Limitations of using Loops¶ There are several limitations using loops. If you look at the below examples related to processing collections using loops, most of the functions have similar logic to iterate through elements. We are spending more time on coding non business logic. It results in too much of code and it can become …
Dict Comprehensions¶ Let us see how we can process Python collections using dict comprehensions. We will be using orders data set which will be available by running notebook related to preparing data sets. We will see how to get a dict using dict comprehensions. The output should contain key value pairs for each order where …
Set Comprehensions¶ Let us see how we can process Python collections using set comprehensions. Set comprehensions can be applied on top of any iterable. We will be using orders data set which will be available by running notebook related to preparing data sets. We will see how to get all unique months from orders data …
List Comprehensions¶ Let us see how we can process data in Python collections using list comprehensions. Let us create list of tuples using id and email from the below employees using list comprehensions. We will ensure that employee id is of type int. Here is the approach with conventional loops. Create a global list. Process …
Manipulate Collections using comprehensions¶ Comprehensions is alternative way of manipulating lists using conventional loops. Comprehensions reduce the effort of coding and improves readability when we want to apply basic transformations on the data. We can apply comprehensions on lists, sets as well as dicts. We can create a new list or set or dict using …
Joining Data Sets¶ Let us perform few tasks to understand how to perform joins over multiple collections using loops and conditionals. There are different strategies for joins. Nested Loops Sort Merge Hash Join We will be using Nested Loops approach using orders and order_items. Build dict for one data set – orders. Iteratively lookup into …
