Exporting and handling GoogleScholar CSV exports can drive you mad. I wrote a simple converter script using Python and Pandas to do speed up the task.
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import pandas as pd import glob # Path path = '/path/to/look/at' # Search given path recursively and find files matching the given pattern. files = [f for f in glob.glob(path + "**/*Google*.csv", recursive=True)] for filename in files: with open(filename, "r", encoding="utf-8") as ins: array =  for line in ins: # Replace quotes line = line.replace('"', '') # Temporary replace comma-followed-by-whitespace # occurences with underscore line = line.replace(', ', '_') # rsplit line from right for 7 occurences of comma line = [x.strip() for x in line.rsplit(',', 7)] # Restore comma-followed-by-whitespace occurences line = [x.replace('_', ', ') for x in line] # store processed line array.append(line) # convert array to pandas dataframe df = pd.DataFrame(array) # Rename file new_name = filename.replace('.csv', '.xlsx') # Store df in Excel format df.to_excel(new_name, index=False, header=None)