pip install matplotlib seaborn plotly pandas numpy

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.figure(figsize=(10, 6))
plt.plot(x, y1, label='sin(x)', linewidth=2, color='#e94560')
plt.plot(x, y2, label='cos(x)', linewidth=2, color='#0f3460')
plt.title('正弦和余弦曲线', fontsize=14, pad=15)
plt.xlabel('X 轴', fontsize=12)
plt.ylabel('Y 轴', fontsize=12)
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

categories = ['Q1', 'Q2', 'Q3', 'Q4']
revenue = [120, 180, 160, 210]
profit = [30, 45, 40, 55]

x = range(len(categories))
width = 0.35

fig, ax = plt.subplots(figsize=(10, 6))
ax.bar([i - width/2 for i in x], revenue, width, label='收入', color='#16213e')
ax.bar([i + width/2 for i in x], profit, width, label='利润', color='#e94560')

ax.set_xlabel('季度')
ax.set_ylabel('金额（万元）')
ax.set_title('2025 年度收入和利润')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()
plt.tight_layout()
plt.show()

import seaborn as sns

tips = sns.load_dataset('tips')
sns.set_theme(style='whitegrid')

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

sns.boxplot(data=tips, x='day', y='total_bill', ax=axes[0, 0])
axes[0, 0].set_title('每日小费分布')

sns.scatterplot(data=tips, x='total_bill', y='tip', hue='time', size='size', ax=axes[0, 1])
axes[0, 1].set_title('小费与账单金额关系')

corr = tips.select_dtypes(include=['float64', 'int64']).corr()
sns.heatmap(corr, annot=True, cmap='RdBu_r', ax=axes[1, 0])
axes[1, 0].set_title('特征相关性')

sns.violinplot(data=tips, x='day', y='total_bill', hue='sex', split=True, ax=axes[1, 1])
axes[1, 1].set_title('性别与消费分布')

plt.tight_layout()
plt.show()

import plotly.express as px

df = px.data.gapminder()

fig = px.scatter(
    df.query("year == 2007"),
    x='gdpPercap', y='lifeExp',
    size='pop', color='continent',
    hover_name='country',
    log_x=True,
    title='2007 年各国 GDP 与预期寿命'
)
fig.show()

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

np.random.seed(42)
dates = pd.date_range('2025-01-01', '2025-12-31', freq='D')
sales = np.random.normal(1000, 200, len(dates))
sales += np.sin(np.arange(len(dates)) * 2 * np.pi / 365) * 300

df = pd.DataFrame({'date': dates, 'sales': sales})
df['month'] = df['date'].dt.month
df['weekday'] = df['date'].dt.day_name()

fig = plt.figure(figsize=(16, 10))
gs = GridSpec(2, 3, figure=fig)

ax1 = fig.add_subplot(gs[0, :])
monthly = df.groupby('month')['sales'].sum()
ax1.plot(monthly.index, monthly.values, marker='o', linewidth=2, color='#e94560')
ax1.set_title('月度销售趋势', fontsize=14)
ax1.grid(True, alpha=0.3)

weekday_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
ax2 = fig.add_subplot(gs[1, 0])
weekly = df.groupby('weekday')['sales'].mean().reindex(weekday_order)
colors = ['#16213e'] * 7
colors[4] = '#e94560'
ax2.bar(weekly.index, weekly.values, color=colors)
ax2.set_title('周内日平均销售额', fontsize=12)
ax2.tick_params(axis='x', rotation=45)

ax3 = fig.add_subplot(gs[1, 1])
ax3.hist(df['sales'], bins=30, color='#0f3460', edgecolor='white', alpha=0.7)
ax3.axvline(df['sales'].mean(), color='#e94560', linestyle='--', label=f'均值: {df["sales"].mean():.0f}')
ax3.set_title('销售额分布', fontsize=12)
ax3.legend()

plt.suptitle('2025 销售数据分析仪表盘', fontsize=18, y=1.02)
plt.tight_layout()
plt.show()

plt.rcParams.update({
    'figure.facecolor': 'white',
    'axes.facecolor': '#fafafa',
    'axes.grid': True,
    'grid.alpha': 0.3,
    'axes.spines.top': False,
    'axes.spines.right': False,
    'font.size': 12,
    'axes.titlesize': 14,
})