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Master the art of data cleaning from "summary" of Python for Data Analysis by Wes McKinney
Data cleaning is an essential step in the data analysis process. It involves identifying and correcting errors in the dataset to ensure that the data is accurate and reliable. Mastering the art of data cleaning requires attention to detail and a systematic approach. One common task in data cleaning is handling missing data. Missing data can arise for various reasons, such as data entry errors or equipment malfunction. It is important to identify missing data and decide how to handle it. This may involve imputing values for missing data or deleting observations with missing values. Another important aspect of data cleaning is handling duplicate data. Duplicate data can distort analysis results and lead to incorrect conclusions...
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