pie图

import matplotlib.pyplot as mp
mp.figure('pie', facecolor='lightgray')
#整理数据
values = [26, 17, 21, 29, 11]
spaces = [0.05, 0.01, 0.01, 0.01, 0.01]
labels = ['Python', 'JavaScript',
          'C++', 'Java', 'PHP']
colors = ['dodgerblue', 'orangered',
          'limegreen', 'violet', 'gold']
mp.figure('Pie', facecolor='lightgray')
mp.title('Pie', fontsize=20)
# 等轴比例
mp.axis('equal')
mp.pie(
    values,         # 值列表
    spaces,         # 扇形之间的间距列表
    labels,         # 标签列表
    colors,         # 颜色列表
    '%d%%',         # 标签所占比例格式
    shadow=True,    # 是否显示阴影
    startangle=90,  # 逆时针绘制饼状图时的起始角度
    radius=1        # 半径
)
mp.show()

屏幕快照 2019-07-10 下午13.15.00 下午.png

条形图

import numpy as np
import matplotlib.pyplot as mp
apples = np.array([30, 25, 22, 36, 21, 29, 20, 24, 33, 19, 27, 15])
oranges = np.array([24, 33, 19, 27, 35, 20, 15, 27, 20, 32, 20, 22])
mp.figure('Bar'  , facecolor='lightgray')
mp.title('Bar', fontsize=20)
mp.xlabel('Month', fontsize=14)
mp.ylabel('Price', fontsize=14)
mp.tick_params(labelsize=10)
mp.grid(axis='y', linestyle=':')
mp.ylim((0, 40))
x = np.arange(len(apples))
mp.bar(x-0.2, apples, 0.4, color='dodgerblue',label='Apple')
mp.bar(x + 0.2, oranges, 0.4, color='orangered',label='Orange', alpha=0.75)
mp.xticks(x, [
    'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
    'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
mp.legend()
mp.show()

屏幕快照 2019-07-10 下午13.04.50 下午.png

等高线图

import numpy as np
import matplotlib.pyplot as mp
n = 1000
# 生成网格化坐标矩阵
x, y = np.meshgrid(np.linspace(-3, 3, n),
                   np.linspace(-3, 3, n))
# 根据每个网格点坐标，通过某个公式计算z高度坐标
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)
mp.figure('Contour', facecolor='lightgray')
mp.title('Contour', fontsize=20)
mp.xlabel('x', fontsize=14)
mp.ylabel('y', fontsize=14)
mp.tick_params(labelsize=10)
mp.grid(linestyle=':')
# 绘制等高线图
mp.contourf(x, y, z, 8, cmap='jet')
cntr = mp.contour(x, y, z, 8, colors='black',
                  linewidths=0.5)
# 为等高线图添加高度标签
mp.clabel(cntr, inline_spacing=1, fmt='%.1f',
          fontsize=10)
mp.show()

屏幕快照 2019-07-10 下午13.06.55 下午.png

散点图

import numpy as np
import matplotlib.pyplot as mp
n = 100
# 172:  期望值
# 10:   标准差
# n:    数字生成数量
x = np.random.normal(172, 20, n)
y = np.random.normal(60, 10, n)
mp.figure('scatter', facecolor='lightgray')
mp.title('scatter')
# mp.scatter(x, y)
mp.scatter(x, y, c='red')           #直接设置颜色
d = (x-172)**2 + (y-60)**2
mp.scatter(x, y, c=d, cmap='jet')    #以c作为参数，取cmap颜色映射表中的颜色值
mp.show()

屏幕快照 2019-07-10 下午13.16.56 下午.png

3D图

import numpy as np
import matplotlib.pyplot as mp
from mpl_toolkits.mplot3d import axes3d
n = 1000
x = np.random.normal(0, 1, n)
y = np.random.normal(0, 1, n)
z = np.random.normal(0, 1, n)
d = np.sqrt(x ** 2 + y ** 2 + z ** 2)
mp.figure('3D Scatter')
ax = mp.gca(projection='3d')  # 创建三维坐标系
mp.title('3D Scatter', fontsize=20)
ax.set_xlabel('x', fontsize=14)
ax.set_ylabel('y', fontsize=14)
ax.set_zlabel('z', fontsize=14)
mp.tick_params(labelsize=10)
ax.scatter(x, y, z, s=60, c=d, cmap='jet_r', alpha=0.5)
mp.show()

