Query datasets from Datasets Server

Datasets Server automatically converts and publishes datasets on the Hub as Parquet files. Parquet files are column-based and they shine when you’re working with big data. There are several ways you can work with Parquet files, and this guide will show you how to:

Polars

Polars is a fast DataFrame library written in Rust with Arrow as its foundation.

πŸ’‘ Learn more about how to get the dataset URLs in the List Parquet files guide.

Let’s start by grabbing the URLs to the train split of the blog_authorship_corpus dataset from Datasets Server:

r = requests.get("https://datasets-server.huggingface.co/parquet?dataset=blog_authorship_corpus")
j = r.json()
urls = [f['url'] for f in j['parquet_files'] if f['split'] == 'train']
urls
['https://huggingface.co/datasets/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/blog_authorship_corpus/blog_authorship_corpus-train-00000-of-00002.parquet',
 'https://huggingface.co/datasets/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/blog_authorship_corpus/blog_authorship_corpus-train-00001-of-00002.parquet']

To read from a single Parquet file, use the read_parquet function to read it into a DataFrame and then execute your query:

import polars as pl

df = (
    pl.read_parquet("https://huggingface.co/datasets/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/blog_authorship_corpus/blog_authorship_corpus-train-00000-of-00002.parquet")
    .groupby("horoscope")
    .agg(
        [
            pl.count(),
            pl.col("text").str.n_chars().mean().alias("avg_blog_length")
        ]
    )
    .sort("avg_blog_length", descending=True)
    .limit(5)
)
print(df)
shape: (5, 3)
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ horoscope ┆ count ┆ avg_blog_length β”‚
β”‚ ---       ┆ ---   ┆ ---             β”‚
β”‚ str       ┆ u32   ┆ f64             β”‚
β•žβ•β•β•β•β•β•β•β•β•β•β•β•ͺ═══════β•ͺ═════════════════║
β”‚ Aquarius  ┆ 34062 ┆ 1129.218836     β”‚
β”‚ Cancer    ┆ 41509 ┆ 1098.366812     β”‚
β”‚ Capricorn ┆ 33961 ┆ 1073.2002       β”‚
β”‚ Libra     ┆ 40302 ┆ 1072.071833     β”‚
β”‚ Leo       ┆ 40587 ┆ 1064.053687     β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

To read multiple Parquet files - for example, if the dataset is sharded - you’ll need to use the concat function to concatenate the files into a single DataFrame:

import polars as pl
df = (
    pl.concat([pl.read_parquet(url) for url in urls])
    .groupby("horoscope")
    .agg(
        [
            pl.count(),
            pl.col("text").str.n_chars().mean().alias("avg_blog_length")
        ]
    )
    .sort("avg_blog_length", descending=True)
    .limit(5)
)
print(df)
shape: (5, 3)
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ horoscope   ┆ count ┆ avg_blog_length β”‚
β”‚ ---         ┆ ---   ┆ ---             β”‚
β”‚ str         ┆ u32   ┆ f64             β”‚
β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ═══════β•ͺ═════════════════║
β”‚ Aquarius    ┆ 49568 ┆ 1125.830677     β”‚
β”‚ Cancer      ┆ 63512 ┆ 1097.956087     β”‚
β”‚ Libra       ┆ 60304 ┆ 1060.611054     β”‚
β”‚ Capricorn   ┆ 49402 ┆ 1059.555261     β”‚
β”‚ Sagittarius ┆ 50431 ┆ 1057.458984     β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Lazy API

Polars offers a lazy API that is more performant and memory-efficient for large Parquet files. The LazyFrame API keeps track of what you want to do, and it’ll only execute the entire query when you’re ready. This way, the lazy API doesn’t load everything into RAM beforehand, and it allows you to work with datasets larger than your available RAM.

To lazily read a Parquet file, use the scan_parquet function instead. Then, execute the entire query with the collect function:

import polars as pl

q = (
    pl.scan_parquet("https://huggingface.co/datasets/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/blog_authorship_corpus/blog_authorship_corpus-train-00000-of-00002.parquet")
    .groupby("horoscope")
    .agg(
        [
            pl.count(),
            pl.col("text").str.n_chars().mean().alias("avg_blog_length")
        ]
    )
    .sort("avg_blog_length", descending=True)
    .limit(5)
)
df = q.collect()

Pandas

You can also use the popular Pandas DataFrame library to read Parquet files.

To read from a single Parquet file, use the read_parquet function to read it into a DataFrame:

import pandas as pd

df = (
    pd.read_parquet("https://huggingface.co/datasets/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/blog_authorship_corpus/blog_authorship_corpus-train-00000-of-00002.parquet")
    .groupby('horoscope')['text']
    .apply(lambda x: x.str.len().mean())
    .sort_values(ascending=False)
    .head(5)
)

To read multiple Parquet files - for example, if the dataset is sharded - you’ll need to use the concat function to concatenate the files into a single DataFrame:

df = (
      pd.concat([pd.read_parquet(url) for url in urls])
      .groupby('horoscope')['text']
      .apply(lambda x: x.str.len().mean())
      .sort_values(ascending=False)
      .head(5)
)

DuckDB

DuckDB is a database that supports reading and querying Parquet files really fast. Begin by creating a connection to DuckDB, and then install and load the httpfs extension to read and write remote files:

Python
JavaScript
import duckdb

url = "https://huggingface.co/datasets/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/blog_authorship_corpus/blog_authorship_corpus-train-00000-of-00002.parquet"

con = duckdb.connect()
con.execute("INSTALL httpfs;")
con.execute("LOAD httpfs;")

Now you can write and execute your SQL query on the Parquet file:

Python
JavaScript
con.sql(f"SELECT horoscope, count(*), AVG(LENGTH(text)) AS avg_blog_length FROM '{url}' GROUP BY horoscope ORDER BY avg_blog_length DESC LIMIT(5)")
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ horoscope β”‚ count_star() β”‚  avg_blog_length   β”‚
β”‚  varchar  β”‚    int64     β”‚       double       β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ Aquarius  β”‚        34062 β”‚  1129.218836239798 β”‚
β”‚ Cancer    β”‚        41509 β”‚  1098.366812016671 β”‚
β”‚ Capricorn β”‚        33961 β”‚ 1073.2002002296751 β”‚
β”‚ Libra     β”‚        40302 β”‚ 1072.0718326633914 β”‚
β”‚ Leo       β”‚        40587 β”‚ 1064.0536871412028 β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

To query multiple files - for example, if the dataset is sharded:

Python
JavaScript
con.sql(f"SELECT horoscope, count(*), AVG(LENGTH(text)) AS avg_blog_length FROM read_parquet({urls[:2]}) GROUP BY horoscope ORDER BY avg_blog_length DESC LIMIT(5)")
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚  horoscope  β”‚ count_star() β”‚  avg_blog_length   β”‚
β”‚   varchar   β”‚    int64     β”‚       double       β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ Aquarius    β”‚        49568 β”‚ 1125.8306770497095 β”‚
β”‚ Cancer      β”‚        63512 β”‚   1097.95608703867 β”‚
β”‚ Libra       β”‚        60304 β”‚ 1060.6110539931017 β”‚
β”‚ Capricorn   β”‚        49402 β”‚ 1059.5552609206104 β”‚
β”‚ Sagittarius β”‚        50431 β”‚ 1057.4589835616982 β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

DuckDB-Wasm, a package powered by , is also availabe for running DuckDB in a browser. This could be useful, for instance, if you want to create a web app to query Parquet files from the browser!