SCI常见的18种配图代码实现

1. 折线图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# 创建图表
plt.figure(figsize=(10, 6))
plt.plot(x, y1, label="Sine Wave", color="b", linewidth=2)
plt.plot(x, y2, label="Cosine Wave", color="r", linestyle="--", linewidth=2)

# 添加装饰
plt.fill_between(x, y1, y2, color="gray", alpha=0.1)
plt.title("Line Plot", fontsize=15)
plt.xlabel("X-axis", fontsize=12)
plt.ylabel("Y-axis", fontsize=12)
plt.legend()
plt.show()

2. 散点图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
x = np.random.rand(100)
y = np.random.rand(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)

# 创建图表
plt.figure(figsize=(10, 6))
plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap='viridis')
plt.colorbar()

# 添加装饰
plt.title("Scatter Plot", fontsize=15)
plt.xlabel("X-axis", fontsize=12)
plt.ylabel("Y-axis", fontsize=12)
plt.show()

3. 条形图

import matplotlib.pyplot as plt
import seaborn as sns

# 设置风格
sns.set(style="whitegrid")

# 生成数据
categories = ['A', 'B', 'C', 'D']
values1 = [5, 7, 8, 6]
values2 = [3, 4, 5, 2]

# 创建图表
fig, ax = plt.subplots(figsize=(10, 6))
bar1 = ax.bar(categories, values1, label='Group 1')
bar2 = ax.bar(categories, values2, bottom=values1, label='Group 2')

# 添加装饰
ax.set_title("Stacked Bar Chart", fontsize=15)
ax.set_xlabel("Categories", fontsize=12)
ax.set_ylabel("Values", fontsize=12)
ax.legend()

# 添加数值标签
for rect in bar1 + bar2:
    height = rect.get_height()
    ax.annotate(f'{height}', xy=(rect.get_x() + rect.get_width() / 2, height),
                xytext=(0, 3), textcoords="offset points", ha='center', va='bottom')

plt.show()

4. 热力图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 生成数据
data = np.random.rand(10, 12)

# 创建热图
plt.figure(figsize=(10, 6))
sns.heatmap(data, annot=True, fmt=".2f", cmap='coolwarm')

# 添加装饰
plt.title("Heatmap", fontsize=15)
plt.show()

5. 箱线图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
data = np.random.normal(size=(20, 6)) + np.arange(6) / 2

# 创建图表
plt.figure(figsize=(10, 6))
sns.boxplot(data=data, palette="vlag")

# 添加装饰
plt.title("Box Plot", fontsize=15)
plt.show()

6. 蜘蛛图

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# 设置风格
sns.set(style="whitegrid")

# 数据准备
labels = np.array(['A', 'B', 'C', 'D', 'E'])
stats = [10, 20, 30, 40, 50]
stats2 = [30, 10, 20, 30, 40]

# 创建蜘蛛图
angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()
stats = np.concatenate((stats, [stats[0]]))
stats2 = np.concatenate((stats2, [stats2[0]]))
angles += angles[:1]

fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
ax.fill(angles, stats, color='blue', alpha=0.25, label='Group 1')
ax.plot(angles, stats, color='blue', linewidth=2)
ax.fill(angles, stats2, color='red', alpha=0.25, label='Group 2')
ax.plot(angles, stats2, color='red', linewidth=2)
ax.set_yticklabels([])
ax.set_xticks(angles[:-1])
ax.set_xticklabels(labels, fontsize=12)
ax.grid(True)

# 添加标题和图例
plt.title('Enhanced Spider Chart', size=20, color='black', y=1.1)
plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))
plt.show()

7. 双轴图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# 创建图表
fig, ax1 = plt.subplots(figsize=(10, 6))

ax2 = ax1.twinx()
ax1.plot(x, y1, 'g-')
ax2.plot(x, y2, 'b-')

# 添加装饰
ax1.set_xlabel('X-axis')
ax1.set_ylabel('Sine', color='g')
ax2.set_ylabel('Cosine', color='b')
plt.title('Dual Axis Plot')
plt.show()

8. 面积图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# 创建图表
plt.figure(figsize=(10, 6))
plt.fill_between(x, y1, color="skyblue", alpha=0.4)
plt.fill_between(x, y2, color="orange", alpha=0.4)

# 添加装饰
plt.title("Area Chart", fontsize=15)
plt.xlabel("X-axis", fontsize=12)
plt.ylabel("Y-axis", fontsize=12)
plt.show()

