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Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions, and SQL Integration
In this tutorial, we delve into building an advanced data analytics pipeline using Polars, a lightning-fast DataFrame library designed for optimal performance and scalability. Our goal is to demonstrate how we can utilize Polars’ lazy evaluation, complex expressions, window functions, and SQL interface to process large-scale financial datasets efficiently. We begin by generating a synthetic […] The post Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions, and SQL Integration appeared first on MarkTechPost.
In this tutorial, we delve into building an advanced data analytics pipeline using Polars, a lightning-fast DataFrame library designed for optimal performance and scalability. Our goal is to demonstrate how we can utilize Polars’ lazy evaluation, complex expressions, window functions, and SQL interface to process large-scale financial datasets efficiently. We begin by generating a synthetic financial time series dataset and move step-by-step through an end-to-end pipeline, from feature engineering and rolling statistics to multi-dimensional analysis and ranking. Throughout, we demonstrate how Polars empowers us to write expressive and performant data transformations, all while maintaining low memory usage and ensuring fast execution.
import polars as pl
import numpy as np
from datetime import datetime, timedelta
import io
try:
import polars as pl
except ImportError:
import subprocess
subprocess.run(["pip", "install", "polars"], check=True)
import polars as pl
print("
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