54. Python Pandas Data Cleaning| drop duplicates| dropna | fillna
Introduction
Data cleaning is an essential step in the data analysis process that involves removing errors, inconsistencies, and inaccuracies from a dataset to ensure that it is reliable and accurate.
Steps Involved in Data Cleaning 1
Data cleaning involves several steps, including removing duplicates, handling missing values, correcting errors, and standardizing formats. These steps are crucial in ensuring that the data is accurate and reliable, and can be used to build robust machine learning models.
Handling Missing Values 1
Missing values can be handled using various techniques, including dropping rows with missing values, filling missing values with a specified value, or using interpolation. For example, missing values can be dropped using the dropna() method.
Correcting Errors 2
Errors in the data can be corrected using various techniques, including data visualization and machine learning algorithms. For example, in Python, errors can be corrected using the pandas library.