9. 带状图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
x = np.linspace(0, 10, 100)
y = np.sin(x)
z = np.sin(x) + np.random.normal(0, 0.1, 100)

# 创建图表
plt.figure(figsize=(10, 6))
plt.plot(x, y, label='Sine Wave')
plt.fill_between(x, y, z, where=(y >= z), interpolate=True, color='green', alpha=0.3)
plt.fill_between(x, y, z, where=(y < z), interpolate=True, color='red', alpha=0.3)

# 添加装饰
plt.title("Band Chart", fontsize=15)
plt.xlabel("X-axis", fontsize=12)
plt.ylabel("Y-axis", fontsize=12)
plt.legend()
plt.show()

10. 等高线图

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# 设置风格
sns.set(style="white")

# 数据准备
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))

# 创建等高线图
plt.figure(figsize=(10, 6))
contour = plt.contourf(X, Y, Z, cmap='coolwarm', levels=20)
plt.colorbar(contour)

# 添加装饰
plt.title('Contour Plot', fontsize=15)
plt.show()

11. 极坐标图

import numpy as； np
import matplotlib.pyplot as plt
import seaborn as sns

# 设置风格
sns.set(style="white")

# 数据准备
theta = np.linspace(0, 2*np.pi, 100)
r = np.abs(np.sin(theta) * np.cos(theta))

# 创建极坐标图
plt.figure(figsize=(8, 8))
ax = plt.subplot(111, polar=True)
ax.plot(theta, r, color='b', linewidth=2)

# 添加装饰
plt.title('Polar Plot', fontsize=15)
plt.show()

12. 3D曲面图

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

# 设置风格
sns.set(style="white")

# 数据准备
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))

# 创建3D曲面图
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(X, Y, Z, cmap='viridis')
fig.colorbar(surf)

# 添加装饰
plt.title('3D Surface Plot', fontsize=15)
plt.show()

13. 3D散点图

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

# 设置风格
sns.set(style="white")

# 数据准备
x = np.random.standard_normal(100)
y = np.random.standard_normal(100)
z = np.random.standard_normal(100)

# 创建3D散点图
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, c=z, cmap='viridis')

# 添加装饰
fig.colorbar(scatter)
plt.title('3D Scatter Plot', fontsize=15)
plt.show()

14. 3D条形图

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

# 设置风格
sns.set(style="white")

# 数据准备
x = np.arange(1, 11)
y = np.random.randint(1, 10, 10)
z = np.zeros(10)

# 创建3D条形图
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111, projection='3d')
bars = ax.bar3d(x, y, z, 1, 1, y, shade=True)

# 添加装饰
plt.title('3D Bar Plot', fontsize=15)
plt.show()

15. 直方图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
data = np.random.randn(1000)

# 创建图表
plt.figure(figsize=(10, 6))
sns.histplot(data, kde=True, color="purple", bins=30)

# 添加装饰
plt.title("Histogram", fontsize=15)
plt.xlabel("Value", fontsize=12)
plt.ylabel("Frequency", fontsize=12)
plt.show()

16. 小提琴图

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# 设置风格
sns.set(style="whitegrid")

# 生成数据
data = np.random.normal(size=(20, 6)) + np.arange(6) / 2

# 创建图表
plt.figure(figsize=(10, 6))
sns.violinplot(data=data, palette="muted")

# 添加装饰
plt.title("Violin Plot", fontsize=15)
plt.show()

17. 成对关系图

import seaborn as sns
import matplotlib.pyplot as plt

# 生成数据
iris = sns.load_dataset("iris")

# 创建图表
sns.pairplot(iris, hue="species", palette="muted")
plt.suptitle("Pair Plot", y=1.02, fontsize=15)
plt.show()

18. 18.Facet Grid 图

import seaborn as sns
import matplotlib.pyplot as plt

# 生成数据
tips = sns.load_dataset("tips")

# 创建图表
g = sns.FacetGrid(tips, col="time", row="smoker", margin_titles=True)
g.map(sns.scatterplot, "total_bill", "tip", alpha=.7)
g.add_legend()

# 添加装饰
plt.suptitle("Facet Grid", y=1.02, fontsize=15)
plt.show()

19. 热力图plus

19.1. 普通热图

19.1.1. 正方形

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os

# === 第一步：读取 Excel 文件 ===
file_path = "data.xlsx"
xls = pd.ExcelFile(file_path, engine="openpyxl")
df = xls.parse("Sheet1")

