Python • Pandas • PyArrow • Spark

How to Read and Load Parquet Files in Python (Pandas, PyArrow, Spark)

Copy-paste ready snippets for the most common ways to open Parquet data. Use tabs to switch libraries and jump directly to the section you need.

Using Pandas (pd.read_parquet)

Ideal for local analysis and quick exploration. Install pandas with a Parquet engine such as pyarrow.

Best for single-node workloads and exploratory analysis.
Supports filters with columns= and row-level predicates via PyArrow.
Use engine="pyarrow" (default in recent pandas) for performance and type fidelity.

Fast for local analysis; requires pandas with pyarrow engine.

import pandas as pd

df = pd.read_parquet("file.parquet")
print(df.head())

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Use Parquet Tools Online

Using PyArrow (For better performance)

PyArrow exposes Parquet metadata, statistics, and row groups. It is excellent when you need control over columns, filters, or schema inspection before converting to pandas.

import pyarrow.parquet as pq

table = pq.read_table("file.parquet", columns=["id", "country"])
print(table.schema)

# Filter rows by predicate pushdown (supported when statistics exist)
dataset = pq.ParquetDataset("file.parquet")
filtered = dataset.read(columns=["id", "country"])
df = filtered.to_pandas()

Tip: call pq.read_metadata to inspect row groups, compression, and column types without loading the full dataset.

Using PySpark (For big data)

Spark handles partitioned datasets on cloud storage with predicate pushdown and column pruning. Use it when the dataset exceeds single-machine memory.

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName("read-parquet").getOrCreate()

df = spark.read.parquet("s3://bucket/path/to/table/")

# Column pruning + predicate pushdown
filtered = df.select("id", "country").where(col("country") == "US")

filtered.show(10)
filtered.write.mode("overwrite").parquet("s3://bucket/tmp/us-users/")

Tip: Keep partitions balanced (avoid millions of small files) and prefer column pruning to reduce shuffle costs.

Troubleshooting (Common errors)

ModuleNotFoundError: install pyarrow orfastparquet (pip install "pandas[parquet]").
ArrowInvalid / schema mismatch: ensure column names and types match across row groups; check with pq.read_metadata.
Corrupted small files: merge tiny Parquet files or rewrite with repartition in Spark.
Memory errors in pandas: load selected columns only, chunk with dataset.to_pandas(split_blocks=True), or sample via Spark then export.