Stephen's blog Manage newline breaks in CSV files with Python Nov 24, 2023 · 4 min read When importing and exporting CSV files, a common challenge is handling newline characters within cells. Let's explore simple solutions to help manage such scenarios effectively with Python. 🤔 Understanding the issue CSV files use specific characters (or delimiters), like commas (',') or pipes ('|') to separate values and newlines to indicate new rows. But what if the data itself contains new lines (within a cell)? Let's imagine we extract the following data from a worksheet: Name | Description -----------|------------ Pump 1 | Not running Pump 2 | Turned off at panel If saved as a CSV file, it may look like this: Name|Description Pump 1|Not running Pump 2|"Turned off at panel" Notice the quotes used around the "Turned off at panel" cell. These exist because the CSV writer used quotes to handle cells with multiple cells i.e. multiline cells. ✍️ Writing data to CSV Before writing data to CSV, you need to handle newlines within cells to avoid any cell content breaking the structure of the CSV file. Let's look at a few ways you could write your data to a CSV while cleaning up newlines in each cell content: Note⚠️: The following steps are shown for a Windows machine using '\r\n' for newlines. Replace with '\n' if you're on a Unix-based machine instead. 🔄 Approach 1: Replace newlines You can swap newlines with a space or another placeholder of your choice: df["Description"] = df["Description"].str.replace('\\r\\n', ' ', regex=True) df.to_csv("new_file.csv", sep='|', index=False, quoting=csv.QUOTE_NONE, escapechar='\\\\') Output: Name|Description Pump 1|Not running Pump 2|Turned off at panel Let's break down the code used: .str.replace('\\r\\n', ' ', regex=True) - searches for newline characters and replaces them with spaces df.to_csv - writes the dataframe to a CSV file sep='|' - sets the pipe character as the column delimiter index=False - excludes row indices from the CSV file quoting=csv.QUOTE_NONE - Stops pandas from adding quotes automatically escapechar='\\\\' - Escapes delimiters that show in the data Pros✅ Easy to implement Preserves the row integrity in the CSV file Cons❌ Loses the original data format Not suitable if a newline has semantic meaning 📦Approach 2: Wrap everything with quotes Configure the CSV writer to place quotes around every field and record, which preserves the newlines as they are: df.to_csv("new_file.csv", sep='|', index=False, quoting=csv.QUOTE_ALL) Output: "Name"|"Description" "Pump 1"|"Not running" "Pump 2"|"Turned off at panel" This code is similar to the previous approach, apart from: quoting=csv.QUOTE_ALL - encloses all fields in quotes Pros✅ Preserves the data in its original form, including the newlines too Doesn't require any data manipulation before saving it Cons❌ CSV files become harder to read in simple text editors More complex parsing logic may be required to read the data back in https://aianddatainsights.beehiiv.com/subscribe 🗃️ Approach 3: Use custom encoding for newlines Encode the newlines in the cells with a unique placeholder before writing to CSV and decode them when reading: df["Description"] = df["Description"].str.replace("\\r\\n", "<br>") df.to_csv("new_file.csv", sep='|', index=False) Output: Name|Description Pump 1|Not running Pump 2|Turned off<br>at panel The .str.replace("\\r\\n", "<br>") line replaces the newlines with an HTML break indicator, which represents the custom encoding solution we're writing into our logic. Pros✅ Keeps the structure of the CSV clean and intact Allows you to restore the original formatting after decoding Cons❌ Requires constant encoding and decoding steps The placeholder may collide with your actual data if it appears anywhere else coincidentally (choose one that doesn't appear anywhere in your data) 📚 Approach 4: Use libraries for multi-line CSV formats Some modules support the handling of CSV files with multiline formats using quotes: import csv df.to_csv("new_file.csv", sep='|', lineterminator='\\r\\n', quoting=csv.QUOTE_NONNUMERIC) Output: ""|"Name"|"Description" 0|"Pump 1"|"Not running" 1|"Pump 2"|"Turned off at panel" Let's break down some lesser-known areas of our code: lineterminator='\\r\\n' - defines the newline character for the end of each record quoting=csv.QUOTE_NONNUMERIC - wraps quotes around every field that isn't numeric Pros✅ Retains the data formatting Matches the CSV standards for handling multi-line fields Cons❌ Not every CSV parser supports multi-line records Can still be confusing if the file is opened and viewed in simple text editors 🏁 Conclusion When deciding on what method to advance with, think about what is important for your data and who will be using it. Each method has its benefits and trade-offs, so select the one that fits your situation best, and most importantly, always test to make sure your data looks right once everything is processed! https://aianddatainsights.beehiiv.com/subscribe https://medium.com/@sdw-online/subscribe Read on web Add a comment © 2023 Stephen's blog 8 The Green, Ste 300, Dover, Delaware, 19901 Unsubscribe Publish with Hashnode