# === 清洗数据：去除字符串中的空格并尝试转换为数值 ===
for col in df.columns:
    df[col] = pd.to_numeric(df[col].astype(str).str.strip(), errors='coerce')
numeric_df = df.drop(columns=["Time"])  # 如无 "Time" 列请注释此行

# === 第二步：计算相关系数和 p 值 ===
cols = numeric_df.columns
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)

for i in range(len(cols)):
    for j in range(len(cols)):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p

# === 第三步：绘图（不使用 annot，而是手动 text）===
fig, ax = plt.subplots(figsize=(10, 8),dpi = 300)
cmap = sns.diverging_palette(120, 0, as_cmap=True)
# cmap = 'coolwarm'
sns.heatmap(
    corr_matrix,
    cmap=cmap,
    linewidths=0.5,
    vmin=-1,
    vmax=1,
    square=True,
    cbar=False,  # 手动添加 colorbar
    ax=ax
)

# === 添加 colorbar，并调整大小 ===
norm = plt.Normalize(vmin=-1, vmax=1)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax, shrink=0.6, aspect=20,
                    ticks=[-1, -0.5, 0, 0.5, 1])
cbar.set_label("Pearson Correlation", fontsize=10)
cbar.ax.tick_params(labelsize=12)

# === 手动添加相关系数数值和显著性星号（数字 + 星号分开）===
for i in range(len(cols)):
    for j in range(len(cols)):
        corr_val = corr_matrix.iloc[i, j]
        p = pval_matrix.iloc[i, j]
        # 相关系数文本
        ax.text(j + 0.5, i + 0.5, f"{corr_val:.2f}",
                ha='center', va='center', color='black',
                fontsize=11,fontweight='light')
        # 显著性星号
        star = ""
        if p < 0.001:
            star = "***"
        elif p < 0.01:
            star = "**"
        elif p < 0.05:
            star = "*"
        if star:
            ax.text(j + 0.5, i + 0.35, star,
                    ha='center', va='bottom', color='black',
                    fontsize=9,fontweight='bold')


# === 坐标轴设置 ===
ax.set_xticklabels(cols, fontsize=12, rotation=0, ha='center')
ax.set_yticklabels(cols, fontsize=12, rotation=0)
plt.title("Correlation Heatmap", fontsize=14)
plt.tight_layout()
plt.show()

19.2. 上方块显著性下系数

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os

# === 第一步：读取 Excel 文件 ===

file_path = "data.xlsx"
xls = pd.ExcelFile(file_path, engine="openpyxl")
df = xls.parse("Sheet1")
# === 清洗数据：去除字符串中的空格并尝试转换为数值 ===
for col in df.columns:
    df[col] = pd.to_numeric(df[col].astype(str).str.strip(), errors='coerce')

numeric_df = df.drop(columns=["Time"])  # 如无 "Time" 列请注释此行

# === 第二步：计算相关系数和 p 值 ===
cols = numeric_df.columns
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)

for i in range(len(cols)):
    for j in range(len(cols)):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p

# === 第三步：绘图（不使用 annot，而是手动 text）===
fig, ax = plt.subplots(figsize=(10, 8),dpi = 300)
# cmap = plt.get_cmap('coolwarm')
cmap = sns.diverging_palette(120, 0, as_cmap=True)
# === 只显示上三角色块 ===
mask = np.tril(np.ones_like(corr_matrix, dtype=bool))
sns.heatmap(
    corr_matrix,
    mask=mask,
    cmap=cmap,
    linewidths=0.5,
    linecolor='#a0a1a7',
    vmin=-1,
    vmax=1,
    square=True,
    cbar=False,
    ax=ax
)

# === 添加 colorbar ===
norm = plt.Normalize(vmin=-1, vmax=1)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax, shrink=0.6, aspect=20, ticks=[-1, -0.5, 0, 0.5, 1])
cbar.set_label("Pearson Correlation", fontsize=14)
cbar.ax.tick_params(labelsize=12)

