Sample() for random rows in Data Analysis Python

Introduction

We use sample() to pick random rows from a table. This helps us look at a small, random part of big data.

You want to check a few random records from a large dataset to understand its structure.

You need to create a smaller dataset for testing or training a model.

You want to randomly select data points for a quick quality check.

You want to split data randomly for experiments or validation.

You want to shuffle data rows before analysis.

Syntax

Data Analysis Python

DataFrame.sample(n=None, frac=None, replace=False, random_state=None)

n is the number of rows to pick randomly.

frac is the fraction of rows to pick (like 0.1 for 10%).

Examples

Pick 3 random rows from the DataFrame df.

Data Analysis Python

df.sample(n=3)

Pick 20% random rows from df.

Data Analysis Python

df.sample(frac=0.2)

Pick 5 rows randomly with replacement, so rows can repeat.

Data Analysis Python

df.sample(n=5, replace=True)

Pick 4 random rows but always the same ones each time you run (for reproducibility).

Data Analysis Python

df.sample(n=4, random_state=42)

Sample Program

This code creates a small table of names and ages. Then it picks 2 random rows from it. Using random_state=1 makes sure the same rows are picked every time you run it.

Data Analysis Python

import pandas as pd

# Create a simple DataFrame
data = {'Name': ['Anna', 'Bob', 'Cara', 'Dan', 'Eva'],
        'Age': [23, 35, 45, 29, 41]}
df = pd.DataFrame(data)

# Pick 2 random rows
sampled_rows = df.sample(n=2, random_state=1)

print(sampled_rows)

Important Notes

If you use frac, do not use n at the same time.

Setting random_state helps get the same random rows every time, useful for sharing results.

By default, replace=False means rows won't repeat in the sample.

Summary

sample() helps pick random rows from data.

You can choose how many rows or what fraction to pick.

Use random_state to get repeatable random samples.