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.

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