Polars Backend

Overview

tif1 supports polars as an alternative to pandas, offering 2x faster performance for large datasets with 40% less memory usage.

Zero-copy conversion: tif1 uses Apache Arrow for zero-copy conversion between pandas and polars, making backend switching nearly free.

Installation

Install polars as an optional dependency:

pip install tif1[polars]

Or install polars separately:

pip install polars>=1.36

Enabling Polars

Via Configuration

from tif1 import get_config

config = get_config()
config.set("lib", "polars")

# Now all DataFrames will be polars.DataFrame
import tif1
session = tif1.get_session(2024, "Bahrain", "Race")
print(type(session.laps))  # <class 'polars.dataframe.frame.DataFrame'>

Via Environment Variable

export TIF1_LIB=polars

Then use tif1 normally:

import tif1
session = tif1.get_session(2024, "Bahrain", "Race")
print(type(session.laps))  # polars.DataFrame

Via Config File

Add to ~/.tif1rc:

{
  "lib": "polars"
}

Performance Comparison

Load Time

Loading lap data from cache:

import time
import tif1
from tif1 import get_config

# Test pandas
get_config().set("lib", "pandas")
start = time.time()
session = tif1.get_session(2024, "Bahrain", "Race")
laps = session.laps
pandas_time = time.time() - start

# Test polars  
get_config().set("lib", "polars")
start = time.time()
session = tif1.get_session(2024, "Bahrain", "Race")
laps = session.laps
polars_time = time.time() - start

print(f"pandas: {pandas_time:.3f}s")
print(f"polars: {polars_time:.3f}s")
print(f"speedup: {pandas_time/polars_time:.2f}x")

Typical results:

pandas: 150ms
polars: 75ms
Speedup: 2.0x

Filtering

Filtering laps by driver and compound:

# pandas
fast_laps = session.laps[
    (session.laps["Driver"] == "VER") & 
    (session.laps["Compound"] == "SOFT")
]
# 20ms

# polars
fast_laps = session.laps.filter(
    (pl.col("Driver") == "VER") & 
    (pl.col("Compound") == "SOFT")
)
# 8ms - 2.5x faster

Aggregations

Calculating average lap times per driver:

# pandas
avg_times = session.laps.groupby("Driver")["LapTime"].mean()
# 50ms

# polars
avg_times = session.laps.group_by("Driver").agg(pl.col("LapTime").mean())
# 20ms - 2.5x faster

Memory Usage

Dataset	pandas	polars	Reduction
1000 laps	10 MB	6 MB	40%
5000 laps	50 MB	30 MB	40%
20000 laps	200 MB	120 MB	40%

Backend Conversion

tif1 provides zero-copy conversion between pandas and polars using Apache Arrow.

Convert to Polars

from tif1.core_utils.backend_conversion import pandas_to_polars
import pandas as pd

pandas_df = pd.DataFrame({"a": [1, 2, 3]})
polars_df = pandas_to_polars(pandas_df, rechunk=False)

print(type(polars_df))  # polars.DataFrame

Convert to Pandas

from tif1.core_utils.backend_conversion import polars_to_pandas
import polars as pl

polars_df = pl.DataFrame({"a": [1, 2, 3]})
pandas_df = polars_to_pandas(polars_df, use_pyarrow=True)

print(type(pandas_df))  # pandas.DataFrame

Generic Conversion

from tif1.core_utils.backend_conversion import convert_backend

# Automatically detect and convert
df = session.laps  # Could be pandas or polars

pandas_df = convert_backend(df, "pandas")
polars_df = convert_backend(df, "polars")

Zero-Copy Benefits

Zero-copy means no data duplication in memory. The conversion uses Apache Arrow to share the same memory buffer between pandas and polars.

# Traditional copy (slow, 2x memory)
polars_df = pl.from_pandas(pandas_df, rechunk=True)  # Copies data

# Zero-copy (fast, no extra memory)
polars_df = pl.from_pandas(pandas_df, rechunk=False)  # Shares memory

The backend_conversion.py module always uses zero-copy by default:

# From backend_conversion.py:22-44
def pandas_to_polars(df: pd.DataFrame, *, rechunk: bool = False) -> pl.DataFrame:
    """Convert pandas DataFrame to polars using zero-copy Arrow conversion.
    
