22.23.24.25.盒须图（boxplot）、棉棒图(Stem Plot； Lollipop plot)、极坐标图、雷达图（Radar Chart）

ゞ浴缸里的玫瑰 2022-11-17 13:56 16阅读 0赞

22.盒须图（boxplot）  
23.棉棒图(Stem Plot; Lollipop plot)  
24.极坐标图  
25.雷达图（Radar Chart）

# 22.盒须图（boxplot） #

盒须图(也称为箱形图）是一种图表类型，通常用于说明性数据分析中，通过显示数据四分位数（或百分位数）和平均值来直观地显示数值数据的分布和偏度（skewness）。

箱形图于1977年由美国著名统计学家约翰·图基（John Tukey）发明。它能显示出一组数据的最大值、最小值、中位数、及上下四分位数。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70]  
最小值(minimum)  
下四分位数(Q1)  
中位数(Median,也就是Q2)  
上四分位数(Q3)  
最大值(maximum)  
四分位间距(IQR)  
箱形图将数据分为几个部分，每个部分包含该集中大约25%的数据。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 1]  
请注意，上图代表的数据是完美的正态分布，大多数箱形图均不符合这种对称性（每个四分位数的长度相同）。  
箱形图形状将显示统计数据集是正态分布还是偏斜。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 2]  
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箱形图很有用，因为它们显示了数据集中的异常值(outliers,离群值)。  
离群值是在数值上与其余数据相距遥远的观测值。  
查看箱形图时，离群值定义为位于箱形图whiskers之外的数据点。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 4]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 5]

import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    
    # Fixing random state for reproducibility
    np.random.seed(19680801)
    
    # fake up some data
    spread = np.random.rand(50) * 100
    center = np.ones(25) * 50
    flier_high = np.random.rand(10) * 100 + 100
    flier_low = np.random.rand(10) * -100
    data = np.concatenate((spread, center, flier_high, flier_low))
    
    fig1, ax1 = plt.subplots()
    ax1.set_title('Basic Plot')
    ax1.boxplot(data)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 6]

import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    
    # Fixing random state for reproducibility
    np.random.seed(19680801)
    
    # fake up some data
    spread = np.random.rand(50) * 100
    center = np.ones(25) * 50
    flier_high = np.random.rand(10) * 100 + 100
    flier_low = np.random.rand(10) * -100
    data = np.concatenate((spread, center, flier_high, flier_low))
    
    fig2, ax2 = plt.subplots()
    ax2.set_title('Notched boxes')
    ax2.boxplot(data, notch=True)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 7]

import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    
    # Fixing random state for reproducibility
    np.random.seed(19680801)
    
    # fake up some data
    spread = np.random.rand(50) * 100
    center = np.ones(25) * 50
    flier_high = np.random.rand(10) * 100 + 100
    flier_low = np.random.rand(10) * -100
    data = np.concatenate((spread, center, flier_high, flier_low))
    
    green_diamond = dict(markerfacecolor='g', marker='D')
    fig3, ax3 = plt.subplots()
    ax3.set_title('Changed Outlier Symbols')
    ax3.boxplot(data, flierprops=green_diamond)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 8]

Fake up some more data

import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    
    # Fixing random state for reproducibility
    np.random.seed(19680801)
    
    # fake up some data
    spread = np.random.rand(50) * 100
    center = np.ones(25) * 50
    flier_high = np.random.rand(10) * 100 + 100
    flier_low = np.random.rand(10) * -100
    data = np.concatenate((spread, center, flier_high, flier_low))
    
    spread = np.random.rand(50) * 100
    center = np.ones(25) * 40
    flier_high = np.random.rand(10) * 100 + 100
    flier_low = np.random.rand(10) * -100
    d2 = np.concatenate((spread, center, flier_high, flier_low))
    data.shape = (-1, 1)
    d2.shape = (-1, 1)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 9]

# 23.棉棒图(Stem Plot; Lollipop plot) #

棉棒图绘制从基线到y坐标的垂直线，并在尖端（tip）放置一个标记。

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(0.1, 2 * np.pi, 41)
    y = np.exp(np.sin(x))
    
    plt.stem(x, y, use_line_collection=True)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 10]  
基线的位置可以使用bottom进行调整。 参数linefmt，markerfmt和basefmt控制plot的基本格式属性。 但是，并非所有属性都可以通过关键字参数进行配置。 对于更高级的控制，可调整pyplot返回的线对象。

