How can I dynamically handle ragged rows in a CSV file using Polars?

You have two main options:

1. Use Polars’ Built-in Option:
   You can enable the built-in parameter that truncates rows with extra columns by setting
   truncate_ragged_lines=True. This option tells Polars to ignore any extra columns beyond what was inferred (or defined by the schema). For example:

   PYTHONCopied
   import polars as pl
   
   df = pl.read_csv(
       "sample.csv",
       has_header=False,
       skip_rows=1,
       infer_schema=False,
       truncate_ragged_lines=True,
       ignore_errors=True
   )

   This approach works well if you’re okay with extra fields being silently dropped.

2. Preprocess the File:
   If you need to preserve all column data and handle ragged rows more dynamically (for instance, if you want to align columns based on the row with the most fields), you’ll need to preprocess the file before reading it into Polars. Common preprocessing steps include:

   • Scanning the file to determine the maximum number of columns.
   • Rewriting all rows to have that many columns (e.g., padding missing columns with null values).

   This approach can be done in Python. For example:

   PYTHONCopied
   import csv
   import io
   import polars as pl
   
   # Read the CSV and determine the maximum number of columns.
   with open("sample.csv", newline="") as f:
       reader = csv.reader(f)
       rows = list(reader)
       max_cols = max(len(row) for row in rows)
   
   # Preprocess: pad each row with empty strings (or None) until row length matches max_cols.
   padded_rows = [row + [None] * (max_cols - len(row)) for row in rows]
   
   # Write the padded rows back to an in-memory string (or file)
   output = io.StringIO()
   writer = csv.writer(output)
   writer.writerows(padded_rows)
   output.seek(0)
   
   # Now read the corrected CSV with Polars.
   df = pl.read_csv(output, has_header=False)

Summary:

• If silently truncating extra columns is acceptable, set truncate_ragged_lines=True.
• Otherwise, preprocess the file to ensure each row has a consistent number of columns before ingesting it with Polars.

Select the approach that best fits your data integrity and processing requirements.