Python中可视化折线图的四种创意方法

# 圆形折线图
# 数据准备
hours = np.linspace(0, 2 * np.pi, 24, endpoint=False)
values = [] # ....
# 闭合数据
values_cycle = np.concatenate((values, [values[0]]))
hours_cycle = np.concatenate((hours, [hours[0]]))
hour_labels = [f"{h}点" for h in range(24)]

fig = plt.figure(figsize=(14, 6))

# --- 左图：普通折线图 ---
ax1 = fig.add_subplot(121)
ax1.plot(range(24), values, marker="o", color="#FF6B6B")
# 省略...

# --- 右图：圆形折线图 ---
ax2 = fig.add_subplot(122, projection="polar")
ax2.plot(hours_cycle, values_cycle, linewidth=2, color="#FF6B6B")
# 省略...

plt.show()

# 斜率图
# 数据
depts = ['销售部', '人事部', '技术部', '研发部', '市场部']
score_before = [65, 70, 88, 85, 60]
score_after = [85, 68, 92, 80, 90]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

# --- 左图：普通折线图 ---
x = [1, 2] # 代表前后两个时间点
for i in range(len(depts)):
    ax1.plot(x, [score_before[i], score_after[i]], marker='o', label=depts[i])
# 省略 ...

# --- 右图：斜率图 ---
for i in range(len(depts)):
    color = 'green' if score_after[i] > score_before[i] else 'red'
    # 绘制线条
    ax2.plot([0, 1], [score_before[i], score_after[i]], color=color, marker='o', linewidth=2)
    # 直接在点旁边标注文字，去除图例查找的负担
    ax2.text(-0.05, score_before[i], f"{depts[i]} {score_before[i]}", ha='right', va='center')
    ax2.text(1.05, score_after[i], f"{score_after[i]}", ha='left', va='center')

# 省略 ...

plt.tight_layout()
plt.show()

# 凹凸图
years = [2019, 2020, 2021, 2022, 2023]
# 排名数据
ranks = {
    '品牌A': [1, 1, 2, 3, 4],
    '品牌B': [4, 3, 1, 1, 2],
    '品牌C': [2, 4, 3, 2, 1],
    '品牌D': [3, 2, 4, 4, 3]
}
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

# --- 左图：普通折线图 (模拟数值非常接近的情况) ---
# 假设排名对应的数值很接近，很难看清
values_simulated = {
    '品牌A': [30, 31, 25, 20, 15],
    '品牌B': [10, 15, 30, 32, 28],
    '品牌C': [25, 12, 22, 28, 35],
    '品牌D': [20, 22, 18, 15, 20]
}

for brand, val_list in values_simulated.items():
    ax1.plot(years, val_list, marker='o', label=brand)

# 省略 ...

# --- 右图：凹凸图 ---
for idx, (brand, rank_list) in enumerate(ranks.items()):
    ax2.plot(years, rank_list, marker='o', markersize=15, linewidth=4, label=brand, color=colors[idx])
    # 在圆点中写上名次
    for x, y in zip(years, rank_list):
        ax2.text(x, y, str(y), color='white', ha='center', va='center', fontweight='bold')

ax2.invert_yaxis() # 关键：倒转Y轴，让第1名在最上面
# 省略 ...

plt.tight_layout()
plt.show()

# 周期图
months = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月']
# 模拟长数据
sales_2021 = [20, 25, 35, 50, 80, 100, 110, 105, 70, 50, 30, 25]
sales_2022 = [22, 28, 40, 55, 85, 105, 115, 100, 75, 55, 35, 28]
sales_2023 = [25, 30, 45, 60, 95, 120, 130, 115, 80, 60, 40, 35]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

# --- 左图：普通折线图 (线性时间轴) ---
# 把所有数据拼成一条长线
all_sales = sales_2021 + sales_2022 + sales_2023
all_months_idx = range(len(all_sales))

ax1.plot(all_months_idx, all_sales, color='#3498db', linewidth=2)
# 省略 ...

# --- 右图：周期图 (叠加时间轴) ---
ax2.plot(months, sales_2021, marker='.', label='2021年', color='lightgrey', linewidth=2, linestyle='--')
ax2.plot(months, sales_2022, marker='.', label='2022年', color='grey', linewidth=2, linestyle='--')
ax2.plot(months, sales_2023, marker='o', label='2023年', color='#3498db', linewidth=3)
# 省略 ...

plt.tight_layout()
plt.show()