python可视化1

- 日理万妓 2023-07-18 02:28 61阅读 0赞

### 文章目录 ###

*  0、颜色配比
 *  1、绘制散点图
 *  2、绘制饼状图
 *  3、多图绘制到一个图片上
 *  4、多图绘制到不同分布上
 *  5、绘制三维立体图
 *  6、绘制三维曲线
 *  7、数据条形图

# 0、颜色配比 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70]

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'}

# 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()

[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>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 1]

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,

[0.15815532 0.65428075 0.80638782 0.17977135 0.86633134 0.60840757
     0.95604924 0.92410809 0.48354069 0.47104306]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 2]

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()

<matplotlib.legend.Legend at 0x1545addeb38>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 3]

# 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()

![在这里插入图片描述][20200323172856946.png]

# 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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 4]

# 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

No handles with labels found to put in legend.

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 5]

# 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()

No handles with labels found to put in legend.

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 6]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 7]

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 8]

# 7、数据条形图 #

第一个图  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 9]

# 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()

<table> 
 <thead> 
  <tr> 
   <th align="center">参数</th> 
   <th align="center">说明</th> 
   <th align="center">类型</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="center">x</td> 
   <td align="center">x坐标</td> 
   <td align="center">int;float</td> 
  </tr> 
  <tr> 
   <td align="center">height</td> 
   <td align="center">条形的高度</td> 
   <td align="center">int, float</td> 
  </tr> 
  <tr> 
   <td align="center">width</td> 
   <td align="center">宽度</td> 
   <td align="center">0~1，默认0.8</td> 
  </tr> 
  <tr> 
   <td align="center">botton</td> 
   <td align="center">条形的起始位置</td> 
   <td align="center">也是y轴的起始坐标</td> 
  </tr> 
  <tr> 
   <td align="center">align</td> 
   <td align="center">条形的中心位置</td> 
   <td align="center">“center”,"lege"边缘</td> 
  </tr> 
  <tr> 
   <td align="center">color</td> 
   <td align="center">条形的颜色</td> 
   <td align="center">“r",“b”,“g”,"#123465"，默认“b"</td> 
  </tr> 
  <tr> 
   <td align="center">edgecolor</td> 
   <td align="center">边框的颜色</td> 
   <td align="center">同上</td> 
  </tr> 
  <tr> 
   <td align="center">linewidth</td> 
   <td align="center">边框的宽度</td> 
   <td align="center">像素，默认无，int</td> 
  </tr> 
  <tr> 
   <td align="center">tick_label</td> 
   <td align="center">下标的标签</td> 
   <td align="center">可以是元组类型的字符组合</td> 
  </tr> 
  <tr> 
   <td align="center">log</td> 
   <td align="center">y轴使用科学计算法表示</td> 
   <td align="center">bool</td> 
  </tr> 
  <tr> 
   <td align="center">orientation</td> 
   <td align="center">是竖直条还是水平条</td> 
   <td align="center">竖直：“vertical”，水平条：“horizontal”</td> 
  </tr> 
  <tr> 
   <td align="center">hatch</td> 
   <td align="center">填充形状</td> 
   <td align="center">str格式 如"+","-"</td> 
  </tr> 
 </tbody> 
</table>

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’
 *

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 10]第二个图, 增加bar不同形状填充  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNDYwODE5_size_16_color_FFFFFF_t_70 11]

# 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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