1.4 直方图
🚩绘制的直方图其实就是一个概率分布，直方图可以看成很多个柱子的柱状图
import numpy as np import matplotlib.pyplot as plt mu = 100 # 平均值 sigma = 15 # 标准差 x = np.random.normal(loc = mu, scale = 15, size = 10000) fig, ax = plt.subplots() # 直方图一般用于描述统计性的数据 # 数据量比较大,通过绘制直方图,看出数据内部的关系 # density = True 统计的是概率 # density = False 统计数字在某个范围内的次数 n, bins, patches = ax.hist(x, 200, density = True) # 直方图 # 概率密度函数 y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu)) ** 2)) plt.plot(bins, y, '--') plt.xlabel('Smarts') plt.ylabel('Probability density') plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$') # 紧凑布局 fig.tight_layout()
1.5 箱形图
import numpy as np import matplotlib.pyplot as plt # 正态分布 data = np.random.normal(size = (500, 4)) lables = ['A', 'B', 'C', 'D'] # 用Matplotlib画箱线图 # 黄色的线就是中位数,红色的圆点是异常值 _ = plt.boxplot(data, 1, 'ro', labels = lables)
1.6 散点图
import numpy as np import matplotlib.pyplot as plt data = np.random.randn(100, 2) s = np.random.randint(100, 300, size = 100) color = np.random.randn(100) plt.scatter(data[:, 0], # 横坐标 data[:, 1], # 纵坐标 s = s, # 尺寸 c = color, # 颜色 alpha = 0.5) # 透明度
1.7 饼图
1.7.1 一般饼图
import numpy as np import matplotlib.pyplot as plt # 解决中文字体乱码的问题 plt.rcParams['font.sans-serif'] = 'KaiTi' labels = ["五星", "四星", "三星", "二星", "一星"] # 标签 percent = [95, 261, 105, 30, 9] # 某市星级酒店数量 # 设置图片大小和分辨率 fig = plt.figure(figsize = (5, 5), dpi = 120) # 偏移中心量，突出某一部分 # 0.1 表示 10%,自身高度的10%,是一个相对值 explode = (0, 0.1, 0, 0, 0) # 绘制饼图：autopct显示百分比，这里保留一位小数；shadow控制是否显示阴影 _ = plt.pie(x = percent, # 数据 explode = explode, # 偏移中心量 labels = labels, # 显示标签 autopct = '%0.1f%%', # 显示百分比 shadow = True) # 阴影，3D效果
1.7.2 甜甜圈
import numpy as np import matplotlib.pyplot as plt plt.figure(figsize = (6, 6)) # 甜甜圈原料 recipe = ["225g flour", "90g sugar", "1 egg", "60g butter", "100ml milk", "1/2package of yeast"] # 原料比例 data = [225, 90, 50, 60, 100, 5] wedges, texts = plt.pie(data, startangle = 40) bbox_props = dict(boxstyle = "square,pad=0.3", fc = "w", ec = "k", lw = 0.72) kw = dict(arrowprops = dict(arrowstyle = "-"), bbox = bbox_props, va = "center") for i, p in enumerate(wedges): ang = (p.theta2 - p.theta1) / 2. + p.theta1 # 角度计算 # 角度转弧度----->弧度转坐标 y = np.sin(np.deg2rad(ang)) x = np.cos(np.deg2rad(ang)) ha = {-1 : "right", 1 : "left"}[int(np.sign(x))] # 水平对齐方式 connectionstyle = "angle,angleA=0,angleB={}".format(ang) # 箭头连接样式 kw["arrowprops"].update({"connectionstyle" : connectionstyle}) # 更新箭头连接方式 plt.annotate(recipe[i], xy=(x, y), xytext = (1.35 * np.sign(x), 1.4 * y), ha = ha, **kw, fontsize = 18, weight = 'bold') plt.title("Matplotlib bakery: A donut", fontsize = 18, pad = 25) plt.tight_layout()
1.8 热力图
import numpy as np import matplotlib import matplotlib.pyplot as plt # 标签 vegetables = ["cucumber", "tomato", "lettuce", "asparagus", "potato", "wheat", "barley"] farmers = list('ABCDEFG') # 创建数据,随机数 harvest = np.random.randn(7, 7) * 5 # 农民丰收数据 plt.rcParams['font.size'] = 18 plt.rcParams['font.weight'] = 'heavy' plt.figure(figsize = (9, 9)) # imshow() 显示图片,因为数值不同,所以图片颜色不同 im = plt.imshow(harvest) plt.xticks(np.arange(len(farmers)), farmers, rotation = 45, ha = 'right') plt.yticks(np.arange(len(vegetables)), vegetables) # 绘制文本 for i in range(len(vegetables)): for j in range(len(farmers)): text = plt.text(j, i, round(harvest[i, j], 1), ha = "center", va = "center", color = 'r') plt.title("Harvest of local farmers (in tons/year)", pad = 20)
1.9 面积图
import matplotlib.pyplot as plt plt.figure(figsize = (9, 6)) days = [1, 2, 3, 4, 5] sleeping = [7, 8, 6, 11, 7] eating = [2, 3, 4, 3, 2] working = [7, 8, 7, 2, 2] playing = [8, 5, 7, 8, 13] plt.stackplot(days, sleeping, eating, working, playing) plt.xlabel('x') plt.ylabel('y') plt.title('Stack Plot', fontsize = 18) plt.legend(['Sleeping', 'Eating', 'Working', 'Playing'], fontsize = 18)
1.10 蜘蛛图
import numpy as np import matplotlib.pyplot as plt # 画图数据 plt.rcParams['font.family'] = 'KaiTi' labels = np.array(["个人能力", "IQ", "服务意识", "团队精神", "解决问题能力", "持续学习"]) y = [83, 61, 95, 67, 76, 88] x = np.linspace(0, 2 * np.pi, len(labels), endpoint = False) # 首位相接,我们要把 "持续学习" 和 "个人能力相连起来" y = np.concatenate((y, [y[0]])) # 首尾相接 x = np.concatenate((x, [x[0]])) # 首尾相接 # 用Matplotlib画蜘蛛图 fig = plt.figure(figsize = (6, 6)) ax = fig.add_subplot(111, polar = True) # 连线 # o:表示形状，圆形 # -:表示实线 # o-:属性连用 ax.plot(x, y, 'o-', linewidth = 2) ax.fill(x, y, alpha = 0.25) # 填充颜色 # 设置角度 ax.set_thetagrids(x[:-1] * 180 / np.pi,# 角度值 # 由于首位相接时候相当于给x增加了一个元素,现在需要切片去掉这个元素 labels, fontsize = 18) _ = ax.set_rgrids([20, 40, 60, 80], fontsize = 18)
