在Python中，如何读取二进制文件并在该文件的每个字节上循环？
在Python中，如何读取二进制文件并在该文件的每个字节上循环？
Answers:
Python 2.4及更早版本
f = open("myfile", "rb")
try:
byte = f.read(1)
while byte != "":
# Do stuff with byte.
byte = f.read(1)
finally:
f.close()Python 2.5-2.7
with open("myfile", "rb") as f:
byte = f.read(1)
while byte != "":
# Do stuff with byte.
byte = f.read(1)请注意，with语句在2.5以下的Python版本中不可用。要在v 2.5中使用它，您需要导入它：
from __future__ import with_statement在2.6中是不需要的。
Python 3
在Python 3中，这有点不同。我们将不再以字节模式而是字节对象从流中获取原始字符，因此我们需要更改条件：
with open("myfile", "rb") as f:
byte = f.read(1)
while byte != b"":
# Do stuff with byte.
byte = f.read(1)或如benhoyt所说，跳过不相等并利用b""评估为false 的事实。这使代码在2.6和3.x之间兼容，而无需进行任何更改。如果从字节模式更改为文本模式或相反，也可以避免更改条件。
with open("myfile", "rb") as f:
byte = f.read(1)
while byte:
# Do stuff with byte.
byte = f.read(1)python 3.8
从现在开始，借助：=运算符，可以以更短的方式编写以上代码。
with open("myfile", "rb") as f:
while (byte := f.read(1)):
# Do stuff with byte.该生成器从文件中产生字节，并分块读取文件：
def bytes_from_file(filename, chunksize=8192):
with open(filename, "rb") as f:
while True:
chunk = f.read(chunksize)
if chunk:
for b in chunk:
yield b
else:
break
# example:
for b in bytes_from_file('filename'):
do_stuff_with(b)8192 Byte = 8 kB（实际上是，KiB但这不是众所周知的）。该值是“完全”随机的，但8 kB似乎是一个适当的值：不会浪费太多的内存，并且仍然没有“太多”的读取操作，如Skurmedel接受的答案...
for b in chunk:循环，将速度提高20-25％yield from chunk。yield在Python 3.3中添加了这种形式（请参见Yield Expressions）。
如果文件不是太大，则将其保存在内存中是一个问题：
with open("filename", "rb") as f:
bytes_read = f.read()
for b in bytes_read:
process_byte(b)其中process_byte表示要对传入的字节执行的某些操作。
如果要一次处理一个块：
with open("filename", "rb") as f:
bytes_read = f.read(CHUNKSIZE)
while bytes_read:
for b in bytes_read:
process_byte(b)
bytes_read = f.read(CHUNKSIZE)该with语句在Python 2.5及更高版本中可用。
要读取一个文件（一次一个字节（忽略缓冲）），可以使用内置两个参数iter(callable, sentinel)的函数：
with open(filename, 'rb') as file:
for byte in iter(lambda: file.read(1), b''):
# Do stuff with byte它调用file.read(1)直到不返回任何内容b''（空字节串）。对于大文件，内存不会无限增长。您可以传递buffering=0 给open()，以禁用缓冲-它确保每次迭代仅读取一个字节（慢速）。
with-statement自动关闭文件-包括下面的代码引发异常的情况。
尽管默认情况下存在内部缓冲，但是每次处理一个字节仍然效率低下。例如，以下是该blackhole.py实用程序，它消耗了给出的所有内容：
#!/usr/bin/env python3
"""Discard all input. `cat > /dev/null` analog."""
