How to Use Polars with Pandas: Read Large Files Efficiently

Mar 9, 2026

I was working with a 5GB CSV file the other day. My standard approach would have been pandas read_csv(), but I kept hearing about Polars being faster and more memory efficient. The problem: I have existing pandas code and I didn’t want to rewrite everything.

The question I kept asking myself: Should I use Polars to read the file and convert to pandas, or just stick with pandas?

The Short Answer

Yes, using Polars to read large files and convert to pandas is beneficial when you need only specific columns. Polars reads only the required columns from disk, reducing memory usage. But if you’re reading the entire file into memory anyway, you won’t see significant benefits.

Let me show you what I tried.

Reading Specific Columns

I started with a simple case: I only needed three columns from that 5GB file. With pandas, I’d do this:

import pandas as pd

# Reads entire file into memory, then selects columns
pandas_df = pd.read_csv("large_file.csv", usecols=["user_id", "timestamp", "revenue"])

This loads all columns into memory before selecting the three I need. Polars does something different:

import polars as pl
import pandas as pd

# Only reads the specified columns from disk
polars_df = pl.read_csv(
    "large_file.csv",
    columns=["user_id", "timestamp", "revenue"]
)

# Convert to pandas for existing workflows
pandas_df = polars_df.to_pandas()

# Continue with pandas operations
result = pandas_df.groupby("user_id")["revenue"].sum()

Polars reads column-wise from the CSV, skipping columns I don’t need. This reduces I/O and memory usage significantly when the file has many columns but you only need a few.

Lazy Loading with Query Optimization

I also tried Polars’ lazy evaluation, which was interesting:

import polars as pl

# Build a lazy query - nothing executes yet
lazy_df = (
    pl.scan_csv("large_file.csv")
    .filter(pl.col("timestamp") > "2024-01-01")
    .select(["user_id", "category", "amount"])
)

# Only when I call collect() does it execute
polars_df = lazy_df.collect()

# Convert to pandas
pandas_df = polars_df.to_pandas()

The scan_csv() function doesn’t read data immediately. Instead, it builds a query plan. When I call collect(), Polars optimizes the query and executes it efficiently. This includes:

Reading only the columns needed for filter and select operations
Applying the filter during reading (predicate pushdown)
Skipping unnecessary I/O

When the Conversion Doesn’t Help

I tried the same approach with a different file where I needed all columns:

import polars as pl

# Reading all columns - no memory benefit
polars_df = pl.read_csv("medium_file.csv")
pandas_df = polars_df.to_pandas()

Result: No significant memory savings. Polars still had to load everything into memory, then convert it to pandas. The Reddit discussion I found confirmed this: “If you’re reading the entire file and keeping it in memory, you’ll hardly see any benefit.”

Converting Both Directions

Sometimes I start with pandas data from an API or database, then want Polars for heavy aggregation:

import polars as pl
import pandas as pd

# Start with pandas (e.g., from API)
pandas_df = pd.read_json("api_response.json")

# Convert to Polars for efficient aggregation
polars_df = pl.from_pandas(pandas_df)

result = (
    polars_df
    .group_by("category")
    .agg([
        pl.col("value").sum().alias("total"),
        pl.col("value").mean().alias("average")
    ])
)

# Convert back to pandas for export
final_df = result.to_pandas()
final_df.to_csv("summary.csv", index=False)

The conversion between Polars and pandas is efficient because both use Apache Arrow as their memory format. There’s minimal overhead for the conversion itself.

When to Skip Conversion Entirely

After experimenting, I realized the best performance comes from using Polars throughout:

import polars as pl

# Better: Use Polars throughout
result = (
    pl.scan_csv("large_file.csv")
    .filter(pl.col("status") == "active")
    .group_by("region")
    .agg([
        pl.col("revenue").sum().alias("total_revenue"),
        pl.col("orders").count().alias("order_count")
    ])
    .sort("total_revenue", descending=True)
    .collect()
)

# Export directly - no pandas conversion
result.write_csv("regional_summary.csv")

No pandas needed. This approach gives you the best performance because:

Lazy evaluation optimizes the entire pipeline
No conversion overhead
Polars handles all operations efficiently

Decision Framework

Based on my testing, here’s when I’d use each approach:

Scenario	Recommended Approach	Why
Reading full file into memory	Use pandas directly	No memory savings from Polars
Reading subset of columns	Polars + `to_pandas()`	Column pruning saves memory
Complex transformations	Use Polars end-to-end	Best performance
Existing pandas codebase	Polars for reading + `to_pandas()`	Minimal code changes
One-time analysis	Use Polars end-to-end	Better performance with less overhead

Performance Considerations

A few things I learned about performance:

Column Pruning: Polars only loads specified columns, reducing I/O and memory. This is the biggest benefit when working with wide datasets.
Lazy Evaluation: Query optimization happens before data is materialized. This includes predicate pushdown and column projection.
Arrow Format: Both libraries use Apache Arrow, making conversions efficient. The overhead is minimal for one-time conversions.
Conversion Overhead: While to_pandas() has minimal overhead, it adds up in loops or repeated operations. Avoid converting back and forth unnecessarily.

What I’m Doing Now

For new projects, I’m using Polars throughout. For existing pandas codebases where I need to read large files but only a few columns, I use Polars for reading and convert to pandas.

The main barrier to full Polars adoption isn’t technical—it’s muscle memory with pandas syntax. The Reddit discussion I referenced put it well: “Just use polars the whole way - it’s way better through the whole process.”

If you’re trying this with your own data, measure memory usage with %memit or memory_profiler to see the actual benefits in your specific case. The gains depend heavily on your data shape and query patterns.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

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Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!