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搜索[python]，共10000篇文章

使用C语言扩展Python(三)

上一篇中我们已经了解如何在Python程序和C模块之间进行值的相互传递，现在我们来进入实作阶段，看看如何将一个C语言开发的开源mp3编解码库LAME包装为一个Python下可以使用的扩展模块。

2018-01-06

使用C语言扩展Python(一)

开发环境：Ubuntu9.10,python2.6,gcc4.4.1 1,ubuntu下的python运行包和开发包是分开的，因此需要在新利得里面安装python-all-dev,从而可以在代码中引用python

2018-01-06

深入理解Python字符编码

不论你是有着多年经验的 Python 老司机还是刚入门 Python 不久，你一定遇到过UnicodeEncodeError、UnicodeDecodeError 错误，每当遇到错误我们就拿着 encode

2018-01-05

理解Python中的装饰器

这个例子仅用于演示，并没有考虑foo带有参数和有返回值的情况，完善它的重任就交给你了：）上面这段代码看起来似乎已经不能再精简了，Python于是提供了一个语法糖来降低字符输入量。

2018-01-04

用PDB库调试Python程序

命令行启动目标程序，加上-m参数，这样调用myscript.py的话断点就是程序的执行第一行之前 python -m pdb myscript.py 2.

2018-01-04

本文转载自http://www.linuxtone.org/thread-29256-1-1.html 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 #coding:utf-8 #by_wangyi #by:QQ917611 #QQ群：251289157 import psutil import time import os import sys import re stats = [ 0 , 1 ] stoptimes = 2 if len (sys.argv)> 1 : interface = sys.argv[ 1 ] else : interface = 'eth0' class Monitor: def __init__( self ,user_uid,stoptime): self .user_uid = user_uid self .stoptime = stoptime if user_uid = = 0 : print "请使用root用户运行此脚本" exit() def meminfo( self ,used_vm,free_vm,buffers,cached): total = psutil.virtual_memory().total / 1024 / 1024 used = used_vm - (buffers + cached) free = total - used print "totalmem:%sM" % total print "usedmem:%sM" % used print "freemem:%sM" % free def diskinfo( self ): list = [] for i in psutil.disk_partitions(): list .append(i[ 1 ]) for k in range ( len ( list )): total = "%-15s分区\ttotal:%s" % ( list [k],psutil.disk_usage( list [k]).total / 1024 / 1024 / 1024 ) used = "used:%s" % (psutil.disk_usage( list [k]).used / 1024 / 1024 / 1024 ) free = "free:%s" % (psutil.disk_usage( list [k]).free / 1024 / 1024 / 1024 ) print "%sG\t%sG\t%sG" % (total,used,free) def cpuinfo( self ): with open ( '/proc/loadavg' )asf: loadavg = f.read() print "5分钟cpuload:%s" % (loadavg.split()[ 0 ]) print "10分钟cpuload:%s" % (loadavg.split()[ 1 ]) print "15分钟cpuload:%s" % (loadavg.split()[ 2 ]) print "当前运行proc:%s" % (loadavg.split()[ 3 ].split( '/' )[ 0 ]) print "最后运行pid:%s" % (loadavg.split()[ 4 ]) print '当前cpu%s' % (psutil.cpu_percent()) def flowinfo( self ): f = open ( '/proc/net/dev' , 'r' ).readlines() for i in f: if re.search(interface,i): rx = i.split( ':' )[ 1 ].split()[ 0 ] tx = i.split()[ 8 ] stats[ 0 ] = rx stats[ 1 ] = tx used_vm = psutil.virtual_memory().used / 1024 / 1024 free_vm = psutil.virtual_memory().free / 1024 / 1024 buffers = psutil.virtual_memory().buffers / 1024 / 1024 cached = psutil.virtual_memory().cached / 1024 / 1024 if __name__ = = '__main__' : user_uid = os.geteuid() stoptime = 2 task = Monitor(user_uid,stoptime) print "servermeminfo：\n" task.meminfo(used_vm,free_vm,buffers,cached) print "==================================" print "serverdiskinfo:\n" task.diskinfo() print "===================================" print "servercpuinfo:\n" task.cpuinfo() print "===================================" print "serverflowinfo：\n" task.flowinfo() RX_one = float (stats[ 0 ]) TX_one = float (stats[ 1 ]) time.sleep(stoptimes) task.flowinfo() RX_two = float (stats[ 0 ]) TX_two = float (stats[ 1 ]) RX_rate = round ( float (RX_two - RX_one) / 1024 , 2 ) TX_rate = round ( float (TX_two - TX_one) / 1024 , 2 ) print time.strftime( "%Y-%m-%d%H:%M:%S" ), 'RXbytes=' ,RX_rate, 'KB' print time.strftime( "%Y-%m-%d%H:%M:%S" ), 'TXbytes=' ,TX_rate, 'KB'

