Sample() for random rows in Data Analysis Python - Step-by-Step Execution

Concept Flow - Sample() for random rows

Start with DataFrame

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Call sample(n=3)

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Randomly select 3 rows

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Return new DataFrame with selected rows

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Use or display sampled rows

The sample() function picks random rows from a DataFrame and returns them as a new smaller DataFrame.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [10,20,30,40,50], 'B': ['a','b','c','d','e']})
sample_df = df.sample(n=3, random_state=1)
print(sample_df)

This code creates a DataFrame and randomly selects 3 rows using sample().

Execution Table

Step	Action	DataFrame Rows Before	Random Rows Selected	Output DataFrame Rows
1	Create DataFrame	[0,1,2,3,4]	N/A	[0,1,2,3,4]
2	Call sample(n=3, random_state=1)	[0,1,2,3,4]	[1,4,2]	[1,4,2]
3	Print sampled DataFrame	[0,1,2,3,4]	[1,4,2]	[1,4,2]
4	End	[0,1,2,3,4]	N/A	[1,4,2]

💡 Sampled 3 random rows; execution ends after printing.

Variable Tracker

Variable	Start	After sample() call	Final
df	[0,1,2,3,4]	[0,1,2,3,4]	[0,1,2,3,4]
sample_df	N/A	[1,4,2]	[1,4,2]

Key Moments - 3 Insights

Why does sample_df have rows with indices [1,4,2] instead of [0,1,2]?

Because sample() picks rows randomly, not sequentially. The execution_table row 2 shows the random indices selected.

What does random_state=1 do in sample()?

It fixes the random selection so the same rows are picked every time. This is why sample_df is consistent across runs (see execution_table row 2).

Does sample() change the original DataFrame df?

No, sample() returns a new DataFrame with selected rows. The original df stays the same as shown in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, which rows were randomly selected?

A [1,4,2]

B [0,1,2]

C [1,3,4]

D [3,4,0]

💡 Hint

Check the 'Random Rows Selected' column at step 2 in execution_table.

At which step does the sample_df variable get its value?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at variable_tracker and execution_table rows where sample_df changes.

If we remove random_state=1, what will happen to the sampled rows?

A No rows will be selected.

B They will always be the same rows.

C They will be randomly different each run.

D An error will occur.

💡 Hint

random_state fixes randomness; without it, sample() picks different rows each time.

Concept Snapshot

sample(n) picks n random rows from a DataFrame.
Use random_state to get repeatable samples.
Returns a new DataFrame; original stays unchanged.
Useful for quick random data checks or splits.

Full Transcript

The sample() function in pandas lets you pick random rows from a DataFrame. You start with your full DataFrame, then call sample(n=3) to get 3 random rows. Setting random_state ensures the same rows are picked every time, which helps with testing or sharing results. The original DataFrame does not change; sample() returns a new smaller DataFrame with the selected rows. This is useful when you want to look at a random subset of your data quickly.