head() and tail() in Data Analysis Python - Step-by-Step Execution

Concept Flow - head() and tail()

Start with DataFrame

↓

Call head(n)

↓

Select first n rows

↓

Return subset DataFrame

The flow shows how calling head(n) or tail(n) on a DataFrame returns the first or last n rows respectively.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': range(1,6), 'B': list('abcde')})
print(df.head(3))
print(df.tail(2))

This code creates a DataFrame and shows the first 3 rows with head(3) and last 2 rows with tail(2).

Execution Table

Step	Action	Input DataFrame	Method Called	Parameter n	Output DataFrame
1	Create DataFrame	{'A':[1,2,3,4,5], 'B':['a','b','c','d','e']}	None	None	Full DataFrame with 5 rows
2	Call head(3)	Full DataFrame	head	3	First 3 rows: rows 0,1,2
3	Call tail(2)	Full DataFrame	tail	2	Last 2 rows: rows 3,4
4	End	N/A	N/A	N/A	Execution complete

💡 All requested rows returned; no more steps.

Variable Tracker

Variable	Start	After head(3)	After tail(2)	Final
df	N/A	Full DataFrame with 5 rows	Full DataFrame with 5 rows	Full DataFrame with 5 rows
head_df	N/A	DataFrame with rows 0,1,2	DataFrame with rows 0,1,2	DataFrame with rows 0,1,2
tail_df	N/A	N/A	DataFrame with rows 3,4	DataFrame with rows 3,4

Key Moments - 2 Insights

Why does head(3) return rows 0,1,2 and not 1,2,3?

Because DataFrame rows are zero-indexed, so the first row is index 0. head(3) returns the first three rows starting at index 0, as shown in execution_table step 2.

What happens if n is larger than the number of rows in the DataFrame?

head() or tail() will return all rows without error. This is because they select up to n rows but do not fail if n exceeds the DataFrame size.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what rows does head(3) return?

A Rows 1,2,3

B Rows 2,3,4

C Rows 0,1,2

D Rows 3,4,5

💡 Hint

Check the Output DataFrame column in step 2 of execution_table.

At which step does tail(2) return the last two rows?

A Step 3

B Step 2

C Step 1

D Step 4

💡 Hint

Look for the method called 'tail' in execution_table.

If we call head(10) on this DataFrame, what will happen?

A Return first 10 rows, error if less than 10

B Return all rows since DataFrame has only 5 rows

C Return empty DataFrame

D Return last 10 rows

💡 Hint

Refer to key_moments about behavior when n exceeds DataFrame size.

Concept Snapshot

head(n) returns the first n rows of a DataFrame.
tail(n) returns the last n rows.
If n > number of rows, all rows are returned.
Rows are zero-indexed.
Useful for quick data preview.

Full Transcript

This lesson shows how to use head() and tail() methods on a DataFrame to view the first or last few rows. The flow starts with a DataFrame, then calls head(n) or tail(n) to select rows. The execution table traces creating a DataFrame with 5 rows, then calling head(3) which returns rows 0,1,2, and tail(2) which returns rows 3,4. Variables track the DataFrame and subsets. Key moments clarify zero-based indexing and behavior when n exceeds row count. The quiz tests understanding of which rows are returned and method behavior. The snapshot summarizes usage and rules for head() and tail().

Practice

1. What does the head() function do when used on a DataFrame?

easy

A. Shows the last few rows of the DataFrame

B. Deletes the first few rows of the DataFrame

C. Shows the total number of rows in the DataFrame

D. Shows the first few rows of the DataFrame

Solution

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

   The head() function is designed to display the first few rows of a DataFrame to give a quick look at the data.

2. Step 2: Compare with other options

   tail() shows the last few rows, counting rows is done by len(), and deleting rows is not the function of head().

3. Final Answer:

   Shows the first few rows of the DataFrame -> Option D

4. Quick Check:

   head() = first rows [OK]

Hint: Remember: head = start, tail = end [OK]

Common Mistakes:

• Confusing head() with tail()
• Thinking head() counts rows
• Assuming head() deletes data

2. Which of the following is the correct syntax to show the last 3 rows of a DataFrame named df?

easy

A. df.tail(3)

B. df.head(3)

C. df.last(3)

D. df.end(3)

Solution

1. Step 1: Identify the function for last rows

   The tail() function returns the last rows of a DataFrame, and it accepts a number to specify how many rows.

2. Step 2: Check syntax correctness

   df.tail(3) is correct; head() shows first rows, and last() or end() are not valid pandas functions.

3. Final Answer:

   df.tail(3) -> Option A

4. Quick Check:

   Last rows = tail() [OK]

Hint: Use tail(n) to get last n rows [OK]

Common Mistakes:

• Using head() instead of tail()
• Using non-existent functions like last()
• Wrong method call syntax

3. Given the DataFrame df below:

import pandas as pd
data = {'Name': ['Anna', 'Bob', 'Cara', 'Dan', 'Eva'], 'Age': [23, 35, 45, 29, 40]}
df = pd.DataFrame(data)

What will df.head(2) return?

medium

A. An error because head() needs no arguments

B. Rows with Name Dan and Eva

C. Rows with Name Anna and Bob

D. Rows with Age 45 and 29

Solution

1. Step 1: Understand what head(2) does

   head(2) returns the first 2 rows of the DataFrame.

2. Step 2: Identify the first 2 rows in the DataFrame

   The first two rows have Names 'Anna' and 'Bob' with Ages 23 and 35 respectively.

3. Final Answer:

   Rows with Name Anna and Bob -> Option C

4. Quick Check:

   head(2) = first 2 rows [OK]

Hint: head(n) shows first n rows [OK]

Common Mistakes:

• Thinking head() shows last rows
• Assuming head() cannot take arguments
• Mixing row order

4. What is wrong with this code snippet?

import pandas as pd
data = {'A': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)
print(df.tail[3])

medium

A. DataFrame creation is incorrect

B. Using square brackets instead of parentheses with tail

C. tail() cannot be used with numbers

D. Missing import statement

Solution

1. Step 1: Check the method call syntax

   The code uses df.tail[3] which uses square brackets. Methods require parentheses.

2. Step 2: Correct the syntax

   It should be df.tail(3) to call the method with argument 3.

3. Final Answer:

   Using square brackets instead of parentheses with tail -> Option B

4. Quick Check:

   Method calls need parentheses [OK]

Hint: Use parentheses () to call functions [OK]

Common Mistakes:

• Using [] instead of () for method calls
• Assuming tail() can't take arguments
• Ignoring Python syntax errors

5. You have a DataFrame df with 1000 rows. You want to quickly check the first 5 and last 5 rows together. Which code snippet correctly shows this?

hard

A. pd.concat([df.head(), df.tail()])

B. df.head() + df.tail()

C. df.head() & df.tail()

D. df.head().append(df.tail())

Solution

1. Step 1: Understand how to combine DataFrames

   To combine two DataFrames vertically, use pd.concat() or append() method.

2. Step 2: Check each option

   df.head() + df.tail() and df.head() & df.tail() are invalid operations for DataFrames. df.head().append(df.tail()) works but is deprecated in recent pandas versions. pd.concat([df.head(), df.tail()]) is the recommended way.

3. Final Answer:

   pd.concat([df.head(), df.tail()]) -> Option A

4. Quick Check:

   Use pd.concat() to combine DataFrames [OK]

Hint: Use pd.concat() to join DataFrames vertically [OK]

Common Mistakes:

• Using + or & operators on DataFrames
• Using deprecated append() method
• Trying to print head() and tail() separately without combining