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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →To keep nested data nested, put the inner comprehension in the expression of the outer one. To flatten it, put both for clauses in one comprehension. The difference is the shape of the result: nested comprehensions produce a collection for each outer item, while chained clauses produce individual values at the deepest loop.
Choose the output shape first
Before writing a comprehension, decide what one output element should represent. For a list of lists, do you need one transformed list per input row, or one flat list containing every transformed item? That decision determines where the inner loop belongs.
Keep one output list per input row
Place the inner comprehension in the leading expression of the outer comprehension:
nested_result = [
[transform(item) for item in row]
for row in rows
]Each pass through for row in rows creates one inner list. The outer comprehension collects those lists, so the result has a list-of-lists shape. For example:
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rows = [[1, 2], [3, 4]]
doubled = [[item * 2 for item in row] for row in rows]
# [[2, 4], [6, 8]]This form is useful when the row boundary carries meaning and should remain visible in the result.
Flatten items into one output list
Put both loops in the same comprehension when each output element should be an individual item:
flat_result = [
transform(item)
for row in rows
for item in row
]The second loop runs for each row, and the expression contributes one transformed item at a time. For the example above, [item * 2 for row in rows for item in row] produces [2, 4, 6, 8], not a list of two lists.
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Trace loop order and expression evaluation
Read clauses from left to right as nested loops. The leading expression is evaluated at the innermost point, once for each combination of loop values that reaches it. The official Python language reference describes comprehension clauses as nested blocks; the Functional Programming HOWTO relates the same pattern to explicit loops.
result = [
transform(item)
for row in rows
for item in row
]Corresponding loop structure:
result = []
for row in rows:
for item in row:
result.append(transform(item))This correspondence makes loop order and available values easier to check. The leftmost iterable, rows, is evaluated in the surrounding scope; later clauses can use targets from earlier clauses, as item‘s loop uses row above.
Use names that describe the data each loop visits. row and item reveal more than repeated generic names such as x and y, especially when a comprehension has multiple levels.
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Put each filter beside the loop it filters
A filter applies at the level where it appears. Put a condition after the loop whose values it examines:
positive_values = [
item
for row in rows
for item in row
if item > 0
]Here the condition checks each inner item, so it follows the item loop. A condition that decides whether an entire row should be considered belongs after the outer loop instead:
nonempty_rows = [
[item for item in row]
for row in rows
if row
]When a condition involves both row-level and item-level decisions, place each filter where the values it needs are available. If the resulting sequence of loops and conditions takes effort to untangle, use an ordinary loop with if statements or give a complicated condition a descriptive name.
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Transpose a matrix: nested comprehension or zip()?
Transposing a matrix turns its columns into rows. The Python tutorial demonstrates a nested comprehension for a 3-by-4 matrix:
matrix = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]
transposed = [
[row[column] for row in matrix]
for column in range(4)
]The outer loop selects a column index. For each index, the inner comprehension gathers that position from every row, yielding one output row. This example uses range(4) because the matrix has four columns.
For the same operation, zip() states the intent more directly:
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transposed = list(zip(*matrix))The result elements from zip() are tuples, while the nested comprehension above produces lists. Choose based on both clarity and the collection type the rest of your code expects. The Python tutorial recommends preferring built-in functions to complex flow statements when a built-in expresses the operation well.
Know when a comprehension stops being readable
A comprehension is a good fit when a reader can quickly identify the output value, each iteration source, and each filter. Nested data does not automatically make a comprehension hard to read: a nested expression is clear when each level has a distinct role, such as “one output row per input row.”
Prefer explicit loops when the transformation combines several steps—such as extracting fields, validating them, converting conditionally, and supplying fallbacks—or when the reader would need to mentally expand a long expression to understand it. Intermediate names and ordinary if statements make those stages visible. There is no measured complexity threshold behind this choice; it is a judgment about how readily the code communicates its operations.
Format multiline comprehensions for scanning
Use the formatting conventions of your project. The Python tutorial’s coding-style guidance points readers to PEP 8 and highlights four-space indentation and a 79-character line limit among its style points. Those are general Python style recommendations, not special rules for comprehensions. For a multiline expression, put loop clauses and filters on separate, consistently indented lines so their order is easy to follow.
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Under Python’s documented comprehension-scope rules, target names such as row and item do not leak into the surrounding scope. The iterable expression for the leftmost for is evaluated in the enclosing scope, while later clauses may depend on earlier targets. The syntax and scope discussion cited here comes from Python 3.15.0rc3 documentation; the related Functional Programming HOWTO and coding-style pages surfaced as Python 3.14.8 documentation.
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