python 基础 —— Matplotlib

最新推荐文章于 2025-05-16 11:45:05 发布

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最新推荐文章于 2025-05-16 11:45:05 发布

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分类专栏： Python基础

本文链接：https://blog.youkuaiyun.com/iCode_girl/article/details/89052035

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这篇博客详细介绍了使用Matplotlib进行数据可视化的各种方法，包括figure图像设置、坐标轴调整、图例添加、注解功能、散点图、柱状图、等高线图、图片显示、3D数据处理、subplot布局以及动画制作等。通过实例演示了每个功能的应用，帮助读者掌握Python数据可视化的基本技巧。

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1. figure 图像

# 显示图像，figure下方的图标作用
x = np.linspace(-3, 3, 50) # [-3, 3]区间生成50个点
y1 = 2 * x + 1
y2 = x**2 + 1
plt.figure(num=1)
plt.plot(x, y1)
x = np.linspace(-3, 3, 50) # [-3, 3]区间生成50个点
y1 = 2 * x + 1
y2 = x**2 + 1

plt.figure(num=3, figsize=(8, 5))
plt.plot(x, y2
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
plt.show()

2. 设置坐标轴

# 修改坐标轴显示参数
x = np.linspace(-3, 3, 50)  # [-3, 3]区间生成50个点
y1 = 2 * x + 1
y2 = x**2 + 1

plt.figure()
plt.plot(x, y2)
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
plt.xlim((-1, 2))  # x轴取值范围
plt.ylim((-2, 3))  # y轴取值范围
plt.xlabel('I am x')
plt.ylabel('I am y')
new_ticks = np.linspace(-1, 2, 5)
print(new_ticks)
plt.xticks(new_ticks)
# plt.yticks([-2, -1.8, -1, 1.22, 3],
#            [r'$really\ bad$', r'$bad\ \alpha$', r'$good$', r'$really\ good$'])

# gca = 'get current axis'
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.spines['bottom'].set_position(('data', 0)) # 定位的方式：outward, axes
ax.spines['left'].set_position(('data', 0))
plt.show()

3. legend 图例

plt.legend(handles=[l1, l2,], labels=['aaa', 'bbb', ], loc='best')

4. annotation 注解

# annotation 注解
x = np.linspace(-3, 3, 50)
y = 2*x + 1

plt.figure(num=1, figsize=(8, 5))
plt.plot(x, y)

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))

x0 = 1
y0 = 2*x0 + 1
plt.scatter(x0, y0, s=50, color='blue')
plt.plot([x0, x0], [y0, 0], 'k--', lw=2.5)

# method 1
plt.annotate(r'$2x+1=%s$' % y0, xy=(x0, y0), xycoords='data', xytext=(+30, -30),
             textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=.2'))

# method2
plt.text(-3.7, 3, r'$This\ is\ the\ some\ text. \mu\ \sigma_i \alpha_t$', fontdict={'size':16, 'color':'r'})
plt.show()

5. 设置 tick 能见度

x = np.linspace(-3, 3, 50)
y = 0.1*x

plt.figure()
plt.plot(x, y, linewidth=10)
plt.ylim(-2, 2)
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))

for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(12)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.7))


plt.show()

6. scatter 散点图

n = 1024
X = np.random.normal(0, 1, n)
Y = np.random.normal(0, 1, n)
T = np.arctan2(Y, X) # for color value

# plt.scatter(X, Y, s=75, c=T, alpha=0.5)
plt.scatter(np.arange(5), np.arange(5))
#plt.xlim((-1.5, 1.5))
#plt.ylim((-1.5, 1.5))
plt.xticks(())
plt.yticks(())
plt.show()

7. bar 柱状图

n = 12
X = np.arange(n)
Y1 = (1 - X/float(n)) * np.random.uniform(0.5, 1.0, n)
Y2 = (1 - X/float(n)) * np.random.uniform(0.5, 1.0, n)
plt.bar(X, +Y1, facecolor='#9990ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
for x, y in zip(X, Y1):
    # ha: horizontal alignment
    plt.text(x, y+0.05, '%.2f' % y, ha='center', va='bottom')

for x, y in zip(X, Y2):
    # ha: horizontal alignment
    plt.text(x, -y-0.05, '%.2f' % y, ha='center', va='top')

plt.show()