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(0.1, 2 * np.pi, 41)
    y = np.exp(np.sin(x))
    
    markerline, stemlines, baseline = plt.stem(
        x, y, linefmt='grey', markerfmt='D', bottom=1.1, use_line_collection=True)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 11]

输出结果：

Product  Turnover
    0     Apple        30
    1    Banana        59
    2      Beer        92
    3     Juice        43
    4    Cheese       123
    5    Coffee        93
    6  Red wine       103
    7    Hotdog        37

![在这里插入图片描述][20210410093439172.png]

import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    
    df = pd.DataFrame({ 'Product':['Apple', 'Banana', 'Beer', 'Juice', 'Cheese',
                                  'Coffee', 'Red wine', 'Hotdog'],
                       'Turnover':[30, 59, 92, 43, 123, 93, 103, 37]},
                      columns=['Product', 'Turnover'])
    print(df)
    
    plt.figure(figsize=(9, 6))
    
    (markerline, stemlines, baseline) = plt.stem(df['Product'], df['Turnover'], use_line_collection=True)
    plt.setp(markerline, marker='*', markersize=15, markeredgewidth=2, color='gold')
    plt.setp(stemlines, color='gold')
    plt.setp(baseline, visible=False)
    
    plt.tick_params(labelsize=12)
    plt.xlabel('Product', size=12)
    plt.ylabel('Turnover(k dollars)', size=12)
    plt.ylim(bottom=0)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 12]

该图描述了每种产品的营业额。 在八种产品中，奶酪的销售额带来了最大的营业额。

import pandas as pd
    import matplotlib.pyplot as plt
    
    df = pd.DataFrame({ 'Product':['Apple', 'Banana', 'Beer', 'Juice', 'Cheese',
                                  'Coffee', 'Red wine', 'Hotdog'],
                       'Turnover':[30, 59, 92, 43, 123, 93, 103, 37]},
                      columns=['Product', 'Turnover'])
    print(df)
    
    ordered_df = df.sort_values(by='Turnover').reset_index(drop=True)
    my_range = range(1, len(df.index) + 1)
    
    plt.figure(figsize=(9, 6))
    
    plt.hlines(y=my_range, xmin=0, xmax=ordered_df['Turnover'], color='skyblue')
    plt.plot(ordered_df['Turnover'], my_range, 'o', markersize=11)
    
    plt.yticks(ordered_df.index+1, ordered_df['Product'])
    plt.tick_params(labelsize=12)
    plt.xlabel('Turnover(k dollars)', size=12)
    plt.ylabel('Product', size=12)
    plt.xlim(left=0)
    
    plt.show()

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# 24.极坐标图 #

调用subplot()创建子图时通过设置projection=’polar’,便可创建一个极坐标子图，然后调用plot()在极坐标子图中绘图。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 14]

import numpy as np
    import matplotlib.pyplot as plt
    
    # 极坐标下需要的数据有极径和角度
    r = np.arange(1, 6, 1)  # 极径
    theta = [i * np.pi / 2 for i in range(5)]  # 角度
    
    # 指定画图坐标为极坐标,projection='polar'
    ax = plt.subplot(111, projection='polar')
    ax.plot(theta, r, linewidth=3, color='r')
    
    ax.grid(True)
    
    plt.show()

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import numpy as np
    import matplotlib.pyplot as plt
    
    # 极坐标参数设置
    theta=np.arange(0,2*np.pi,0.02)
    plt.figure(figsize=(8,4))
    ax1= plt.subplot(121, projection='polar')
    ax2= plt.subplot(122, projection='polar')
    ax1.plot(theta,theta/6,'--',lw=2)
    ax2.plot(theta,theta/6,'--',lw=2)
    # 创建极坐标子图ax
    
    ax2.set_theta_direction(-1) # 坐标轴正方向改为顺时针
    # set_theta_direction()：坐标轴正方向，默认逆时针
    
