python drawing library

Basic reading of CSV file and drawing pie chartSince there is no prior experience of actual processing, this procedure is still worth seeing, involving the basic methods of working with tabular data:Import Matplotlib.pyplot as Pltimport pandas as pd# csv read file data = pd.read_csv (' outorder.csv ', encoding= ' gb2312 ') # Each column is compatible with the NumPy method A = data[' way '].values# gets the various possible contents of this column Type

This matplotlib installation process to a certain extent, the reference http://blog.csdn.net/qrlhl/article/details/48978107 Because the need to learn machine learning, but also ready to refer to the "machine learning Combat" This book to get started. After installing python3.4 and the scientific calculation package numpy, you need to use the drawing package matplotlib. The installation of the goods has really wasted my effort. After reading a variety

, how to do? For more information please go to other blogs, where more detailed instructions are available .Pandas import time data for format conversion　　Draw multiple graphs on one canvas and add legends1 fromMatplotlib.font_managerImportfontproperties2Font = fontproperties (fname=r"C:\windows\fonts\STKAITI. TTF", size=14)3colors = ["Red","Green"]#the color used to specify the line4Labels = ["Jingdong","12306"]#used to specify the legend5Plt.plot (range (0, D), C, C=colors[0], label=Labels[0]

Turtle.pendown () turtle.circle (20,180) Turtle.penup () Turtle.goto ( -25,195) Turtle.pendown ( ) TURTLE.FD (+) #画笔向前移到距离 turtle.left (TURTLE.FD) turtle.left (+) TURTLE.FD (turtle.penup) Turtle.goto (- 90,170) turtle.left (180) Turtle.down () for I in range (1,5): If I%2!=0:TURTLE.FD ($) ELSE:TURTLE.FD ) Turtle.left (all) for i in [ -90,90]: Turtle.penup () Turtle.goto (i,120) if I==-90:turtle.right (Else:turtle.left) Turtle.down () Turtle. FD (+) Turtle.left (TURTLE.FD) turtle.right (turtle.c

on the hexagonal grid.Back to the original high-dimensional feature space, the songs are clustered into a user-defined number of groups (k=10 can do a good job of visualizing them). For each group, find the song that is closest to the group center.On the hexagon grid, use different colors to color the song in the K Group Center.Insert different colors on the XY screen, depending on the distance from the other songs to each group center. Below, let's take a look at some of these steps for more i

song on the XY plane to a point on the hexagonal grid.Back to the original high dimensional feature space, the song clustering to the user-defined number of groups (k=10 can achieve a good visual purposes). For each group, find the song closest to the group center.On the hexagonal grid, use different colors to color the song in the K Group Center.Insert a different color into each of the other songs, depending on the distance to the center of each group on the XY screen. Next, let's take a loo