head() and tail() for previewing in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the head() function do in pandas?

The head() function shows the first few rows of a DataFrame or Series, helping you quickly see the start of your data.

beginner

How do you use tail() in pandas and what does it show?

tail() shows the last few rows of a DataFrame or Series, letting you quickly check the end of your data.

beginner

By default, how many rows do head() and tail() show?

Both head() and tail() show 5 rows by default if no number is given.

beginner

How can you see the first 10 rows of a DataFrame named df?

Use df.head(10) to see the first 10 rows.

beginner

Why is it useful to preview data with head() and tail()?

Previewing helps you quickly understand the data structure, check for missing values, and confirm data loading without looking at the whole dataset.

What does df.head() return by default?

A First 5 rows of df

B Last 5 rows of df

C First 10 rows of df

D All rows of df

head() returns the first 5 rows by default.

How do you get the last 3 rows of a DataFrame df?

A df.end(3)

B df.head(3)

C df.last(3)

D df.tail(3)

tail(3) returns the last 3 rows.

If you want to preview the start and end of your data, which two functions do you use?

A start() and end()

B head() and tail()

C first() and last()

D top() and bottom()

Use head() for the start and tail() for the end.

What type of object do head() and tail() return?

A A plot

B A list of values

C A new DataFrame or Series with selected rows

D A summary statistic

They return a new DataFrame or Series containing the selected rows.

Can you specify how many rows to preview with head() and tail()?

A Yes, by passing a number as an argument

B No, they always show 5 rows

C Only <code>head()</code> allows this

D Only <code>tail()</code> allows this

Both functions accept a number to specify how many rows to show.

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

Think about what you want to learn quickly from the data.

Describe the default behavior of head() and tail() and how to change the number of rows they show.

Remember the default and how to customize.

Practice

1. What does the head() function do in pandas?

easy

A. Sorts the DataFrame by index

B. Shows the last few rows of the DataFrame

C. Shows the first few rows of the DataFrame

D. Deletes 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 preview data.

2. Step 2: Compare with other options

   tail() shows the last few rows, not the first. Deleting or sorting are unrelated to head().

3. Final Answer:

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

4. Quick Check:

   head() = first rows [OK]

Hint: head() previews start; tail() previews end [OK]

Common Mistakes:

• Confusing head() with tail()
• Thinking head() deletes rows
• Assuming head() sorts 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.tail[-3]

D. df.last(3)

Solution

1. Step 1: Recall the syntax for tail()

   The correct syntax to get last rows is df.tail(n), where n is the number of rows.

2. Step 2: Check each option

   df.tail(3) is correct. df.head(3) shows first rows. df.tail[-3] is invalid syntax. df.last(3) does not exist.

3. Final Answer:

   df.tail(3) -> Option A

4. Quick Check:

   Use tail(n) for last n rows [OK]

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

Common Mistakes:

• Using head() instead of tail()
• Using square brackets with tail()
• Using non-existent last() method

3. Given the DataFrame df below:

   A  B
0  5  10
1  6  11
2  7  12
3  8  13
4  9  14

What will df.head(2) return?

medium

A. Rows with index 3 and 4

B. Rows with index 0 and 1

C. Rows with index 2 and 3

D. Rows with index 1 and 2

Solution

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

   head(2) returns the first 2 rows of the DataFrame, which are rows with index 0 and 1.

2. Step 2: Identify the rows in the DataFrame

   Rows 0 and 1 have values (5,10) and (6,11) respectively, matching the first two rows.

3. Final Answer:

   Rows with index 0 and 1 -> Option B

4. Quick Check:

   head(2) = first 2 rows [OK]

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

Common Mistakes:

• Selecting last rows instead of first
• Confusing index numbers
• Assuming head() returns columns

4. You run df.tail(3) but get an error: TypeError: 'int' object is not callable. What is the most likely cause?

medium

A. You assigned a variable named tail as an integer earlier

B. You used square brackets instead of parentheses

C. The DataFrame df is empty

D. You forgot to import pandas

Solution

1. Step 1: Understand the error message

   TypeError: 'int' object is not callable means you tried to call an integer like a function.

2. Step 2: Identify cause related to tail

   If you assigned tail as an integer variable, it overrides the method, causing this error when calling df.tail(3).

3. Final Answer:

   You assigned a variable named tail as an integer earlier -> Option A

4. Quick Check:

   Variable name conflicts cause callable errors [OK]

Hint: Avoid naming variables same as methods [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Assuming empty DataFrame causes this error
• Ignoring import errors

5. You have a DataFrame df with 1000 rows. You want to preview the first 5 rows and the last 5 rows together in one DataFrame. Which code correctly does this?

hard

A. df.head(5) + df.tail(5)

B. df.head(5).merge(df.tail(5))

C. df.head(5).append(df.tail(5))

D. pd.concat([df.head(5), df.tail(5)])

Solution

1. Step 1: Understand how to combine DataFrames vertically

   To combine rows from two DataFrames, use pd.concat() or append(). The plus operator and merge are incorrect here.

2. Step 2: Check each option

   pd.concat([df.head(5), df.tail(5)]) correctly stacks first and last 5 rows. df.head(5) + df.tail(5) causes error. append() is deprecated in recent pandas versions. merge() is for joining on columns, not stacking rows.

3. Final Answer:

   pd.concat([df.head(5), df.tail(5)]) -> Option D

4. Quick Check:

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

Hint: Use pd.concat() to combine head and tail rows [OK]

Common Mistakes:

• Using + operator to add DataFrames
• Using deprecated append() method
• Using merge() instead of concat()