Generate clean plain-text files from pandas groupby: skip to_csv, write lines directly, and eliminate CSV quoting issues

Learn why pandas.to_csv breaks plain text layouts and how to write grouped output directly in Python to avoid CSV quoting artifacts. See code and results.

When the target format is free-form text rather than a CSV table, pushing your output through a CSV writer often backfires. Grouped values become embedded inside quoted cells, commas creep in, and line breaks are not where you want them. If your goal is a plain text layout like an ID header followed by its lines and a separator, you’ll get a cleaner result by writing the file directly instead of using pandas.to_csv.

Problem setup

We need to read a CSV with columns ID and OTHER_FIELDS_2, group by ID, and produce a text file where each group starts with the ID, then lists the non-empty values from OTHER_FIELDS_2, and ends with a line of asterisks. Using DataFrame-to-CSV formatting for this layout leads to extra quotes and misplaced line breaks.

Code that triggers the formatting issue

The following snippet groups rows, turns grouped data into strings, and then writes it using to_csv. The logic is clear, but the final write step forces CSV rules onto a non-CSV format, which is why the output contains quotes and artifacts.

import pandas as pd
import csv
tbl = pd.read_csv('idDetails.csv')
row_buffer = []
key_col = 'ID'
value_cols = ['OTHER_FIELDS_2']
clusters = tbl.groupby(key_col)[value_cols]
for k, sub in clusters:
    lines_as_is = sub.to_string(header=False, index=False)
    cleaned = sub.fillna('').dropna(axis=0, how='all')
    flat_text = cleaned.to_string(header=False, index=False)
    sep_line = '**********'
    row_buffer.append([k, '\n', flat_text, '\n', sep_line])
out = pd.DataFrame(row_buffer)
out.to_csv('idDetailsoutput.txt', header=False, index=False)

Why this fails for plain text

Rows are assembled into a DataFrame and exported with to_csv, which is designed to produce a well-formed CSV table. That means fields are quoted and separated for CSV correctness. Because each group is effectively a single row containing multiline text, the writer preserves it by wrapping it in quotes and embedding line breaks inside a cell, creating the extra quoting and awkward layout. CSV expects a rectangular table with values for columns, while the desired output is a custom text layout.

Direct-write fix

Skip DataFrame-to-CSV for this task. Iterate over groups and write exactly what you need to a text file. This avoids CSV quoting rules altogether and keeps the formatting under your control.

import pandas as pd
# Read input
records = pd.read_csv('idDetails.csv')
# Group by ID and select the target column
by_id = records.groupby('ID')['OTHER_FIELDS_2']
# Write custom text output
with open('idDetailsoutput.txt', 'w') as out_f:
    for gid, series in by_id:
        out_f.write(f"{gid}\n")
        for val in series:
            if pd.notna(val) and val.strip() != "":
                out_f.write(f"{val.strip()}\n")
        out_f.write("**********\n")

Resulting output matches the requested format:

18
20 BA-10  12  06  2  30  S
20 BA-20  12  06  2  30  S
**********
66
20 AD-38  12  06  B
20 AD-38  30  07  B
**********
70
20 OL-45  19  11  B
20 EM-45  19  08  B
**********
77
**********
87
25 R160  22  13  E
25 R165  22  08  E
**********
88
20 TH-42  02  02  5  30  MT
**********

Why this is worth knowing

Even when you prepare data with pandas, not every output should be a CSV. If the end result is a human-readable, structured text block, a simple write pass gives you precise control and avoids CSV quoting, commas, and cell boundaries leaking into your final layout.

Takeaways

Use groupby to organize your records, but choose an output method that matches the format you need. For custom text layouts, write lines directly and filter out empty values as you go. This keeps the output clean, predictable, and aligned with your specification.