python可视化1

0、颜色配比

cnames = {
'aliceblue':            '#F0F8FF',
'antiquewhite':         '#FAEBD7',
'aqua':                 '#00FFFF',
'aquamarine':           '#7FFFD4',
'azure':                '#F0FFFF',
'beige':                '#F5F5DC',
'bisque':               '#FFE4C4',
'black':                '#000000',
'blanchedalmond':       '#FFEBCD',
'blue':                 '#0000FF',
'blueviolet':           '#8A2BE2',
'brown':                '#A52A2A',
'burlywood':            '#DEB887',
'cadetblue':            '#5F9EA0',
'chartreuse':           '#7FFF00',
'chocolate':            '#D2691E',
'coral':                '#FF7F50',
'cornflowerblue':       '#6495ED',
'cornsilk':             '#FFF8DC',
'crimson':              '#DC143C',
'cyan':                 '#00FFFF',
'darkblue':             '#00008B',
'darkcyan':             '#008B8B',
'darkgoldenrod':        '#B8860B',
'darkgray':             '#A9A9A9',
'darkgreen':            '#006400',
'darkkhaki':            '#BDB76B',
'darkmagenta':          '#8B008B',
'darkolivegreen':       '#556B2F',
'darkorange':           '#FF8C00',
'darkorchid':           '#9932CC',
'darkred':              '#8B0000',
'darksalmon':           '#E9967A',
'darkseagreen':         '#8FBC8F',
'darkslateblue':        '#483D8B',
'darkslategray':        '#2F4F4F',
'darkturquoise':        '#00CED1',
'darkviolet':           '#9400D3',
'deeppink':             '#FF1493',
'deepskyblue':          '#00BFFF',
'dimgray':              '#696969',
'dodgerblue':           '#1E90FF',
'firebrick':            '#B22222',
'floralwhite':          '#FFFAF0',
'forestgreen':          '#228B22',
'fuchsia':              '#FF00FF',
'gainsboro':            '#DCDCDC',
'ghostwhite':           '#F8F8FF',
'gold':                 '#FFD700',
'goldenrod':            '#DAA520',
'gray':                 '#808080',
'green':                '#008000',
'greenyellow':          '#ADFF2F',
'honeydew':             '#F0FFF0',
'hotpink':              '#FF69B4',
'indianred':            '#CD5C5C',
'indigo':               '#4B0082',
'ivory':                '#FFFFF0',
'khaki':                '#F0E68C',
'lavender':             '#E6E6FA',
'lavenderblush':        '#FFF0F5',
'lawngreen':            '#7CFC00',
'lemonchiffon':         '#FFFACD',
'lightblue':            '#ADD8E6',
'lightcoral':           '#F08080',
'lightcyan':            '#E0FFFF',
'lightgoldenrodyellow': '#FAFAD2',
'lightgreen':           '#90EE90',
'lightgray':            '#D3D3D3',
'lightpink':            '#FFB6C1',
'lightsalmon':          '#FFA07A',
'lightseagreen':        '#20B2AA',
'lightskyblue':         '#87CEFA',
'lightslategray':       '#778899',
'lightsteelblue':       '#B0C4DE',
'lightyellow':          '#FFFFE0',
'lime':                 '#00FF00',
'limegreen':            '#32CD32',
'linen':                '#FAF0E6',
'magenta':              '#FF00FF',
'maroon':               '#800000',
'mediumaquamarine':     '#66CDAA',
'mediumblue':           '#0000CD',
'mediumorchid':         '#BA55D3',
'mediumpurple':         '#9370DB',
'mediumseagreen':       '#3CB371',
'mediumslateblue':      '#7B68EE',
'mediumspringgreen':    '#00FA9A',
'mediumturquoise':      '#48D1CC',
'mediumvioletred':      '#C71585',
'midnightblue':         '#191970',
'mintcream':            '#F5FFFA',
'mistyrose':            '#FFE4E1',
'moccasin':             '#FFE4B5',
'navajowhite':          '#FFDEAD',
'navy':                 '#000080',
'oldlace':              '#FDF5E6',
'olive':                '#808000',
'olivedrab':            '#6B8E23',
'orange':               '#FFA500',
'orangered':            '#FF4500',
'orchid':               '#DA70D6',
'palegoldenrod':        '#EEE8AA',
'palegreen':            '#98FB98',
'paleturquoise':        '#AFEEEE',
'palevioletred':        '#DB7093',
'papayawhip':           '#FFEFD5',
'peachpuff':            '#FFDAB9',
'peru':                 '#CD853F',
'pink':                 '#FFC0CB',
'plum':                 '#DDA0DD',
'powderblue':           '#B0E0E6',
'purple':               '#800080',
'red':                  '#FF0000',
'rosybrown':            '#BC8F8F',
'royalblue':            '#4169E1',
'saddlebrown':          '#8B4513',
'salmon':               '#FA8072',
'sandybrown':           '#FAA460',
'seagreen':             '#2E8B57',
'seashell':             '#FFF5EE',
'sienna':               '#A0522D',
'silver':               '#C0C0C0',
'skyblue':              '#87CEEB',
'slateblue':            '#6A5ACD',
'slategray':            '#708090',
'snow':                 '#FFFAFA',
'springgreen':          '#00FF7F',
'steelblue':            '#4682B4',
'tan':                  '#D2B48C',
'teal':                 '#008080',
'thistle':              '#D8BFD8',
'tomato':               '#FF6347',
'turquoise':            '#40E0D0',
'violet':               '#EE82EE',
'wheat':                '#F5DEB3',
'white':                '#FFFFFF',
'whitesmoke':           '#F5F5F5',
'yellow':               '#FFFF00',
'yellowgreen':          '#9ACD32'}

