We use cookies (including Google cookies) to personalize ads and analyze traffic. By continuing to use our site, you accept our Privacy Policy.

SimplyLeet
TagsCompaniesDisclaimersPrivacyTermsContactAbout Us

Rename Columns

Number: 3068

Difficulty: Easy

Paid? No

Companies: N/A

Problem Description

Given a table named students with columns id, first, last, and age, rename the columns as follows: id to student_id, first to first_name, last to last_name, and age to age_in_years. The output should display the entire rows with the renamed column headers.

Key Insights

  • The main objective is to change the column names.
  • In SQL, you can use the AS keyword to alias a column.
  • When using structured data in other programming languages (e.g., pandas DataFrame in Python, arrays/objects in JavaScript), you manually map the old column names to the new ones.
  • The challenge is straightforward as it mostly involves renaming identifiers, without computations or complex data transformations.

Space and Time Complexity

Time Complexity: O(n) — each row is processed once. Space Complexity: O(n) — additional data structures may be created to store the renamed data.

Solution

The idea is to transform the original data structure by mapping each old column name to its new name. In SQL, this is accomplished by selecting each column with a new alias (using AS). In programming languages, if the data is in a DataFrame or an array of objects, you either use built-in renaming functions (like in pandas) or iterate through the records and create new objects with the required keys. The key point is that the transformation is direct without altering the row content.

Code Solutions

Below are sample implementations in Python, JavaScript, C++, and Java with line-by-line comments.

import pandas as pd

# Create a sample DataFrame to simulate the "students" table
data = {
    'id': [1, 2, 3, 4, 5],
    'first': ['Mason', 'Ava', 'Taylor', 'Georgia', 'Thomas'],
    'last': ['King', 'Wright', 'Hall', 'Thompson', 'Moore'],
    'age': [6, 7, 16, 18, 10]
}
df = pd.DataFrame(data)

# Rename the columns as specified
df = df.rename(columns={
    'id': 'student_id',
    'first': 'first_name',
    'last': 'last_name',
    'age': 'age_in_years'
})

# Print the updated DataFrame
print(df)
← Back to All Questions