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🔍 *What is .sample() in Apache Spark?*
`.sample()` is used to randomly sample a subset of data from a DataFrame or RDD. It's perfect for testing, exploratory data analysis, and performance checks without processing the full dataset.

💡 *Syntax of .sample() in PySpark*
df.sample(withReplacement, fraction, seed=None)

📌 *Example*
df = spark.read.csv("data.csv", header=True)
sampled_df = df.sample(False, 0.1, seed=42)
sampled_df.show()

✅ *Tips*
Set `withReplacement` to `True` for sampling with replacement, or `False` for without.
Use a fixed `seed` for reproducibility.
Ideal for ML experiments or debugging without touching the full dataset.
Be cautious: a small fraction might still pull in lots of data if your dataset is huge!

📘 *Official Documentation*
Spark DataFrame sample(): https://spark.apache.org/docs/latest/api/p...

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