如何使用Oracle REGEXP_LIKE识别含指定特殊字符的行

Hey there! I get it—those tricky special characters can be a pain because many of them have special meanings in regex or SQL wildcards, which is probably why your previous attempts didn't work. Let's break this down for different scenarios, since the approach varies a bit depending on whether you're using SQL or a programming language like Python.

Using SQL (Common Databases) #

The core idea is to use a regex or pattern-matching function that correctly treats those special characters as literal values, not syntax.

MySQL/MariaDB #

Use the REGEXP operator. Most of your target characters can go directly inside a regex character class [], but you need to escape [, ], and \ (since they have special meaning in regex or SQL string escaping):

SELECT * FROM your_table
WHERE LONGDSC REGEXP '[_*%?:;=()\\[\\]\\\\/|#."]';

• Why the double backslashes? In SQL strings, a single \ is an escape character, so we need \\ to represent a literal \ in the regex.
• Characters like *, (, ) lose their special regex meaning inside [], so no need to escape them here.

PostgreSQL #

PostgreSQL uses the ~ operator for regex matches. The syntax is similar, but you only need single backslashes (since PostgreSQL handles string escaping differently):

SELECT * FROM your_table
WHERE LONGDSC ~ '[_*%?:;=()\[\]\\/|#."]';

SQL Server #

Use PATINDEX to find the position of any matching character. Inside the pattern, wrapping % and _ in [] turns off their wildcard behavior:

SELECT * FROM your_table
WHERE PATINDEX('%[_*%?:;=()\[\]\\/|#."]%', LONGDSC) > 0;

Using Python (Pandas) #

If you're working with a DataFrame in pandas, use str.contains() with a raw regex string to avoid escaping headaches:

import pandas as pd

# Load your data
df = pd.read_csv("your_data.csv")  # or read from database

# Define the regex pattern (raw string avoids double escaping)
special_chars_pattern = r'[_*%?:;=()\[\]\\/|#."]'

# Filter rows where LONGDSC has any of the special characters
filtered_df = df[df["LONGDSC"].str.contains(special_chars_pattern, na=False)]

# View the result
print(filtered_df)

• The r prefix makes it a raw string, so \ is treated as a literal backslash in the regex.
• na=False excludes rows where LONGDSC is null (adjust if you want to include them).

Quick Tip #

If you're still having issues, test the pattern with a single character first (e.g., just match _ or %) to confirm your base syntax works, then add the rest of the characters one by one. That helps narrow down which character is causing the problem.

内容的提问来源于stack exchange，提问作者sf_gray