如果放入results.show(false)，结果将不会被截断|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4453119|entityMap^0|4|J|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

If you put <code>results.show(false)</code> , results will not be truncated

blocks|key|4453171|text|下面的代码有助于在不截断每列的情况下查看所有行|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4453172|df.show(df.count(),+False)|code-block|syntax|javascript|4453173|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Below code would help to view all rows without truncation in each column

<pre><code>df.show(df.count(), False)
</code></pre>

blocks|key|4453318|text|results.show(20,+False)还是results.show(20,+false)，这取决于您是在Java/Scala/Python上运行它|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4453319|entityMap^0|0|N|P|N|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]|$9|K|A|L|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|M|8|@]|D|@]|E|$]]]|G|$]]

<code>results.show(20, False)</code> or <code>results.show(20, false)</code>
depending on whether you are running it on Java/Scala/Python

blocks|key|4453338|text|results.show(false)将向您显示完整的列内容。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4453339|显示方法默认限制为20，在false前添加一个数字将显示更多行。|4453340|entityMap^0|0|J|0|D|5|0^^$0|@$1|2|3|4|5|6|7|J|8|@$9|K|A|L|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|H|3|-4|5|6|7|P|8|@]|D|@]|E|$]]]|I|$]]

<code>results.show(false)</code> will show you the full column content. 

Show method by default limit to 20, and adding a number before <code>false</code> will show more rows.

blocks|key|4453228|text|在Scala中，results.show(20,false)为我做到了这一点。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4453229|entityMap^0|8|M|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

<code>results.show(20,false)</code> did the trick for me in Scala.

blocks|key|4453633|text|以下答案适用于Spark+Streaming应用程序。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4453634|通过将"truncate“选项设置为false，您可以告诉输出接收器显示完整的列。|4453635|val+query+=+out.writeStream
++++++++++.outputMode(OutputMode.Update())
++++++++++.format("console")
++++++++++.option("truncate",+false)
++++++++++.trigger(Trigger.ProcessingTime("5+seconds"))
++++++++++.start()|code-block|syntax|javascript|4453636|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

The following answer applies to a Spark Streaming application.

By setting the "truncate" option to false, you can tell the output sink to display the full column.

<pre><code>val query = out.writeStream
 .outputMode(OutputMode.Update())
 .format("console")
 .option("truncate", false)
 .trigger(Trigger.ProcessingTime("5 seconds"))
 .start()
</code></pre>

blocks|key|4453498|text|尝试df.show(20，False)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4453499|请注意，如果您不指定要显示的行数，它将显示20行，但将执行所有数据帧，这将花费更多时间！|4453500|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Try
df.show(20,False)
Notice that if you do not specify the number of rows you want to show, it will show
20 rows but will execute all your dataframe which will take more time !

blocks|key|4453272|text|尝试执行以下命令：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4453273|df.show(df.count())|code-block|syntax|javascript|4453274|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

try this command :

<pre><code>df.show(df.count())
</code></pre>

blocks|key|4453267|text|在Databricks中，您可以以表格格式可视化数据帧。使用以下命令：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4453268|display(results)|code-block|syntax|javascript|4453269|它看起来就像|4453270|​|4453271|📷|atomic|4453272|4453273|entityMap|0|LINK|mutability|MUTABLE|url|https://databricks.com/try-databricks|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/45c7e1441291d962ce2879b546b9f021.png|imageAlt^0|1|A|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@]|9|@$A|15|B|16|1|17]]|C|$]]|$1|D|3|E|5|F|7|18|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|19|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|1A|8|@]|9|@]|C|$]]|$1|M|3|N|5|O|7|1B|8|@]|9|@$A|1C|B|1D|1|1E]]|C|$]]|$1|P|3|L|5|6|7|1F|8|@]|9|@]|C|$]]|$1|Q|3|-4|5|6|7|1G|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|Z|U|10|C|$11|12|13|-4]]]]

Within <a href="https://databricks.com/try-databricks" rel="nofollow noreferrer">Databricks</a> you can visualize the dataframe in a tabular format. With the command:

<pre><code>display(results)
</code></pre>

It will look like

<a href="https://i.stack.imgur.com/g0Svi.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/g0Svi.png" alt="enter image description here"></a>

blocks|key|4453339|text|在c#中，Option("truncate",+false)不会截断输出中的数据。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4453340|StreamingQuery+query+=+spark
++++++++++++++++++++.Sql("SELECT+*+FROM+Messages")
++++++++++++++++++++.WriteStream()
++++++++++++++++++++.OutputMode("append")
++++++++++++++++++++.Format("console")
++++++++++++++++++++.Option("truncate",+false)
++++++++++++++++++++.Start();|code-block|syntax|javascript|4453341|entityMap^0|5|P|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

In c# <code>Option("truncate", false)</code> does not truncate data in the output.

<pre><code>StreamingQuery query = spark
 .Sql("SELECT * FROM Messages")
 .WriteStream()
 .OutputMode("append")
 .Format("console")
 .Option("truncate", false)
 .Start();
</code></pre>

blocks|key|4453452|text|在pyspark中尝试过|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4453453|df.show(truncate=0)|code-block|syntax|javascript|4453454|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Tried this in pyspark
<pre><code>df.show(truncate=0)
</code></pre>

blocks|key|4453284|text|我使用插件Chrome扩展运行得很好：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4453285|[https://userstyles.org/styles/157357/jupyter-notebook-wide][1]|offset|length|4453286|entityMap|0|LINK|mutability|MUTABLE|url|https://userstyles.org/styles/157357/jupyter-notebook-wide][1]^0|0|0|1R|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

I use the plugin Chrome extension works pretty well: 

[<a href="https://userstyles.org/styles/157357/jupyter-notebook-wide][1]" rel="nofollow noreferrer">https://userstyles.org/styles/157357/jupyter-notebook-wide][1]</a>

blocks|key|4453292|text|在scala中试试：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4453293|df.show(df.count.toInt,+false)|code-block|syntax|javascript|4453294|show方法接受整数和布尔值，但df.count返回Long...so类型转换是必需的|4453295|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Try this in scala:

<pre><code>df.show(df.count.toInt, false)
</code></pre>

The show method accepts an integer and a Boolean value but df.count returns Long...so type casting is required

I am using spark-csv to load data into a DataFrame. I want to do a simple query and display the content:

<pre><code>val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load("my.csv")
df.registerTempTable("tasks")
results = sqlContext.sql("select col from tasks");
results.show()
</code></pre>

The col seems truncated:

<pre><code>scala&gt; results.show();
+--------------------+
| col|
+--------------------+
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-06 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:21:...|
|2015-11-16 07:21:...|
|2015-11-16 07:21:...|
+--------------------+
</code></pre>

How do I show the full content of the column?

How to show full column content in a Spark Dataframe?

我正在使用spark-csv将数据加载到DataFrame中。我想做一个简单的查询并显示内容：val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load("my.csv")df.registerTempTable("tasks")results = sqlContext.sql(

问如何在Spark Dataframe中显示完整的列内容？
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问如何在Spark Dataframe中显示完整的列内容？
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