更新：

In [11]: df['ReceiptNumber'] = (df.assign(ReceiptNumber=np.where(pd.notnull(df.Tender),

....: df['Rcpt#'],

....: np.nan))['ReceiptNumber']

....: .fillna(method='pad')

....: .astype(int))

In [12]: df[['Rcpt#','Tender','ReceiptNumber']]

Out[12]:

Rcpt# Tender ReceiptNumber

0 32381 Cash 32381

1 18924 NaN 32381

2 505101 NaN 32381

3 32382 CrCd 32382

4 502201 NaN 32382

5 502201 NaN 32382

6 32383 Cash 32383

7 502201 NaN 32383

8 32384 CrCd 32384

9 505201 NaN 32384

10 505201 NaN 32384

11 32385 Cash 32385

12 18924 NaN 32385

13 32386 Cash 32386

14 18924 NaN 32386

老答案：

df.assign(ReceiptNumber=np.where(pd.notnull(df.Tender),

df['Rcpt#'],

np.nan))['ReceiptNumber']

.fillna(method='pad')

PS这个片段没有经过测试,因为你没有在文本表格中提供你的数据集,所以我无法复制&粘贴它