While Power Query doesn't directly support list comprehensions like Python or other languages, it offers similar functionalities using built-in functions and expressions. Here's how to achieve list comprehension-like logic for concise and efficient data manipulation:

1. Leveraging `List.Generate`:

This function allows you to generate a list based on a custom logic. It takes three arguments:

* Starting value: The initial value for your list.
* Condition: A logical expression that determines how long the list generation continues.
* Change: The expression that defines how the value changes for each iteration in the list.

Example:

Suppose you want to create a list of numbers from 1 to 10. Here's how you can achieve it using `List.Generate`:

```
List.Generate(1, x => x <= 10, x => x + 1)
```

This generates a list starting from 1, iterates as long as the value is less than or equal to 10, and increments the value by 1 in each iteration.

2. Combining `List.Generate` with Other Functions:

The power of `List.Generate` comes from combining it with other list manipulation functions like `List.Transform` and `List.Filter`.

Example:

Let's say you have a list of product names and want to create a new list with their lengths. You can achieve this using a combination of functions:

```
let
Products = {"Apple", "Banana", "Orange"},
Lengths = List.Transform(
List.Generate(1, x => x <= List.Count(Products), x => List.Nth(Products, x-1)),
each Text.Length(_)
)
in
Lengths
```

This code first generates a list of numbers from 1 to the count of products (acting as an index). Then it uses `List.Transform` to iterate through this index list and uses `List.Nth` to get the corresponding product name. Finally, it applies `Text.Length` to each product name to obtain a list of lengths.

3. Using List Comprehension Logic with `Table.SelectRows`:

While not strictly list comprehensions, you can achieve similar filtering logic using `Table.SelectRows`.

Example:

Imagine you have a sales table and want to filter for orders above a certain amount. Here's how you can do it:

```
let
FilteredSales = Table.SelectRows(SalesTable, each Sales[Amount] > 100)
in
FilteredSales
```

This code iterates through each row in the SalesTable and selects only those rows where the Amount is greater than 100.

By understanding these techniques, you can achieve concise and efficient data manipulation in Power Query, mimicking the functionality of list comprehensions found in other programming languages. Remember, these are just a few examples, and Power Query offers a vast range of functions for working with lists and tables.