折れ線グラフと散布図

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(x, y)  # basic line plot
ax.set_title('Sine Wave')
ax.set_xlabel('x')
ax.set_ylabel('sin(x)')
# plt.show()
plt.close()

fig, ax = plt.subplots(figsize=(7, 4))

# Solid blue line with circle markers
ax.plot(x, np.sin(x), color='steelblue', lw=2, ls='-',
        marker='o', markersize=4, label='sin(x)')

# Dashed red line
ax.plot(x, np.cos(x), color='crimson', lw=1.5, ls='--', label='cos(x)')

ax.legend()
plt.close()

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

np.random.seed(0)
dates = pd.date_range('2024-01', periods=12, freq='ME')
fig, ax = plt.subplots(figsize=(8, 4))

for product in ['A', 'B', 'C']:
    sales = np.random.randint(50, 200, 12)
    ax.plot(dates, sales, marker='o', label=f'Product {product}')

ax.set_title('Monthly Sales by Product')
ax.legend()
plt.close()

df = pd.DataFrame(
    np.random.randn(12, 3),
    index=pd.date_range('2024', periods=12, freq='ME'),
    columns=['North', 'South', 'West']
).cumsum()

fig, ax = plt.subplots(figsize=(8, 4))
df.plot(ax=ax, linewidth=1.5)
ax.set_title('Cumulative Revenue by Region')
ax.set_ylabel('Revenue (cumulative)')
plt.close()

np.random.seed(42)
x = np.random.randn(100)
y = 2 * x + np.random.randn(100)

fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, color='steelblue', alpha=0.6, s=40)
ax.set_xlabel('Feature X')
ax.set_ylabel('Target Y')
ax.set_title('Feature vs Target')
plt.close()

categories = np.random.choice([0, 1, 2], size=100)

fig, ax = plt.subplots(figsize=(6, 5))
scatter = ax.scatter(x, y, c=categories, cmap='Set1',
                     alpha=0.7, s=60, edgecolors='none')
fig.colorbar(scatter, ax=ax, label='Category')
ax.set_title('Scatter Coloured by Category')
plt.close()

fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.4, s=30, color='steelblue', label='Data')

# Fit and plot linear trend line
coeffs = np.polyfit(x, y, 1)
x_line = np.linspace(x.min(), x.max(), 100)
y_line = np.poly1d(coeffs)(x_line)
ax.plot(x_line, y_line, color='red', lw=2, label='Linear fit')

ax.legend()
ax.set_title('Scatter with Trend Line')
plt.close()

x_range = np.linspace(0, 2 * np.pi, 100)
mean = np.sin(x_range)
upper = mean + 0.2
lower = mean - 0.2

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(x_range, mean, color='steelblue', lw=2, label='Mean')
ax.fill_between(x_range, lower, upper,
                color='steelblue', alpha=0.2, label='±0.2 band')
ax.legend()
ax.set_title('Line with Confidence Band')
plt.close()

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(x, np.sin(x), 'b-', lw=2)

ax.set_xlim(0, 2 * np.pi)
ax.set_ylim(-1.2, 1.2)
ax.grid(True, linestyle='--', alpha=0.5)
ax.set_axisbelow(True)  # grid behind data
ax.set_title('Sine with Grid')
plt.close()

np.random.seed(1)
x_dense = np.random.randn(1000)
y_dense = np.random.randn(1000)

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

# Without alpha: overplotting hides density
ax1.scatter(x_dense, y_dense, s=10)
ax1.set_title('No alpha (overplotted)')

# With alpha: density visible
ax2.scatter(x_dense, y_dense, s=10, alpha=0.2)
ax2.set_title('alpha=0.2 (density visible)')

plt.tight_layout()
plt.close()