One (not so flexible) approach would be to firstly remove all " quotes from the csv, and then enclose the elements of the specific column with "" quotes(this is done to avoid misinterpreting the "," seperator while parsing), like this:
import csv
# Specify the column index (0-based)
column_index = 1
# Open the input CSV file
with open('input.csv', 'r') as f:
reader = csv.reader(f)
# Open the output CSV file
with open('output.csv', 'w', newline='') as g:
writer = csv.writer(g)
# Iterate through the rows of the input CSV file
for row in reader:
# Replace the " character with an empty string
row[column_index] = row[column_index].replace('"', '')
# Enclose the modified element in "" quotes
row[column_index] = f'"{row[column_index]}"'
# Write the modified row to the output CSV file
writer.writerow(row)
This code creates a new modified csv file
Then your problematic csv row will look like that:
3,"Unfortunately,I get an error",15"
Then you can import the data like you did: df = pd.read_csv(filename, sep = ',', quotechar='"')
To automate this conversion for all csv files within a directory:
import csv
import glob
# Specify the column index (0-based)
column_index = 1
# Get a list of all CSV files in the current directory
csv_files = glob.glob('*.csv')
# Iterate through the CSV files
for csv_file in csv_files:
# Open the input CSV file
with open(csv_file, 'r') as f:
reader = csv.reader(f)
# Open the output CSV file
output_file = csv_file.replace('.csv', '_new.csv')
with open(output_file, 'w', newline='') as g:
writer = csv.writer(g)
# Iterate through the rows of the input CSV file
for row in reader:
# Replace the " character with an empty string
row[column_index] = row[column_index].replace('"', '')
# Enclose the modified element in "" quotes
row[column_index] = f'"{row[column_index]}"'
# Write the modified row to the output CSV file
writer.writerow(row)
this names the new csv files as the old ones but with "_new.csv" instead of just ".csv".