Python Day 19 – Data Cleaning with Pandas

Welcome to Day 19 of the Python Programming Series

In this video, we focus on Data Cleaning — one of the most important steps in any data science, analytics, or machine learning workflow.

You’ll learn how to clean raw data using Python and Pandas, fix missing values, remove inconsistencies, and get your dataset ready for analysis or modeling.

✅ What You Will Learn
Understanding the importance of data cleaning

Detecting and handling missing values

Using fillna, dropna, and interpolation techniques

Removing or replacing outliers

Identifying and deleting duplicates

Fixing incorrect data types

Cleaning strings and column names

Stripping whitespaces and handling casing

Parsing and formatting dates

Using regex for cleaning textual data

Validating values and correcting common errors

Building a reusable cleaning pipeline in Pandas

Technologies Used
Python 3

Pandas

Jupyter Notebook or Google Colab

Perfect For
Anyone working with CSV, Excel, or messy real-world data who needs to clean and prepare it for analysis, reports, or machine learning.

Resources
Practice Notebook:
https://colab.research.google.com/drive/1OifHwZ5xAlTLNkz5Er1NJmVD8tA0lHQX?usp=sharing

Data Set : https://drive.google.com/file/d/1Vr0GhmIGT6YUxKF6xBFp95dvp-2l0z0O/view?usp=sharing
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Видео Python Day 19 – Data Cleaning with Pandas канала Abhishek Mishra