# === 手动添加文本 ===
for i in range(len(cols)):
    for j in range(len(cols)):
        corr_val = corr_matrix.iloc[i, j]
        p = pval_matrix.iloc[i, j]
        # 上三角（右上角）：显著性星号
        if i < j:
            star = ""
            if p < 0.001:
                star = "***"
            elif p < 0.01:
                star = "**"
            elif p < 0.05:
                star = "*"
            if star:
                ax.text(j + 0.5, i + 0.5, star, ha='center', va='center', color='black', fontsize=13, fontweight='bold')
        # 下三角（左下角）：相关系数，颜色与色条对应
        elif i > j:
            color = cmap(norm(corr_val)) if not np.isnan(corr_val) else 'black'
            ax.text(j + 0.5, i + 0.5, f"{corr_val:.2f}", ha='center', va='center', color=color, fontsize=12, fontweight='bold')
        # 对角线：显示变量名
        elif i == j:
            ax.text(j + 0.5, i + 0.5, cols[i], ha='center', va='center', fontsize=12, color='black', fontweight='bold')

# === 坐标轴设置 ===
ax.set_xticks([])
ax.set_yticks([])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_xlabel('')
ax.set_ylabel('')
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
plt.title("Correlation Heatmap", fontsize=14)
plt.tight_layout()
plt.show()

19.3. 气泡热力图（方块）

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os

# === 第一步：读取 Excel 文件 ===
file_path = "data.xlsx"
xls = pd.ExcelFile(file_path, engine="openpyxl")
df = xls.parse("Sheet1")
# === 清洗数据：去除字符串中的空格并尝试转换为数值 ===
for col in df.columns:
    df[col] = pd.to_numeric(df[col].astype(str).str.strip(), errors='coerce')

numeric_df = df.drop(columns=["Time"])  # 如无 "Time" 列请注释此行

# === 第二步：计算相关系数和 p 值 ===
cols = numeric_df.columns
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)

for i in range(len(cols)):
    for j in range(len(cols)):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p

# === 第三步：绘图（不使用 annot，而是手动 text）===
fig, ax = plt.subplots(figsize=(10, 8),dpi = 300)
cmap = sns.diverging_palette(120, 0, as_cmap=True)
# cmap = plt.get_cmap('coolwarm')
norm = plt.Normalize(vmin=-1, vmax=1)

# === 只显示白色方格和灰色边框 ===
sns.heatmap(
    np.ones_like(corr_matrix),  # 全1，配合白色cmap
    cmap='Greys',
    linewidths=1,
    linecolor='grey',
    vmin=1, vmax=1,  # 保证格子为白色
    square=True,
    cbar=False,
    ax=ax
)

# === 添加气泡 ===
for i in range(len(cols)):
    for j in range(len(cols)):
        corr_val = corr_matrix.iloc[i, j]
        # 只在上三角画气泡
        if not np.isnan(corr_val) and i < j:
            size = 800 * abs(corr_val)
            color = cmap(norm(corr_val))
            ax.scatter(j + 0.5, i + 0.5, s=size, color=color, alpha=0.8, edgecolors='grey', linewidth=0.5, zorder=3)

sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax, shrink=0.6, aspect=20, ticks=[-1, -0.5, 0, 0.5, 1])
cbar.set_label("Pearson Correlation", fontsize=14)
cbar.ax.tick_params(labelsize=12)

# === 手动添加文本 ===
for i in range(len(cols)):
    for j in range(len(cols)):
        corr_val = corr_matrix.iloc[i, j]
        p = pval_matrix.iloc[i, j]
        # 上三角（右上角）：显著性星号
        if i < j:
            star = ""
            if p < 0.001:
                star = "***"
            elif p < 0.01:
                star = "**"
            elif p < 0.05:
                star = "*"
            if star:
                ax.text(j + 0.5, i + 0.5, star, ha='center', va='center', color='black', fontsize=13, fontweight='bold', zorder=4)
        # 下三角（左下角）：相关系数，颜色与色条对应
        elif i > j:
            color = cmap(norm(corr_val)) if not np.isnan(corr_val) else 'black'
            ax.text(j + 0.5, i + 0.5, f"{corr_val:.2f}", ha='center', va='center', color=color, fontsize=12, fontweight='bold', zorder=4)
        # 对角线：显示变量名
        elif i == j:
            ax.text(j + 0.5, i + 0.5, cols[i], ha='center', va='center', fontsize=12, color='black', fontweight='bold', zorder=4)

# === 隐藏xy轴刻度和label ===
ax.set_xticks([])
ax.set_yticks([])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_xlabel('')
ax.set_ylabel('')
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
plt.title("Correlation Bubble Heatmap", fontsize=14)
plt.tight_layout()
plt.show()

