Pandas is a popular Python library for data manipulation and analysis, often used in conjunction with NumPy for efficient numerical computing. However, with the release of Pandas version 2.0, there’s been a major change in the library’s default engine for certain operations. In particular, Pandas has now introduced support for using PyArrow as the default engine, replacing NumPy in some instances.
What is PyArrow?
Before diving into the changes introduced by Pandas 2.0, let’s first understand what PyArrow is. PyArrow is a Python library that provides a cross-language development platform for in-memory data, with a focus on fast and efficient data processing. It’s based on the Arrow data format, which is designed to be highly interoperable and optimized for high-performance computing.
PyArrow provides a Python interface to Arrow data, allowing users to manipulate and process large datasets in-memory using Python. It supports a variety of data sources and formats, including CSV, Parquet, and more.
Why the Switch to PyArrow?
So, why did Pandas decide to switch to PyArrow as the default engine for certain operations? The answer lies in the efficiency and performance gains that PyArrow can offer over NumPy for certain types of data manipulations.
In particular, PyArrow excels at reading and writing data to and from disk, as well as certain types of data transformation. For these operations, PyArrow can be significantly faster and more memory-efficient than NumPy, making it a natural choice for certain use cases.
It’s worth noting that this change in default engine only applies to certain operations and is not a complete replacement of NumPy with PyArrow. Pandas still relies heavily on NumPy and will continue to do so for the foreseeable future.
How to Use PyArrow with Pandas
If you’re already familiar with Pandas and want to start using PyArrow with it, the good news is that it’s relatively easy to do so. In fact, if you’re using Pandas 1.0 or later, PyArrow is already included by default.
To use PyArrow with Pandas, you’ll need to specify it as the engine for certain operations. For example, to read a Parquet file using PyArrow, you can use the following code:
import pandas as pd
df = pd.read_parquet(‘example.parquet’, engine=’pyarrow’)Similarly, to write a DataFrame to a Parquet file using PyArrow, you can use the following code:
df.to_parquet('example.parquet', engine='pyarrow')In general, PyArrow is used as the default engine for reading and writing Parquet files, but you can also specify it as the engine for other file formats and operations as well.
Conclusion
Pandas 2.0 introduced a major change in the library’s default engine, with support for using PyArrow for certain operations instead of NumPy. While this change is not a complete replacement of NumPy, it can offer significant efficiency and performance gains for certain types of data manipulations.
If you’re already using Pandas and want to start using PyArrow, it’s relatively easy to do so by specifying it as the engine for certain operations. With PyArrow’s focus on fast and efficient data processing, it’s a powerful tool for manipulating and analyzing large datasets in Python.
