Sample() for random rows in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the sample() function do in pandas?

The sample() function selects random rows from a DataFrame. It helps to get a random subset of data for analysis or testing.

beginner

How do you select 5 random rows from a DataFrame df?

Use df.sample(n=5). This picks 5 random rows without replacement by default.

beginner

What does the frac parameter do in sample()?

The frac parameter lets you select a fraction of rows randomly. For example, df.sample(frac=0.1) selects 10% of rows.

intermediate

How can you make the random selection reproducible using sample()?

Use the random_state parameter with a fixed number, like df.sample(n=3, random_state=42). This ensures the same rows are picked every time.

intermediate

What happens if you set replace=True in sample()?

Setting replace=True allows sampling with replacement. This means the same row can be picked multiple times.

Which pandas function is used to select random rows from a DataFrame?

A random()

B sample()

C select()

D shuffle()

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

How do you select 20% of rows randomly from a DataFrame df?

A df.sample(size=0.2)

B df.sample(n=20)

C df.sample(percent=20)

D df.sample(frac=0.2)

Use the frac parameter with a decimal fraction like 0.2 to select 20% of rows.

What does setting random_state in sample() do?

A Makes the random selection reproducible

B Sorts the sampled rows

C Samples rows with replacement

D Changes the number of rows sampled

Setting random_state fixes the random seed so the same rows are selected every time.

If you want to allow the same row to be picked multiple times, which parameter should you use?

A replace=True

B random_state=True

C replace=False

D frac=True

Setting replace=True allows sampling with replacement, so rows can repeat.

What is the default behavior of sample() regarding replacement?

A Sampling with replacement

B Depends on the DataFrame size

C Sampling without replacement

D Depends on the random_state

By default, sample() samples without replacement, so rows are unique in the sample.

Explain how to use pandas sample() to get a random subset of rows from a DataFrame.

Think about how to pick random rows and keep results consistent.

Describe the effect of the replace parameter in the sample() function.

Consider if the same row can appear multiple times or not.

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