数据可视化之绘图基础

╰半夏微凉° 2021-12-23 15:51 527阅读 0赞

## 1.一个简单的实例 ##

import matplotlib.pyplot as plt
    
    x = [1, 2, 3, 4]
    y = [5, 4, 3, 2]
    
    plt.figure()  # 创建一个figure()
    
    plt.subplot(231) # divide subplots into 2*3 grid and select 1
    plt.plot(x, y)
    
    plt.subplot(232) # divide subplots into 2*3 grid and select 2
    plt.bar(x, y)
    
    plt.subplot(233)
    plt.barh(x, y)
    
    plt.subplot(234)
    plt.bar(x, y)
    y1 = [7, 8, 5, 3]
    plt.bar(x, y1, bottom=y, color='r')
    
    plt.subplot(235)
    plt.boxplot(x)
    
    plt.subplot(236)
    plt.scatter(x, y)
    
    plt.show()

![1542450-20190516153729153-1991120286.png][]

### 工作原理 ###

![1542450-20190516153806632-615579855.png][]![1542450-20190516153854146-67370875.png][]

## 2.简单的正弦和余弦图 ##

![1542450-20190516154455167-973493871.png][]

以这个简单图表为基础，可以进一步定制化增加更多的信息，并且让坐标轴及其边界更精确些。

import matplotlib.pyplot as plt
    import numpy as np
    
    # generate uniformly distributed
    # 256 points from -pi to pi, inclusive
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    
    # these are vectorised versions
    # of math.cos, and math.sin in built-in Python maths
    # compute cos for every x
    y = np.cos(x)
    
    # compute sin for every x
    y1 = np.sin(x)
    
    # plot cos
    plt.plot(x, y)
    
    # plot sin
    plt.plot(x, y1)
    
    # define plot title
    plt.title("Functions $\sin$ and $\cos$")
    
    # set x limit
    plt.xlim(-3.0, 3.0)
    # set y limit
    plt.ylim(-1.0, 1.0)
    
    # format ticks at specific values
    plt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
               [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$']) # 为了用希腊字母表示
    plt.yticks([-1, 0, +1],
               [r'$-1$', r'$0$', r'$+1$']) # 为了用希腊字母表示

plt.show()

![1542450-20190516155050421-1348930364.png][]

## 3.设置坐标轴长度和范围 ##

![1542450-20190516155920069-585115521.png][]

## 4.设置图表的线型、属性和格式化字符串 ##

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    
    y = np.cos(x)
    
    y1 = np.sin(x)
    
    plt.plot(x, y, lw=5,c='c')
    plt.plot(x, y1,marker='+')
    
    plt.show()

![1542450-20190516161337849-1701575688.png][]

### 工作原理 ###

![1542450-20190516161447954-2120396630.png][]![1542450-20190516161508914-1229443699.png][]![1542450-20190516161546989-128755756.png][]![1542450-20190516161608726-778758075.png][]

![1542450-20190516161628144-246683540.png][]![1542450-20190516161738434-776267710.png][]

## 5.折线图 ##

### 5.1 坐标化成XY的对数坐标 ###

import matplotlib.pyplot as plt
    
    import numpy as np
    
    import pandas as pd
    
    # 以下两行来显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    
    plt.figure(num=1)
    # np.arange(20)生成数组[1..20], np.exp():返回e(2.71828182846)的幂次方
    x = pd.Series(np.exp(np.arange(20)))
    x.plot(label=u'原始数据图', legend=True)  # legend=True让label标签在子图上可见
    plt.show()
    plt.figure(num=2)
    x.plot(logy=True, label=u'对数数据图', legend=True)  # logy=True：y轴坐标为10的n次方
    plt.show()

![1542450-20190516203217451-504871155.png][]![1542450-20190516203223136-1930018927.png][]

### 5.2 figure语法及操作 ###

figure语法说明

figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True)

num:图像编号或名称，数字为编号 ，字符串为名称  
figsize:指定figure的宽和高，单位为英寸；  
dpi参数指定绘图对象的分辨率，即每英寸多少个像素，缺省值为80      1英寸等于2.5cm,A4纸是 21\*30cm的纸张   
facecolor:背景颜色  
edgecolor:边框颜色  
frameon:是否显示边框