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1、绘制散点图

import numpy as np
import matplotlib.pylab as pl
%matplotlib inline

a = np.arange(0, 2.0 * np.pi, 0.1)
print(a)
b = np.cos(a)
pl.scatter(a, b)
# pl.show()
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[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7
 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.  3.1 3.2 3.3 3.4 3.5
 3.6 3.7 3.8 3.9 4.  4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.  5.1 5.2 5.3
 5.4 5.5 5.6 5.7 5.8 5.9 6.  6.1 6.2]

<matplotlib.collections.PathCollection at 0x15465e2ff98>
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import numpy as np
import matplotlib.pylab as pl
%matplotlib inline
x = np.random.random(100)  # 0-1
print(x[0:10])
y = np.random.random(100)
pl.scatter(x, y, s=x * 500, c=u'r', marker=u'*')
pl.show()
# s 指定大小, c指定颜色, marker指定符号形状, 
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[0.15815532 0.65428075 0.80638782 0.17977135 0.86633134 0.60840757
 0.95604924 0.92410809 0.48354069 0.47104306]
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import numpy as np
import matplotlib.pyplot as plt
%matplotlib  inline
# x = np.linspace(0,10,100)
x = np.arange(0, 6, 0.1) # 以0.1为单位，生成0到6的数据
y1 = np.sin(x)
y2 = np.cos(x)
plt.plot(x, y1, label="sin")
plt.plot(x, y2, linestyle = "--", label="cos") # 用虚线绘制
plt.xlabel("x") # x轴标签
plt.ylabel("y") # y轴标签
plt.title('sin & cos') # 标题
plt.legend()
# plt.show()
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<matplotlib.legend.Legend at 0x1545addeb38>
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2、绘制饼状图

import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline

labels = ("Frogs", "Hogs", "Dogs", "Logs")
colors = ["yellowgreen", "gold", "#FF0000", "lightcoral"]
explode = (0, 0.1, 0, 0.1)  # 使饼状图中第二片和第四片分开
fig = plt.figure()
ax = fig.gca()

ax.pie(np.random.random(4),  		# 四个值
		explode    =	explode,
		labels     =	labels,
		colors     =	colors,
		autopct    =	"%1.1f%%",  # 百分比的格式 小数点保留一位, %% = "%"
		shadow     =	True, 		# 是否显示阴影
		startangle =	90,  		# 起始角度, 第一块从90度开始绘制, 从北向西转
		radius     =	0.25,		# 饼的半径
		center     =	(0, 0), 	# 中心位置
		frame      =	True)		# 

ax.set_xticks([0,1]) #设置坐标轴刻度
ax.set_yticks([0,1]) #设置坐标轴刻度
						# 0, 1被替换
ax.set_xticklabels(["Sunny", "Cloudy"]) #设置坐标轴显示的标签
ax.set_yticklabels(["Dry", "Rainy"]) #设置坐标轴显示的标签

