For those who ask which Python library is most incessantly utilized by data scientists, the reply is undoubtedly Pandas. Pandas is used for working with datasets via the functionalities as analyzing, cleansing, exploring, and manipulating data. Moreover, Pandas may be used to run descriptive statistical evaluation. Data scientists who use Python for his or her projects change into aware of Pandas from day one. So, why am I discussing Pandas today?
The truth is, there are several Pandas functions that many users are inclined to neglect or fail to explore fully. Hence, I’ll discuss these functions in today’s article.
The apply() method applies custom functions along the axis of a DataFrame or Series. This method is helpful for complex computations where it is advisable manipulate data with user-defined functions and make your data transformation more versatile. For instance, in the event you’d like to scrub the dataset with messy product names and costs, you would want to align product names right, use the word “Inch” as a substitute of the symbol, add appropriate spacing, preserve words of their correct cases, and take away dollar signs in the worth column. You possibly can manage all these tasks…
