python networkx visualization

‘)plt.legend(loc=‘upper right‘)plt.xticks((0,2,4,6,8,10),(‘1月‘,‘3月‘,‘5月‘,‘7月‘,‘9月‘,‘11月‘))plt.xlabel(‘月份‘)plt.ylabel(‘XX事件数‘)plt.grid(x1)plt.show()5. Read the hourly frequency data, draw the overlapping bar chartdata_hour2015 = pd.read_csv(‘data_hour2015.txt‘)data_hour2016 = pd.read_csv(‘data_hour2016.txt‘)plt.figure(figsize=(10, 6))data_hour2015[‘nums‘].T.plot.bar(color=‘g‘,alpha=0.6,label=‘2015年‘)data_hour2016[‘nums‘].T.plot.bar(color=‘r‘,alpha=0.4,label=‘2016年‘)plt.xlabel(‘小时‘)plt.ylabel(‘XX事

Introduction URL: Https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebookImport Matplotlib.pyplot as PltImport Seaborn as SNSImport Pandas as PDImport data:Iris=pd.read_csv (' E:\\data\\iris.csv ')Iris.head ()To make a histogram:Plt.hist (iris[' SEPALLENGTHCM '],bins=15)Plt.xlabel (' SEPALLENGTHCM ')Plt.ylabel (' quantity ')Plt.title (' Distribution of SEPALLENGTHCM ')Plt.show ()To make a scatter plot:But such a diagram does

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-7-8@author:guaguastd@name:plot_frequencies_words.py" if _ _name__ = = ' __main__ ': #import JSON # import Counter from collections Import Counter # import Search From search Import Search_for_tweet # import visualize from visualize import visualize_for_frequencies # import login, see http://blog.csdn.net/guaguastd/article/details/31706155 from login import Twitter_login # ge T the tw

the volumeImport NumPy as NPX=np.random.randint (1,100,100) (generates 100 random integers from 1 to 100)BINS=[0,10,20,30,40,50,60,70,80,90,100] (Specify the range of divisions)Plt.hist (X,bins) (the number of conforming data in this range according to the specified range)Plt.hist (x,bins,rwidth=0.7) (Make bar chart spacing)Plt.show ()To plot a scatter plot:X=np.random.randint (1,10,50) (generates random numbers)Y=np.random.randint (1,10,50)Plt.scatter (x, y) (scatter plot, or multiple sets of

Matplotlib Brief Description: Matplotlib is a desktop drawing package for creating high-quality charts (mainly 2D). The project was launched by John Hunter in 2002 to build a MATLAB-style drawing interface for Python. If you use a Python IDE, such as Pycharm,matplotlib, you also have interactive features such as zoom and pan. It not only supports many different GUI backend on various operating systems, but

How the data is clear, accurate, interactive, and visualized through data, will achieve these effects.Libraries needed for Python visualization: pandas,matplotlibRefer to the official tutorial: http://matplotlib.org/index.htmlScatter plot:Plot function: Plot (x, Y, '. ', Color (r,g,b))X, y,x axis and y-axis sequence; '. ', the size of the midpoint of the scatter plot; Color:rgb definition#-*-coding:utf-8-*-

(filename):"" "Load single batch of Cifar" "with open (filename,' RB ')As F:datadict = P.load (f) X = datadict[' Data '] Y = datadict[' Labels '] X = X.reshape (10000,3,32,y = Np.array (y)Return X, YDefLoad_cifar_labels(filename):with open (filename,' RB ')As F:lines = [xFor XIn F.readlines ()] Print (lines)if __name__ = ="__main__": Load_cifar_labels ("/data/cifar-10-batches-py/batches.meta") imgx, imgy = Load_cifar_batch ("/data/cifar-10-batches-py/data_batch_1")Print Imgx.shapePrint"Saving Pi

Python folders like data visualization Import Matplotlib.pyplot as Plt Import Matplotlib.image as Mpimg Import NumPy as NP Import Urllib2 Import Urllib Import OS Import Shutil Subdir= "/7" Homedir = OS.GETCWD () + subdir # "/home/haoyou/dev/last_caffe_with_stn/myprojects/spn-mnistcluttered/mnist-cluttered/" +subdir Import OS def walk_dir (dir,fileinfo,topdown=true): For

For example, the Python Tornado framework for data visualization tutorial, pythontornado Extended modules used Xlrd: An extension tool for reading Excel in Python. You can read a specified form or cell.Installation is required before use.: Https://pypi.python.org/pypi/xlrdDecompress the package and cd it to the decompressed directory. Execute

