_csv.error: field larger than field limit (131072)

Exception handling when setting limit

At times, setting sys.maxsize can trigger an OverflowError. What's the solution here? Time for elegant bypassing with an iterative approach. This loop gradually decreases maxInt, effectively bringing you to a pipeline-safe limit:

import csv
import sys

maxInt = sys.maxsize
decrement = True

# When life gives you OverflowErrors, make code loops
while decrement:
    decrement = False
    try:
        csv.field_size_limit(maxInt)
    except OverflowError:
        maxInt = int(maxInt/10)
        decrement = True
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Adjusting to specific CSV structures

What if your CSV files include large, unquoted fields? Use csv.QUOTE_NONE to avert csv.Error. This neutralizes Python's default quote handling and avoids conflicts with large unquoted fields:

import csv
# Hiding from quoting errors in a CSV.NO_QUOTE zone
with open('yourfile.csv', 'r', encoding='utf-8') as csvfile:
    reader = csv.reader(csvfile, quoting=csv.QUOTE_NONE)
    for row in reader:
        # Your row handling code here
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Oftentimes, CSV files possess unique structures. In such cases, adjust the quotechar and quoting parameters to match.

Hooking up with ctypes for larger limits

If sys.maxsize falls short for larger fields, switch to ctypes. Here's how to set an even higher limit on systems with ctypes support:

import ctypes
import csv

# ctYES to ctypes!
try:
    libc = ctypes.cdll.LoadLibrary("libc.so.6")
    limit = -1
    _CSV_FIELD_SIZE_LIMIT = libc.csv_field_size_limit
    _CSV_FIELD_SIZE_LIMIT(limit)
except (AttributeError, OSError):
    # ctypes not available? We got fallback options
    csv.field_size_limit(sys.maxsize)
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This higher limit can be crucial in sidestepping int too large to convert to C long errors.

Considerations for system architecture

Understand how the C types and system architecture (64bit/32bit) influence Python CSV field size handling. On 64-bit systems, sys.maxsize theoretically offers greater room. However, the practical limit depends on C’s long type size and your system's memory capabilities.

Handing tab-delimited files

Using csv.QUOTE_NONE works great for avoiding csv.Error with large, unquoted fields. However, ensure it plays nicely with tab-delimited files by also setting the delimiter:

# Tabs and quotes, sitting in a tree, p-a-r-s-i-n-g
with open('yourlargefile.tsv', 'r', encoding='utf-8') as tsvfile:
    reader = csv.reader(tsvfile, delimiter="\t", quoting=csv.QUOTE_NONE)
    # Process every row smoothly
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Tailoring CSV reader settings for your use case

Optimal handling of your specific CSV files hinges on finding the perfect csv.reader settings. Experiment with parameters like doublequote, escapechar, and dialect. Every CSV file is a new mystery waiting to be solved!