from matplotlib import pyplot as plt import numpy as np import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] # 中文字体支持
1 饼图-pie()
1.1 pie()方法参数说明
pie()是matplotlib中画饼图的方法，其主要参数如下：
1.2 基础作图
labels = 'Python组', 'Java组', 'C组', 'Go组' sizes = [25, 45, 30, 10] fig = plt.figure(figsize=(8, 4)) ax1 = fig.add_subplot(111) ax1.pie(sizes, labels=labels) ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle. plt.show()
1.3 字符标签与数值标签
labels = 'Python组', 'Java组', 'C组', 'Go组' sizes = [25, 45, 30, 10] fig = plt.figure(figsize=(8, 4)) ax1 = fig.add_subplot(121) ax1.pie(sizes, labels=labels, # 字符标签 labeldistance=1.1, # 字符标签到中心点的距离 autopct='%1.1f%%', # 显示数值标签 pctdistance=0.5 #数值标签到中心点的距离 ) ax2 = fig.add_subplot(122) ax2.pie(sizes, labels=labels, # 字符标签 labeldistance=0.4, # 字符标签到中心点的距离 autopct='%1.2f%%', # 显示数值标签 pctdistance=1.2, #数值标签到中心点的距离 rotatelabels=True # 旋转标签 ) plt.show()
1.4 扇形分隔距离
labels = 'Python组', 'Java组', 'C组', 'Go组' sizes = [25, 45, 30, 10] fig = plt.figure(figsize=(8, 4)) explode1 = (0.1, 0.1, 0.1, 0.1) ax1 = fig.add_subplot(121) ax1.pie(sizes, explode=explode1, # 分隔扇形 labels=labels, autopct='%1.1f%%') explode2 = (0.1, 0, 0, 0) ax2 = fig.add_subplot(122) ax2.pie(sizes, explode=explode2, # 分隔扇形 labels=labels, autopct='%1.1f%%') plt.show()
1.5 阴影与边框
labels = 'Python组', 'Java组', 'C组', 'Go组' sizes = [25, 45, 30, 10] explode = (0.1, 0, 0, 0) fig1, ax1 = plt.subplots() ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, # 显示阴影 wedgeprops = {'linewidth': 3} # 设置边框宽度 ) plt.show()
1.6 旋转图形
labels = 'Python组', 'Java组', 'C组', 'Go组' sizes = [25, 45, 30, 10] explode = (0.1, 0, 0, 0) fig1, ax1 = plt.subplots() ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90 # 旋转 ) plt.show()
1.7 单独设置某一扇形
pie()方法返回一个tuple，第一个元素为每个扇形对象组成的list，第二个元素为每个扇形的标签Text对象，第三个元素为每个扇形的数值标签对象，通过这三个对象，可以实现对单一扇形的设置。
labels = 'Python组', 'Java组', 'C组', 'Go组' sizes = [25, 45, 30, 10] explode = (0.1, 0, 0, 0) fig1, ax1 = plt.subplots() patches, texts, autotexts = ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%') print('第1个返回值：',patches) print('第2个返回值：',texts) print('第3个返回值：',autotexts) texts[0].set_color('red') # 设置第一个扇形的字符标签字体颜色 texts[0].set_fontsize(30) # 设置第一个扇形的字符标签字体大小 autotexts[0].set_color('white') # 设置第一个扇形的数值标签字体颜色 plt.show()
第1个返回值：[<matplotlib.patches.Wedge object at 0x7efe1de27450>, <matplotlib.patches.Wedge object at 0x7efe1de27a50>, <matplotlib.patches.Wedge object at 0x7efe1de311d0>, <matplotlib.patches.Wedge object at 0x7efe1de31a90>] 第2个返回值：[Text(0.9068994725035225, 0.7858329000320824, 'Python组'), Text(-1.0005952104475537, 0.4569564802357176, 'Java组'), Text(0.15654637770487598, -1.0888035780743386, 'C组'), Text(1.055442297353649, -0.30990572269135586, 'Go组')] 第3个返回值：[Text(0.5290246922937214, 0.4584025250187147, '22.7%'), Text(-0.5457792056986657, 0.2492489892194823, '40.9%'), Text(0.0853889332935687, -0.593892860767821, '27.3%'), Text(0.5756957985565357, -0.1690394851043759, '9.1%')]
labels = 'Python组', 'Java组', 'C组', 'Go组' sizes = [25, 45, 30, 10] explode = (0.1, 0, 0, 0) fig1, ax1 = plt.subplots(figsize=(5, 5)) patches, texts, autotexts = ax1.pie(sizes, explode=explode, autopct='%1.1f%%') ax1.legend(patches, labels, loc="upper right",bbox_to_anchor=(0.75, 0, 0.5, 0.4)) plt.show()
1.8 嵌套饼图