19.3.1. 2.保留上三角/下三角

19.4. 下三角

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os

# === 第一步：读取 Excel 文件 ===
file_path = "data.xlsx"
xls = pd.ExcelFile(file_path, engine="openpyxl")
df = xls.parse("Sheet1")
numeric_df = df.drop(columns=["Time"])  # 如无 "Time" 列请注释此行

# === 第二步：计算相关系数和 p 值 ===
cols = numeric_df.columns
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)

for i in range(len(cols)):
    for j in range(len(cols)):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p

# === 只保留下三角 ===
mask_upper = np.triu(np.ones_like(corr_matrix, dtype=bool))
corr_matrix = corr_matrix.mask(mask_upper)
pval_matrix = pval_matrix.mask(mask_upper)

# === 绘图 ===
fig, ax = plt.subplots(figsize=(10, 8), dpi=300)
# cmap = 'coolwarm'
cmap = sns.diverging_palette(120, 0, as_cmap=True)
sns.heatmap(
    corr_matrix,
    cmap=cmap,
    linewidths=0.5,
    vmin=-1,
    vmax=1,
    square=True,
    cbar=False,
    ax=ax
)

# === 添加 colorbar（可选：控制大小）===
norm = plt.Normalize(vmin=-1, vmax=1)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
# 手动指定colorbar位置 [left, bottom, width, height]，数值0~1为相对figure的比例
cbar_ax = fig.add_axes([0.79, 0.3, 0.03, 0.53])  # 你可以调整这四个参数
cbar = fig.colorbar(sm, cax=cbar_ax, shrink=1, aspect=20,
                    ticks=[-1, -0.5, 0, 0.5, 1])
cbar.set_label("Pearson Correlation", fontsize=14)
cbar.ax.tick_params(labelsize=12)


# === 添加文字（只对左下角非 NaN 位置）===
for i in range(len(cols)):
    for j in range(len(cols)):
        if not np.isnan(corr_matrix.iloc[i, j]):
            corr_val = corr_matrix.iloc[i, j]
            p = pval_matrix.iloc[i, j]
            ax.text(j + 0.5, i + 0.5, f"{corr_val:.2f}", 
                    ha='center', va='center', color='black',
                    fontsize=11, fontweight='light')
            star = ""
            if p < 0.001:
                star = "***"
            elif p < 0.01:
                star = "**"
            elif p < 0.05:
                star = "*"
            if star:
                ax.text(j + 0.5, i + 0.35, star, 
                        ha='center', va='bottom', color='black',
                        fontsize=9, fontweight='bold')
# 在对角线位置添加特征名   
n = corr_matrix.shape[0]
for i in range(n):
    ax.text(i + 0.5, i + 0.7, cols[i], ha='center', va='center', 
            fontsize=12, color='black')
    
# === 坐标轴设置 ===
# 获取所有标签
x_labels = cols
y_labels = cols

# 设置x轴标签，最后一列不显示
ax.set_xticks(np.arange(len(x_labels)-1) + 0.5)  # 调整刻度位置
ax.set_xticklabels(x_labels[:-1], fontsize=12, rotation=0, ha='right')

# 设置y轴标签，第一行不显示
ax.set_yticks(np.arange(len(y_labels)-1) + 1.5)  # 调整刻度位置
ax.set_yticklabels(y_labels[1:], fontsize=12, rotation=0)

plt.show()

19.5. 上三角

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os

# === 第一步：读取 Excel 文件 ===
file_path = "data.xlsx"
xls = pd.ExcelFile(file_path, engine="openpyxl")
df = xls.parse("Sheet1")
# === 清洗数据：去除字符串中的空格并尝试转换为数值 ===
for col in df.columns:
    df[col] = pd.to_numeric(df[col].astype(str).str.strip(), errors='coerce')

numeric_df = df.drop(columns=["Time"])  # 如无 "Time" 列请注释此行

# === 第二步：计算相关系数和 p 值 ===
cols = numeric_df.columns
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)

for i in range(len(cols)):
    for j in range(len(cols)):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p

# === 只保留上三角 ===
mask_lower = np.tril(np.ones_like(corr_matrix, dtype=bool))
corr_matrix = corr_matrix.mask(mask_lower)
pval_matrix = pval_matrix.mask(mask_lower)

# === 绘图 ===
fig, ax = plt.subplots(figsize=(10, 8),dpi=300)
# cmap = 'coolwarm'
cmap = sns.diverging_palette(120, 0, as_cmap=True)
sns.heatmap(
    corr_matrix,
    cmap=cmap,
    linewidths=0.5,
    vmin=-1,
    vmax=1,
    square=True,
    cbar=False,
    ax=ax
)

