head() and tail() in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the head() function do in a DataFrame?

The head() function shows the first few rows of a DataFrame, usually the first 5 by default. It helps you quickly see the start of your data.

beginner

What is the purpose of the tail() function in data analysis?

The tail() function shows the last few rows of a DataFrame, usually the last 5 by default. It helps you quickly check the end of your data.

beginner

How can you change the number of rows shown by head() or tail()?

You can pass a number inside the parentheses, like head(3) or tail(10), to show that many rows from the start or end.

beginner

Why is it useful to use head() and tail() when exploring data?

They let you quickly check the structure and content of your data without loading everything. This helps find errors or understand data format early.

beginner

If a DataFrame has 100 rows, what will df.tail(5) show?

It will show the last 5 rows, rows 95 to 99, giving you a quick look at the end of the data.

What is the default number of rows shown by head() in pandas?

A 10

B 5

C 1

D All rows

head() shows the first 5 rows by default.

Which function shows the last rows of a DataFrame?

A tail()

B end()

C first()

D head()

tail() shows the last rows of a DataFrame.

How do you show the first 3 rows of a DataFrame named df?

A df.head(3)

B df.tail(3)

C df.first(3)

D df.show(3)

Use df.head(3) to show the first 3 rows.

Why might you use head() or tail() when working with big data?

A To see all data at once

B To delete rows

C To quickly check a small part of data

D To sort data

head() and tail() help quickly check a small part of data without loading everything.

If you want to see the last 10 rows of a DataFrame, which command is correct?

A df.head(10)

B df.end(10)

C df.last(10)

D df.tail(10)

Use df.tail(10) to see the last 10 rows.

Explain how and why you would use the head() and tail() functions when starting to analyze a new dataset.

Think about how you peek at the start and end of a book to get an idea of its content.

Describe a situation where using tail() would be more helpful than head().

Imagine you want to see the latest sales records in a big sales dataset.

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