CC00059.python——|Hadoop&Python.v06|——|Arithmetic.v06|Matplotlib数据可视化：Matplotlib|

一、常用视图：折线图

import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(0,10,size = 15)
# 一图多线
plt.figure(figsize=(9,6))
plt.plot(x,marker = '*',color = 'r')
plt.plot(x.cumsum(),marker = 'o')

# 多图布局
fig,axs = plt.subplots(2,1)
fig.set_figwidth(9)
fig.set_figheight(6)
axs[0].plot(x,marker = '*',color = 'red')
axs[1].plot(x.cumsum(),marker = 'o')

二、柱状图：堆叠柱状图

import numpy as np
import matplotlib.pyplot as plt
labels = ['G1', 'G2', 'G3', 'G4', 'G5','G6']                        # 级别
men_means = np.random.randint(20,35,size = 6)
women_means = np.random.randint(20,35,size = 6)
men_std = np.random.randint(1,7,size = 6)
women_std = np.random.randint(1,7,size = 6)
width = 0.35
plt.bar(labels,                                                     # 横坐标
        men_means,                                                  # 柱高
        width,                                                      # 线宽
        yerr=4,                                                     # 误差条
        label='Men')                                                # 标签
plt.bar(labels, women_means, width, yerr=2, bottom=men_means,
        label='Women')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.legend()

分组带标签柱状图

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

labels = ['G1', 'G2', 'G3', 'G4', 'G5','G6']                            # 级别
men_means = np.random.randint(20,35,size = 6)
women_means = np.random.randint(20,35,size = 6)
x = np.arange(len(men_means))
plt.figure(figsize=(9,6))
rects1 = plt.bar(x - width/2, men_means, width)                         # 返回绘图区域对象
rects2 = plt.bar(x + width/2, women_means, width)

# 设置标签标题，图例
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(x,labels)
plt.legend(['Men','Women'])

# 添加注释
def set_label(rects):
    for rect in rects:
        height = rect.get_height()                                      # 获取高度
        plt.text(x = rect.get_x() + rect.get_width()/2,                 # 水平坐标
                 y = height + 0.5,                                      # 竖直坐标
                s = height,                                             # 文本
                ha = 'center')                                          # 水平居中
set_label(rects1)
set_label(rects2)
plt.tight_layout()                                                      # 设置紧凑布局
plt.savefig('./分组带标签柱状图.png')

三、极坐标图：极坐标线形图

import numpy as np
import matplotlib.pyplot as plt

r = np.arange(0, 4*np.pi, 0.01)                                     # 弧度值
y = np.linspace(0,2,len(r))                                         # 目标值

ax = plt.subplot(111,projection = 'polar',facecolor = 'lightgreen') # 定义极坐标
ax.plot(r, y,color = 'red')
ax.set_rmax(3)                                                      # 设置半径最大值
ax.set_rticks([0.5, 1, 1.5, 2])                                     # 设置半径刻度
ax.set_rlabel_position(-22.5)                                       # 设置半径刻度位置
ax.grid(True)                                                       # 网格线

ax.set_title("A line plot on a polar axis", va='center',ha = 'center',pad = 30)

极坐标柱状图

import numpy as np
import matplotlib.pyplot as plt
N = 8                                                                   # 分成8份
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = np.random.randint(3,15,size = N)
width = np.pi / 4
colors = np.random.rand(8,3)                                            # 随机生成颜色
ax = plt.subplot(111, projection='polar')                               # polar表示极坐标
ax.bar(theta, radii, width=width, bottom=0.0,color = colors)

四、直方图

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()

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()
plt.savefig('./直方图.png')

五、箱型图

import numpy as np
import matplotlib.pyplot as plt
data=np.random.normal(size=(500,4))
lables = ['A','B','C','D']
# 用Matplotlib画箱线图
plt.boxplot(data,1,'gD',labels=lables)                                  # 红色的圆点是异常值

 六、散点图：

散点图的英文叫做scatter plot，它将两个变量的值显示在二维码中，
飞涨适合展示两个变量之间的关系。

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

七、拼图：

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
matplotlib.rcParams['font.sans-serif']='Kaiti SC'

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=150)
# 偏移中心量，突出某一部分
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
plt.savefig("./饼图.jpg")

2、嵌套饼图

fig=plt.figure(figsize=(5,5),dpi=100)

#数据集，p1, p2分别对应外部、内部百分比例
p1=[43,25,32]
p2=[7,22,14,5,14,6,32]

labels = ['小狗','小猫','小鸟']
def func(pct):
    return r'%0.1f'%(pct) + '%'

plt.pie(p1,
        autopct=lambda pct: func(pct),
        radius=1,                                                       # 半径
        pctdistance=0.85,                                               # 百分比位置
        wedgeprops=dict(linewidth=3,width=0.4,edgecolor='w'),           # 饼图格式：间隔线宽、饼图宽度、边界颜色
        labels=labels)
# 绘制内部饼图
plt.pie(p2,
        autopct='%0.1f%%',
        radius=0.7,
        pctdistance=0.7,
        wedgeprops=dict(linewidth=3,width=0.7,edgecolor='w'))
# 设置图例标题、位置，frameon控制是否显示图例边框，bbox_to_anchor控制图例显示在饼图的外面
plt.legend(labels,loc = 'upper right',bbox_to_anchor = (0.75,0,0.4,1),title = '宠物占比')

3、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()

八、热力图

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

vegetables = ["cucumber", "tomato", "lettuce", "asparagus","potato", "wheat", "barley"]
farmers = list('ABCDEFG')

harvest = np.random.rand(7,7)*5                                         # 农民丰收数据
plt.rcParams['font.size'] = 18
plt.rcParams['font.weight'] = 'heavy'
plt.figure(figsize=(9,9))
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)
fig.tight_layout()
plt.savefig('./热力图.png')

九、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)

十、蜘蛛图

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'Kaiti SC'
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"])
stats=[83, 61, 95, 67, 76, 88]
# 画图数据准备，角度、状态值
angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
stats=np.concatenate((stats,[stats[0]]))
angles=np.concatenate((angles,[angles[0]]))
# 用Matplotlib画蜘蛛图
fig = plt.figure(figsize=(9,9))
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2)                               # 连线
ax.fill(angles, stats, alpha=0.25)                                      # 填充
# 设置角度
ax.set_thetagrids(angles*180/np.pi,                                     # 角度值
                  labels,
                  fontsize = 18)
ax.set_rgrids([20,40,60,80],fontsize = 18)