屏幕快照 2019-07-10 下午13.08.29 下午.png

极坐标图

import numpy as np
import matplotlib.pyplot as mp
mp.figure('Polar', facecolor='orangered')
mp.gca(projection='polar')
mp.title('Polar')
mp.xlabel(r'$\theta$', fontsize=14)
mp.xlabel(r'$\rho$', fontsize=14)
mp.grid(linestyle=':')
# 绘制线性关系
# t = np.linspace(0, 4*np.pi, 1000)
# r = 0.8*t
# mp.plot(t, r)
# mp.show()
# 绘制sin曲线
x = np.linspace(0, 6*np.pi, 1000)
y = 3*np.sin(6*x)
mp.plot(x, y)
mp.show()

屏幕快照 2019-07-10 下午13.09.18 下午.png

3D平面图

import numpy as np
import matplotlib.pyplot as mp
from mpl_toolkits.mplot3d import axes3d
n = 1000
# 生成网格化坐标矩阵
x, y = np.meshgrid(np.linspace(-3, 3, n),
                   np.linspace(-3, 3, n))
# 根据每个网格点坐标，通过某个公式计算z高度坐标
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)
mp.figure('3D', facecolor='lightgray')
ax3d = mp.gca(projection='3d')
mp.title('3D', fontsize=20)
# ax3d.set_xlabel('x', fontsize=14)
# ax3d.set_ylabel('y', fontsize=14)
# ax3d.set_zlabel('z', fontsize=14)
mp.tick_params(labelsize=10)
# 绘制3D平面图
# rstride: 行跨距
# cstride: 列跨距
ax3d.plot_surface(x, y, z, rstride=10, cstride=10, cmap='jet')
mp.show()

屏幕快照 2019-07-10 下午13.11.20 下午.png

sin(),cos()

import numpy as np
import matplotlib.pyplot as mp
n = 1000
x = np.linspace(0, 8 * np.pi, n)
sin_y = np.sin(x)
cos_y = np.cos(x / 2) / 2
mp.figure('Fill', facecolor='lightgray')
mp.title('Fill', fontsize=20)
mp.xlabel('x', fontsize=14)
mp.ylabel('y', fontsize=14)
mp.tick_params(labelsize=10)
mp.grid(linestyle=':')
mp.plot(x, sin_y, c='dodgerblue',
        label=r'$y=sin(x)$')
mp.plot(x, cos_y, c='orangered',
        label=r'$y=\frac{1}{2}cos(\frac{x}{2})$')
mp.fill_between(x, cos_y, sin_y, cos_y < sin_y,
                color='dodgerblue', alpha=0.5)
mp.fill_between(x, cos_y, sin_y, cos_y > sin_y,
                color='orangered', alpha=0.5)
mp.legend()
mp.show()

屏幕快照 2019-07-10 下午13.14.04 下午.png

动态气泡图

# 生成动画泡泡
import numpy as np
import matplotlib.pyplot as mp
import matplotlib.animation as ma

# 构建100个泡泡，确定属性
n = 100
balls = np.zeros(100, dtype=[
    ('position', float, 2),
    ('size', float, 1),
    ('growth', float, 1),
    ('color', float, 4)])
# 初始化所有ball的属性值
# 随机生成最小值为0，最大值为1的N行2列的数组
# 初始化所有ball图标
balls['position'] = np.random.uniform(0, 1, (n, 2))
balls['size'] = np.random.uniform(40, 70, n)
balls['growth'] = np.random.uniform(10, 20, n)
balls['color'] = np.random.uniform(0, 1, (n, 4))
# 画图
mp.figure('Animation', facecolor='lightgray')
mp.title('Animation', fontsize=16)
mp.xticks([])
mp.yticks([])
sc = mp.scatter(balls['position'][:, 0], balls['position'][:, 1], balls['size'], color=balls['color'])
def update(number):
    # 定义更新图像
    balls['size'] += balls['growth']
    # 让某个球重新初始化属性
    ind = number % n
    balls[ind]['size'] = np.random.uniform(40, 70, 1)
    balls[ind]['position'] = np.random.uniform(0, 1, (1, 2))
    sc.set_sizes(balls['size'])
    sc.set_offsets(balls['position'])
anim = ma.FuncAnimation(mp.gcf(), update, interval=30)
mp.show()

屏幕快照 2019-07-10 下午13.17.46 下午.png

利用numpy，matplotlib画图