sizes = { 'Python组':{'男':10, '女': 15}, 'Java组':{'男':15, '女': 30}, 'C组':{'男':5, '女': 25}, 'Go组':{'男':4, '女': 6} } def func(sizes): """提取数据和标签""" data1 = [] data2 = [] data2_label = [] for key in sizes.keys(): data1.append(sizes.get(key).get('男') + sizes.get(key).get('女')) data2.append(sizes.get(key).get('男')) data2_label.append(key+'-'+'男') data2.append(sizes.get(key).get('女')) data2_label.append(key+'-'+'女') return data1, data2, sizes.keys(), data2_label data1, data2, data1_label, data2_label = func(sizes) cmap = plt.get_cmap("tab20c") outer_colors = cmap(np.arange(4)*4) inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10, 13, 14])) fig = plt.figure(figsize=(10, 4)) ax1 = fig.add_subplot(121) ax1.pie(data1, labels=data1_label, radius=1.5, colors=outer_colors, autopct='%1.1f%%', labeldistance=1, pctdistance=0.8) ax1.pie(data2, labels=data2_label, radius=1, colors=inner_colors, autopct='%1.1f%%', labeldistance=0.4, pctdistance=0.9) ax2 = fig.add_subplot(122) ax2.pie(data1, labels=data1_label, radius=1.5, colors=outer_colors, autopct='%1.1f%%', labeldistance=1, pctdistance=0.8,wedgeprops=dict(width=0.8, edgecolor='w')) ax2.pie(data2, labels=data2_label, radius=1, colors=inner_colors, autopct='%1.1f%%', labeldistance=0.4, pctdistance=0.9,wedgeprops=dict(width=0.6, edgecolor='w')) plt.show()
2 箱线图
2.1 参数说明
matplotlib绘制箱线图通过boxplot()方法实现，主要参数如下：
boxplot()方法返回值是一个dict，键值包
括'whiskers'、'caps'、'boxes'、'fliers'、'means'，分别表示须线、顶端末端线段、箱体、异常数据、均值等绘图对象分别组成的列表，通过这些对象可以横放把您的实现箱线图各个部分的自定义设置。
2.2 基础作图
data=np.random.normal(0,4,100) fig = plt.figure(figsize=(8, 8)) ax1 = fig.add_subplot(221) ax1.set_title('图1 常规作图') ax1.boxplot(data) muti_data=[np.random.normal(0,std,100) for std in range(1,4)] ax2 = fig.add_subplot(222) ax2.set_title('图2 多图绘制') ax2.boxplot(muti_data) ax3 = fig.add_subplot(223) ax3.set_title('图3 水平箱线图') ax3.boxplot(data, vert=False) ax4 = fig.add_subplot(224) ax4.set_title('图4 中间凹陷') ax4.boxplot(data, notch=True) plt.show()
2.3 修改标签
data=np.random.normal(0,4,100) fig = plt.figure(figsize=(8, 4)) muti_data=[np.random.normal(0,std,100) for std in range(1,4)] ax2 = fig.add_subplot(121) ax2.set_title('图1') ax2.boxplot(muti_data, labels=['第1组', '第2组', '第3组']) muti_data=[np.random.normal(0,std,100) for std in range(1,4)] ax2 = fig.add_subplot(122) ax2.set_title('图2') ax2.boxplot(muti_data, vert=False, labels=['第1组', '第2组', '第3组']) plt.show()
2.4 显示均值
data=np.random.normal(0,4,100) fig = plt.figure(figsize=(8, 4)) muti_data=[np.random.normal(0,std,100) for std in range(1,4)] ax2 = fig.add_subplot(121) ax2.set_title('图1') ax2.boxplot(muti_data, labels=['第1组', '第2组', '第3组'], showmeans=True) # 显示均值，默认以点的方式显示 muti_data=[np.random.normal(0,std,100) for std in range(1,4)] ax2 = fig.add_subplot(122) ax2.set_title('图2') ax2.boxplot(muti_data, labels=['第1组', '第2组', '第3组'], showmeans=True, meanline=True) # 显示均值，并以横线方式显示 plt.show()
2.5 箱体设置
data=np.random.normal(0,4,100) fig = plt.figure(figsize=(8, 4)) muti_data=[np.random.normal(0,std,100) for std in range(1,4)] ax2 = fig.add_subplot(121) ax2.set_title('图1') box_dict = ax2.boxplot(muti_data, labels=['第1组', '第2组', '第3组'], patch_artist=True) # 注意，patch_artist一定要设置为True，下面的设置才会生效 box_dict.get('boxes')[0].set_color('red') # 箱体边框颜色 box_dict.get('boxes')[1].set_color('blue') box_dict.get('boxes')[2].set_color('green') plt.show()
