Master these techniques to stand out as a Python developer
If you ask which Python library is most frequently used by data scientists, the answer is undoubtedly Pandas. Pandas is used for working with datasets via the functionalities as analyzing, cleaning, exploring, and manipulating data. Additionally, Pandas can be used to run descriptive statistical analysis. Data scientists who use Python for their projects become familiar with Pandas from day one. So, why am I discussing Pandas today?
In fact, there are several Pandas functions that many users tend 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 useful for complex computations where you need to manipulate data with user-defined functions and make your data transformation more versatile. For example, if you’d like to clean the dataset with messy product names and prices, you would need to align product names right, use the word “Inch” instead of the symbol, add appropriate spacing, preserve words in their correct cases, and remove dollar signs in the price column. You could manage all these tasks…