Sample() for random rows in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this sample() code?

Given the DataFrame df below, what will df.sample(n=3, random_state=1) return?

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)

A

   A  B
2  30  c
0  10  a
1  20  b

B

   A  B
1  20  b
4  50  e
2  30  c

C

   A  B
4  50  e
0  10  a
3  40  d

D

   A  B
3  40  d
1  20  b
0  10  a

💡 Hint

Remember that setting random_state fixes the random selection order.

The sample() method with n=3 and random_state=1 picks rows with indices 2, 0, and 1 in that order.

❓ data_output

intermediate

How many rows are returned by sample() with frac=0.4?

If a DataFrame has 10 rows, what is the number of rows returned by df.sample(frac=0.4, random_state=5)?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'X': range(10)})
sample_df = df.sample(frac=0.4, random_state=5)
print(len(sample_df))

A 6

B 4

C 3

D 5

💡 Hint

frac means fraction of total rows.

frac=0.4 means 40% of 10 rows, which is 4 rows.

🔧 Debug

advanced

What error does this sample() code raise?

What error will this code produce?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
sample_df = df.sample(n=5)

A KeyError: 'n'

B TypeError: sample() got an unexpected keyword argument 'n'

C No error, returns 5 rows with NaNs

D ValueError: Cannot take a larger sample than population when 'replace=False'

💡 Hint

Check if sample size is larger than DataFrame size without replacement.

By default, sample() does not allow sampling more rows than exist without replacement, so it raises a ValueError.

🚀 Application

advanced

Which code produces a random sample with replacement?

You want to randomly select 4 rows from a DataFrame of 3 rows, allowing repeats. Which code does this?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})

A df.sample(frac=1.33, random_state=0)

B df.sample(n=4, replace=False, random_state=0)

C df.sample(n=4, replace=True, random_state=0)

D df.sample(n=4)

💡 Hint

Sampling with replacement allows repeats and can exceed original size.

Only option C uses replace=True to allow sampling more rows than exist with repeats.

🧠 Conceptual

expert

What is the effect of setting random_state in sample()?

Why do we set the random_state parameter in df.sample()?

A To ensure the sample is the same every time the code runs

B To increase the sample size automatically

C To speed up the sampling process

D To sort the sampled rows by their index

💡 Hint

Think about reproducibility in random operations.

random_state fixes the random seed so the sample is reproducible and consistent across runs.