readlines()
version is actually slower than readline()
on the largest dataset. It is attempting to store about 1GB in memory here, causing a slowdown, but still faster than input()
.
This is an old revision of the document!
There is no magic bullet that leads to the fastest input and output in a programming contest. Having said this, there are typically multiple ways of performing and processing input and output in Python3. By understanding the options that are available and how to optimize them (e.g., conducting tests) you can see a real world performance increase, in particular when faced with high volumes of reads and writes.
Output is handled with the print()
function.
>>> print() # '\n'
>>> print("Hello World!") # "Hello World!\n"
>>> print("Hello", "World!") # "Hello World!\n"
>>> print(1, 2, 3, sep='') # "123\n"
>>> print(1, 2, 3, end='-') >>> print("a b c") # "1 2 3-a b c\n"
The following benchmarks demonstrate the increased likelihood of failure of print()
as output sizes increase. All files used for testing can be found here.
10 characters per line (n= number of lines):
n | input() | sys.stdin.readline() | sys.stdin.readlines() |
104 | .034s | .016s | .018s |
105 | .146s | .052s | .030s |
106 | 1.301s | .301s | .130s |
1000 characters per line (n= number of lines):
n | input() | sys.stdin.readline() | sys.stdin.readlines() |
104 | .046s | .037s | .033s |
105 | .282s | .183s | .143s |
106 | 2.728s | 1.430s | 1.723s1) |
The above tests were designed to showcase minimal reading functionality other than temporary storage.2)
readlines()
version is actually slower than readline()
on the largest dataset. It is attempting to store about 1GB in memory here, causing a slowdown, but still faster than input()
.
map()
, and split()
, as these are non-IO considerations in Python3.