Building a DataFrame from Parquet Format

Apache Parquet is an open-source columnar storage data file format designed for efficient data storage and retrieval. It offers efficient data compression and encoding schemes, capable of handling large and complex datasets. Additionally, it supports multiple programming languages. Compared to the simple CSV format, Parquet has significant advantages when storing and processing large datasets:

• Storage Efficiency: Parquet files are much smaller than CSV files and support various data compression methods.
• Query Performance: The import and query speed of Parquet is much higher than that of CSV, especially when dealing with large datasets.
• Self-Describing: Parquet files contain metadata about the data structure, including detailed information about fields and field types.
• Compatibility and Performance: Parquet is compatible with many data processing frameworks, such as Hadoop and Spark.

#![allow(unused)]
fn main() {
// This code demonstrates how to load a Parquet format file
use std::fs::File;
use polars::prelude::*;
let mut input_file = File::open("d:/output.parquet")?;
let df = ParquetReader::new(&mut input_file).finish()?;
println!("{}", df);
}