Python One-Liners That Will Boost Your Data Preparation Workflow
In the data project lifecycle, data preparation is the stage where we get the raw data ready for further procedures like data analysis and ML modeling.
Several transformations are frequently chained together in sequences as part of data preparation. For instance, we need to filter rows in addition to
Chain Transformation with Pipe
Pivot Data with Multiple Aggregation
Rearranging data into formats that are simpler for users to examine and comprehend is known as data pivoting.
Conditional Selection For Assigning Values
We frequently have to develop new features when working with raw data. We might wish to classify employee salaries into three different values, for instance.
Conditional Replacement For Several Columns
Sometimes we want to substitute a different value that is appropriate for our work for the data we have chosen, rather than filtering it out.
Multiple Columns Combination
When we work with data, sometimes we represent multiple columns as one feature instead of leaving them as they are.
Must know Docker Commands for every Data Engineering