← Boston Consulting Group Interview Insights

Boston Consulting Group·Data Scientist·Online Assessment (OA)·Intermediate

Intermediate
Jun 2026Remote

Summary

BCG CodeSignal notebook assessment for a Data Scientist role, one question involving pandas and some annoying schema mismatch between two CSV files. Pretty straightforward if you've done any data wrangling before, but the column name typo is the kind of thing that bites you if you're moving fast.

Questions Asked (1)

Q1

Given two CSV files of order data from different years with inconsistent column names, use pandas to load both files, standardize the column names to a common schema, cast the amount column to float, and concatenate them into a single DataFrame.

Data ModelingTechnical Trade-offs
Author's notes

The actual pandas steps are not the hard part.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by loading both CSV files with pandas, then inspect their column names to identify inconsistencies. Define a mapping from each file's columns to a common schema, rename the columns, cast the amount column to float, and finally concatenate the DataFrames. Emphasize data validation and handling edge cases like missing values or type conversion errors.

Pro tip: Use a configuration-driven approach for column mapping to make the solution scalable and maintainable, and always validate data after concatenation to catch issues early.

1. Load and Inspect Data

Read both CSV files into pandas DataFrames and examine their column names, data types, and sample rows to understand the inconsistencies.

2. Define Common Schema and Mapping

Determine the target column names and create a mapping dictionary for each file that translates its columns to the common schema.

3. Standardize Columns and Cast Types

Rename columns using the mapping and convert the amount column to float, handling any conversion errors or missing values appropriately.

4. Concatenate and Validate

Concatenate the standardized DataFrames into one, then validate the result by checking shape, data types, and summary statistics.

Key Points to Mention

  • Handling inconsistent column names via mapping or renaming
  • Using pandas functions like read_csv, rename, astype, and concat
  • Data type conversion and error handling (e.g., errors='coerce')
  • Ensuring schema alignment before concatenation
  • Validating the final DataFrame for correctness
  • Scalability and maintainability of the solution

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.