ax.set_xlim((-0.5,1.5)) # 设置坐标轴跨度
ax.set_ylim((-0.5,1.5)) # 设置坐标轴跨度

ax.set_aspect('equal')  # 设置横纵比相等

plt.show()
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3、多图绘制到一个图片上

import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline
x = np.linspace(0, 2*np.pi, 500)
y = np.sin(x)
z = np.cos(x*x)
plt.figure(figsize=(8,4))

plt.plot(x,y,
	label="$sin(x)$", 
	color="red",  # 红色, 两个像素宽度
	linewidth=2) 

plt.plot(x,z,"b--", # 蓝色虚线
	label="$cos(x^2)$")

plt.xlabel("time(s)")
plt.ylabel("volt")
plt.title("sin and cos figure using pyplot")
plt.ylim(-1.2, 1.2)
plt.legend()  # 显示图例 左下角线的标识
plt.show()		# 显示窗口

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4、多图绘制到不同分布上

import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline
x = np.linspace(0, 2*np.pi, 500)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.cos(x*x)

plt.figure(1)# 创建第一个图形

ax1 = plt.subplot(2,2,1) #第一行第一列第一个图形
plt.sca(ax1) # 选择ax1
plt.plot(x,y1,color="red")
plt.ylim = (-1.2, 1.2) # 限制y轴范围

ax2 = plt.subplot(2,2,2) #第一行第2列第2个图形
plt.sca(ax2) # 选择ax2
plt.plot(x,y2,"b--")
plt.ylim = (-1.2, 1.2) # 限制y轴范围

ax3 = plt.subplot(212, facecolor='r') # 背景色red
plt.sca(ax3) # 选择ax3
plt.plot(x,y3,"g--")
plt.ylim = (-1.2, 1.2) # 限制y轴范围

plt.legend()  # 显示图例 左下角线的标识
plt.show()		# 显示窗口

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No handles with labels found to put in legend.
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5、绘制三维立体图

import matplotlib.pyplot as plt
import numpy as np
import mpl_toolkits.mplot3d

x,y = np.mgrid[-2:2:20j, -2:2:20j] #步长使用j, 从-2到2分为了20个点
# 得到了20*20  400个点, 后面的j标识包含终点2
# print(x)
z = 50*np.sin(x+y)

ax = plt.subplot(111, projection='3d')
ax.plot_surface(x, y, z,  # 画一个面
	rstride=2, 
	cstride=1, 
	cmap=plt.cm.Blues_r)

ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")

plt.legend()  # 显示图例 左下角线的标识
plt.show()		# 显示窗口


rho,theta = np.mgrid[0:1:40j, 0:2*np.pi:40j]
z = rho**2
x = rho*np.cos(theta)
y = rho*np.sin(theta)
ax = plt.subplot(111, projection="3d")
ax.plot_surface(x,y,z)
plt.show()

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'
运行
运行

No handles with labels found to put in legend.
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6、绘制三维曲线

import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
# import mpl_toolkits.mplot3d


mpl.rcParams["legend.fontsize"] = 10  # 图例字号
fig = plt.figure()
ax = fig.gca(projection="3d")
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-4, 4, 100) * 0.3
r = z**3 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label="parametric curve")
ax.legend()
plt.show()

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7、数据条形图

第一个图

 # 绘制三个平台的准确率
# 读取不同的csv数据
import numpy as np
import pandas as pd
import json

#绘制多个条形图
from matplotlib import pyplot as plt
from pylab import *

# 中文显示问题
mpl.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

#绘制多个条形图
from matplotlib import pyplot as plt
%matplotlib inline 
# jupyter专用

# 输入统计数据
display_index = ["a","b","c","d","e","f"]
platform = ['x轴1','x轴2','x轴3']
colors = ["red",'b','purple','mediumblue','g','navy','red']
data = {"a": [1.071, 2.559, 2.292], 
"b": [1.218, 2.682, 2.381], 
"c": [1.104, 2.755, 2.381], 
"d": [1.217, 2.762, 2.325], 
"e": [1.27, 2.904, 2.307], 
"f": [1.254, 3.4, 2.376]}
# with open("data.json", "r") as f:
#     data = json.loads(f.read())
print(data) #
print(display_index)

