Hey Arnold, I’ve dealt with this exact headache before—those semicolons wrapped in quotes are classic CSV gotchas that break data parsing. Let’s get your ETL pipeline back on track quickly.

The Root Problem #

Pandas’ default CSV parser splits on every semicolon by default, even if it’s inside quotes. That’s why your line abide;acdet;"adds;dsss";acde gets split into 5 parts instead of 4, shoving "dsss" into a new column/row and breaking everything.

The Simple Fix: Configure read_csv Properly #

You just need to tell Pandas to respect quoted fields so it ignores semicolons inside them. Here’s how to adjust your code:

First, import the csv module (it’s part of Python’s standard library, no extra installs needed), then use these parameters with pd.read_csv:

import pandas as pd
import csv

# If you're fetching from a URL, you can pass the URL directly here
df = pd.read_csv(
    "your_csv_file.csv",  # Replace with your web URL or local file path
    sep=";",               # Your actual delimiter
    quotechar='"',         # The character used to wrap fields with special characters
    quoting=csv.QUOTE_MINIMAL,  # Only quote fields that contain delimiters (perfect for your case)
    skipinitialspace=False  # Optional: set to True if there are spaces after semicolons
)

What Each Parameter Does: #

• sep=";": Tells Pandas to use semicolons as the main delimiter.
• quotechar='"': Identifies double quotes as the wrapper for fields that contain the delimiter.
• quoting=csv.QUOTE_MINIMAL: This tells Pandas to treat any field wrapped in quotes as a single value, even if it has semicolons inside. It’s ideal here because only the fields with semicolons are quoted in your data.
• If you ever run into fields with escaped quotes (like "adds""dsss"), add escapechar='"' to handle those edge cases.

Verify the Fix #

After reading the CSV, quickly check that your data is intact:

# Check the number of columns (should match your expected count)
print(df.shape[1])

# Inspect the problematic field to confirm it's a single value
print(df.loc[0, 2])  # Should output adds;dsss (without the quotes)

Once your DataFrame is correctly parsed, your downstream preprocessing and SQL Server import steps should work without interruptions.

内容的提问来源于stack exchange，提问作者Arnold