CSV Trimming: A Python Package for Cleaning Messy CSV Files

CSV Trimming is a Python package designed to convert chaotic CSV files—often obtained from websites, legacy systems, or poorly managed data—into clean, well‑formatted CSVs using just one line of code, without requiring complex configuration or large language models.

Installation

pip install csv_trimming

Basic Usage

from csv_trimming import CSVTrimmer

# Load your csv
csv = pd.read_csv("path/to/csv.csv")
# Instantiate the trimmer
trimmer = CSVTrimmer()
# And trim it
trimmed_csv = trimmer.trim(csv)
# That's it!

The package can clean a messy input CSV such as the example shown, removing stray symbols, empty cells, and misaligned rows, producing a tidy table with only the relevant columns.

Advanced Feature – Row Correlation

When rows are split across multiple lines (a common issue in real‑world CSVs), CSV Trimmer can merge them by providing a callback that defines which rows are related.

def simple_correlation_callback(current_row: pd.Series, next_row: pd.Series) -> Tuple[bool, pd.Series]:
    """Return the correlation between two rows."""
    # All of the rows that have a subsequent correlated row are
    # non‑empty, and the subsequent correlated rows are always
    # with the first cell empty.
    if pd.isna(next_row.iloc[0]) and all(pd.notna(current_row)):
        return True, pd.concat([
            current_row,
            pd.Series({"surname": next_row.iloc[-1]}),
        ])
    return False, current_row

trimmer = CSVTrimmer(simple_correlation_callback)
result = trimmer.trim(csv)

Using this callback, the library merges split rows and produces a final CSV where each logical record occupies a single row, as demonstrated by the before‑and‑after tables in the original article.

For more details and the source code, visit the project repository at https://github.com/LucaCappelletti94/csv_trimming .