#定义变量
bar_width = 0.1 # 条形宽度
# 定义横坐标 共三组
a = np.arange(len(platform)) 

# 使用7次 bar 函数画出三组组条形图
for i,display_ind in enumerate(display_index):
    plt.bar(a+bar_width*i, # 每增加一个bar, 就以a向右移动width宽
            height=data[display_ind], 
            width=bar_width-0.02,  # -0.02的目的是为了让显示出来空隙
            color=colors[i], # 颜色, 这里需要看颜色对照表
            label=display_ind) # 标签

# 显示图例 bbox_to_anchor
plt.legend(loc='center left', 
           bbox_to_anchor=(0, 1.12), # 表示上下左右 ncol与(左,下)的距离
           ncol=3)  # 每行显示3个

# 设置坐标系x轴
plt.xticks(a + bar_width*3, # a+bar_width*3是为了显示刻度中间  
           platform, # platform x轴显示的数据
           size=12, # size是用来设置显示字体大小 
           rotation=30)  # 设置X轴的下标旋转30度   

plt.xlabel('平台',size = 12)  # 
plt.ylabel("y轴显示", size = 12)  # 纵坐标轴标题
# plt.title('xxxxxxxx', size=20)  # 图形标题
# plt.savefig("保存结果.pdf",dpi=150,bbox_inches="tight") # 保存为pdf格式
plt.savefig("保存结果.png",dpi=150,bbox_inches="tight") # 保存为图片格式
plt.show()    
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解决的问题:

• 颜色使用不同颜色需要查看颜色配比, 在本文开头
• 保存时保存不完整 ,加入bbox_inches=“tight”
• 增加条形之间的空隙
• 设置x轴下标旋转
• 设置条形标识位置在条形图外侧loc=‘center left’

第二个图, 增加bar不同形状填充

 # 绘制三个平台的准确率
# 读取不同的csv数据
import numpy as np
import pandas as pd
import json

#绘制多个条形图
from matplotlib import pyplot as plt
from pylab import *

# 中文显示问题
mpl.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

#绘制多个条形图
from matplotlib import pyplot as plt
%matplotlib inline 
# jupyter专用

# 输入统计数据
display_index = ["a","b","c","d","e","f"]
platform = ['x轴1','x轴2','x轴3']
colors = ["red",'b','purple','mediumblue','g','navy','red']
hatchs = ['///', '\\\\\\', '||||', '--', '+++', '***', '...']

data = {"a": [1.071, 2.559, 2.292], 
"b": [1.218, 2.682, 2.381], 
"c": [1.104, 2.755, 2.381], 
"d": [1.217, 2.762, 2.325], 
"e": [1.27, 2.904, 2.307], 
"f": [1.254, 3.4, 2.376]}
# with open("data.json", "r") as f:
#     data = json.loads(f.read())
print(data) #
print(display_index)

#定义变量
bar_width = 0.1 # 条形宽度
# 定义横坐标 共三组
a = np.arange(len(platform)) 

# 使用7次 bar 函数画出三组组条形图
for i,display_ind in enumerate(display_index):
    plt.bar(a+bar_width*i, # 每增加一个bar, 就以a向右移动width宽
            height=data[display_ind], 
            width=bar_width-0.02,  # -0.02的目的是为了让显示出来空隙
            color=colors[i], # 颜色, 这里需要看颜色对照表
            hatch = hatchs[i],
            label=display_ind) # 标签

# 显示图例 bbox_to_anchor
plt.legend(loc='center left', 
           bbox_to_anchor=(0, 1.12), # 表示上下左右 ncol与(左,下)的距离
           ncol=3)  # 每行显示3个

# 设置坐标系x轴
plt.xticks(a + bar_width*3, # a+bar_width*3是为了显示刻度中间  
           platform, # platform x轴显示的数据
           size=12, # size是用来设置显示字体大小 
           rotation=30)  # 设置X轴的下标旋转30度   

plt.xlabel('平台',size = 12)  # 
plt.ylabel("y轴显示", size = 12)  # 纵坐标轴标题
# plt.title('xxxxxxxx', size=20)  # 图形标题
# plt.savefig("保存结果.pdf",dpi=150,bbox_inches="tight") # 保存为pdf格式
plt.savefig("保存结果.png",dpi=150,bbox_inches="tight") # 保存为图片格式
plt.show()    

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