Introduction to PyArrow

PyArrow is a Python library for Apache Arrow, a cross-language development platform designed for high-performance data processing and analytics. It provides an efficient, columnar in-memory data format that enables fast data interchange between systems like Pandas, NumPy, Spark, and Parquet-based storage engines.

PyArrow is widely used in big data pipelines, data engineering, and analytics workflows where performance and memory efficiency are critical.

What is Apache Arrow?

Apache Arrow defines a standardized columnar memory format that allows data to be shared across different programming languages without expensive serialization or copying.

PyArrow is the Python implementation of Apache Arrow.

Key Benefits

• Zero-copy data sharing
• Columnar memory layout for faster analytics
• Seamless interoperability with Pandas and NumPy
• Efficient reading and writing of Parquet and Feather files

Core Components of PyArrow

• Arrow Arrays: Columnar data structures
• Arrow Tables: Collection of arrays (similar to a DataFrame)
• Parquet / Feather: High-performance file formats
• Compute Module: Vectorized operations

To install PyArrow refer to: Installing Python PyArrow

Let's look at some of example use cases of PyArrow:

1. Creating an Arrow Array

This example shows how to create a basic Arrow array from a Python list.

import pyarrow as pa

data = [1, 2, 3, 4, 5]
arr = pa.array(data)

print(arr)

Output:

[
1,
2,
3,
4,
5
]

Explanation:

• pa.array() converts a Python list into an Arrow array.
• Elements are stored in a columnar, immutable structure optimized for analytics.

2. Creating an Arrow Table

Arrow Tables are similar to Pandas DataFrames but optimized for performance.

import pyarrow as pa

data = {
    "name": ["Xavier", "Logan", "Pheonix"],
    "age": [60, 120, 35]
}

table = pa.table(data)
print(table)

Output

pyarrow.Table
name: string
age: int64
----
name: [["Xavier","Logan","Pheonix"]]
age: [[60,120,35]]

Explanation:

• pa.table(data) creates an Arrow Table where each dictionary key becomes a column.
• Each column is internally represented as an Arrow Array.

3. Converting Between Pandas and PyArrow

PyArrow integrates seamlessly with Pandas:

import pandas as pd
import pyarrow as pa

df = pd.DataFrame({
    "city": ["Delhi", "Mumbai", "Dubai"],
    "population": [19, 20, 10]
})

table = pa.Table.from_pandas(df)
df_back = table.to_pandas()

print(df_back)

Output

city population

0 Delhi 19
1 Mumbai 20
2 Dubai 10

Explanation:

• pa.Table.from_pandas(df) converts a Pandas DataFrame into an Arrow Table.
• table.to_pandas() converts the Arrow Table back to a DataFrame with minimal data copying.

4. Reading and Writing Parquet Files

Parquet is a columnar storage format commonly used in big data systems.

import pyarrow as pa
import pyarrow.parquet as pq

table = pa.table({
    "id": [1, 2, 3],
    "score": [90, 85, 88]
})

pq.write_table(table, "data.parquet")

read_table = pq.read_table("data.parquet")
print(read_table)

Output

pyarrow.Table
id: int64
score: int64
----
id: [[1,2,3]]
score: [[90,85,88]]

Explanation:

• pq.write_table() writes the Arrow Table to a Parquet file in columnar format.
• pq.read_table() loads the Parquet file back into an Arrow Table.

5. Basic Compute Operations

PyArrow supports vectorized computations.

import pyarrow as pa
import pyarrow.compute as pc

arr = pa.array([10, 20, 30, 40])
res = pc.multiply(arr, 2)

print(res)

Output

[
20,
40,
60,
80
]

Explanation:

• pc.multiply() applies a vectorized multiplication on every element of arr.
• The operation runs at the Arrow compute layer without Python loops.

Common Use Cases of PyArrow

• Data Engineering Pipelines: Efficient data transfer between systems
• Big Data Analytics: Used with Spark, Dask, and DuckDB
• Machine Learning: Fast dataset loading and preprocessing
• Columnar Storage: Parquet and Feather file handling
• Interoperability: Sharing data across Python, Java, C++, and R