Python Interview Questions: Polars, DuckDB, PyCaret, Kedro & Ray! 🚀 #Python #DataScience #BigData

1️⃣ Polars for High-Performance DataFrames

Polars is a DataFrame library written in Rust with a Python API, offering lightning-fast operations and lazy evaluation.

Unlike pandas, it uses Arrow memory format and supports multi-threaded execution.

Example:

import polars as pl
df = pl.read_csv("large.csv")
result = df.groupby("category").agg(pl.col("sales").mean())
print(result)
2️⃣ DuckDB for In-Process Analytics

DuckDB is an embedded OLAP database, designed for analytical queries directly from Python, R, or command line.

Think of it as SQLite but optimized for analytics.

Example:

import duckdb
con = duckdb.connect()
df = con.execute("SELECT category, AVG(price) FROM 'large.csv' GROUP BY category").df()
print(df)
3️⃣ PyCaret for Low-Code Machine Learning

PyCaret automates the ML workflow: preprocessing, model training, tuning, and deployment.

Perfect for fast prototyping without writing long code.

Example:

from pycaret.classification import setup, compare_models

data = pd.read_csv("data.csv")
s = setup(data, target="label")
best_model = compare_models() # Auto-selects the best classifier
4️⃣ Kedro for Data Pipelines

Kedro is a Python framework for reproducible, modular data pipelines.

Encourages clean project structure with nodes (functions), pipelines, and configuration.

Example:

# node.py
def preprocess(data: pd.DataFrame) -v pd.DataFrame:
return data.dropna()

# pipeline
from kedro.pipeline import node, Pipeline
pipeline = Pipeline([node(preprocess, "raw_data", "clean_data")])
5️⃣ Ray for Distributed Data Science

Ray is a Python framework for parallel computing, scaling ML, DL, and reinforcement learning workloads.

Frameworks like Modin, RLlib, and Tune run on Ray.

Example:

import ray

ray.init()
@ray.remote
def f(x):
return x * x

futures = [f.remote(i) for i in range(4)]
print(ray.get(futures))

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