### 5.3 更换坐标轴 ###

import matplotlib.pyplot as plt
    
    import numpy as np
    
    # 生成一系列数据
    
    x = np.linspace(-1, 1, 50)
    y1 = x ** 2
    y2 = 2 * x + 1
    
    plt.figure(num=3, figsize=(8, 5))
    plt.plot(x, y2)
    plt.plot(x, y1, color='red', linewidth=10, linestyle='--')
    # 图像的属性：颜色线宽线条样式
    
    plt.xlim((-1, 2))
    plt.ylim((-2, 3))
    # 坐标轴的取值范围
    
    plt.xlabel('I am x.')
    plt.ylabel('I am y.')
    # 坐标轴的标识
    
    new_ticks = np.linspace(-1, 2, 5)
    print(new_ticks)
    # 打印坐标轴新的单位长度
    plt.xticks(new_ticks)
    # 更换坐标轴的单位长度
    plt.yticks(np.linspace(-2, 5, 5))
    # # 更换坐标轴不同单位长度上的标识，换成文字形式
    
    #
    # gca='get current axis'
    # ax = plt.gca()
    # ax.spines['right'].set_color('c')
    # ax.spines['left'].set_color('r')
    
    plt.show()

![1542450-20190516204902876-550580855.png][]

### 5.4刻度标记大小 ###

import matplotlib.pyplot as plt
    
    # 导入模块pyplot,并给它赋别名plt，以免后续重复输入pyplot，pyplot中包含了许多用于生成图表的函数
    
    # 简单绘图
    squares = [1, 4, 9, 16, 25]  # 创建一个列表，平方数
    plt.plot(squares)  # 再将这个列表传递给函数plot(),这个函数会根据根据这些数字绘出有意义的图
    # plt.show()#打开matplotlib查看器，并显示绘制图形
    
    # 修改标签文字和线条粗细
    plt.plot(squares, linewidth=5)  # linewidth决定了plot()绘制图像线条的粗细
    plt.title("Square Numbers", fontsize=24)  # 设置图标标题，fontsize字型大小
    plt.xlabel("Value", fontsize=14)  # 给x轴加标签
    plt.ylabel("Square of Value", fontsize=14)  # 给y轴加标签
    plt.tick_params(axis='both', labelsize=10)  # 设置刻度标记的大小
    # tick_params设置刻度的样式，其中指定的实参将影响x轴y轴的刻度
    # plt.show()
    
    # 校正图形
    # 当你向plot提供一系列数据时，他假设第一个数据点对应的x坐标值为0，
    # 但我们第一个点对应的坐标值为1，为改变这种默认行为，我们可以给plot同时提供输入值和输出值
    input_values = [1, 2, 3, 4, 5]
    squares = [1, 4, 9, 16, 25]
    plt.plot(input_values, squares, linewidth=5)
    plt.show()

![1542450-20190516205425846-751410732.png][]

## 6.散点图 ##

### 6.1 ###

import numpy as np
    
    import matplotlib.pyplot as plt
    
    x = np.arange(0., 5., 0.2)
    
    plt.plot(x, x, 'r--', x, x**2, 'bs', x, x**3, 'g^')
    
    plt.show()

![1542450-20190516211007911-1078063451.png][]

### 6.2 ###

import matplotlib.pyplot as plt
    import numpy as np
    
    n = 1024    # data size
    # 正太分布
    X = np.random.normal(0, 1, n)
    Y = np.random.normal(0, 1, n)
    T = np.arctan2(Y, X)    # 根据xy值设置颜色
    
    plt.scatter(X, Y, s=75, c=T, alpha=.5)
    
    plt.xlim(-1.5, 1.5)
    plt.ylim(-1.5, 1.5)
    
    plt.show()

![1542450-20190516211808071-1333356557.png][]

## 7.柱状图 ##

### 7.1 ###

import matplotlib.pyplot as plt
    import numpy as np
    
    # 12个柱状图
    n = 12
    X = np.arange(n)  # x会生成0到11
    Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)  # 随机随机生成0.5到1的数
    Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)
    
    plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
    plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
    
    # zip是把X，Y1中的值分别给x和y
    # plt.text(x位置，y位置，值）
    for x, y in zip(X, Y1):
        # ha:horizontal alignment对齐方式
        plt.text(x, y + 0.05, '%.2f' % y, ha='center', va='bottom')
    
    for x, y in zip(X, Y2):
        # ha:horizontal alignment对齐方式
        plt.text(x, -y - 0.05, '-%.2f' % y, ha='center', va='top')
    
    plt.xlim(-.5, n)
    plt.xticks(())
    plt.ylim(-1.25, 1.25)
    plt.yticks(())
    plt.show()

![1542450-20190516212142591-1308536304.png][]

