Avoid Unintentional Line Beaks in Python csv File

This Python solution helps efficiently handle unintentional line breaks in CSV files by using pd.read_csv() with quoting=csv.QUOTE_ALL to correctly parse quoted text containing newlines.

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To handle the unintentional line breaks in CSV files, where some rows split across multiple lines due to quoted text containing newlines (such as descriptions in quotes), you can adjust how pandas.read_csv() reads the file.

Here’s how you can achieve this:

1. Use pd.read_csv() with proper handling of line breaks within quotes. The pd.read_csv() function has a parameter quoting that can handle quotes properly, and using the Python csv.QUOTE_ALL flag ensures that line breaks inside quotes are ignored.
2. Check for file encoding issues that may arise while reading large datasets.

Here’s a Python code snippet to solve this problem:

codeimport pandas as pd
import csv

# Read the CSV file while handling line breaks inside quoted strings
df = pd.read_csv('yourfile.csv', delimiter=';', quoting=csv.QUOTE_ALL, encoding='utf-8')

# Display the first few rows to verify the output
print(df.head())

• delimiter=’;’: Specifies that the file is using a semicolon (;) as the separator.
• quoting=csv.QUOTE_ALL: Ensures that text wrapped in quotes is treated as a single entry, even if it contains newlines.
• encoding=’utf-8′: Handles any special characters in the file.

Handling errors:

• If you get errors related to missing data or other issues, consider adding additional parameters like error_bad_lines=False to skip problematic rows or engine='python' for more complex parsing.

Explanation:

Managing CSV Files with Unintentional Line Breaks in Python

When dealing with large datasets, especially in CSV format, you might encounter unexpected issues such as unintentional line breaks. This is common in data where certain fields, like descriptions or comments, contain newlines. If these fields are wrapped in quotes ("), these newlines should be treated as part of the text. However, without proper handling, this can lead to parsing errors or broken rows during data analysis.

One simple and effective solution in Python is to use pandas, a powerful data manipulation library, with proper CSV reading configurations. By using the quoting=csv.QUOTE_ALL parameter in pd.read_csv(), you can instruct Python to treat quoted text as a single entity, regardless of line breaks inside the quotes.

For example, consider the following dataset where a description spills over to the next line:

code0964;Ship-3;096415000201;1;;"Wire for prov. crane 10 mm x 20 mtr.  
galv. 85 kn. Thimble in one end.";1;05/01/2015 10:08;16/07/1934 04:01;0;0;Supplier-634;18/12/2014;02/02/2015 16:31;Delivery Place-105

Without proper handling, the line break in the description would break the parsing process. Using quoting=csv.QUOTE_ALL ensures that pandas recognizes the entire quoted text, including line breaks, as a single field. This makes your data processing smoother and avoids errors during analysis.