foundations for analytics with python

1Python Socket Base Server-Foundations of Python sockets2 3A server that provides a TCP link service through a Python socket module can be divided into 4a step,41, set up the socket object5s =Socket.socket (socket.af_inet, socket. SOCK_STREAM)62, set the socket option (optional)7S.setsockopt (socket. Sol_socket, SOCKET. SO_REUSEADDR, 1)8 after a server process te

Foundations of Python Network programming third Edition downloadhttp://www.amazon.com/Foundations-Python-Network-Programming-Brandon/dp/1430258543This book was published at the end of 2014, based on the latest version of python3.4.Book Source LinkHttps://github.com/brandon-rhodes/fopnpDirectoryChapter 1:introduction to

',Normed=True,histtype= "bar" ,=0.5) Axitset_xlabel ( "values" ) . Set_title) Plt . savefig (,dpi150) Plt. Show () This one's not written as a function.ImportNumPyAsNpImportMatplotlib.PyplotAsPltx=Np.Linspace(0,10,1200)Y=Np.Sin(X)+1Z=Np.Cos(X**2)+1#设置图像大小Plt.Figure(Figsize=(8,6))Plt.Plot(X,Y,Label=' $\cosx+1$ ',Color="Red",LineWidth=2)Plt.Plot(X,Z,Label=' $\cos x^2+1$ ')#设置x轴Plt.Xlabel("Time (s)")Plt.ylabel (plttitle () pltylim (0,2 plt. () plt. ( "5.png" ,dpi= 120) plt. ()

application development, covering Python data types and structures, data visualization with Matplotlib, Financial time series data processing, high performance input/output operations, high-performance Python technology and libraries, multiple mathematical tools required in finance, random number generation and stochastic process simulations, Python statistics a

1 #-*-coding:utf-8-*-2 ImportSYS3 Import Time4 5 defcheck ():6p =07 whileTrue:8f = open ("Log.txt","r+")9F1 = open ("Result.txt","A +")Ten One #Positioning Pointers A F.seek (p, 0) - -FileList =F.readlines () the iffilelist: - forLineinchfilelist: - #working with line content -F1.write (line*10) + Print Line - + #get the current location of the file Ap =F.tell () at Print 'Now P', P - f.close () - f1.close () -T

DrawingPlt.axis ([0,5,0,20]): Coordinate rangePlt.title (' PLOT ', fontsize=20): Picture titlePlt.xlable (' Row '): Row headerPlt.ylable (' COL '): column headerPlt.text (' text '): Write text in the specified coordinatesPlt.grid (True): Draw MeshPlt.plot (x, y): Line chartPlt.plot (x, Y, '-'): Line chartPlt.plot (x, y, ' o '): Scatter plotPlt.hist (x,bins=20): HistogramPlt.bar (x, y): Bar chartPlt.bar (x,y1,0.3,color= ' B ');p Lt.bar (x+0.3,y2,0.3,color= ' G '): Multi-sequence bar graphDat.plot

] 2.7.4 Indent 22 123456789101112131415 >>> A = 4>>> If a > 3:... if a ... print ("I ' M Four").. else:... print ("I ' m a little number")...I ' M four>>> If a > 3:... if a ... print ("I ' M Four").. else:... print ("I ' m a big number")...I ' M four 2.8 IPython 232.8.1 IPython Shell 232.8.2 IPython Qt-console 242.9 PyPI Warehouse--python Package Index 252.1 + Python IDE 262

Based on Python, there are several scientific and data analysis libraries, which are very convenient to use. Combined with OpenStack (http://www.openstack.org), RabbitMQ (http://www.rabbitmq.com), celery (HTTP/ www.celeryproject.org) can create an analytics platform for real-time data.OpenStack is a python-based cloud computing platform that enables the schedulin

=" ahr0chm6ly9tbwjpei5xbg9nby5jbi9tbwjpel9w "/>After execution, the certificate can be automatically created and saved, and the corresponding Nginx configuration file will be modified, which is very convenient.You can now log in and use Jupyter notebook by https://your.domain.name/access, entering the password you configured earlier. Raptor Source: http://blog.csdn.net/raptor/article/details/78035782 This article is from the "Marco Linux Training" blog, so be sure to keep this

This script reads SQL Server, just given the table name or view name, and if there is data, it will output each data distribution map that meets the requirements for each field.#-*-coding:utf-8-*-#python 3.5.0#Exploratory Analytics (exploratory data Analysis,eda)__author__='HZC'ImportMathImportSQLAlchemyImportNumPy as NPImportPandas as PDImportMatplotlib.pyplot as PltclassEDA:def __init__(self,d): Self.engi

Recently, there is a need to compare the time of a single activity load of two versions before and afterIn the Android log we can see similar Info/activitymanager (2486): Displayed activity com.teleca/. contextmenuactivity:240 MS (total 41289 ms) log, which is the load time of the activityFirst get the log through adb logcat > xx.txt, and then analyze the log with the following code:#-*-coding:utf-8-*-#analyze Android adb logcat log extract activity displayed logImportOSImportRedefAndroiddisplay

) Print(e)defHandleline (logline): Susptmp= Logline.split (" ") ifLen (susptmp) > 2: ifSUSPTMP[6] = =Keypage[0]:delSusptmp[1:3] delSusptmp[7:] delSusptmp[2:6] ifLen (susptmp) > 2: IP=Susptmp[0] time1= ((Susptmp[1].split (':', 1)) [1]) [0:5] Date= DateFormat ((Susptmp[1].split (':', 1) ) [0] _, _, User, _, PASSD, _= Re.split (r'[= "]', susptmp[2]) #Print (IP + '---', time1 + '---', date + '---', user + '---', PASSD + '---')sql =

], cmap=plt.cm.gray_r, interpolation=' nearest ') 1 Svc.fit (digits.data[1:1790], digits.target[1:1790]) SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001, kernel=‘rbf‘, max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) 123456789101112131415 Import Matplotlib.pyplotAs Plt%matplotlib InlinePlt.subplot (321)Plt.imshow (digits.images[1791], Cmap=plt.cm.gray_r, int

#!/usr/bin/env python#coding =utf-8Import MySQLdbImport RedisImport OSImport Socket,fcntl,struct#print Redis._file_def get_ip_address (ifname):s = socket.socket (socket.af_inet, socket. SOCK_DGRAM)Return Socket.inet_ntoa (Fcntl.ioctl (S.fileno (),0x8915, # SIOCGIFADDRStruct.pack (' 256s ', ifname[:15])) [20:24])ip = get_ip_address (' eth0 ')R = Redis. Redis (host= ' 192.168.100.89 ', port=6379,db=1)info = R.info ()WR = open ("A.txt", ' W ')For key in

Initializing an array arr = Np.array ([[1,2],[3,4]])arr = Np.array ([[1,2],[3,4]], Dtype=complex)arr = Np.zeros ((3,4))arr = Np.ones (())arr = Np.arange (4,10). Reshape (2,3)arr = Np.linspace (0,1,6) #[0.0,0.2,0.4,0.6,0.8,1.0]arr = Np.random.random (3)arr = Np.random.random ((5,2)) Array built-in functions Type (arr): Numpy.ndarrayArr.dtype:int32, float64 ...Arr.ndim:2Arr.size:4Arr.shape: (2l,2l)Arr.itemsize:4 Array calculation function Arr+1: Elements andArr*2: