python interactive visualization

FrontierThrough the previous section on the Python combat data visualization of the Pygal module (Basic) Learning, we have a preliminary understanding of the use of Pygal module, this section will be a practical project to deepen the use of Pygal module. The JSON-formatted population data can be downloaded from the Web and processed using JSON modules, and the Pygal module provides a map creation tool for b

Data visualization is an important part of financial, financial, and other statistics work. In the early stages of the project, we often need exploratory data analysis to gain insight into the data. Python data visualization makes the process clearer, especially when dealing with large, high dimensional datasets. Matplotlib is a popular

GitHub URL to read the JSON format data. 2. Use the requests module to access the specified URL and read the content. 3. Read the content and convert it to a JSON-formatted object. 4. Iterate through the JSON object and, for each of these items, read the URL value for each code base.Principle: First, use the requests module to obtain remote resources. The Requests module provides a simple API to define HTTP verbs, and we only need to emit a get () method call. We are only interested in the Resp

Python captures financial data, pandas performs data analysis and visualization series (to understand the needs), pythonpandasFinally, I hope that it is not the preface of the preface. It is equivalent to chatting and chatting. I think a lot of things are coming from the discussion. For example, if you need something, you can only communicate with yourself, only by summing up some things can we better chat

Background: Based on the huge demand for visualizations and cost factors, using the Pyecharts + Django visualization is clearly a better choiceVisualize to find: patterns, relationships, and anomaliesEnvironment: People with obsessive-compulsive disorder have always used the latest versiondjango:2.1.0python:3.x (Win10 is 3.7,ubuntu is 3.5)Operating system environment: WIN10 and Ubuntu1. Django Installation:Django is a free, open-source web framework d

Author: vamei Source: http://www.cnblogs.com/vamei welcome reprint, please also keep this statement. Thank you! In the process of data visualization, we often need to display the data on the map based on the collected geographical location. For example, we want to map cities, aircraft routes, military bases, and so on. Generally, a Geographic Information System has such a function. Today we will discuss how to implement it in

FrontierPython provides a number of modules for data visualization, including Matplotlib, Pygal. I refer to the online popular books "Python programming from the beginning to the actual combat", in the test and learning process encountered a few problems to solve, just write down this project experience, for the basic part of the Python is not detailed, mainly th

Http://pbpython.com/visualization-tools-1.htmlhttps://bokeh.pydata.org/en/latest/Https://github.com/bokeh/bokehHttp://biobits.org/bokeh-jupyter-embed.htmlImport Pandas as PDFrom bokeh.plotting import figure, ShowFrom Bokeh.io import Output_notebook, output_filePower = pd.read_csv (' power.csv ')Time = power[' time '].tolist ()Current = power[' current '].tolist ()Plot = figure (title= ' Power Curve ', plot_width=1900, plot_height=500)Plot.line (X=time

segmentation situation. The red part is the process of running the program.11, continue to write code, the frequency of the statistical summary, the code implementation as shown.12, the program run, get a TXT and Excel file, inside is about the word frequency statistics information, as shown. The red part is the result of the program running, and there is no error.13. Import these keywords into WordArt for visualization, as shown in.14, set a case, f

‘)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

The Bokeh package is an interactive visual library. It is displayed using a Web browser, with the goal of drawing the pattern in d3.js style, so that it looks beautiful and easy to construct. Bokeh supports a large number of streaming datasets. You can use this library to create a variety of charts/graphs. One of its main rivals may be plotly. D3.js is a JavaScript library that can be used to create "data-driven documents" (Information driven documen

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

(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