In the field of data science and analytics, raw data is rarely perfect. Real-world datasets often contain missing values, duplicate records, incorrect formats, inconsistent text, and outliers that can affect the accuracy of analysis and machine learning models. Data cleaning is the process of detecting, correcting, and preparing raw data so that it becomes reliable and ready for analysis.
One of the most powerful tools for data cleaning in Python is Pandas. Pandas is an open-source Python library that provides easy-to-use data structures and functions for manipulating and analyzing structured data. With its DataFrame and Series objects, Pandas allows data professionals to efficiently clean datasets of any size.
Loading Data into Pandas
Before cleaning data, the first step is importing it into a Pandas DataFrame.
import pandas as pd
