head() and tail() for previewing in Pandas - Mini Project: Build & Apply

Previewing Data with head() and tail() in pandas

📖 Scenario: You have a small dataset of daily temperatures recorded in a city for one week. You want to quickly look at the first few and last few days to understand the data.

🎯 Goal: Learn how to use head() and tail() methods in pandas to preview the first and last rows of a dataset.

📋 What You'll Learn

Create a pandas DataFrame with exact temperature data

Use a variable to set the number of rows to preview

Use head() to get the first rows

Use tail() to get the last rows

Print the previewed data

💡 Why This Matters

🌍 Real World

Previewing data quickly helps data scientists understand the structure and spot any obvious issues before deep analysis.

💼 Career

Knowing how to use head() and tail() is a basic skill for data analysts and scientists to inspect datasets efficiently.

1

Create the temperature DataFrame

Create a pandas DataFrame called temps with these exact data: dates as strings in 'Date' column: '2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07'. Temperatures in Celsius in 'Temp' column: 22, 24, 19, 23, 25, 20, 21.

Pandas

import pandas as pd
# Create the DataFrame temps with exact dates and temperatures
# Your code here

Hint

Use pd.DataFrame with a dictionary containing 'Date' and 'Temp' keys and lists of values.

2

Set the number of rows to preview

Create a variable called preview_rows and set it to 3. This will control how many rows to preview from the start and end of the DataFrame.

Pandas

import pandas as pd

temps = pd.DataFrame({
    'Date': ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07'],
    'Temp': [22, 24, 19, 23, 25, 20, 21]
})

# Create preview_rows variable and set it to 3
# Your code here

Hint

Just create a variable preview_rows and assign the number 3.

3

Use head() and tail() to preview data

Create two variables: first_rows to store the first preview_rows rows of temps using head(), and last_rows to store the last preview_rows rows of temps using tail().

Pandas

import pandas as pd

temps = pd.DataFrame({
    'Date': ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07'],
    'Temp': [22, 24, 19, 23, 25, 20, 21]
})

preview_rows = 3

# Use head() and tail() to get first_rows and last_rows
# Your code here

Hint

Use temps.head(preview_rows) and temps.tail(preview_rows) to get the first and last rows.

4

Print the previewed rows

Print the variables first_rows and last_rows to display the first and last 3 rows of the temperature data.

Pandas

import pandas as pd

temps = pd.DataFrame({
    'Date': ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07'],
    'Temp': [22, 24, 19, 23, 25, 20, 21]
})

preview_rows = 3

first_rows = temps.head(preview_rows)
last_rows = temps.tail(preview_rows)

# Print first_rows and last_rows
# Your code here

Hint

Use print(first_rows) and print(last_rows) to show the previews.

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()