How to Use pandas read parquet to Load Parquet Files into DataFrames

Use pandas.read_parquet() to load Apache Parquet files into DataFrames, automatically selecting between PyArrow and fastparquet engines while supporting local paths, cloud storage, and column filtering.

The pandas.read_parquet function provides the primary interface for reading Apache Parquet files into pandas DataFrames. According to the pandas-dev/pandas source code, this high-level API resides in pandas/io/parquet.py (definition starts around line 509) and orchestrates engine selection, path resolution, and DataFrame construction while delegating actual parsing to specialized backend libraries.

How pandas read parquet Works Internally

Understanding the internal flow helps optimize performance and debug issues. The implementation follows a five-stage pipeline:

Path Handling and Normalization

The function first normalizes the supplied file-system path or URL. It expands ~ to the user home directory, accepts pathlib.Path objects, and prepares the location string for the underlying engine.

Engine Selection: PyArrow vs fastparquet

The engine parameter controls which backend parses the file:

• engine="auto" (default): The code attempts to import PyArrow first; if unavailable, it falls back to fastparquet.
• engine="pyarrow": Forces the PyArrow backend via pandas/io/parquet/_arrow.py.
• engine="fastparquet": Forces the fastparquet backend via pandas/io/parquet/_fastparquet.py.

Reading and Converting Data

The chosen engine's ParquetFile class reads file metadata, applies optional column filtering, and converts data to NumPy arrays or Arrow tables. The pandas/io/parquet.py wrapper then constructs the final DataFrame from these structures.

Remote Storage with storage_options

When reading from remote storage (S3, GCS, Azure), the storage_options dictionary passes directly to the engine, which uses fsspec to open the object. This allows authentication credentials and connection parameters to flow through without pandas handling the network layer.

pandas read parquet Examples

Basic Usage with Automatic Engine Detection

Let pandas select the best available engine automatically:

import pandas as pd

df = pd.read_parquet("data/example.parquet")
print(df.head())

Explicit Engine Selection

Force a specific backend when you need particular features or reproducibility:

df = pd.read_parquet("data/example.parquet", engine="pyarrow")
print(df.describe())

Reading Specific Columns

Reduce memory usage and I/O by loading only required columns:

df = pd.read_parquet(
    "data/example.parquet",
    columns=["order_id", "order_date", "total_amount"],
)
print(df.columns)

Reading from S3 and Cloud Storage

Access remote Parquet files using fsspec-compatible storage options:

df = pd.read_parquet(
    "s3://my-bucket/sales/2023.parquet",
    storage_options={"key": "ACCESS_KEY", "secret": "SECRET_KEY"},
)
print(df.shape)

Using pathlib.Path Objects

Pass Path objects directly without manual string conversion:

from pathlib import Path

path = Path("data/example.parquet")
df = pd.read_parquet(path)
print(df.info())

Key Implementation Files in pandas-dev/pandas

The pandas.read_parquet function relies on a modular architecture spread across several files:

• pandas/io/parquet.py – Contains the main read_parquet definition (around line 509) and orchestrates engine dispatch, path normalization, and DataFrame construction.
• pandas/io/parquet/_arrow.py – Implements PyArrow-specific reading logic via _read_pyarrow helper functions.
• pandas/io/parquet/_fastparquet.py – Implements fastparquet-specific reading logic via _read_fastparquet helper functions.
• pandas/util/_validators.py – Provides argument validation utilities used to sanitize engine, columns, and storage_options parameters.
• pandas/_libs/parsers.pyx – Contains low-level Cython parsing helpers used indirectly when converting column data to NumPy arrays.

Summary

• pandas.read_parquet in pandas/io/parquet.py serves as the primary interface for loading Parquet files into DataFrames.
• The function automatically selects between PyArrow and fastparquet engines unless explicitly specified.
• It supports column filtering, remote storage via fsspec, and pathlib.Path objects for flexible I/O operations.
• Heavy lifting is delegated to engine-specific modules (_arrow.py, _fastparquet.py), keeping the pandas wrapper lightweight and maintainable.

Frequently Asked Questions

What engines does pandas read parquet support?

pandas.read_parquet supports two backend engines: PyArrow and fastparquet. When engine="auto" (the default), pandas attempts to use PyArrow first and falls back to fastparquet if PyArrow is not installed. You can force a specific engine by passing engine="pyarrow" or engine="fastparquet".

How do I read only specific columns from a Parquet file?

Pass a list of column names to the columns parameter. This pushes the column selection down to the engine level, reducing both I/O bandwidth and memory usage because unneeded columns are never loaded into memory. For example: pd.read_parquet("file.parquet", columns=["col_a", "col_b"]).

Can pandas read parquet read files directly from S3 or cloud storage?

Yes. Provide the full URI (e.g., s3://bucket/file.parquet) and supply authentication credentials or connection parameters via the storage_options dictionary. The function passes these options to the underlying engine, which uses fsspec to handle the remote file system protocol, enabling seamless access to S3, GCS, Azure, and other cloud storage backends.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}