python interactive visualization

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

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

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

Matplotlib's official website address: http://matplotlib.org/ When using Python to do data processing, a lot of the data we don't seem to be intuitive, and sometimes it's graphically shown that it's easier to observe the changing characteristics of the data, and so on. Matplotlib is a Python 2D drawing library that generates publishing quality-level graphics in a variety of hard copy formats and cross-platf

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

[0, 1].plot (data[0], data[1]) OneAxs[1, 1].HIST2D (data[0], data[1]) A -Plt.show ()5. Add Text: Axis label, property label1 ImportMatplotlib.pyplot as Plt2 ImportNumPy as NP3Mu, sigma = 100, 154x = mu + sigma * NP.RANDOM.RANDN (10000)5 6 #The histogram of the data7N, bins, patches = plt.hist (x, Normed=true, facecolor='g', alpha=0.75)8 9 TenPlt.xlabel ('Smarts') OnePlt.ylabel ('probability') APlt.title ('Histogram of IQ') -Plt.text (. 025, R'$\mu=100,\ \sigma=15$')#Support Latex Format -Plt.ax

die import Dieimport pygal# 实例化两个Die类对象die_1 = Die()die_2 = Die(10) # 注意这里传入10results = []for roll_num in range(50000): result = die_1.roll() + die_2.roll() results.append(result) # 将结果放入results列表frequencies = []max_result = die_1.num_sides + die_2.num_sides# 将实验的结果数据统计出每个数字出现的次数for value in range(2, max_result + 1): frequency = results.count(value) frequencies.append(frequency)# 绘制直方图# 实例化一个bar对象，对该对象的title、x_labels、x_title、y_title属性设置相当于在直方图设置。hist = pygal.Bar()hist.title = "Res

Echarts Baidu is very famous also very diao.Echarts is Baidu Open source of a data visualization JS library. Mainly used for data visualization.Pyecharts is a class library that is used to generate echarts charts. is actually the butt of echarts and Python. Url:Https://github.com/chenjiandongx/pyecharts/blob/master/docs/zh-cn/documentation.md#%E5%BC%80%E5%A7%8B%E4%BD%BF%E7%94%A8 http://pyecharts.org/#/zh-cn

columns spanned label.grid(row=1,column=0)Four. EventsBinding an event using the bind () function窗体对象.bind(事件类型,回调函数)Five. dialog boxes and message boxes1. Message boxfromimport *print showerror(title=‘‘,message=‘‘)#其中，还有其他类型消息框，show...,ask...。2. dialog boxfrom SimpleDialog import *dlg=SimpleDialog(root,text=‘‘,buttons=[‘Yes‘,‘NO‘,...])print dlg.go()#用户点击了那个按钮fromimport *print askfloat(title=‘‘,prompt=‘‘,minivalue=0,maxvalue=100)#与askfloat()相同的方法还有ask integer、asserting，只不过属性有所不同fromimport

,timevar) 3) Data preprocessing sst = dataset.variables['sst'][timeindex,:].squeeze()ice = dataset.variables['ice'][timeindex,:].squeeze()lats = dataset.variables['lat'][:]lons = dataset.variables['lon'][:]lons, lats = np.meshgrid(lons,lats) 4) set and draw an illustration fig = plt.figure()ax = fig.add_axes([0.05,0.05,0.9,0.9])m = Basemap(projection='kav7',lon_0=0,resolution=None)m.drawmapboundary(fill_color='0.3')im1 = m.pcolormesh(lons,lats,sst,shading='flat',cmap=plt.cm.jet,latlon=True)im2 =

1 ImportCSV2 3filename ='Ch02-data.csv'4data = []5 6 Try:7with open (filename) as f://binding a data file to an object F with the WITH statement8Reader =Csv.reader (f)9Header = Next (reader)//python 3. X is for next ()Tendata = [row forRowinchReader] One exceptCSV. Error as E: A Print('Error reading CSV file at line%s:%s'%(reader.line_num,e)) -Sys.exit (-1) - the ifHeader: - Print(header) - Print("=======================") - forRowinchDa

': ' Spring air ', ' value ': ' 601021 '},], value= ' 600933 '), DCC. Graph (id= ' my-graph ')]) @app. Callback (Output (' my-graph ', ' figure '), [Input (' My-dropdown ', ' value ')] def update_graph (selected_dropdown_value): # df = web. DataReader (# selected_dropdown_value, data_source= ' Yahoo ', # START=DT (2018, 1, 1), End=dt.now () #) d f = Ts.get_k_data (Selected_dropdown_value, ktype= ' 30') return {' data ': [{' X ': Df.index, ' y ':d f.close}]}if __name__ = = ' __main__ ': App.run_