## 8直方图 ##

### 8.1 ###

import numpy as np
    import matplotlib.pyplot as plt
    
    np.random.seed(1)
    mu1, sigma1 = 100, 15
    mu2, sigma2 = 80, 15
    x1 = mu1 + sigma1 * np.random.randn(10000)
    x2 = mu2 + sigma2 * np.random.randn(10000)
    n1, bins1, patches1 = plt.hist(x1, 50, density=True, facecolor='g', alpha=1)
    n2, bins2, patches2 = plt.hist(x2, 50, density=True, facecolor='r', alpha=0.2)
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.xlabel('智商')
    plt.ylabel('置信度')
    plt.title('IQ直方图')
    plt.text(110, .025, r'$mu=100, sigma=15$')
    plt.text(50, .025, r'$mu=80, sigma=15$')
    # 设置坐标范围
    plt.axis([40, 160, 0, 0.03])
    plt.grid(True)
    plt.show()

![1542450-20190516212747857-1884987603.png][]

## 9条形图 ##

### 9.1平行条形图 ###

import numpy as np
    import matplotlib.pyplot as plt
    
    size = 4
    a = np.random.random(size)
    b = np.random.random(size)
    c = np.random.random(size)
    
    x = np.arange(size)
    total_width, n = 0.8, 3
    width = total_width / n
    # redraw the coordinates of x
    x = x - (total_width - width) / 2
    # here is the offset
    plt.bar(x, a, width=width, label='a')
    plt.bar(x + width, b, width=width, label='b')
    plt.bar(x + 2 * width, c, width=width, label='c')
    
    plt.legend()
    plt.show()

![1542450-20190516213549510-2132048453.png][]

### 9.2堆积条形图 ###

import numpy as np
    import matplotlib.pyplot as plt
    
    size = 5
    a = np.random.random(size)
    b = np.random.random(size)
    c = np.random.random(size)
    x = np.arange(size)
    plt.bar(x, a, width=0.5, label='a',fc='r')
    plt.bar(x, b, bottom=a, width=0.5, label='b', fc='g')
    plt.bar(x, c, bottom=a+b, width=0.5, label='c', fc='b')
    plt.ylim(0, 2.5)
    plt.legend()
    # plt.grid(True)
    plt.show()

![1542450-20190516213837240-1879997343.png][]

## 10.饼状图 ##

### 10.1一般饼状图 ###

import matplotlib.pyplot as plt
    
    labels = 'A', 'B', 'C', 'D'
    sizes = [15, 30, 45, 10]
    explode = (0, 0.1, 0, 0)
    plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=False, startangle=90)
    plt.axis('equal')
    plt.show()

![1542450-20190516214457268-256709439.png][]

### 10.2嵌套饼状图 ###

import numpy as np
    import matplotlib.pyplot as plt
    
    size = 0.3
    vals = np.array([[60., 32.], [37., 40.], [29., 10.]])
    cmap = plt.get_cmap("tab20c")
    outer_colors = cmap(np.arange(3)*4)
    inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))
    print(vals.sum(axis=1))
    # [92. 77. 39.]
    plt.pie(vals.sum(axis=1), radius=1, colors=outer_colors,
    wedgeprops=dict(width=size, edgecolor='w'))
    print(vals.flatten())
    # [60. 32. 37. 40. 29. 10.]
    plt.pie(vals.flatten(), radius=1-size, colors=inner_colors,
    wedgeprops=dict(width=size, edgecolor='w'))
    # equal makes it a perfect circle
    plt.axis('equal')
    plt.show()

![1542450-20190516214749097-2091579929.png][]

### 10.3极轴饼状图 ###

import numpy as np
    
    import matplotlib.pyplot as plt
    
    np.random.seed(19680801)
    N = 10
    theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
    radii = 10 * np.random.rand(N)
    
    width = np.pi / 4 * np.random.rand(N)
    
    ax = plt.subplot(111, projection='polar')
    
    bars = ax.bar(theta, radii, width=width, bottom=0.0)
    
    for r, bar in zip(radii, bars):
        bar.set_facecolor(plt.cm.viridis(r / 10.))
        bar.set_alpha(0.5)
    plt.show()

![1542450-20190516215027247-1306547183.png][]

## 11三维图 ##

### 11.1三维散点图 ###

import numpy as np
    
    import matplotlib.pyplot as plt
    
    from mpl_toolkits.mplot3d import Axes3D
    
    data = np.random.randint(0, 255, size=[40, 40, 40])
    
    x, y, z = data[0], data[1], data[2]
    
    ax = plt.subplot(111, projection='3d')
    
    ax.scatter(x[:10], y[:10], z[:10], c='y')
    
    ax.scatter(x[10:20], y[10:20], z[10:20], c='r')
    
    ax.scatter(x[30:40], y[30:40], z[30:40], c='g')
    
    ax.set_zlabel('Z')
    
    ax.set_ylabel('Y')
    
    ax.set_xlabel('X')
    
    plt.show()

![1542450-20190516215308807-699564869.png][]

转载于:https://www.cnblogs.com/xinmomoyan/p/10888233.html

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