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Data Analysis Pythondata~10 mins

head() and tail() in Data Analysis Python - Interactive Code Practice

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Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to show the first 3 rows of the DataFrame.

Data Analysis Python
df.[1](3)
Drag options to blanks, or click blank then click option'
Atail
Bhead
Cshow
Dfirst
Attempts:
3 left
💡 Hint
Common Mistakes
Using tail() instead of head() to get the first rows.
Using show() which is not a pandas DataFrame method.
Using first() which is not a pandas DataFrame method.
2fill in blank
medium

Complete the code to display the last 5 rows of the DataFrame.

Data Analysis Python
df.[1](5)
Drag options to blanks, or click blank then click option'
Atail
Bhead
Cend
Dlast
Attempts:
3 left
💡 Hint
Common Mistakes
Using head() instead of tail() to get the last rows.
Using end() or last() which are not pandas DataFrame methods.
3fill in blank
hard

Fix the error in the code to correctly show the first 4 rows of the DataFrame.

Data Analysis Python
df.[1](4)
Drag options to blanks, or click blank then click option'
Atail
Bfirst
Chead
Dshow
Attempts:
3 left
💡 Hint
Common Mistakes
Using tail() instead of head() to get the first rows.
Using show() which is not a pandas DataFrame method.
4fill in blank
hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python
{word: [1] for word in words if len(word) [2] 3}
Drag options to blanks, or click blank then click option'
Alen(word)
B<=
C>
Dword
Attempts:
3 left
💡 Hint
Common Mistakes
Using the word itself as the value instead of its length.
Using <= instead of > in the condition.
5fill in blank
hard

Fill all three blanks to create a dictionary with uppercase words as keys, their lengths as values, for words longer than 4 characters.

Data Analysis Python
{ [1]: [2] for word in words if len(word) [3] 4 }
Drag options to blanks, or click blank then click option'
Aword.upper()
Blen(word)
C>
Dword
Attempts:
3 left
💡 Hint
Common Mistakes
Using the original word as key instead of uppercase.
Using <= instead of > in the condition.
Using the word itself as value instead of length.

Practice

(1/5)
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
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10 questions~5 mineasy hard