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

大家好，欢迎来到IT知识分享网。

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）。

import matplotlib import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed() # 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() 

import matplotlib import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed() # 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() 

import matplotlib import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed() # 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() 

Fake up some more data

import matplotlib import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed() # 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() 

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() 

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() 

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) 

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 

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() 

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() 

24.极坐标图

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() 

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() 

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() 

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() 

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 

# 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() 

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() 

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。