18.1 Data Cleaning in Python and SQL
Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. This is a critical step in the data analysis process because the results of your analysis are only as good as the quality of your data.
Python and SQL each have unique strengths that can be used in different stages of the data cleaning process. Let's look at some examples of how these two powerful tools can be used to clean data.
Firstly, we will fetch some data from a SQL database and load it into a DataFrame using Python's pandas library. Note that in these examples, we will be using the SQLite database. However, the same principles apply to other databases that can be accessed through Python, such as MySQL and PostgreSQL.
Example:
In this data, you might encounter a number of common data cleaning tasks. Let's go through some of them and demonstrate how to address...