8. Contours 等高线图

def f(x, y):
    # the height function
    return (1 - x / 2 + x**5 + y**3) * np.exp(-x**2-y**2)

n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)

X, Y = np.meshgrid(x, y)

# use plt.contourf to filling contours
# X, Y and value for (X, Y) points
plt.contourf(X, Y, f(X, Y), 8, alpha=0.75, cmap=plt.cm.hot)
# use plt.contour to add contour lines
C = plt.contour(X, Y, f(X, Y), 8, colors='black', linewidth=0.5)

# adding label
plt.clabel(C, inline=True, fontsize=10)
plt.show()

9. Image 图片

# image data
a = np.array([0.31360827978, 0.365348418405, 0.423733120134,
              0.365348418405, 0.439599930621, 0.525083754405,
              0.4233733120134, 0.525083754405, 0.651536351379]).reshape(3,3)

plt.imshow(a, interpolation='nearest', cmap='bone', origin='upper')
plt.colorbar(shrink=0.9)

plt.xticks(())
plt.yticks(())
plt.show()

10. 3D数据

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
# X, Y value
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
# height value
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='rainbow')
ax.set_zlim(-2, 2)

plt.show()

11. subplot 多合一显示

plt.figure()
plt.subplot(2, 1, 1)
plt.plot([0, 1], [0, 1])
plt.subplot(2, 3, 4)
plt.plot([0, 2], [0, 2])
plt.subplot(2, 3, 5)
plt.plot([0, 3], [0, 3])
plt.subplot(2, 3, 6)
plt.plot([0, 4], [0, 4])
plt.show()

12. subplot 分格显示

# method1: subplot2grid
plt.figure()
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3, rowspan=1)
ax1.plot([1, 2], [1, 2])
ax1.set_title('ax1_title')

ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=1)
ax3 = plt.subplot2grid((3, 3), (1, 2), colspan=1, rowspan=2)
ax4 = plt.subplot2grid((3, 3), (2, 0), colspan=1, rowspan=1)
ax1 = plt.subplot2grid((3, 3), (2, 1), colspan=1, rowspan=1)
plt.show()

# method 2: gridspec
plt.figure()
gs = gridspec.GridSpec(3, 3)
ax1 = plt.subplot(gs[0, :])
ax2 = plt.subplot(gs[1, :2])
ax3 = plt.subplot(gs[1:, 2])
ax4 = plt.subplot(gs[-1, 0])
ax5 = plt.subplot(gs[-1, -2])
plt.show()

# method 3: ease to define structure
plt.figure()
f, ((ax11, ax12), (ax21, ax22)) = plt.subplots(2, 2, sharex=True, sharey=True)
ax11.scatter([1, 2], [1, 2])
plt.show()

13. 图中图

fig = plt.figure()
x = [1, 2, 3, 4, 5, 6, 7]
y = [1, 3, 4, 2, 5, 8, 6]

left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax1 = fig.add_axes([left, bottom, width, height])
ax1.plot(x, y, 'r')
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax1.set_title('title')

left, bottom, width, height = 0.2, 0.6, 0.2, 0.2
ax2 = fig.add_axes([left, bottom, width, height])
ax2.plot(y, x, 'b')
ax2.set_xlabel('x')
ax2.set_ylabel('y')
ax2.set_title('inside1')

plt.axes([0.6, 0.2, 0.25, 0.25])
plt.plot(y[::-1], x, 'g')
plt.xlabel('x')
plt.ylabel('y')
plt.title('inside2')
plt.show()

14. 次坐标轴

x = np.arange(0, 10, 0.1)
y1 = 0.05*x**2
y2 = -1*y1

fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x, y1, 'g-')
ax2.plot(x, y2, 'b--')
ax1.set_xlabel('X data')
ax1.set_ylabel('Y1', color='g')
ax2.set_ylabel('Y2', color='b')
plt.show()

15. Animation 动画

from matplotlib import animation
fig, ax = plt.subplots()

x = np.arange(0, 2*np.pi, 0.01)
line, = ax.plot(x, np.sin(x))

def animate(i):
    line.set_ydata(np.sin(x+i/10))
    return line,

def init():
    line.set_ydata(np.sin(x))
    return line,

ani = animation.FuncAnimation(fig=fig, func=animate, frames=100, init_func=init, interval=20, blit=False, )
plt.show()