Talks - Christopher Ariza: Building NumPy Arrays from CSV Files, Faster than Pandas

Learn how to build NumPy arrays from CSV files faster than using Pandas by leveraging the efficient CSV reader from the standard library and C extensions like CPO and Limited Arrays.

Key takeaways

NumPy arrays can be built faster from CSV files without using Pandas.
The CSV reader from the standard library is efficient due to its C implementation.
The limite contiguous sets (CPO) is a C extension that creates and populates grids where we have the shape from tall to square and wide, and the d-type is very nice.
The CSV reader’s type parsing is very useful for processing tall, square, and wide data frames.
The limited arrays are a C extension implemented in array kit that outperforms pandas in most scenarios.
The code point grid is a tool that is used to serve as an interface between the limited arrays and the readers.
Pandas’ type parsing is not suitable for processing tall, square, and wide data frames, making it slower.
C code with no py objects performs better than Python code with py objects.
The performance of a data frame is determined by its processing time and the parser used.
The CSV reader is configured for a wide range of CSV dialects and supports all NumPy D types.
Delimited arrays can be used to build columnar arrays from CSV files.
The C implementation of delimited_to_arrays is the core functionality of building NumPy arrays.
Python’s NumPy gem-from-text offers better performance than Python and Pandas’ type parsing.
The performance comparison between the limited arrays and pandas shows that the limited arrays outperform pandas in most scenarios.
The C code that uses the CSV reader and the code point grid is more efficient than Python code with py objects.

Talks - Christopher Ariza: Building NumPy Arrays from CSV Files, Faster than Pandas

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