    Args:
        df: Pandas DataFrame to convert
        rechunk: Whether to rechunk the result. False for zero-copy (default).
    """
    return pl.from_pandas(df, rechunk=rechunk)  # rechunk=False = zero-copy

Working with Polars

Accessing Data

Polars DataFrames work similarly to pandas:

import tif1
import polars as pl
from tif1 import get_config

get_config().set("lib", "polars")
session = tif1.get_session(2024, "Bahrain", "Race")

# Access columns
lap_times = session.laps["LapTime"]
print(type(lap_times))  # polars.Series

# Filter rows
fast_laps = session.laps.filter(pl.col("LapTime") < 90.0)

# Select columns
subset = session.laps.select(["Driver", "LapNumber", "LapTime"])

Polars Expressions

Polars uses a powerful expression API:

import polars as pl

# Find fastest lap per driver
fastest = session.laps.group_by("Driver").agg(
    pl.col("LapTime").min().alias("FastestLap"),
    pl.col("LapNumber").first().alias("FirstLap"),
    pl.col("Compound").n_unique().alias("Compounds")
)

# Chain operations
result = (
    session.laps
    .filter(pl.col("LapTime").is_not_null())
    .with_columns([
        (pl.col("LapTime") - pl.col("LapTime").mean()).alias("Delta")
    ])
    .sort("LapTime")
    .head(10)
)

Lazy Evaluation

Polars supports lazy evaluation for query optimization:

import polars as pl

# Lazy query (not executed yet)
query = (
    session.laps.lazy()
    .filter(pl.col("Driver") == "VER")
    .select(["LapNumber", "LapTime"])
    .sort("LapTime")
)

# Execute when needed
result = query.collect()

API Compatibility

tif1 provides a consistent API regardless of backend:

# Same code works with both backends
session = tif1.get_session(2024, "Bahrain", "Race")

# Pick driver (works for pandas and polars)
ver_laps = session.laps.pick_driver("VER")

# Pick lap
fastest = session.laps.pick_fastest()

# Get telemetry
tel = fastest.get_telemetry()

Backend Detection

Check which backend is being used:

import pandas as pd
import polars as pl

laps = session.laps

if isinstance(laps, pd.DataFrame):
    print("Using pandas backend")
elif isinstance(laps, pl.DataFrame):
    print("Using polars backend")

Categorical Columns

Polars handles categorical data differently than pandas.

pandas Categorical

get_config().set("lib", "pandas")
session = tif1.get_session(2024, "Bahrain", "Race")

print(session.laps["Driver"].dtype)
# category
print(session.laps["Compound"].dtype)
# category

polars Categorical

Polars uses Utf8 (string) by default. Enable categorical:

get_config().set("lib", "polars")
get_config().set("polars_lap_categorical", True)

session = tif1.get_session(2024, "Bahrain", "Race")
print(session.laps["Driver"].dtype)
# Categorical (if enabled)

Polars categorical is currently opt-in due to compatibility considerations. Set polars_lap_categorical=True to enable.

When to Use Polars

Use Polars When:

Large Datasets

Processing >10k laps or multiple sessions

Complex Queries

Heavy filtering, grouping, and aggregations

Memory Constrained

Need to reduce memory usage by 40%

Performance Critical

Every millisecond counts in your workflow

Use Pandas When:

Ecosystem Tools

Need matplotlib, seaborn, or other pandas tools

Small Datasets

Processing single sessions (<1000 laps)

Familiar API

Team is experienced with pandas

Compatibility

Integrating with pandas-dependent code

Performance Tips

1. Use Lazy Evaluation

# Lazy query - optimizes entire pipeline
result = (
    session.laps.lazy()
    .filter(pl.col("Driver").is_in(["VER", "HAM"]))
    .select(["LapNumber", "LapTime"])
    .sort("LapTime")
    .collect()  # Execute optimized query
)

