Python csv.QUOTE_NONNUMERIC: Handle Mixed Data Types in CSV

When working with CSV files containing mixed numeric and text data, Python's csv.QUOTE_NONNUMERIC provides a smart quoting strategy that automatically handles different data types appropriately.

Understanding csv.QUOTE_NONNUMERIC

Unlike csv.QUOTE_MINIMAL or csv.QUOTE_ALL, csv.QUOTE_NONNUMERIC specifically quotes non-numeric fields while leaving numeric values unquoted.

Writing CSV Files with QUOTE_NONNUMERIC

import csv

data = [
    ['Name', 'Age', 'City'],
    ['John', 25, 'New York'],
    ['Alice', 30, 'London'],
]

with open('example.csv', 'w', newline='') as file:
    writer = csv.writer(file, quoting=csv.QUOTE_NONNUMERIC)
    writer.writerows(data)

The resulting CSV file will automatically quote text fields while leaving numbers unquoted:

"Name","Age","City"
"John",25,"New York"
"Alice",30,"London"

Reading CSV Files with QUOTE_NONNUMERIC

Important: When reading files with csv.QUOTE_NONNUMERIC, unquoted fields are automatically converted to float numbers.

import csv

with open('example.csv', 'r') as file:
    reader = csv.reader(file, quoting=csv.QUOTE_NONNUMERIC)
    for row in reader:
        print(row, type(row[1]))

['Name', 'Age', 'City'] 
['John', 25.0, 'New York'] 
['Alice', 30.0, 'London'] 

Error Handling

When using csv.QUOTE_NONNUMERIC for reading, ensure all numeric values are unquoted, or you'll encounter ValueError exceptions.

# This will raise an error
data = '"Name","25","City"'  # Number is quoted
reader = csv.reader([data], quoting=csv.QUOTE_NONNUMERIC)
list(reader)  # ValueError: could not convert string to float: '25'

Integration with DictWriter

You can also use csv.QUOTE_NONNUMERIC with DictWriter for dictionary-based CSV operations.

Conclusion

csv.QUOTE_NONNUMERIC is ideal for handling mixed data types in CSV files, automatically managing quotation marks and type conversion. Just be careful with quoted numeric values when reading files.