Python Map Examples

Map. In loops we call methods.

But sometimes this leads to code that is unclear. It is hard to maintain. Bugs emerge. With map, we declaratively apply methods to collections.

The result. Map requires fewer statements and variables. It is a form of declarative programming. We tell the program the result we want, not how to compute it.

An example. 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.

Based on:

Python 3

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):
    print(r)

Output

2
3
4

Iterator. 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.
print(items)
print(items2)

Output

[7, 8, 9]
[14, 16, 18]

Sum, predicate. 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

# Cities.
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.
print(count)

Output

3

Two iterables. 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.
print(result)

Output

[2, 6, 12]

Performance. 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

import time

numbers = [5, 10, 15, 20, 30, 40]

print(time.time())

# Version 1: map.
for c in range(0, 1000000):
    sum = 0
    for i in map(lambda v: v + 20, numbers):
	sum += i

print(time.time())

# Version 2: for-loop.
for c in range(0, 1000000):
    sum = 0
    for v in numbers:
	sum += (v + 20)

print(time.time())

Results

1411254268.364547
1411254271.106704    map:      2.742 s
1411254272.373777    for-loop: 1.267 s

A summary. 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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