Feature Type

• Adding new functionality to pandas

• Changing existing functionality in pandas

• Removing existing functionality in pandas

Problem Description

Currently, pandas has separate IO methods for each file format (to_csv, read_parquet, etc.). This requires users to:

• Remember multiple method names
• Change code when switching formats

Feature Description

A unified save/read API would simplify common IO operations while maintaining explicit control when needed:

• File type is inferred from the filepath extension, but a format arg can be passed to be explicit, raising an error in some cases where the inferred file type disagrees with passed file type.
• Both methods accept **kwargs and pass them along to the underlying file-type-specific pandas IO methods.
• Optionally, support some basic translation across discrepancies in arg names in existing IO methods (i.e. "usecols" in read_csv vs "columns" in read_parquet).

# Simplest happy path:
df.save('data.csv')  # Uses to_csv
df = pd.read('data.parquet')  # Uses read_parquet

# Optionally, be explicit about expected file type
df.save('data.csv', format="csv")  # Uses to_csv
df = pd.read('data.parquet', format="parquet")  # Uses read_parquet

# Raises ValueError for conflicting format info:
df.save('data.csv', format='parquet')  # Conflicting types
df.save('data.txt', format='csv')  # .txt implies text format

# Reading allows overrides for misnamed files (or should we require users to rename their files properly first?)
df = pd.read('mislabeled.txt', format='parquet')

# Not sure if we should allow save when inferred file type is not a standard type:
df.save('data', format='csv')  # No extension, needs type
df.save('mydata.unknown', format='csv')  # Unclear extension

Alternative Solutions

Existing functionality is OK, just not the simplest to use.

Additional Context

No response