Is Change Data Type easy or hard?

Change Data Type is considered an easy problem because the algorithm only requires a single pass through the dataset. The main concept is understanding how to apply type casting correctly across all elements.

Change Data Type Python/Java solution

In Python, pandas provides df['column'] = df['column'].astype(int) to convert the column type. In Java or C#, similar transformations can be done by iterating through rows and parsing values using Integer.parseInt or equivalent casting utilities.

How to solve Change Data Type in O(n)?

Iterate through the dataset once and convert each value to the desired type using a casting function such as int(), parseInt(), or a library equivalent. Replace the original value with the converted value during iteration. DataFrame libraries internally follow the same linear scan approach.

What is the best approach for Change Data Type?

The most practical approach is using built-in DataFrame or table libraries that support column casting. Functions like pandas astype() convert an entire column in O(n) time while keeping code concise and readable. These libraries also handle edge cases such as missing values and invalid formats.

Is Change Data Type asked at Google/Amazon/Meta?

Data type conversion problems commonly appear in data engineering and analytics interviews at companies like Google, Amazon, and Meta. They often appear as part of larger data manipulation tasks involving datasets, parsing, or preprocessing pipelines.

What data structure is used in Change Data Type?

The problem typically operates on tabular datasets such as DataFrames, tables, or arrays of records. The conversion is applied column-wise while iterating through rows. No advanced data structures are required beyond basic collections.

What is the time complexity of Change Data Type?

The time complexity is O(n) because each value in the column must be converted once. Whether using a DataFrame library or manual iteration, the algorithm processes every row exactly one time. Space complexity is typically O(1) if the conversion happens in place.

Description Hints 1 Solution Video 1:53

2886. Change Data Type

Easy87.3% acceptance1 company

DataFrame students
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| student_id  | int    |
| name        | object |
| age         | int    |
| grade       | float  |
+-------------+--------+

Write a solution to correct the errors:

The grade column is stored as floats, convert it to integers.

The result format is in the following example.

Example 1:
Input:
DataFrame students:
+------------+------+-----+-------+
| student_id | name | age | grade |
+------------+------+-----+-------+
| 1          | Ava  | 6   | 73.0  |
| 2          | Kate | 15  | 87.0  |
+------------+------+-----+-------+
Output:
+------------+------+-----+-------+
| student_id | name | age | grade |
+------------+------+-----+-------+
| 1          | Ava  | 6   | 73    |
| 2          | Kate | 15  | 87    |
+------------+------+-----+-------+
Explanation: 
The data types of the column grade is converted to int.

Asked at 1 company

Google

Hints open one at a time

Hint 1

Consider using a build-in function in pandas library with a dictionary to convert the datatype of columns as specified.

1:53

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Case 1

Input

{"headers":{"students":["student_id","name","age","grade"]},"rows":{"students":[[1,"Ava",6,73.0],[2,"Kate",15,87.0]]}}

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