    ax2.set_thetagrids(np.arange(0.0, 360.0, 90),['a','b','c','d'])
    ax2.set_rgrids(np.arange(0.2,2,0.4))
    # set_thetagrids()：设置极坐标角度网格线显示及标签 → 网格和标签数量一致
    # set_rgrids()：设置极径网格线显示，其中参数必须是正数
    
    ax2.set_theta_offset(np.pi/2)
    # set_theta_offset()：设置角度偏移，逆时针，弧度制
    
    ax2.set_rlim(0.2,1.2)
    ax2.set_rmax(2)
    ax2.set_rticks(np.arange(0.1, 1.5, 0.2))
    # set_rlim()：设置显示的极径范围
    # set_rmax()：设置显示的极径最大值
    # set_rticks()：设置极径网格线的显示范围
    
    plt.show()

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# 25.雷达图（Radar Chart） #

雷达图是一种由一系列等角辐条（称为radii）组成的图表，每个辐条代表一个变量。 轮辐的数据长度与数据点变量的大小(magnitude )相对于所有数据点上变量的最大大小(maximum magnitude)成正比。 绘制一条线连接每个辐条的数据值。 这使该图块具有星形外观，并且是该图块的流行名称之一的起源。

**适用时机：**  
比较两个或两个以上具有不同特征的items或groups。  
检查一个个数据点的相对值。  
在一张雷达图上显示少于十个因素。

没有内置函数允许使用Matplotlib制作雷达图。 因此，我们必须使用基本函数来构建它。

输入数据是pandas data frame，其中每行代表一个个体，每列代表一个变量。

import matplotlib.pyplot as plt
    import pandas as pd
    from math import pi
    
    # Set data
    df = pd.DataFrame({ 
        'group': ['A', 'B', 'C', 'D'],
        'var1': [38, 1.5, 30, 4],
        'var2': [29, 10, 9, 34],
        'var3': [8, 39, 23, 24],
        'var4': [7, 31, 33, 14],
        'var5': [28, 15, 32, 14]
    })
    
    # number of variable
    categories = list(df)[1:]
    N = len(categories)
    print(N)
    
    # We are going to plot the first line of the data frame.
    # But we need to repeat the first value to close the circular graph:
    values=df.loc[0].drop('group').values.flatten().tolist()
    values += values[:1]
    print(values)
    
    # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]
    print(angles)
    
    # Initialise the spider plot
    ax = plt.subplot(111, polar=True)
    
    # Draw one axe per variable + add labels labels yet
    plt.xticks(angles[:-1], categories, color='grey', size=8)
    
    # Draw ylabels
    ax.set_rlabel_position(0)
    plt.yticks([10, 20, 30], ["10", "20", "30"], color="grey", size=7)
    plt.ylim(0, 40)
    
    # Plot data
    ax.plot(angles, values, linewidth=1, linestyle='solid')
    
    # Fill area
    ax.fill(angles, values, 'b', alpha=0.1)
    
    plt.show()

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## 25.1.Radar chart with several individuals ##

# Libraries
    import matplotlib.pyplot as plt
    import pandas as pd
    from math import pi
    
    # Set data
    df = pd.DataFrame({ 
        'group': ['A', 'B', 'C', 'D'],
        'var1': [38, 1.5, 30, 4],
        'var2': [29, 10, 9, 34],
        'var3': [8, 39, 23, 24],
        'var4': [7, 31, 33, 14],
        'var5': [28, 15, 32, 14]
    })
    
    # ------- PART 1: Create background
    
    # number of variable
    categories = list(df)[1:]
    N = len(categories)
    
    # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]
    
    # Initialise the spider plot
    ax = plt.subplot(111, polar=True)
    
    # If you want the first axis to be on top:
    ax.set_theta_offset(pi / 2)
    ax.set_theta_direction(-1)
    
    # Draw one axe per variable + add labels labels yet
    plt.xticks(angles[:-1], categories)
    
    # Draw ylabels
    ax.set_rlabel_position(0)
    plt.yticks([10, 20, 30], ["10", "20", "30"], color="grey", size=7)
    plt.ylim(0, 40)
    
    # ------- PART 2: Add plots
    
    # Plot each individual = each line of the data
    # I don't do a loop, because plotting more than 3 groups makes the chart unreadable
    
    # Ind1
    values = df.loc[0].drop('group').values.flatten().tolist()
    values += values[:1]
    ax.plot(angles, values, linewidth=1, linestyle='solid', label="group A")
    ax.fill(angles, values, 'b', alpha=0.1)
    
