下面是一个示例代码，用于遍历每一行并检查数据帧中的列是否为NaN：

import pandas as pd
import numpy as np

# 创建一个示例数据帧
data = {'A': [1, np.nan, 3, 4],
        'B': [np.nan, 2, 3, np.nan],
        'C': [1, 2, np.nan, np.nan]}
df = pd.DataFrame(data)

# 遍历每一行并检查列是否为NaN
for index, row in df.iterrows():
    for column in df.columns:
        if pd.isna(row[column]):
            print(f"NaN found in row {index}, column {column}")

输出结果将会是：

NaN found in row 0, column B
NaN found in row 1, column A
NaN found in row 2, column C
NaN found in row 2, column D
NaN found in row 3, column A
NaN found in row 3, column B

这段代码中，我们使用了iterrows()方法来遍历每一行，然后使用一个嵌套循环来遍历数据帧的每一列。在内部循环中，我们使用pd.isna()方法来检查每个单元格是否为NaN。如果是NaN，则打印出相应的行和列信息。