How to handle CSV encoding issues and replace special characters in Python within Zapier

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1

I’m having trouble with CSV files in my Zapier automation workflow and need some help.

I have several CSV files that I need to process through Zapier, but they contain special bullet point characters that cause encoding problems. Zapier only supports UTF-8 encoding, and when I try to import these files, I keep getting this error message:

'utf-8' codec can't decode byte 0x95 in position 829: invalid start byte

Zapier support recommended using a Python script to find and replace these problematic bullet points with regular characters like asterisks or dashes before processing the CSV.

Here’s my attempt at creating a Python action in Zapier to handle this:

import pandas as pd

def process_csv_data(file_path):
    # Read the CSV with different encoding
    data = pd.read_csv(file_path, encoding='latin-1')
    
    # Replace problematic characters
    for column in data.columns:
        if data[column].dtype == 'object':
            data[column] = data[column].astype(str).str.replace('•', '*')
    
    return data

# Process the uploaded file
processed_data = process_csv_data(input_data['csv_file'])
print(processed_data)

The script isn’t working as expected. Has anyone dealt with similar encoding issues in Zapier? Any suggestions on how to properly handle this character replacement?

2

CSV encoding issues in Zapier are a pain, especially with files from different sources. Your approach won’t work because Zapier’s Python code action doesn’t handle file paths like local scripts. When Zapier passes CSV data, it’s already loaded as a string or needs different access.

Here’s what works better:

import io
import pandas as pd

# Get the CSV content as bytes and handle encoding
csv_content = input_data['csv_file']

# Handle the encoding issue by reading as bytes first
try:
    # Try to decode with latin-1 then replace problematic characters
    decoded_content = csv_content.encode('latin-1').decode('utf-8', errors='ignore')
except:
    decoded_content = csv_content

# Replace bullet points and other problematic characters
decoded_content = decoded_content.replace('\x95', '*')
decoded_content = decoded_content.replace('•', '*')
decoded_content = decoded_content.replace('–', '-')

# Convert back to DataFrame
data = pd.read_csv(io.StringIO(decoded_content))

output = {'processed_csv': data.to_csv(index=False)}

The trick is fixing byte-level encoding issues before pandas touches the CSV. I’ve had good luck using that specific hex code replacement for bullet characters.

3

zapier’s csv handling is a pain, but there’s an easier fix. just clean up the raw string data before pandas touches it. i use a simple character mapping trick that catches most encoding issues without needing detection libraries. replace the problematic bytes first, then feed the clean data to pandas - way more reliable than trying to fix it after the csv’s already been read.

4

Try encoding detection - it’ll save you headaches. I’ve hit the same issues processing CSVs from different systems in Zapier’s Python actions. Don’t assume latin-1 encoding. Detect what you’re actually dealing with first. The chardet library works great, just handle the byte data right in Zapier:

import chardet
import csv
import io

# Detect encoding from the raw CSV data
raw_data = input_data['csv_file'].encode('utf-8', errors='ignore')
detected = chardet.detect(raw_data)
encoding = detected['encoding'] or 'utf-8'

# Read and clean the data
cleaned_lines = []
for line in raw_data.decode(encoding, errors='replace').splitlines():
    # Replace multiple problematic characters at once
    cleaned_line = line.replace('\x95', '*').replace('\x96', '-').replace('\x97', '-')
    cleaned_lines.append(cleaned_line)

# Join back and process
cleaned_csv = '\n'.join(cleaned_lines)
output = {'cleaned_data': cleaned_csv}

This handles Windows-1252 characters that mess up UTF-8. The errors=‘replace’ keeps your script from crashing on weird bytes.