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.

Practice

1. What does the sample() function do when used on a DataFrame in pandas?

easy

A. It groups the DataFrame by a column.

B. It sorts the DataFrame by index.

C. It selects random rows from the DataFrame.

D. It removes duplicate rows from the DataFrame.

Solution

1. Step 1: Understand the purpose of sample()

   The sample() function is designed to pick random rows from a DataFrame.

2. Step 2: Compare with other options

   Sorting, removing duplicates, and grouping are different operations and not related to sample().

3. Final Answer:

   It selects random rows from the DataFrame. -> Option C

4. Quick Check:

   sample() = random rows [OK]

Hint: Remember: sample() picks random rows only [OK]

Common Mistakes:

• Confusing sample() with sort()
• Thinking sample() removes duplicates
• Assuming sample() groups data

2. Which of the following is the correct syntax to get 5 random rows from a DataFrame named df?

easy

A. df.sample(n=5)

B. df.sample(frac=5)

C. df.sample(rows=5)

D. df.sample(size=5)

Solution

1. Step 1: Recall sample() parameters

   The correct parameter to specify number of rows is n, so df.sample(n=5) is correct.

2. Step 2: Check other options

   df.sample(frac=5) is invalid because frac must be between 0 and 1; rows and size are invalid parameters.

3. Final Answer:

   df.sample(n=5) -> Option A

4. Quick Check:

   Use n= to specify row count [OK]

Hint: Use n=5 to specify number of rows in sample() [OK]

Common Mistakes:

• Using invalid parameters like rows or size
• Forgetting to use n= when specifying count
• Using frac with count value instead of fraction

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({'A': [10, 20, 30, 40, 50]})
sample_df = df.sample(n=2, random_state=1)

What will sample_df contain?

medium

A. Rows with index 1 and 4

B. Rows with index 2 and 4

C. Rows with index 0 and 3

D. Rows with index 0 and 1

Solution

1. Step 1: Understand random_state effect

   Setting random_state=1 makes sampling reproducible, so the same rows are picked every time.

2. Step 2: Check which rows are picked

   Running the code picks rows with index 1 and 4 (values 20 and 50).

3. Final Answer:

   Rows with index 1 and 4 -> Option A

4. Quick Check:

   random_state=1 fixes sample rows [OK]

Hint: Use random_state to get same sample every time [OK]

Common Mistakes:

• Ignoring random_state and expecting different rows
• Assuming sample picks first rows always
• Confusing index with row position

4. What is wrong with this code snippet to get 3 random rows from df?

sampled = df.sample(3, random_state=42, frac=0.5)

medium

A. The sample size must be greater than the DataFrame length.

B. You cannot use both n and frac together.

C. random_state must be an integer greater than 100.

D. The code is correct and will run without errors.

Solution

1. Step 1: Understand sample() parameters

   You can specify either n (number of rows) or frac (fraction of rows), but not both at the same time.

2. Step 2: Identify the error

   Using both 3 (positional for n) and frac=0.5 causes an error.

3. Final Answer:

   You cannot use both n and frac together. -> Option B

4. Quick Check:

   Use either n or frac, not both [OK]

Hint: Use only n or frac in sample(), never both [OK]

Common Mistakes:

• Trying to use n and frac together
• Thinking random_state must be >100
• Assuming sample size can exceed DataFrame length

5. You have a DataFrame df with 1000 rows. You want to randomly select 10% of the rows but ensure the sample is the same every time you run the code. Which code snippet correctly does this?

hard

A. df.sample(n=0.1, random_state=42)

B. df.sample(n=100)

C. df.sample(frac=10, random_state=42)

D. df.sample(frac=0.1, random_state=42)

Solution

1. Step 1: Understand fraction sampling

   To select 10% of rows, use frac=0.1. Using n=100 selects fixed rows (not percentage-based) and df.sample(n=100) lacks random_state so not reproducible every time.

2. Step 2: Check random_state for reproducibility

   Setting random_state=42 ensures the sample is the same every run.

3. Step 3: Identify incorrect options

   frac=10 is invalid (fraction >1), and n=0.1 is invalid because n must be integer.

4. Final Answer:

   df.sample(frac=0.1, random_state=42) -> Option D

5. Quick Check:

   Use frac=0.1 and random_state for fixed 10% sample [OK]

Hint: Use frac=0.1 and random_state for fixed 10% sample [OK]

Common Mistakes:

• Using frac > 1 instead of fraction
• Passing float to n instead of int
• Not setting random_state for reproducibility