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View Code? Open in Web Editor NEWCode for the book "High Performance Python 2e" by Micha Gorelick and Ian Ozsvald with OReilly
License: Other
Code for the book "High Performance Python 2e" by Micha Gorelick and Ian Ozsvald with OReilly
License: Other
Hi. I have an issue on where I can find and download the file described in
.I would like to run the code but I cannot quite find this file to download. Plus in the book there seems no clear instruction on how I can find and download it. Could you help me on this.
Thanks.
Hi, I'm not sure if the code will be added, but in
Example 3-7 Memory and time consequences of appends versus list comprehensions
>>> %memit [i*i for i in range(100_000)]
peak memory: 70.50 MiB, increment: 3.02 MiB
>>> %%memit l = []
... for i in range(100_000):
... l.append(i * 2)
...
peak memory: 67.47 MiB, increment: 8.17 MiB
it is said "In Example 3-7, we can see that even for 100,000 elements, we use 2.7ร the memory by building the list with appends versus a list comprehension."
I can not reproduce this with Python 3.8.5.
Neither in a Notebook:
[1]
%load_ext memory_profiler
%load_ext line_profiler
[2]
%memit [i*i for i in range(100_000)]
peak memory: 52.02 MiB, increment: 3.74 MiB
[3]
%%memit
l = []
for i in range(100_000):
l.append(i * 2)
peak memory: 52.66 MiB, increment: 2.95 MiB
nor outside a notebook:
Filename: list_append.py
Line # Mem usage Increment Line Contents
================================================
8 38.5 MiB 38.5 MiB @profile
9 def append_to_list() -> List[int]:
10 """Append to list example."""
11 38.5 MiB 0.0 MiB result = []
12 42.4 MiB 0.0 MiB for i in range(100_000):
13 42.4 MiB 0.3 MiB result.append(i * 2)
14 42.4 MiB 0.0 MiB return result
Filename: list_append.py
Line # Mem usage Increment Line Contents
================================================
17 39.4 MiB 39.4 MiB @profile
18 def comp() -> List[int]:
19 """Comprehend example."""
20 42.2 MiB 0.2 MiB return [i * i for i in range(100_000)]
Same is true for larger lists:
[2]
%memit [i*i for i in range(100_000_000)]
peak memory: 3849.21 MiB, increment: 3801.23 MiB
[3]
%%memit
l = []
for i in range(100_000_000):
l.append(i * 2)
peak memory: 3912.98 MiB, increment: 3863.53 MiB
Thanks for the excellent book! I'm enjoying working my way through it.
In Example 6.5 on page 117, I think the following sentence:
This statement takes such a long time per hit because
grid_shape
must be retrieved from the local namespace
should read:
This statement takes such a long time per hit because
grid_shape
must be retrieved from the global namespace
Hi there. Thanks for writing the book. This is just a friendly question of whether or not the code for 2e will be available at some moment. I noticed this repo contains many empty directories etc and appears to be scaffolding
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