This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.
Python is a high-level, interpreted programming language widely used in data science for data manipulation, analysis, and visualization. Libraries such as Pandas and NumPy provide powerful tools for data wrangling, including handling missing values, filtering, and reshaping datasets.
Description: Identify and fill missing values in a dataset using methods such as mean imputation or forward/backward filling to ensure data completeness and accuracy.
Description: Filter rows or columns based on specified criteria, such as removing outliers or selecting data within a certain range to refine datasets for analysis.
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