Matplotlib プロット集

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)
# サイン波を折れ線で描画
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.title('Line Plot')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(0)
x = np.random.rand(50)
y = np.random.rand(50)
# x の値で色分けした散布図
plt.scatter(x, y, c=x, cmap='viridis', marker='o')
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter Plot')
plt.colorbar()
plt.show()

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']
values = [10, 24, 36, 18]
# カテゴリ別の棒グラフ
plt.bar(categories, values, color='skyblue')
plt.title('Bar Chart')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

data = np.random.randn(1000)
# 分布をヒストグラムで可視化
plt.hist(data, bins=30, color='gray', edgecolor='black')
plt.title('Histogram')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

data = [np.random.randn(100) + i for i in range(4)]
# 複数系列の分布を箱ひげ図で比較
plt.boxplot(data)
plt.title('Box Plot')
plt.show()

import matplotlib.pyplot as plt

labels = ['Apple', 'Banana', 'Cherry', 'Date']
sizes = [30, 15, 45, 10]
# 割合の円グラフ
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('Pie Chart')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

img = np.random.rand(10, 10)
# 2D 配列を画像として表示
plt.imshow(img, cmap='viridis')
plt.colorbar()
plt.title('Image Display')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.exp(-(X**2 + Y**2))
# 等高線の塗りつぶし
plt.contourf(X, Y, Z, levels=20, cmap='coolwarm')
plt.colorbar()
plt.title('Filled Contour Plot')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

Y, X = np.mgrid[-3:3:100j, -3:3:100j]
U = -1 - X**2 + Y
V = 1 + X - Y**2
# ベクトル場の可視化
plt.quiver(X, Y, U, V)
plt.title('Quiver Plot')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

theta = np.linspace(0, 2*np.pi, 100)
r = 1 + np.sin(4*theta)
# 極座標で描画
ax = plt.subplot(projection='polar')
ax.plot(theta, r)
ax.set_title('Polar Plot')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

X = np.linspace(-5, 5, 50)
Y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(X, Y)
Z = np.sin(np.sqrt(X**2 + Y**2))

fig = plt.figure()
ax = fig.add_subplot(projection='3d')
# 3D サーフェスを描画
ax.plot_surface(X, Y, Z, cmap='viridis')
ax.set_title('3D Surface Plot')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(1)
x = np.linspace(0, 10, 50)
noise = np.random.normal(scale=1.2, size=x.size)
y = 1.8 * x + 2.5 + noise

slope, intercept = np.polyfit(x, y, 1)
y_fit = slope * x + intercept

# 回帰直線を重ねる
plt.scatter(x, y, color='tab:blue', alpha=0.75, label='Data')
plt.plot(x, y_fit, color='tab:orange', linewidth=2, label='Fit')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Regression Plot')
plt.legend()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(2)
x = np.linspace(0, 12, 60)
noise = np.random.normal(scale=1.5, size=x.size)
y = 2.2 * x - 1.0 + noise

slope, intercept = np.polyfit(x, y, 1)
y_fit = slope * x + intercept
residuals = y - y_fit

fig, axes = plt.subplots(2, 1, figsize=(6, 7), sharex=True)
# 上段: 元データと回帰直線
axes[0].scatter(x, y, color='tab:blue', alpha=0.75, label='Data')
axes[0].plot(x, y_fit, color='tab:orange', linewidth=2, label='Fit')
axes[0].set_ylabel('y')
axes[0].set_title('Residuals Plot')
axes[0].legend()

# 下段: 残差
axes[1].axhline(0, color='gray', linewidth=1)
axes[1].scatter(x, residuals, color='tab:green', alpha=0.75)
axes[1].set_xlabel('x')
axes[1].set_ylabel('Residuals')

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(3)
data = np.random.randn(200, 4)
data[:, 1] = 0.6 * data[:, 0] + 0.4 * data[:, 1]
data[:, 2] = -0.5 * data[:, 0] + 0.3 * data[:, 2]

corr = np.corrcoef(data, rowvar=False)

# 相関行列をヒートマップ表示
plt.imshow(corr, cmap='coolwarm', vmin=-1, vmax=1)
plt.colorbar(label='Correlation')
plt.xticks(range(4), ['Var1', 'Var2', 'Var3', 'Var4'])
plt.yticks(range(4), ['Var1', 'Var2', 'Var3', 'Var4'])
plt.title('Correlation Heatmap')
plt.show()