Python development [module]: CSV file data visualization,CSV Module 1. CSV file format To store data in a text file, the simplest way is to write data into a file as a series of comma-separated values (CSV). Such a file becomes a CSV file, as shown below: AKDT,Max TemperatureF,Mean TemperatureF,Min TemperatureF,Max Dew PointF,MeanDew PointF,Min DewpointF,Max Humidity, Mean Humidity, Min Humidity, Max Sea Le

After entering http://127.0.0.1:8050/in Google Chrome, enter to see visual results#-*-Coding:utf-8-*-"" "Created on Sun Mar one 10:16:43 2018@author:administrator" "" Import Dashimport Dash_core_componen TS as Dccimport dash_html_components as Htmlapp = Dash. Dash () App.layout = html. DIV (children=[ HTML. H1 (children= ' Dash tutorials '), DCC. Graph ( id= ' example ', figure={ ' data ': [ {' x ': [1, 2, 3, 4, 5], ' Y ': [9, 6, 2, 1, 5], ' type ':

Spectral Clustering Introduction: This blog for the introduction of spectral clustering, including formula derivation is quite in place, then the class ppt is cut this figure, so can understand the words pretty good. http://www.cnblogs.com/FengYan/archive/2012/06/21/2553999.html Algorithm python implementation: For the derivation of the formula what the individual understanding is not very deep, the following directly say the implementation of the a

When writing a program today, an interesting phenomenon was found. When the import statement is executed, a __pycache__ file is generated in the script directory after it is run . so I made the following summary explanation:I. Python basic operating mechanismPython programs run without the need to compile into binary code, and directly from the source to run the program, in short, the Python interpreter wil

This article mainly introduces examples of Python Tornado framework to achieve data visualization of the tutorial, Tornado is an asynchronous development framework for high man, the need for friends can refer to the Expansion module used XLRD: In the Python language, read the extension tool for Excel. You can implement the specified form, read the specified ce

(types): Length=0ifLength Len (area_index): forArea,timesinchZip (area_index,post_times): Data= { 'name': Area,'Data': [Times],'type': Types}yieldData Length+ = 1 for in Data_gen ('column'): print(i) for in Data_gen ('column')]charts.plot (series,show=' ) inline ', Options=dict (title=dict (text=' Hangzhou Post Data statistics- Wang ')))Final Run Result:Summarize the points of knowledge:1, the introduction and use of charts module;2, the list of append () function use;3, COU

Continue with the previous example: http://blog.51cto.com/lavenliu/2126344Look at the example of the plural in the front, add the __str__ attribute here,class Complex: def __init__(self, real, imag): self.real = read self.imag = imag def __add__(self, other): return Complex(self.real + other.real, self.imag + other.imag) def __sub__(self, other): return Complex(self.real - other.read, self.imag - other.imag) def __str__(self): if self.imag >= 0:

that the effect is particularly poor, all the back from the Baidu Encyclopedia on a number of role list down, that this and the original text for comparison, to achieve the role of extraction.2. Merging of roles with the same chapter When you are writing a reptile, you can pull the characters while you crawl.3. Use the data in step 2 for the matrix calculation Read the database and use the keyword-sharing matrix algorithm to build the matrix.Algorithm please refer to

Reference: http://www.cnblogs.com/denny402/p/5088399.htmlThis section configures the Python interface to encounter a lot of pits.1, I use anaconda to configure the Python environment, in the Caffe root directory to join the Python folder to the environment variable this step encounteredQuestion, I didn't know how to add the export after I opened it with that comm

] = uin[::-1]; U[:,-1] = Uin[::-1,0]v = Np.zeros ((vin.shape[0],vin.shape[1]+1), np.float64) v[:,0:-1] = vin[::-1]; V[:,-1] = vin[::-1,0]longitudes.append (360.); longitudes = Np.array (longitudes) lons, lats = Np.meshgrid (longitudes,latitudes) 5) Set and draw the diagram m = Basemap (resolution= ' C ', projection= ' ortho ', lat_0=60.,lon_0=-60.) FIG1 = Plt.figure (figsize= (8,10)) ax = Fig1.add_axes ([0.1,0.1,0.8,0.8]) Clevs = Np.arange (960,1061,5) x, y = m (lons, lats) Parallels = Np.arange

the internal relationship of data. The interactive GUI is a good choice for interactive support.MayaviThis is a 3D graphics toolkit based on the open source C + + graphics library VTK. can be integrated into Ipython for interactive use.Other librariesOther libraries or applications include: PYQWT, Veusz, Gnuplotpy, Biggles, and so on, and large libraries are developing to web-based technologies and moving away from desktop graphics technology.The future of graphical toolsWeb-based technology (s