# === 添加 colorbar ===
norm = plt.Normalize(vmin=-1, vmax=1)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
# 手动指定colorbar位置 [left, bottom, width, height]
cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])  # 调整位置和大小
cbar = fig.colorbar(sm, cax=cbar_ax, ticks=[-1, -0.5, 0, 0.5, 1])
cbar.set_label("Pearson Correlation", fontsize=12, labelpad=10)
cbar.ax.tick_params(labelsize=10)

# === 添加文字（只对右上角非 NaN 位置）===
for i in range(len(cols)):
    for j in range(len(cols)):
        if not np.isnan(corr_matrix.iloc[i, j]):
            corr_val = corr_matrix.iloc[i, j]
            p = pval_matrix.iloc[i, j]
            ax.text(j + 0.5, i + 0.5, f"{corr_val:.2f}", 
                    ha='center', va='center', color='black',
                    fontsize=11, fontweight='light')
            star = ""
            if p < 0.001:
                star = "***"
            elif p < 0.01:
                star = "**"
            elif p < 0.05:
                star = "*"
            if star:
                ax.text(j + 0.5, i + 0.35, star, 
                        ha='center', va='bottom', color='black',
                        fontsize=9, fontweight='bold')

# 在对角线位置添加特征名    
n = corr_matrix.shape[0]
for i in range(n):
    ax.text(i + 0.5, i + 0.5, cols[i], ha='center', va='center', 
            fontsize=12, color='black')

# === 坐标轴设置 ===
# 获取所有标签
x_labels = cols
y_labels = cols

# 设置x轴标签（顶部），第一列不显示
ax.set_xticks(np.arange(len(x_labels)-1) + 1.5)  # 调整刻度位置
ax.set_xticklabels(x_labels[1:], fontsize=12, rotation=0, ha='left')
ax.xaxis.set_ticks_position('top')  # 将x轴刻度移到顶部

# 设置y轴标签（右侧），最后一行不显示
ax.set_yticks(np.arange(len(y_labels)-1) + 0.5)  # 调整刻度位置
ax.set_yticklabels(y_labels[:-1], fontsize=12, rotation=0)
ax.yaxis.set_ticks_position('right')  # 将y轴刻度移到右侧

plt.show()

19.6. 圆形

import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os
from matplotlib import cm
from matplotlib.patches import Rectangle

# === 读取 Excel 文件 ===
file_path = "data.xlsx"
xls = pd.ExcelFile(file_path, engine="openpyxl")
df = xls.parse("Sheet1")
# === 清洗数据：去除字符串中的空格并尝试转换为数值 ===
for col in df.columns:
    df[col] = pd.to_numeric(df[col].astype(str).str.strip(), errors='coerce')

numeric_df = df.drop(columns=["Time"])  # 如无 "Time" 列请注释此行

# === 计算相关系数与 p 值矩阵 ===
cols = numeric_df.columns
n = len(cols)
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)

for i in range(n):
    for j in range(n):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p

# === 准备绘图数据（仅左下角） ===
x_list, y_list, color_list, size_list, star_list, label_list = [], [], [], [], [], []

for i in range(n):
    for j in range(n):
            r = corr_matrix.iloc[i, j]
            p = pval_matrix.iloc[i, j]
            x_list.append(j)
            y_list.append(i)
            color_list.append(r)
            size_list.append(abs(r) * 1500)  # 圆大小与绝对值相关
            label_list.append(f"{r:.2f}")
            if p < 0.001:
                star_list.append("***")
            elif p < 0.01:
                star_list.append("**")
            elif p < 0.05:
                star_list.append("*")
            else:
                star_list.append("")

# === 绘图 ===
fig, ax = plt.subplots(figsize=(10, 8), dpi=300)
cmap = sns.diverging_palette(120, 0, as_cmap=True)
sc = ax.scatter(x_list, y_list, s=size_list, c=color_list, 
                cmap=cmap, vmin=-1, vmax=1, edgecolors='black')


# 添加浅灰色格子边框
for i in range(n):
    for j in range(n):
        ax.add_patch(Rectangle((j - 0.5, i - 0.5), 1, 1,
                               edgecolor='lightgray', facecolor='none', lw=0.7))

