🚀 Day 16 of My Python Learning Journey

Data Cleaning in Python 🧹

Before analysis, data needs to be cleaned to ensure accuracy and reliability.
Here are the key steps:

🔹 Steps in Data Cleaning

1. Remove Duplicates – drop repeated rows.
2. Handle Missing Values – fill, drop, or impute.
3. Fix Data Types – ensure correct formats (int, float, datetime).
4. Handle Outliers – detect & treat extreme values.
5. Standardize Text – trim spaces, lowercase, fix typos.
6. Normalize/Scale Data – prepare for modeling.

⚡ Fun Fact: Nearly 70–80% of data analysis time goes into cleaning and preparing data — not building models!

✨ Data cleaning may feel boring, but it’s the foundation of trustworthy insights.

Python #Pandas #DataCleaning #DataAnalytics #100DaysOfCode