If you are asking which Python library is most frequently used by data scientists, the answer is undoubtedly Pandas. Pandas is used to work with data sets through functionalities such as analyzing, cleaning, exploring, and manipulating data. Additionally, Pandas can be used to run descriptive statistical analyses. Data scientists who use Python for their projects become familiar with Pandas from day one. So why am I talking about Pandas today?
In fact, there are several Pandas features that many users tend to neglect or do not explore in depth. Therefore, I will discuss these features in today's article.
The apply() method applies custom functions along the axis of a DataFrame or a series. This method is useful for complex calculations where you need to manipulate data with user-defined functions and make your data transformation more versatile. For example, if you want to clean the data set with messy product names and prices, you would need to align the product names correctly, use the word “inches” instead of the symbol, add appropriate spacing, keep the words in their correct cases , and remove the dollar signs in the price column. You could manage all these tasks…
