Matplotlib 二维数据与统计图函数

Matplotlib 参考文档 Matplotlib 参考文档

用于二维数据展示和统计分布可视化的函数。

函数一览

函数功能
hist2d()二维直方图
hexbin()六边形分箱图（Hexbin）
stairs()阶梯直方图（替代 hist 的 step 模式）
matshow()在新 Figure 中显示矩阵
pcolor()伪彩色图（PolyCollection）
pcolormesh()伪彩色图（QuadMesh，性能更好）
spy()稀疏矩阵非零元素图案
figimage()在 Figure 级别放置图像
ecdf()经验累积分布函数
violinplot()小提琴图

hist2d() - 二维直方图

matplotlib.pyplot.hist2d(x, y, bins=10, range=None, density=False,
    weights=None, cmin=None, cmax=None, *, **kwargs)

实例

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
x = np.random.randn(5000)
y = x * 0.5 + np.random.randn(5000) * 0.5

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5),
                                layout='constrained')

# 二维直方图
h, xedges, yedges, im = ax1.hist2d(x, y, bins=40, cmap='Blues')
fig.colorbar(im, ax=ax1, label='Count')
ax1.set_title('hist2d()')
ax1.set_xlabel('X')
ax1.set_ylabel('Y')

# 六边形分箱
hb = ax2.hexbin(x, y, gridsize=30, cmap='YlOrRd')
fig.colorbar(hb, ax=ax2, label='Count')
ax2.set_title('hexbin()')
ax2.set_xlabel('X')
ax2.set_ylabel('Y')

plt.show()

stairs() - 阶梯直方图

matplotlib.pyplot.stairs(values, edges=None, *, orientation='vertical',
    baseline=0, fill=False, **kwargs)

实例

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data = np.random.randn(500)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4),
                                layout='constrained')

# hist + stairs 对比
counts, bins = np.histogram(data, bins=30)
ax1.hist(data, bins=30, alpha=0.3, color='steelblue')
ax1.stairs(counts, bins, fill=False, color='red',
           linewidth=2, label='stairs outline')
ax1.set_title('stairs() outline on hist()')
ax1.legend()

# fill=True 的 stairs
ax2.stairs(counts, bins, fill=True, color='coral',
           alpha=0.7, edgecolor='black', linewidth=1)
ax2.set_title('stairs() with fill=True')

plt.show()

pcolormesh() - 伪彩色图

matplotlib.pyplot.pcolormesh(*args, alpha=None, norm=None,
    cmap=None, vmin=None, vmax=None, shading=None, **kwargs)

实例

import matplotlib.pyplot as plt
import numpy as np

# 创建二维数据
x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5),
                                layout='constrained')

# pcolormesh - 默认 shading='flat'（丢最后一行/列）
pc1 = ax1.pcolormesh(X, Y, Z, cmap='RdYlBu',
                      shading='auto')
fig.colorbar(pc1, ax=ax1, label='Value')
ax1.set_title('pcolormesh()')
ax1.set_xlabel('X')
ax1.set_ylabel('Y')

# pcolor - 渲染为 PolyCollection
pc2 = ax2.pcolor(X, Y, Z, cmap='RdYlBu')
fig.colorbar(pc2, ax=ax2, label='Value')
ax2.set_title('pcolor()')
ax2.set_xlabel('X')
ax2.set_ylabel('Y')

plt.show()

spy() - 稀疏矩阵图案

matplotlib.pyplot.spy(Z, precision=0, marker=None, markersize=None,
    aspect='equal', origin='upper', **kwargs)

实例

import matplotlib.pyplot as plt
import numpy as np
from scipy import sparse

# 创建稀疏矩阵
np.random.seed(42)
dense = np.random.rand(50, 50)
dense[dense < 0.9] = 0      # 90% 为零
sparse_mat = sparse.csr_matrix(dense)

fig, ax = plt.subplots(figsize=(5, 5), layout='constrained')

ax.spy(sparse_mat, markersize=5, color='steelblue')
ax.set_title('spy() - Sparse Matrix Pattern')
plt.show()

matshow() / figimage()

matplotlib.pyplot.matshow(A, fignum=None, **kwargs)
matplotlib.pyplot.figimage(X, xo=0, yo=0, alpha=None, norm=None,
    cmap=None, vmin=None, vmax=None, origin=None, **kwargs)

实例

import matplotlib.pyplot as plt
import numpy as np

# matshow - 自动创建新 Figure 显示矩阵
matrix = np.random.rand(8, 8)
plt.matshow(matrix, cmap='viridis')
plt.title('matshow()')
plt.colorbar(label='Value')
plt.show()
print("runoob: matshow displayed")

ecdf() - 经验累积分布

matplotlib.pyplot.ecdf(x, *, ax=None, complementary=False,
    **kwargs)

实例

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data1 = np.random.normal(0, 1, 200)
data2 = np.random.normal(2, 0.5, 200)

fig, ax = plt.subplots(figsize=(7, 5), layout='constrained')

ax.ecdf(data1, label='N(0, 1)')
ax.ecdf(data2, label='N(2, 0.5)')

ax.set_title('ecdf() - Empirical CDF')
ax.set_xlabel('Value')
ax.set_ylabel('Cumulative Probability')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()

violinplot() - 小提琴图

matplotlib.pyplot.violinplot(dataset, positions=None, vert=True,
    widths=0.5, showmeans=False, showmedians=False,
    showextrema=True, **kwargs)

实例

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data = [
    np.random.normal(0, 1, 200),
    np.random.normal(2, 1.5, 200),
    np.random.gamma(2, 1, 200),
]

fig, ax = plt.subplots(figsize=(7, 5), layout='constrained')

vp = ax.violinplot(data, showmeans=True, showmedians=True)

# 自定义颜色
colors = ['#3498db', '#e74c3c', '#2ecc71']
for body, color in zip(vp['bodies'], colors):
    body.set_facecolor(color)
    body.set_alpha(0.7)

ax.set_title('violinplot()')
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(['Normal', 'Normal(wide)', 'Gamma'])
ax.grid(axis='y', alpha=0.3)
plt.show()

Matplotlib 参考文档 Matplotlib 参考文档