Data Engineering Mastery

# Data Cleaning with Python Pandas: The Practical 2026 Guide for Freshers

By Vinay

Founder of Vtricks Technologies

Domain: Technical Interview Preparation • June 2026

Ask any Senior Data Analyst in Bangalore what they actually do for 80% of their workday, and they will give you the same answer: Cleaning Data.

Real-world data is disastrous. It’s filled with missing values, inconsistent formats, duplicate records, and massive outliers. If you hand that messy data to a machine learning model or a visualization tool, the result is garbage. This is why recruiters use "Take Home Assignments" as the primary filter for hiring. They want to see if you can take a disaster file and turn it into a clean, analytical structure using Python Pandas.

In this guide, we provide a structured cheat sheet for the most common cleaning operations that appear in Bangalore’s top tech firm interview assignments.

## The "Dirty Data" Toolkit

#### 1. Handling Missing Values

Never just delete rows. You must decide whether to impute (fill) them with the mean, median, or a placeholder.

`df.fillna(df.mean(), inplace=True)`

#### 2. Removing Duplicates

A rookie keeps duplicates; a pro identifies and drops them based on specific unique identifiers.

`df.drop_duplicates(subset=['TransactionID'], keep='first')`

#### 3. Fixing Data Types

You cannot perform math on strings. You must cast your currency or date columns correctly before analysis.

`df['Price'] = df['Price'].astype(float)`

#### 4. Filtering Outliers

Extreme values (e.g., a transaction of 0 or a salary of 100 Billion) destroy your averages. Filter them out using Z-Score logic.

`df = df[(df['Age'] > 18) & (df['Age'] < 100)]`

## Why Your Assignment Will Be Rejected

Candidates fail assignment tests because they rush to the "insights" phase while ignoring the "cleaning" phase. If your final report shows a sudden, unrealistic spike in revenue, the interviewer isn't going to praise your insight—they are going to assume you didn't check for outliers or duplicate transactions.

In Bangalore's competitive market, interviewers explicitly look for reproducible code. They want to see a clear Pandas script that explains exactly how you arrived at your conclusion. At Vtricks, we don't just teach the syntax; we teach the defensive analysis mindset—a professional discipline that ensures every single row of your data is accounted for.