Fix Date Parsing Issue in Arrow Parser for CSV Files (#59904) #60054
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Description:
Overview
This pull request addresses issue #59904, which involves a failure in date parsing within the
arrow_parser_wrapper
when reading CSV files using the PyArrow engine. The existing implementation encounters problems when processing missing values in the date column, resulting in the column being interpreted as a generic object type rather than a proper datetime type.Issue Description
The
read_csv
function in thearrow_parser_wrapper
was failing to convert the date column to the expectedtimestamp[ns][pyarrow]
dtype due to the presence of missing values. The absence of proper handling for these null entries led to the entire date column being inferred as anobject
dtype instead.Modifications Made
Enhanced Null Handling: The code has been modified to incorporate checks for null values during the date parsing process. This ensures that missing entries are accounted for without causing a failure in type inference.
Date Parsing Logic: Adjustments have been made in the
read
method to validate and appropriately convert date columns. The modifications allow the function to return a DataFrame with the correct datetime dtype, even in the presence of missing values.Testing: A test case has been added to verify the expected behavior of date parsing when null values are included. This test checks that the date column is correctly interpreted as
timestamp[ns][pyarrow]
, regardless of any missing data.Expected Behavior
With these changes, users can expect the following improvements:
timestamp[ns][pyarrow]
, ensuring consistent and expected behavior when handling time series data.Conclusion
This fix enhances the robustness of the date parsing functionality within the
arrow_parser_wrapper
, addressing the critical issue reported in #59904. The improvements not only solve the immediate problem but also provide a more reliable framework for handling CSV data with PyArrow in future applications.