Python Map Examples (Use Lambda on Each Element)Use the map built-in function. Map applies a lambda to each element.
Map. A cold wave comes over the land. All things freeze at once. A function (the freeze) affects all elements of the terrain (the lake, the trees).
With map, we apply a method to a collections. Map requires fewer statements and variables. It is declarative. We tell the program the result we want, not how to compute it.
Here we apply map() to a small list. As the first argument to map, we pass a lambda expression. This lambda increments each element it receives.
Then: The map method passes all elements of the list to the lambda. It does this one at a time.
Result: We receive an iterable collection containing the elements of the list. All elements have had 1 added to their values.
Tip: The lambda is not required. A def-function may be passed to the map built-in method.
Python program that uses map
# An input list.
items = [1, 2, 3]
# Apply lambda to all elements with map.
for r in map(lambda x: x + 1, items):
Map returns an iterator. We often must convert this back into the desired collection type. Here, we use map on a list. We then convert the result of map back into a list.
Caution: This code creates a copy of the original list. Both exist in memory. This may be inefficient if you want to modify a list.
Python program that creates list with map
# Original list.
items = [7, 8, 9]
# Map into a new list.
items2 = list(map(lambda z: z * 2, items))
# Display two lists.
[7, 8, 9]
[14, 16, 18]
How many times in a collection is a condition true? A predicate method is one that returns true or false based on its argument.
Here: We use map with a predicate method. Then we use sum() to count true results of the predicate.
Lambda: We use a lambda expression: this one receives a string parameter. It calls the startswith method.
And: They are counted by sum. Three of the four strings start with the substring "San" in the list.
Python program that sums result of map
names = ["San Jose", "San Francisco", "Santa Fe", "Houston"]
# Sum result of map.
count = sum(map(lambda s: s.startswith("San"), names))
# Count of cities starting with San.
More than one iterable can be used as arguments to map. Here we use two lists in a map call. The two lists have an unequal number of elements.
However: Map does not care. It proceeds as far as it can, which is three elements.
Tip: The lambda here accepts two arguments. This is required when using two iterables.
Info: The first argument is the first list's element. And the second argument is from the other list.
Python program that uses two lists in map
# Two input lists.
a = [1, 2, 3]
b = [2, 3, 4, 5]
# Multiply elements of two lists together.
result = list(map(lambda x, y: x * y, a, b))
# Three elements are present.
[2, 6, 12]
In tests, map tends to be slower than an equivalent for-loop. There is some overhead to invoking a method and constructing a map result.
In this test: I found that map was about twice as slow. The loops add 20 to each element in a list, and sum the total.
Tip: Map() may be best reserved for situations where the clarity of code, and not its speed, is more important.
Python program that times map, for-loop
numbers = [5, 10, 15, 20, 30, 40]
# Version 1: map.
for c in range(0, 1000000):
sum = 0
for i in map(lambda v: v + 20, numbers):
sum += i
# Version 2: for-loop.
for c in range(0, 1000000):
sum = 0
for v in numbers:
sum += (v + 20)
1411254271.106704 map: 2.742 s
1411254272.373777 for-loop: 1.267 s
Solutions that use map can become complex. Sometimes, using statements in a loop is simpler. In programming, "advanced" code is often inferior to understandable code.
Simpler things, like for-loops, are often easier to keep correct. Map meanwhile has benefits in programs. Entire programs can be designed around it.
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