Скачать или смотреть How to Efficiently Read Parquet Files into Pandas and Track File Origins with pyarrow

Discover how to read multiple parquet files into a pandas DataFrame while capturing the filenames as a new column using `pyarrow`.
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Efficiently Reading Parquet Files with Filenames in Python

When working with large datasets stored as parquet files, a common challenge arises: how can we efficiently read multiple parquet files into a pandas DataFrame while also keeping track of the filenames? In this post, we'll explore a solution using pyarrow, which offers improved performance over the standard pandas approach.

The Problem

Imagine you have a folder full of parquet files, each containing vital data that you want to analyze in pandas. While reading these files, you also want to add a new column called file_origin that contains the names of these files.

The naïve way to achieve this in pandas, as shown below, can be slow, especially when dealing with a large number of files.

[[See Video to Reveal this Text or Code Snippet]]

By using this approach, you may experience sluggish performance, leading to frustration as your data workflow becomes bottlenecked.

A Solution with pyarrow

Fortunately, there's a more efficient method employing pyarrow, which is designed for high-performance data processing. Here's how to implement it:

Step 1: Setup Your Environment

Before we dive into the code, make sure you have the necessary library installed. You can set up pyarrow using pip:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Reading Parquet Files with Filename Tracking

Here's how to read the parquet files and include the filename as a new column in an efficient manner:

[[See Video to Reveal this Text or Code Snippet]]

Key Components of the Code:

Reading Files: We use pq.read_table(file_name) to read each parquet file efficiently.

Appending the Filename: The append_column method adds a new column called file_name which replicates the current filename for each row.

Batches for Efficiency: By collecting batches of data instead of building a DataFrame row by row, we enhance performance, particularly with larger datasets.

Performance Considerations

While using pyarrow may yield significant performance benefits, the speedup particularly shines if your DataFrames contain many strings or object types, which can be slower with pandas. If your datasets are numeric-heavy, you may notice less of an improvement.

Conclusion

In this guide, we explored a strategy for efficiently reading parquet files into a pandas DataFrame while tracking the source filenames. Using pyarrow, you can streamline your data processing workflow and overcome the performance limitations of the pandas-only approach.

Don't hesitate to put this knowledge into practice next time you're dealing with multiple parquet files. Efficiency and clarity can significantly enhance your data analysis tasks!