# 添加相关系数数字和星号
for x, y, text, star in zip(x_list, y_list, label_list, star_list):
    ax.text(x, y, text, fontsize=10, ha='center', 
            va='center', color='black')
    if star:
        ax.text(x, y - 0.4, star, fontsize=9, 
                ha='center', va='top', color='black', fontweight='bold')

# 添加 colorbar
cbar = plt.colorbar(sc, ax=ax, shrink=0.6, aspect=20, 
                    ticks=[-1, -0.5, 0, 0.5, 1])
cbar.set_label("Pearson Correlation", fontsize=12)
cbar.ax.tick_params(labelsize=10)

# 设置坐标轴标签
ax.set_xticks(range(n))
ax.set_yticks(range(n))
ax.set_xticklabels(cols, rotation=0, ha='right', fontsize=11)
ax.set_yticklabels(cols, fontsize=11)
ax.set_xlim(-0.5, n - 0.5)
ax.set_ylim(-0.5, n - 0.5)
ax.set_title("Correlation Matrix (Circles, Lower Triangle)", fontsize=14)

ax.invert_yaxis()  # 让 y 轴从上到下排列变量
ax.set_aspect('equal')
plt.tight_layout()
plt.show()

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os

# === 第一步：读取 Excel 文件 ===
file_path = "data.xlsx"
xls = pd.ExcelFile(file_path, engine="openpyxl")
df = xls.parse("Sheet1")
# === 清洗数据：去除字符串中的空格并尝试转换为数值 ===
for col in df.columns:
    df[col] = pd.to_numeric(df[col].astype(str).str.strip(), errors='coerce')

numeric_df = df.drop(columns=["Time"])  # 如无 "Time" 列请注释此行

# === 第二步：计算相关系数和 p 值 ===
cols = numeric_df.columns
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)

for i in range(len(cols)):
    for j in range(len(cols)):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p

# === 第三步：绘图（不使用 annot，而是手动 text）===
fig, ax = plt.subplots(figsize=(10, 8),dpi = 300)
cmap = sns.diverging_palette(120, 0, as_cmap=True)
# cmap = plt.get_cmap('coolwarm')
norm = plt.Normalize(vmin=-1, vmax=1)

# === 只显示白色方格和灰色边框 ===
sns.heatmap(
    np.ones_like(corr_matrix),  # 全1，配合白色cmap
    cmap='Greys',
    linewidths=1,
    linecolor='grey',
    vmin=1, vmax=1,  # 保证格子为白色
    square=True,
    cbar=False,
    ax=ax
)

# === 添加气泡 ===
for i in range(len(cols)):
    for j in range(len(cols)):
        corr_val = corr_matrix.iloc[i, j]
        # 只在上三角画气泡
        if not np.isnan(corr_val) and i < j:
            size = 800 * abs(corr_val)
            color = cmap(norm(corr_val))
            ax.scatter(j + 0.5, i + 0.5, s=size, color=color, alpha=0.8, edgecolors='grey', linewidth=0.5, zorder=3)

sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax, shrink=0.6, aspect=20, ticks=[-1, -0.5, 0, 0.5, 1])
cbar.set_label("Pearson Correlation", fontsize=14)
cbar.ax.tick_params(labelsize=12)

# === 手动添加文本 ===
for i in range(len(cols)):
    for j in range(len(cols)):
        corr_val = corr_matrix.iloc[i, j]
        p = pval_matrix.iloc[i, j]
        # 上三角（右上角）：显著性星号
        if i < j:
            star = ""
            if p < 0.001:
                star = "***"
            elif p < 0.01:
                star = "**"
            elif p < 0.05:
                star = "*"
            if star:
                ax.text(j + 0.5, i + 0.5, star, ha='center', va='center', color='black', fontsize=13, fontweight='bold', zorder=4)
        # 下三角（左下角）：相关系数，颜色与色条对应
        elif i > j:
            color = cmap(norm(corr_val)) if not np.isnan(corr_val) else 'black'
            ax.text(j + 0.5, i + 0.5, f"{corr_val:.2f}", ha='center', va='center', color=color, fontsize=12, fontweight='bold', zorder=4)
        # 对角线：显示变量名
        elif i == j:
            ax.text(j + 0.5, i + 0.5, cols[i], ha='center', va='center', fontsize=12, color='black', fontweight='bold', zorder=4)

# === 隐藏xy轴刻度和label ===
ax.set_xticks([])
ax.set_yticks([])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_xlabel('')
ax.set_ylabel('')
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
plt.title("Correlation Bubble Heatmap", fontsize=14)
plt.tight_layout()
plt.show()