2017-12-30

[Spark][Python]DataFrame select 操作例子

[Spark][Python]DataFrame中取出有限个记录的例子的继续 In [4]: peopleDF.select("age") Out[4]: DataFrame[age: bigint

2017-12-18

[Spark][Python]RDD flatMap 操作例子

RDD flatMap 操作例子： flatMap，对原RDD的每个元素(行)执行函数操作，然后把每行都“拍扁” [training@localhost ~]$ hdfs dfs -put cats.txt [training@localhost ~]$ hdfs dfa -cat cats.txt Error: Could not find or load main class dfa [training@localhost ~]$ hdfs dfs -cat cats.txt The cat on the mat The aardvark sat on the sofa mydata=sc.textFile("cats.txt") mydata.count() Out[14]: 2 mydata.take(2) Out[15]: [u'The cat on the mat', u'The aardvark sat on the sofa'] myflatdata=mydata.flatMap(lambda line: line.split(' ')) myflatdta.count() Out[19]: 11 myflatdata.take(2) Out[20]: [u'The', u'cat'] myflatdata.take(11) Out[21]: [u'The', u'cat', u'on', u'the', u'mat', u'The', u'aardvark', u'sat', u'on', u'the', u'sofa'] 本文转自健哥的数据花园博客园博客，原文链接：http://www.cnblogs.com/gaojian/p/7593885.html，如需转载请自行联系原作者

2017-12-17

为“Python将纳入高考”降温

从2018年起浙江省信息技术教材编程语言将会从VB 更换为Python。这意味着Python确定进入浙江省信息技术高考。”

2017-12-12

[Spark][Python]Spark Join 小例子

[training@localhost ~]$ hdfs dfs -cat people.json {"name":"Alice","pcode":"94304"} {"name":"Brayden","age":30,"pcode":"94304"} {"name":"Carla","age":19,"pcoe":"10036"} {"name":"Diana","age":46} {"name":"Etienne","pcode":"94104"} [training@localhost ~]$ hdfs dfs -cat pcodes.json {"pcode":"10036","city":"New York","state":"NY"} {"pcode:"87501","city":"Santa Fe","state":"NM"} {"pcode":"94304","city":"Palo Alto","state":"CA"} {"pcode":"94104","city":"San Francisco","state":"CA"} sqlContext = HiveContext(sc) peopleDF = sqlContext.read.json("people.json") sqlContext = HiveContext(sc) pcodesDF = sqlContext.read.json("pcodes.json") mydf001=peopleDF.join(pcodesDF,"pcode") mydf001.limit(5).show() +-----+----+-------+----+---------------+-------------+-----+ |pcode| age| name|pcoe|_corrupt_record| city|state| +-----+----+-------+----+---------------+-------------+-----+ |94304|null| Alice|null| null| Palo Alto| CA| |94304| 30|Brayden|null| null| Palo Alto| CA| |94104|null|Etienne|null| null|San Francisco| CA| +-----+----+-------+----+---------------+-------------+-----+ 本文转自健哥的数据花园博客园博客，原文链接：http://www.cnblogs.com/gaojian/p/7630003.html，如需转载请自行联系原作者

