How do you quickly extract top/bottom rows and random samples from a Pandas DataFrame?

During EDA you often need to inspect extremes (the highest-revenue customers, the worst-performing products) or draw a random sample for quick analysis. Pandas provides concise methods for each of these.

import pandas as pd
import numpy as np

rng = np.random.default_rng(42)
df = pd.DataFrame({
    'product': [f'P{i}' for i in range(100)],
    'revenue': rng.integers(1_000, 100_000, 100),
    'returns': rng.integers(0, 500, 100),
})

# --- Top and bottom N rows ---
df.nlargest(5, 'revenue')   # 5 highest revenue products
df.nsmallest(5, 'revenue')  # 5 lowest revenue products

# Multiple columns — break ties by second column
df.nlargest(5, ['revenue', 'returns'])

# --- Random sampling ---
df.sample(n=10, random_state=42)        # 10 random rows
df.sample(frac=0.1, random_state=42)    # 10% of rows
df.sample(n=10, replace=True)           # with replacement (bootstrapping)

# Stratified sample — same proportion from each category
df['tier'] = pd.cut(df['revenue'], bins=3, labels=['low','mid','high'])
stratified = df.groupby('tier', group_keys=False).apply(
    lambda g: g.sample(frac=0.1, random_state=42)
)

# --- Head, tail, every Nth row ---
df.head(10)       # first 10 rows
df.tail(10)       # last 10 rows
df.iloc[::5]      # every 5th row — useful for large datasets

nlargest and nsmallest are significantly faster than sort_values(...).head(n) for large DataFrames because they use a partial sort (heap) under the hood — O(N log k) instead of O(N log N) for the full sort. Use them whenever you only need the extremes, not a fully sorted result.

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