learning python for data analysis and visualization github

': ' 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.c

(true, Which= ' Major ') #x坐标轴的网格使用主刻度ax. Yaxis.grid (true,which= ' major ') #x坐标轴的网格使用主刻度plt. Xlabel (' time/t ', Fontsize= ' Xx-large ') #Valid fontsizearelarge,none,medium,smaller, small,x-large,xx-small,larger,x-small,xx-largeplt.ylabel (' Y-label ', Fontsize= ' Xx-large ') plt.title (' title ', fontsize= ' Xx-large ') Plt.xlim (0,110) Plt.ylim (0,1) line1, =ax.plot (x,y, ' g.-', label= "category One",) Line2,=ax.plot (x,y2, ' b*-', label= "category II",) Line3, =ax.plot (x,y3, ' rd-', la

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) - Pri

actually executes the imported module once, as follows:First look at the module being called test.py :def haha(): print("哈哈")haha()Look at the main program again main.py :import testprint("一条鱼")The execution results are:哈哈一条鱼How can you simply invoke the code without executing the called module? To be called module code is not executed, the premise is to know __name__ what the variable means, in short, if not involved in the module import, __name__ The value is " __main__ ", if the module is

First on:NOTE: Reprint please indicate the sourceMaking charts with MatplotlibTake the file as a variable and communicate with the OPENCV.Parsing images with OpenCV#-*-Coding:utf-8-*-from huai_zh import *from Mpl_toolkits.mplot3d import axes3dimport numpy as Npimport MATPLOTLIB.PYPL OT as Pltimport showimport cv2import osfrom matplotlib import pyplot as Pltimport numpy as Npfrom Mpl_toolkits.mplot3d Imp Ort axes3dfig = plt.figure () ax = axes3d (fig) x = Np.arange ( -4, 4, 0.25) Y = Np.arange (

Spit Groove Online Search a lot of matplotlib installation method (do not believe, you can try.) )I can only say, except too cumbersome, it is useless!If you are a python3.6.5 versionI give you the most correct advice :Open cmd directly, find pip with command pip install MatplotlibPIP helps you solve all the problems, do not believe you can try! (To help you install NumPy ...)Bo Master does not blow not black! Try it yourself!See a lot of either cumbersome or useless things also follow a few hou

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/doc

Monday), list (on-duty person ID), user_id:["Start_time", "end_time" (User duty start and end time) Read the XLS file and save the new attendance record and the new user to the database. According to the date parameter query corresponding record, check the day on duty arrangements, matching to get the day attendance record of the students on duty. will be on duty classmate's punch-in time and the duty time comparison pair, judge whether the normal punch-in, calculates the actual duty time,

[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, facecolo

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

to recommend MIT python on The EDX platform. Data analysis: What I know is: 1. JHU's data science is a bit confusing! 2. The R language is required! Therefore, Duke statistics and analysis are strongly promoted. Note: There is a mooc navigation website under the fruit shel

Rt Reply content:I highly recommend the Python class at Rice University, which is very well designed and the teacher is very responsible. ----------------------------------------------------------- Last night mobile phone answer, updated today; Rice University has a total of 3 courses, now seemingly dismantled into 6 doors, 8 weeks per course, according to the order of the more-than-digest. The first course is the

-----15:18 2016/10/14-----1.Import NumPy as Np;import pandas as Pdvalues = PD. Series (Np.random.normal (0,1,size=2000))#Series可看作一个定长的有序字典.The probability density function corresponding to the Gaussian distribution corresponds to the numpy:Np.random.normal (Loc=mu, Scale=sigma, Size=non) standard normal distribution (mu=0,sigma=1) np.random.normal (loc=0, scale=1, Size=non) Values.hist (bins=100, alpha=0.3, color= ' K ', normed= True) #bins interval number alpha Transparency normed=true paramet

Za003-python data analysis and machine learning Combat (Tang Yudi)The beginning of the new year, learning to be early, drip records, learning is progress!Do not look everywhere, seize the promotion of their own.For

GitHub user Followers analysis How to analyze a github user's followers. Weekend preface, with Python analysis of their own GitHub followers users, the statistical results of the following problems

learning with Scikit-learnBooks: "Ten minutes to Pandas" Chinese translation version: http://www.cnblogs.com/chaosimple/p/4153083.html Founder of Pandas: Data analysis using Python (watercress) (recommend) The collection of textbooks: Scipy lecture Notes (very good writing!) Regret missing Pandas)

The author Matthew May is a computer postgraduate in parallel machine learning algorithms, and Matthew is also a data mining learner, a data enthusiast, and a dedicated machine-learning scientist. Open source tools play an increasingly important role in data science workflow

)-i]] pca.append (Sort[len (input)-i]) I+ = 1" "The eigenvalues and eigenvectors corresponding to each principal component are saved and returned as a return value ." "Pca_eig= {} forIinchRange (len (PCA)): pca_eig['{} principal component'. Format (str (i+1))] =[Eigvalue[pca[i]], Eigvector[pca[i] ]returnPca_eig" "assigning the class that the algorithm resides to a custom variable" "Test=MY_PCA ()" "invoke the PCA algorithm in the class to produce the required principal component correspo

python data analysis requirements are not software development requirements , indeed, for a tool, different purposes of the user, the required skills are not the same, such as knife This tool, the butcher used it to kill pigs, the chef used it is cut vegetables, military use it is defend, the guests use it is cut steak, Everyone uses different ways, there are spe

I. Related NumPy(i) Official explanationsNumPy is the fundamental package for scientific computing with Python. It contains among other things: A powerful N-dimensional Array object Sophisticated (broadcasting) functions Tools for integrating C + + and Fortran code Useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses, NumPy can also is used as an efficient multi-dimensio