    # Ind2
    values = df.loc[1].drop('group').values.flatten().tolist()
    values += values[:1]
    ax.plot(angles, values, linewidth=1, linestyle='solid', label="group B")
    ax.fill(angles, values, 'r', alpha=0.1)
    
    # Add legend
    plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))
    
    plt.show()

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df = pd.DataFrame({ 'client_id': ['c1','c2','c3','c4'],
                       'cheese': [2, 5, 4, 3],
                       'beer': [2, 1, 3, 5],
                       'chicken': [5, 3, 3, 4],
                       'bread': [4, 3, 2, 1],
                       'coffee': [2, 3, 5, 3]},
                      columns=['client_id', 'cheese', 'beer', 'chicken', 'bread', 'coffee'])
    df

![在这里插入图片描述][20210410093909637.png]

# Libraries
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from math import pi
    
    # Set data
    df = pd.DataFrame({ 'client_id': ['c1','c2','c3','c4'],
                       'cheese': [2, 5, 4, 3],
                       'beer': [2, 1, 3, 5],
                       'chicken': [5, 3, 3, 4],
                       'bread': [4, 3, 2, 1],
                       'coffee': [2, 3, 5, 3]},
                      columns=['client_id', 'cheese', 'beer', 'chicken', 'bread', 'coffee'])
    
    
    categories = list(df)[1:]
    
    values = df.mean().values.flatten().tolist()
    values += values[:1] # repeat the first value to close the circular graph
    
    angles = [n / float(len(categories)) * 2 * pi for n in range(len(categories))]
    angles += angles[:1]
    
    
    
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8), subplot_kw=dict(polar=True))
    
    plt.xticks(angles[:-1], categories, color='grey', size=12)
    plt.yticks(np.arange(1, 6), ['1', '2', '3', '4', '5'], color='grey', size=12)
    plt.ylim(0, 5)
    ax.set_rlabel_position(30)
    
    ax.plot(angles, values, linewidth=1, linestyle='solid')
    ax.fill(angles, values, 'skyblue', alpha=0.4)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 19]

该雷达图描述了4个客户的平均产品偏好。 chicken是最受欢迎的产品.

# Libraries
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from math import pi
    
    # Set data
    df = pd.DataFrame({ 'client_id': ['c1','c2','c3','c4'],
                       'cheese': [2, 5, 4, 3],
                       'beer': [2, 1, 3, 5],
                       'chicken': [5, 3, 3, 4],
                       'bread': [4, 3, 2, 1],
                       'coffee': [2, 3, 5, 3]},
                      columns=['client_id', 'cheese', 'beer', 'chicken', 'bread', 'coffee'])
    
    
    categories = list(df)[1:]
    
    values = df.mean().values.flatten().tolist()
    values += values[:1] # repeat the first value to close the circular graph
    
    angles = [n / float(len(categories)) * 2 * pi for n in range(len(categories))]
    angles += angles[:1]
    
    
    
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8), subplot_kw=dict(polar=True))
    
    plt.xticks(angles[:-1], categories, color='grey', size=12)
    plt.yticks(np.arange(1, 6), ['1', '2', '3', '4', '5'], color='grey', size=12)
    plt.ylim(0, 5)
    ax.set_rlabel_position(30)
    
    # part 1
    val_c1 = df.loc[0].drop('client_id').values.flatten().tolist()
    val_c1 += val_c1[:1]
    ax.plot(angles, val_c1, linewidth=1, linestyle='solid', label='Client c1')
    ax.fill(angles, val_c1, 'skyblue', alpha=0.4)
    
    # part 2
    val_c2 = df.loc[1].drop('client_id').values.flatten().tolist()
    val_c2 += val_c2[:1]
    ax.plot(angles, val_c2, linewidth=1, linestyle='solid', label='Client c2')
    ax.fill(angles, val_c2, 'lightpink', alpha=0.4)
    
    plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 20]

该雷达图显示了4个客户中2个客户的偏好。客户c1喜欢鸡肉和面包，不那么喜欢奶酪。 但是，客户c2比其他4种产品更喜欢奶酪，并且不喜欢啤酒。

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