Reading Parquet Files with Polars

Oct 13, 2025 by wbrum.org 1 views

Rust: Extracting Value of Type List[i16] from Parquet Cell (Polars)

In this article, we will explore how to extract values of type list[i16] from a Parquet file using the Polars library in Rust. Parquet is a columnar storage format that is widely used in big data processing, and Polars is a Rust library that provides a high-performance and efficient way to work with Parquet files.

What is Polars?

Polars is a Rust library that provides a high-performance and efficient way to work with Parquet files. It is designed to be fast, scalable, and easy to use, making it an ideal choice for big data processing tasks. Polars provides a simple and intuitive API that allows developers to easily read and write Parquet files, as well as perform various operations on the data.

Reading Parquet Files with Polars

To read a Parquet file with Polars, you can use the pl::io::ParquetReader struct. This struct provides a way to read a Parquet file and return a pl::DataFrame object, which is a table-like data structure that can be used to perform various operations on the data.

Here is an example of how to read a Parquet file with Polars:

use polars::prelude::*;
fn main() 
let file_path = "path/to/parquet/file.parquet";
let df = pl:io::ParquetReader::new(file_path).finish().unwrap();
println!("{:?", df);
}

This code reads a Parquet file from the specified path and returns a pl::DataFrame object, which is then printed to the console.

Extracting Values of Type List[i16]

Now that we have read the Parquet file, we can extract the values of type list[i16] from the file. To do this, we can use the df.column() method to access the column that contains the list[i16] values, and then use the iter() method to iterate over the values in the column.

Here is an example of how to extract the values of type list[i16] from the Parquet file:

use polars::prelude::*;
fn main() 
let file_path = "path/to/parquet/file.parquet";
let df = pl:io::ParquetReader::new(file_path).finish().unwrap();
let column_a = df.column("Column A");
let list_i16_values = column_a.iter().map(|value| value.as_list::<i16>().unwrap());
for value in list_i16_values {
println!("{:?", value);
}
}

This code extracts the values of type list[i16] from the "Column A" column in the Parquet file and prints them to the console.

Handling Errors

When working with Parquet files, it is possible that errors may occur. For example, the file may be corrupted or the data may be invalid. To handle these errors, we can use the Result type in Rust to return an error value if an error occurs.

Here is an example of how to handle errors when reading a Parquet file:

use polars::prelude::*;
fn main() 
let file_path = "path/to/parquet/file.parquet";
match pl:io::ParquetReader::new(file_path).finish() {
Ok(df) => println!("{:?", df),
Err(e) => println!("Error: {}", e),
}
}

This code reads a Parquet file from the specified path and returns a Result value. If the file is read successfully, the Result value is Ok and the DataFrame object is printed to the console. If an error occurs, the Result value is Err and the error message is printed to the console.

In this article, we have explored how to extract values of type list[i16] from a Parquet file using the Polars library in Rust. We have seen how to read a Parquet file, extract the values of type list[i16], and handle errors that may occur. By following the examples in this article, you should be able to extract values of type list[i16] from a Parquet file using Polars in Rust.

Here are some example use cases for extracting values of type list[i16] from a Parquet file:

Data Analysis: Extracting values of type list[i16] from a Parquet file can be useful for data analysis tasks, such as calculating statistics or performing data visualization.
Machine Learning: Extracting values of type list[i16] from a Parquet file can be useful for machine learning tasks, such as training models or making predictions.
Data Integration: Extracting values of type list[i16] from a Parquet file can be useful for data integration tasks, such as combining data from multiple sources or transforming data into a different format.

In conclusion, extracting values of type list[i16] from a Parquet file using the Polars library in Rust is a powerful and efficient way to work with big data. By following the examples in this article, you should be able to extract values of type list[i16] from a Parquet file and perform various operations on the data.
Rust: Extracting Value of Type List[i16] from Parquet Cell (Polars) - Q&A

In our previous article, we explored how to extract values of type list[i16] from a Parquet file using the Polars library in Rust. In this article, we will answer some frequently asked questions (FAQs) about extracting values of type list[i16] from a Parquet file using Polars.

Q: What is the difference between Polars and other Parquet libraries?

A: Polars is a Rust library that provides a high-performance and efficient way to work with Parquet files. It is designed to be fast, scalable, and easy to use, making it an ideal choice for big data processing tasks. Other Parquet libraries, such as Apache Arrow and Apache Parquet, are written in C++ and provide similar functionality to Polars, but with a different API and performance characteristics.

Q: How do I install Polars?

A: To install Polars, you can use the following command:

cargo add polars

This will add the Polars library to your Rust project and make it available for use.

Q: What is the difference between `list[i16]` and `i16`?

A: list[i16] is a type that represents a list of 16-bit signed integers, while i16 is a type that represents a single 16-bit signed integer. When working with Parquet files, it is common to encounter lists of integers, which are represented by the list[i16] type.

Q: How do I extract values of type `list[i16]` from a Parquet file?

A: To extract values of type list[i16] from a Parquet file, you can use the following code:

use polars::prelude::*;
fn main() 
let file_path = "path/to/parquet/file.parquet";
let df = pl:io::ParquetReader::new(file_path).finish().unwrap();
let column_a = df.column("Column A");
let list_i16_values = column_a.iter().map(|value| value.as_list::<i16>().unwrap());
for value in list_i16_values {
println!("{:?", value);
}
}

This code reads a Parquet file from the specified path, extracts the values of type list[i16] from the "Column A" column, and prints them to the console.

Q: How do I handle errors when extracting values of type `list[i16]` from a Parquet file?

A: To handle errors when extracting values of type list[i16] from a Parquet file, you can use the Result type in Rust to return an error value if an error occurs. Here is an example of how to handle errors:

use polars::prelude::*;
fn main() 
let file_path = "path/to/parquet/file.parquet";
match pl:io::ParquetReader::new(file_path).finish() {
Ok(df) => println!("{:?", df),
Err(e) => println!("Error: {}", e),
}
}

Q: Can I use Polars with other data formats, such as CSV or JSON?

A: Yes, you can use Polars with other data formats, such as CSV or JSON. Polars provides a flexible API that allows you to read and write data from various formats, including CSV, JSON, and Parquet.

Q: How do I optimize the performance of Polars?

A: To optimize the performance of Polars, you can use the following techniques:

Use the pl::io::ParquetReader struct to read Parquet files, which is optimized for performance.
Use the pl::DataFrame object to perform operations on the data, which is optimized for performance.
Use the pl::iter module to iterate over the data, which is optimized for performance.
Use the pl::chunk module to process the data in chunks, which can improve performance for large datasets.

By following these techniques, you can optimize the performance of Polars and improve the efficiency of your data processing tasks.

In this article, we have answered some frequently asked questions (FAQs) about extracting values of type list[i16] from a Parquet file using Polars. We have covered topics such as installing Polars, extracting values of type list[i16], handling errors, and optimizing performance. By following the examples and techniques in this article, you should be able to extract values of type list[i16] from a Parquet file using Polars and perform various operations on the data.