2017-12-10

Spark之导出PMML文件（Python）

本文将介绍如何在Spark中导出PMML文件（Python语言）。首先我们需要安装jpmml-sparkml-package。

2017-12-03

Python开篇——简介、pip和conda

Python常用库 Python安装 pip和conda 那我还是来介绍一下它（凑一波字数）吧。

2017-11-28

python基础教程函数参数

python里有很多的内置函数给我们的工作带来了很多发便利，在我们实现某些功能或者优化代码的时候，可以自己定义一个函数，同时我们在定义一个类的时候也会用到函数的一些知识去构造一个方法，这里就涉及到一些关于函数的基础知识和函数传参的知识

2017-11-27

Python中列表list常见操作

主要涉及知识点列表是我们python里面最常见的数据类型，我们主要有以下的操作。

2017-11-26

python文件基础之（文件操作）

在之前学习了python的列表、元组、集合等知识，接下来将python的文件相关的知识做一总结和分析。

2017-11-26

python 多用户发邮件

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 #!/usr/bin/envpython #-*-coding:utf-8-*- #导入smtplib和MIMEText import smtplib from email.header import Header from email.mime.text import MIMEText def send_server(to_list,sub,content): #设置服务器，用户名、口令以及邮箱的后缀 mail_host = "smtp.163.com" mail_user = "w67300444@163.com" mail_pass = "1234556" mail_postfix = "163.com" me = mail_user + "<" + mail_user + "@" + mail_postfix + ">" msg = MIMEText(content) msg[ 'Subject' ] = sub #msg['From']=me msg[ 'From' ] = '%s<%s>' % (Header( '运维团队' , 'utf-8' ), 'w67300444@163.com' ) msg[ 'To' ] = to_list try : s = smtplib.SMTP() s.connect(mail_host) s.login(mail_user,mail_pass) s.sendmail(me,to_list,msg.as_string()) s.close() #print'1' return True except Exception,e: #print'2' print str (e) return False def send_to(): #发到用户的列表 mail_list = [ "buser.com" , "cuser.com" ] for i in range ( 0 , len (mail_list)): if send_server(mail_list[i], "ECS启动汇总" , "ECS启动完毕" ): print "发送成功" i = i + 1 ; else : print "发送失败" i = i + 1 ; if __name__ = = '__main__' : send_to() 本文转自小小三郎1 51CTO博客，原文链接：http://blog.51cto.com/wsxxsl/2068667，如需转载请自行联系原作者

2017-11-21

windows端部署python开发环境

应用场景在本地windows端，想开发python代码，或者运行python代码，需要在windows端安装python开发环境，来测试python代码，学习python。操作步骤 1.

