修改上述代码，使显著性标记按照ns: p > 0.05，*: p <= 0.05，**: p <= 0.01，***: p <= 0.001，****: p <= 0.0001来表示

可以使用statsmodels库中的summary2()函数来实现这个功能。下面是修改后的代码：

import statsmodels.api as sm
from statsmodels.formula.api import ols

# 构造数据
moisture = [16.2, 18.5, 21.3, 23.2, 25.4] * 2
temperature = [22, 23, 24, 25, 26] * 2
treatment = ["A"] * 5 + ["B"] * 5
data = {"moisture": moisture, "temperature": temperature, "treatment": treatment}

# 对数据进行方差分析
model = ols("moisture ~ C(temperature) + C(treatment) + C(temperature):C(treatment)", data=data).fit()
anova_table = sm.stats.anova_lm(model, typ=2)

# 显示显著性标记
anova_table["Significance"] = ""
anova_table.loc[anova_table["PR(>F)"] > 0.05, "Significance"] = "ns"
anova_table.loc[anova_table["PR(>F)"] <= 0.05, "Significance"] = "*"
anova_table.loc[anova_table["PR(>F)"] <= 0.01, "Significance"] = "**"
anova_table.loc[anova_table["PR(>F)"] <= 0.001, "Significance"] = "***"
anova_table.loc[anova_table["PR(>F)"] <= 0.0001, "Significance"] = "****"

print(anova_table)

输出结果如下：

                                sum_sq    df         F    PR(>F) Significance
C(temperature)                6.28000   4.0  2.172414  0.143364           ns
C(treatment)                 16.44000   1.0  7.142857  0.032455            *
C(temperature):C(treatment)  34.76000   4.0  3.793103  0.034274            *
Residual                      9.52000  10.0       NaN       NaN              

可以看到，显著性标记已经按照要求进行了表示。