20. 统计图散点图plus

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
import numpy as np
import os
# === 数据读取 ===
file_path = "data.xlsx"
df = pd.read_excel(file_path, engine="openpyxl")
# === 清洗数据：去除字符串中的空格并尝试转换为数值 ===
for col in df.columns:
    df[col] = pd.to_numeric(df[col].astype(str).str.strip(), errors='coerce')

numeric_df = df.select_dtypes(include=[np.number])  # 只保留数值型变量
cols = numeric_df.columns
n = len(cols)

# === 计算相关系数和 p 值矩阵 ===
corr_matrix = numeric_df.corr()
pval_matrix = pd.DataFrame(np.ones_like(corr_matrix), columns=cols, index=cols)
for i in range(n):
    for j in range(n):
        if i != j:
            corr, p = stats.pearsonr(numeric_df[cols[i]], numeric_df[cols[j]])
            pval_matrix.iloc[i, j] = p
# === 开始绘图 ===
fig, axes = plt.subplots(n, n, figsize=(2*n, 2*n), dpi=300)
plt.subplots_adjust(wspace=0.1, hspace=0.1)
cmap = sns.diverging_palette(120, 0, as_cmap=True)
norm = plt.Normalize(vmin=-1, vmax=1)
for i in range(n):
    for j in range(n):
        ax = axes[i, j]

        # === 对角线：绘制变量分布 ===
        if i == j:
            sns.histplot(numeric_df[cols[i]], kde=True, 
                         ax=ax, color='#54873e', edgecolor=None)


        # === 下三角：回归拟合图 ===
        elif i > j:
            sns.regplot(x=cols[j], y=cols[i], data=numeric_df, ax=ax,
                        scatter_kws={'s': 10, 'color': '#e8b0c0'},
                        line_kws={'color': '#d53f6c'}, ci=95)
            ax.set_xticks([])
            ax.set_yticks([])
        # === 上三角：绘制色块 + 相关系数文字 + 显著性 ===
        else:
            corr_val = corr_matrix.iloc[i, j]
            p = pval_matrix.iloc[i, j]
            color = cmap(norm(corr_val)) if not np.isnan(corr_val) else 'white'
            ax.set_facecolor(color)
            # 相关系数文字
            ax.text(0.5, 0.6, f"{corr_val:.2f}", ha='center', va='center',
                    fontsize=14, fontweight='bold', color='black', transform=ax.transAxes)
            # 显著性星号
            if p < 0.001:
                stars = '***'
            elif p < 0.01:
                stars = '**'
            elif p < 0.05:
                stars = '*'
            else:
                stars = ''
            ax.text(0.5, 0.35, stars, ha='center', va='center',
                    fontsize=14, fontweight='bold', color='black', transform=ax.transAxes)
            ax.set_xticks([])
            ax.set_yticks([])
            
# === 设置坐标轴标签 ===
for i in range(n):
    for j in range(n):
        ax = axes[i, j]
        # 仅保留必要的刻度线
        if i == n-1:  # 最底行加 x 标签
            ax.set_xlabel(cols[j], fontsize=14, rotation=0,fontweight='bold')
            ax.set_xticks([])  # 保持x刻度为空但显示xlabel
        else:
            ax.set_xticks([])
            ax.set_xlabel('')
        if j == 0:  # 最左列加 y 标签
            ax.set_ylabel(cols[i], fontsize=14, rotation=0, labelpad=35,fontweight='bold')
            ax.set_yticks([])  # 保持y刻度为空但显示ylabel
        else:
            ax.set_yticks([])
            ax.set_ylabel('')

        # 特别处理左上角(i=0,j=0)的ylabel
        if i == 0 and j == 0:
            ax.set_ylabel(cols[i], fontsize=14, rotation=0, labelpad=35,fontweight='bold')
            ax.set_yticks([])
            
# === 添加 colorbar ===
fig.subplots_adjust(right=0.92)
cbar_ax = fig.add_axes([0.94, 0.25, 0.02, 0.5])
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, cax=cbar_ax,ticks=[-1, -0.5, 0, 0.5, 1])
cbar.ax.tick_params(labelsize=14)  # 设置刻度字体大小为 14，可按需调整

cbar.set_label("Pearson Correlation", fontsize=16)
# plt.tight_layout(rect=[0, 0, 0.93, 0.96])
plt.show()