2017-11-20

Java,Python,Scala比较（三）wordcount

本文并不分析wordcount的计算方法，而是直接给出代码，目的是为了比较Spark中Java，Python，Scala的区别。

2017-11-16

随机森林算法demo python spark

关键参数最重要的，常常需要调试以提高算法效果的有两个参数：numTrees，maxDepth。 numTrees（决策树的个数）：增加决策树的个数会降低预测结果的方差，这样在测试时会有更高的accuracy。训练时间大致与numTrees呈线性增长关系。 maxDepth：是指森林中每一棵决策树最大可能depth，在决策树中提到了这个参数。更深的一棵树意味模型预测更有力，但同时训练时间更长，也更倾向于过拟合。但是值得注意的是，随机森林算法和单一决策树算法对这个参数的要求是不一样的。随机森林由于是多个的决策树预测结果的投票或平均而降低而预测结果的方差，因此相对于单一决策树而言，不容易出现过拟合的情况。所以随机森林可以选择比决策树模型中更大的maxDepth。甚至有的文献说，随机森林的每棵决策树都最大可能地进行生长而不进行剪枝。但是不管怎样，还是建议对maxDepth参数进行一定的实验，看看是否可以提高预测的效果。另外还有两个参数，subsamplingRate，featureSubsetStrategy一般不需要调试，但是这两个参数也可以重新设置以加快训练，但是值得注意的是可能会影响模型的预测效果（如果需要调试的仔细读下面英文吧）。 We include a few guidelines for using random forests by discussing the various parameters. We omit some decision tree parameters since those are covered in the decision tree guide. The first two parameters we mention are the most important, and tuning them can often improve performance: （1）numTrees: Number of trees in the forest. Increasing the number of trees will decrease the variance in predictions, improving the model’s test-time accuracy. Training time increases roughly linearly in the number of trees. （2）maxDepth: Maximum depth of each tree in the forest. Increasing the depth makes the model more expressive and powerful. However, deep trees take longer to train and are also more prone to overfitting. In general, it is acceptable to train deeper trees when using random forests than when using a single decision tree. One tree is more likely to overfit than a random forest (because of the variance reduction from averaging multiple trees in the forest). The next two parameters generally do not require tuning. However, they can be tuned to speed up training. （3）subsamplingRate: This parameter specifies the size of the dataset used for training each tree in the forest, as a fraction of the size of the original dataset. The default (1.0) is recommended, but decreasing this fraction can speed up training. （4）featureSubsetStrategy: Number of features to use as candidates for splitting at each tree node. The number is specified as a fraction or function of the total number of features. Decreasing this number will speed up training, but can sometimes impact performance if too low. We include a few guidelines for using random forests by discussing the various parameters. We omit some decision tree parameters since those are covered in the decision tree guide. """ Random Forest Classification Example. """ from __future__ import print_function from pyspark import SparkContext # $example on$ from pyspark.mllib.tree import RandomForest, RandomForestModel from pyspark.mllib.util import MLUtils # $example off$ if __name__ == "__main__": sc = SparkContext(appName="PythonRandomForestClassificationExample") # $example on$ # Load and parse the data file into an RDD of LabeledPoint. data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt') # Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3]) # Train a RandomForest model. # Empty categoricalFeaturesInfo indicates all features are continuous. # Note: Use larger numTrees in practice. # Setting featureSubsetStrategy="auto" lets the algorithm choose. model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, numTrees=3, featureSubsetStrategy="auto", impurity='gini', maxDepth=4, maxBins=32) # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count()) print('Test Error = ' + str(testErr)) print('Learned classification forest model:') print(model.toDebugString()) # Save and load model model.save(sc, "target/tmp/myRandomForestClassificationModel") sameModel = RandomForestModel.load(sc, "target/tmp/myRandomForestClassificationModel") # $example off$ 模型样子： TreeEnsembleModel classifier with 3 trees Tree 0: If (feature 511 <= 0.0) If (feature 434 <= 0.0) Predict: 0.0 Else (feature 434 > 0.0) Predict: 1.0 Else (feature 511 > 0.0) Predict: 0.0 Tree 1: If (feature 490 <= 31.0) Predict: 0.0 Else (feature 490 > 31.0) Predict: 1.0 Tree 2: If (feature 302 <= 0.0) If (feature 461 <= 0.0) If (feature 208 <= 107.0) Predict: 1.0 Else (feature 208 > 107.0) Predict: 0.0 Else (feature 461 > 0.0) Predict: 1.0 Else (feature 302 > 0.0) Predict: 0.0 本文转自张昺华-sky博客园博客，原文链接：http://www.cnblogs.com/bonelee/p/7204096.html，如需转载请自行联系原作者

2017-11-15

python spark 随机森林入门demo

摘自：https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.tree.DecisionTree

2017-11-15

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