假设"Slot"列中有字符串：

mask = df["Slot"].str.contains(r"^(?:\d|1[0-2])$|Slot")
print(df[mask])

图纸：

Server  Model     Slot
0   server1  Cisco        1
1   server1  Cisco        2
2   server1  Cisco        3
3   server1  Cisco        4
4   server1  Cisco        8
7   server2    IBM   Slot 5
8   server2    IBM   Slot 8
9   server2    IBM   Slot 9
10  server3   Micr  Slot 22
11  server3   Micr  Slot 18
12  server3   Micr   Slot 1

如果您有数字/字符串的混合，请将所有转换为字符串：

mask = df["Slot"].astype(str).str.contains(r"^(?:\d|1[0-2])$|Slot")
print(df[mask])

对于您的特定问题，使用apply创建遮罩将有效：

threshold =12
mask = df['Slot'].apply(lambda x: int(x) <= threshold if x.isdigit() else 'slot' in x.lower())
df[mask]

输出量：

Server  Model     Slot
0   server1  Cisco        1
1   server1  Cisco        2
2   server1  Cisco        3
3   server1  Cisco        4
4   server1  Cisco        8
7   server2    IBM   Slot 5
8   server2    IBM   Slot 8
9   server2    IBM   Slot 9
10  server3   Micr  Slot 22
11  server3   Micr  Slot 18
12  server3   Micr   Slot 1

我认为一般的代码是这样的。

cond1 = pd.to_numeric(df['Slot'], errors='coerce').le(12)
cond2 = df['Slot'].str.contains('Slot')
df[cond1 | cond2]