Data Preparation

Data preparation involves the process of transforming raw data into a format suitable for analysis or modeling. This includes tasks such as cleaning, handling missing values, encoding categorical variables, scaling, normalization, and feature engineering. The goal is to ensure that the data is accurate, complete, and structured in a way that facilitates effective analysis and modeling.

Methods description

• .drop(): A DataFrame method used to remove columns or rows from a DataFrame. It takes the labels parameter to specify the columns or rows to be removed and the axis parameter to indicate whether to remove columns (axis=1) or rows (axis=0). The inplace parameter, if set to True, modifies the DataFrame in place;
• .info(): A DataFrame method that prints a concise summary of a DataFrame, including the number of non-null values in each column and the data types of each column. It provides a quick overview of the DataFrame's structure and content.