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为Spark安装正确的python版本(Install the right python version for Spark)

 我在我的机器上使用Python 2.7.6  
$ python --version
Python 2.7.6
 
 我在我的机器上Spark 1.1.0依赖于Python 2.7.6。 如果我执行：  
user@user:~/bin/spark-1.1.0$ ./bin/pyspark
 
 我明白了  
Python 2.7.6 (default, Mar 22 2014, 22:59:56) 
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
.
.
.
 
 今天我安装了Spark 1.3.1的新预构建版本（我不知道为什么，但是依赖于python 2.7.5）。 如果我现在为新版本执行相同的命令：  
user@user:~/bin/spark-1.3.1-bin-hadoop2.6$ ./bin/pyspark
 
 我找回了旧的Python版本  
Python 2.7.5 (default, Jun 18 2014, 09:37:37) 
[GCC 4.6.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
.
.
.
 
 主要的区别在于，在较旧的Spark版本中，我可以执行import numpy ，而不是新的。  
 我在.bashrc文件中创建了Python的下一个路径：  
export PYTHONPATH=$PYTHONPATH:usr/lib/python2.7
 
 我没有找到在Python中区分版本2.7.6和2.7.5的方法，因此我不知道存储python 2.7.6的地方（缺少命令find ）。 

I use Python 2.7.6 on my machine 
$ python --version
Python 2.7.6
 
I have on my machine Spark 1.1.0 depended from Python 2.7.6. If I execute: 
user@user:~/bin/spark-1.1.0$ ./bin/pyspark
 
I get  
Python 2.7.6 (default, Mar 22 2014, 22:59:56) 
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
.
.
.
 
Today I install the new pre-built version of Spark 1.3.1 (I don't know why, but depended from python 2.7.5). If I execute now the same command for the new version: 
user@user:~/bin/spark-1.3.1-bin-hadoop2.6$ ./bin/pyspark
 
I get back the older Python version 
Python 2.7.5 (default, Jun 18 2014, 09:37:37) 
[GCC 4.6.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
.
.
.
 
The main difference is that in the older Spark version I can execute import numpy, in the new one not. 
I created the next path for Python in .bashrc file: 
export PYTHONPATH=$PYTHONPATH:usr/lib/python2.7
 
I don't find the way to distinguish between version 2.7.6 and 2.7.5 in Python, thus I don't know a place, where python 2.7.6 is stored (command find is missing). 

原文：https://stackoverflow.com/questions/29943042

更新时间：2023-03-28 13:03

最满意答案

 每次数据更改时，都应使用notifyDataSetChanged()通知适配器中的这些更改  
 
  notifyDataSetChanged（）：通知附加的观察者基础数据已更改，反映数据集的任何视图都应刷新自身。  
 
 喜欢这个：  
adapter = new CoursesAdapter(players);
myList.setAdapter(adapter);
....
....
adapter.notifyDataSetChanged();

Every time that your data change you should notify those changes in the adapter, using notifyDataSetChanged() 
 
 notifyDataSetChanged(): Notifies the attached observers that the underlying data has been changed and any View reflecting the data set should refresh itself. 
 
like this:  
adapter = new CoursesAdapter(players);
myList.setAdapter(adapter);
....
....
adapter.notifyDataSetChanged();

为Spark安装正确的python版本(Install the right python version for Spark)

最满意答案

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