khlood-almohammadi/Data-Cleaning-Process

Cleaning our data is the third step in data wrangling. It is where we fix the quality and tidiness issues that our identified in the assess step. In this training, we'll clean all of the issues we identified in using Python and pandas. This Jupyter Notebooks will be structured as follows: we'll learn about the data cleaning process: defining, coding, and testing we'll address the missing data first (and learn why it is usually important to address these completeness issues first) we'll tackle the tidiness issues next (and learn why this is usually the next logical step) And finally, we'll clean up the quality issues This training will consist primarily of Jupyter Notebooks we will leverage the most common cleaning functions and methods in the pandas library to clean the nineteen quality issues and four tidiness issues identified .