Python 画热力图 带P值 一半的

1. 数据准备

import pandas as pd
heatmap_data = pd.read_excel(r"相关性热力图_P值.xlsx")
heatmap_data.head()

2. 计算相关性

3. 基本样式

from colormaps import parula
from matplotlib.ticker import FormatStrFormatter
import matplotlib.pyplot as plt

fig,ax = plt.subplots(figsize=(4,3.5),dpi=100,facecolor="w")
sns.heatmap(heatmap_data.corr(),annot=True,fmt='.2f',cmap=parula,vmin=-1, vmax=1,
            annot_kws={
    
    "size":7.5,"fontweight":"bold"},linecolor="k",linewidths=.2,
            cbar_kws={
    
    "aspect":13},ax=ax)
ax.yaxis.set_tick_params(labelrotation=0)
# 使用 matplotlib.colorbar.Colorbar object
cbar = ax.collections[0].colorbar
cbar.ax.tick_params(direction="in",width=.5,labelsize=10)
cbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
cbar.outline.set_visible(True)
cbar.outline.set_linewidth(.5)
plt.tight_layout()

4. 只画一半

# 生成形状一样的 1 矩阵
np.ones(corr_df.shape)

# 第k条对角线上方的元素全部置零。k=0（默认值），为主对角线
np.tril(np.ones(corr_df.shape))

mask1 = np.tril(np.ones(corr_df.shape))
lower_triang_df = corr_df.where(np.tril(np.ones(corr_df.shape)).astype(np.bool))
lower_triang_df


fig,ax = plt.subplots(figsize=(4,3.5),dpi=100,facecolor="w")
cor_up = heatmap_data.corr().where(np.triu(np.ones(heatmap_data.corr().shape)).astype(np.bool))
sns.heatmap(cor_up,annot=True,fmt='.2f',cmap=parula,vmin=-1, vmax=1,
            annot_kws={
    
    "size":8,"fontweight":"bold"},linecolor="k",linewidths=.2,
            cbar_kws={
    
    "aspect":13},ax=ax)
ax.yaxis.set_tick_params(labelrotation=0)
# 使用 matplotlib.colorbar.Colorbar object
cbar = ax.collections[0].colorbar
cbar.ax.tick_params(direction="in",width=.5,labelsize=10)
cbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
cbar.outline.set_visible(True)
cbar.outline.set_linewidth(.5)
plt.tight_layout()

5. 进阶相关性矩阵图(带P值)绘制.

import pandas as pd
import seaborn as sns
import numpy as np

heatmap_data = pd.read_excel(r"相关性热力图_P值.xlsx")
heatmap_data.head()

from scipy.stats import pearsonr
pvals = heatmap_data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(heatmap_data.columns)) 
pvals

#转换P值为星号
def convert_pvalue_to_asterisks(pvalue):
    if pvalue <= 0.001:
        return "***"
    elif pvalue <= 0.01:
        return "**"
    elif pvalue <= 0.05:
        return "*"
    return ""

pval_star = pvals.applymap(lambda x:convert_pvalue_to_asterisks(x))

# 转换成numpy 类型
corr_star_annot = pval_star.to_numpy()
corr_star_annot``
import matplotlib.pyplot as plt
from colormaps import parula
from matplotlib.ticker import FormatStrFormatter

fig,ax = plt.subplots(figsize=(4,3.5),dpi=100,facecolor="w")
sns.heatmap(heatmap_data.corr(),annot=corr_star_annot,fmt='',cmap=parula,vmin=-1, vmax=1,
            annot_kws={
    
    "size":12,"fontweight":"bold"},linecolor="k",linewidths=.2,
            cbar_kws={
    
    "aspect":13},ax=ax)
ax.tick_params(bottom=False, labelbottom=True,labeltop=False,left=False,pad=1,labelsize=12)
ax.yaxis.set_tick_params(labelrotation=0)
# 使用 matplotlib.colorbar.Colorbar object
cbar = ax.collections[0].colorbar
cbar.ax.tick_params(direction="in",width=.5,labelsize=10)
cbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
cbar.outline.set_visible(True)
cbar.outline.set_linewidth(.5)
plt.tight_layout()

6. P值和值都带

import pandas as pd
import seaborn as sns
import numpy as np

heatmap_data = pd.read_excel(r"相关性热力图_P值.xlsx")
heatmap_data.head()

from scipy.stats import pearsonr
pvals = heatmap_data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(heatmap_data.columns)) 
pvals

#转换P值为星号
def convert_pvalue_to_asterisks(pvalue):
    if pvalue <= 0.001:
        return "***"
    elif pvalue <= 0.01:
        return "**"
    elif pvalue <= 0.05:
        return "*"
    return ""

pval_star = pvals.applymap(lambda x:convert_pvalue_to_asterisks(x))

# 转换成numpy 类型
corr_star_annot = pval_star.to_numpy()
corr_star_annot``
import matplotlib.pyplot as plt
from colormaps import parula
from matplotlib.ticker import FormatStrFormatter

# 定制label
corr_labels = heatmap_data.corr().to_numpy()
p_labels = corr_star_annot
shape = corr_labels.shape
#合并labels:列表表达式
labels = (np.asarray(["{0:.2f}\n{1}".format(data,p) for data, p in zip(corr_labels.flatten(), p_labels.flatten())])).reshape(shape)
labels

import matplotlib.pyplot as plt
from colormaps import parula
fig,ax = plt.subplots(figsize=(6,5),dpi=100,facecolor="w")
sns.heatmap(heatmap_data.corr(),annot=labels,fmt='',cmap=parula,vmin=-1, vmax=1,
            annot_kws={
    
    "size":7.5,"fontweight":"bold"},linecolor="k",linewidths=.2,
            cbar_kws={
    
    "aspect":13},ax=ax)
ax.tick_params(bottom=False, labelbottom=True,labeltop=False,left=False,pad=1,labelsize=12)
ax.yaxis.set_tick_params(labelrotation=0)
# 使用 matplotlib.colorbar.Colorbar object
cbar = ax.collections[0].colorbar
cbar.ax.tick_params(direction="in",width=.5,labelsize=10)
cbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
cbar.outline.set_visible(True)
cbar.outline.set_linewidth(.5)
plt.tight_layout()