python visualization tools

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

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

First set up the basic environment, assuming there is already a Python operating environment. Then need to install some common basic library, such as NumPy, scipy for numerical calculation, pandas for data analysis, Matplotlib/bokeh/seaborn for data visualization. And then on demand to load the library of data acquisition, such as Tushare (http://pythonhosted.org/tushare/), Quandl (https://www.quandl.com/)

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

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

with the GGPLOT2 in the R language, it seems that two packages are used and the likelihood is developed by the same person! The original author also said on GITHUB that the PYTHON library will no longer be updated! However, ggplot2 is really a drawing artifact, which is almost the only reason I am still using the R language. Therefore, matplotlib is required no matter which library you want to use. Although his syntax is complex, he is flexible. yo

‘)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

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-*-

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

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

, matplotlib is required no matter which library you want to use. Although his syntax is complex, he is flexible. You can draw almost any image you want. Here we go: Ggplot Seaborn Bokeh Pygal Python-igraph Folium NetworkX Mayavi VisPy PyQtGraph Vincent Plotly @ Vincent is good. The backend uses d3 for visualization. Seabornpyqtgraph: similar to pyside or pyqt. Both of them are common and can

(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

All of the Python code in this tutorial can be obtained from Ipython notebook on the web. Consider using plotly in your company? You can take a look at Plotly's On-premises Enterprise Edition. (Note: On-premises refers to the software running in the workplace or within the company, as detailed in Wikipedia) Note the operating system: Although Windows or Mac users can follow this article, this article assumes you are using an Ubuntu system (Ubuntu De