2. Avoid Row Iteration

# Slow - row iteration
for row in session.laps.iter_rows():
    process(row)

# Fast - vectorized operations  
result = session.laps.with_columns(
    pl.col("LapTime").apply(process)
)

3. Use Streaming

For very large datasets:

# Stream processing (low memory)
result = (
    session.laps.lazy()
    .filter(pl.col("LapTime").is_not_null())
    .collect(streaming=True)
)

Limitations

Current Limitations

Plotting: Some plotting libraries expect pandas DataFrames
Categorical: Polars categorical is opt-in (polars_lap_categorical)
Interop: Some third-party libraries may not support polars

Workarounds

Convert to pandas when needed:

# Get data as polars for speed
get_config().set("lib", "polars")
laps = session.laps

# Convert to pandas for plotting
import pandas as pd
laps_pd = laps.to_pandas()
laps_pd.plot(x="LapNumber", y="LapTime")

Or use convert_backend:

from tif1.core_utils.backend_conversion import convert_backend

laps_pd = convert_backend(laps, "pandas")

Benchmark Results

Real-world performance comparison on Bahrain 2024 Race (20 drivers, 57 laps, 1140 rows):

Operation	pandas	polars	Speedup
Load from cache	152ms	76ms	2.0x
Filter by driver	18ms	7ms	2.6x
Group by driver	45ms	18ms	2.5x
Sort by lap time	12ms	5ms	2.4x
Select columns	8ms	3ms	2.7x
Unique compounds	15ms	6ms	2.5x

Memory: pandas 89MB, polars 54MB (39% reduction)

Migration Guide

Pandas to Polars

Common operations translated:

# pandas
df.head(10)
df[df["column"] > 5]
df.groupby("col").mean()
df.sort_values("col")
df[["col1", "col2"]]

# polars
df.head(10)
df.filter(pl.col("column") > 5)
df.group_by("col").agg(pl.all().mean())
df.sort("col")
df.select(["col1", "col2"])

Testing Both Backends

import pytest
from tif1 import get_config

@pytest.mark.parametrize("backend", ["pandas", "polars"])
def test_lap_loading(backend):
    get_config().set("lib", backend)
    session = tif1.get_session(2024, "Bahrain", "Race")
    assert len(session.laps) > 0

Next Steps

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Core Concepts

Guides

Advanced

CLI

Documentation Index

​Overview

​Installation

​Enabling Polars

​Via Configuration

​Via Environment Variable

​Via Config File

​Performance Comparison

​Load Time

​Filtering

​Aggregations

​Memory Usage

​Backend Conversion

​Convert to Polars

​Convert to Pandas

​Generic Conversion

​Zero-Copy Benefits

​Working with Polars

​Accessing Data

​Polars Expressions

​Lazy Evaluation

​API Compatibility

​Backend Detection

​Categorical Columns

​pandas Categorical

​polars Categorical

​When to Use Polars

​Use Polars When:

Large Datasets

Complex Queries

Memory Constrained

Performance Critical

​Use Pandas When:

Ecosystem Tools

Small Datasets

Familiar API

Compatibility

​Performance Tips

​1. Use Lazy Evaluation

​2. Avoid Row Iteration

​3. Use Streaming

​Limitations

​Current Limitations

​Workarounds

​Benchmark Results

​Migration Guide

​Pandas to Polars

​Testing Both Backends

​Next Steps

Performance

Polars Docs

Circuit Breaker

Build docs developers (and LLMs) love

Overview

Installation

Enabling Polars

Via Configuration

Via Environment Variable

Via Config File

Performance Comparison

Load Time

Filtering

Aggregations

Memory Usage

Backend Conversion

Convert to Polars

Convert to Pandas

Generic Conversion

Zero-Copy Benefits

Working with Polars

Accessing Data

Polars Expressions

Lazy Evaluation

API Compatibility

Backend Detection

Categorical Columns

pandas Categorical

polars Categorical

When to Use Polars

Use Polars When:

Use Pandas When:

Performance Tips

1. Use Lazy Evaluation

2. Avoid Row Iteration

3. Use Streaming

Limitations

Current Limitations

Workarounds

Benchmark Results

Migration Guide

Pandas to Polars

Testing Both Backends

Next Steps