Why You Should Avoid Pandas in Production Applications

If you're coming from a Data Science or Machine Learning background, chances are you've used pandas extensively. It's one of the first libraries taught in universities and widely adopted in data science projects. There's no issue with that— pandas is a fantastic tool for exploratory data analysis, data manipulation, an
d visualization.

However, should you use pandas in production applications? Absolutely not! Let me explain why.

The Problem with Using Pandas in Production

I recently came across a Python desktop application developed with PySide6 that handled financial computations involving gigabytes of data. The developer used pandas for data processing, and while it worked initially, the app became sluggish, unresponsive, and inefficient as the data grew.

So why is pandas not a good choice for production apps?

1. Pandas is Slow for Large Datasets

Pandas is optimized for small to medium-sized datasets. It is single-threaded and operates in memory, making it inefficient for large-scale data processing. If you're dealing with gigabytes of data, pandas will likely slow down your application.

1. Lack of Asynchronous Processing

Many production applications, especially those handling real-time data, require asynchronous operations. Pandas does not provide native support for async processing, which means it can block the main thread, leading to performance bottlenecks in event-driven applications.

1. High Memory Usage

Pandas loads the entire dataset into memory, making it impractical for handling large-scale data. This can lead to excessive memory consumption, slow processing, and even crashes in extreme cases.

What Are the Better Alternatives?

If pandas isn’t the best choice for production applications, what should you use instead? Here are some powerful alternatives:

1. Polars (Best for Performance)

Polars is a high-performance DataFrame library built for speed. It utilizes Apache Arrow under the hood and supports multi-threading, making it significantly faster than pandas for large datasets.

Example: Pandas vs. Polars Performance Test

import pandas as pd
import polars as pl
import time

# Create a large dataset
data = {
    "col1": range(1, 10_000_000),
    "col2": range(10_000_000, 1, -1)
}

# Using Pandas
t1 = time.time()
df_pandas = pd.DataFrame(data)
df_pandas["sum"] = df_pandas["col1"] + df_pandas["col2"]
t2 = time.time()
print(f"Pandas execution time: {t2 - t1} seconds")

# Using Polars
t3 = time.time()
df_polars = pl.DataFrame(data)
df_polars = df_polars.with_columns((df_polars["col1"] + df_polars["col2"]).alias("sum"))
t4 = time.time()
print(f"Polars execution time: {t4 - t3} seconds")

In most cases, Polars is 5x–10x faster than pandas while consuming significantly less memory!

1. PyArrow (Best for Efficient Memory Management)

Apache PyArrow is another excellent alternative, especially for working with columnar data formats like Parquet and Feather.

import pyarrow as pa
import pyarrow.parquet as pq

# Creating an Arrow table
data = pa.table({
    "col1": range(1, 10_000_000),
    "col2": range(10_000_000, 1, -1)
})

# Save as a Parquet file
pq.write_table(data, "data.parquet")

PyArrow is lightweight and highly optimized for reading/writing large datasets efficiently.

1. Apache Spark (PySpark) (Best for Distributed Computing)

If you need to handle massive datasets in production, Apache Spark (PySpark) is the way to go. Unlike pandas, Spark runs computations in parallel across multiple nodes, making it highly scalable.

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Example").getOrCreate()

# Load a dataset
df = spark.read.csv("large_dataset.csv", header=True, inferSchema=True)

# Perform operations
df = df.withColumn("sum", df["col1"] + df["col2"])
df.show()

For enterprise-level applications dealing with terabytes of data, Spark is the most reliable solution.

Conclusion

Pandas is great for data science, prototyping, and small datasets, but when it comes to production applications, it can be a major bottleneck.

What should you use instead?

Polars → Best for speed and low memory consumption.

PyArrow → Best for efficient memory management and Parquet/Feather data handling.

PySpark → Best for distributed computing and large-scale data processing.

If you're working on a production-level application, choosing the right tool can significantly improve performance and scalability.

What do you think?

Have you faced performance issues with pandas in production? What alternative tools do you use? Let me know in the comments!