import sys
from functools import partial
from collections import deque
chunksize = int(sys.argv[1]) if len(sys.argv) > 1 else (1 << 15)
deque(iter(partial(sys.stdin.detach().read, chunksize), b''), maxlen=0)例：
$ dd if=/dev/zero bs=1M count=1000 | python3 blackhole.py它处理〜1.5 Gb / s的时候chunksize == 32768我的机器，只有在〜7.5 MB / s的时候chunksize == 1。也就是说，一次读取一个字节要慢200倍。考虑到这一点，如果你可以重写你的处理同时使用多个字节，如果你需要的性能。
mmap允许您同时将文件视为bytearray和文件对象。如果您需要访问两个接口，它可以作为将整个文件加载到内存中的替代方法。特别是，您可以仅使用普通for-loop 一次遍历一个内存映射文件一个字节：
from mmap import ACCESS_READ, mmap
with open(filename, 'rb', 0) as f, mmap(f.fileno(), 0, access=ACCESS_READ) as s:
for byte in s: # length is equal to the current file size
# Do stuff with bytemmap支持切片符号。例如，从文件中从position开始mm[i:i+len]返回len字节i。Python 3.2之前不支持上下文管理器协议。mm.close()在这种情况下，您需要显式调用。使用遍历每个字节mmap比消耗更多的内存file.read(1)，但mmap速度要快一个数量级。
numpy内存映射（字节）数组。
numpy.memmap()，您可以一次获取一个字节的数据（ctypes.data）。您可以将numpy数组视为仅比内存+元数据中的blob多一点。
用Python读取二进制文件并遍历每个字节
Python 3.5中的新功能是 pathlib模块，它具有一种便捷的方法，专门用于将文件读取为字节，从而允许我们遍历字节。我认为这是一个不错的回答（如果又快又脏）：
import pathlib
for byte in pathlib.Path(path).read_bytes():
print(byte)有趣的是，这是唯一要提及的答案pathlib。
在Python 2中，您可能会这样做（正如Vinay Sajip也建议的那样）：
with open(path, 'b') as file:
for byte in file.read():
print(byte)如果文件太大而无法在内存中进行迭代，则可以使用iter带有callable, sentinel签名的函数（Python 2版本）来惯用地对其进行分块：
with open(path, 'b') as file:
callable = lambda: file.read(1024)
sentinel = bytes() # or b''
for chunk in iter(callable, sentinel):
for byte in chunk:
print(byte)（其他几个答案都提到了这一点，但很少有人提供合理的读取大小。）
让我们创建一个函数来做到这一点，包括对Python 3.5+标准库的惯用用法：
from pathlib import Path
from functools import partial
from io import DEFAULT_BUFFER_SIZE
def file_byte_iterator(path):
"""given a path, return an iterator over the file
that lazily loads the file
"""
path = Path(path)
with path.open('rb') as file:
reader = partial(file.read1, DEFAULT_BUFFER_SIZE)
file_iterator = iter(reader, bytes())
for chunk in file_iterator:
yield from chunk请注意，我们使用file.read1。file.read阻塞，直到获得所有请求的字节或EOF。file.read1让我们避免阻塞，因此它可以更快地返回。没有其他答案也提到这一点。
让我们制作一个具有兆字节（实际上是兆字节）的伪随机数据的文件：
import random
import pathlib
path = 'pseudorandom_bytes'
pathobj = pathlib.Path(path)
pathobj.write_bytes(
bytes(random.randint(0, 255) for _ in range(2**20)))现在让我们对其进行迭代并在内存中实现它：
>>> l = list(file_byte_iterator(path))
>>> len(l)
1048576我们可以检查数据的任何部分，例如，最后100个字节和前100个字节：
>>> l[-100:]
[208, 5, 156, 186, 58, 107, 24, 12, 75, 15, 1, 252, 216, 183, 235, 6, 136, 50, 222, 218, 7, 65, 234, 129, 240, 195, 165, 215, 245, 201, 222, 95, 87, 71, 232, 235, 36, 224, 190, 185, 12, 40, 131, 54, 79, 93, 210, 6, 154, 184, 82, 222, 80, 141, 117, 110, 254, 82, 29, 166, 91, 42, 232, 72, 231, 235, 33, 180, 238, 29, 61, 250, 38, 86, 120, 38, 49, 141, 17, 190, 191, 107, 95, 223, 222, 162, 116, 153, 232, 85, 100, 97, 41, 61, 219, 233, 237, 55, 246, 181]
>>> l[:100]
[28, 172, 79, 126, 36, 99, 103, 191, 146, 225, 24, 48, 113, 187, 48, 185, 31, 142, 216, 187, 27, 146, 215, 61, 111, 218, 171, 4, 160, 250, 110, 51, 128, 106, 3, 10, 116, 123, 128, 31, 73, 152, 58, 49, 184, 223, 17, 176, 166, 195, 6, 35, 206, 206, 39, 231, 89, 249, 21, 112, 168, 4, 88, 169, 215, 132, 255, 168, 129, 127, 60, 252, 244, 160, 80, 155, 246, 147, 234, 227, 157, 137, 101, 84, 115, 103, 77, 44, 84, 134, 140, 77, 224, 176, 242, 254, 171, 115, 193, 29]不要执行以下操作-这会拉出任意大小的块，直到到达换行符为止-当块太小且可能也太大时，太慢：
with open(path, 'rb') as file:
for chunk in file: # text newline iteration - not for bytes
yield from chunk上面的内容仅适用于语义上人类可读的文本文件（例如纯文本，代码，标记，降价等……本质上是ascii，utf，拉丁语等……已编码），您应该在不带'b'标志的情况下打开它们。
path = Path(path), with path.open('rb') as file:而不是使用内置的开放函数呢？他们俩都做正确的事吗？
Path对象，因为它是处理路径的一种非常方便的新方法。无需将字符串传递到经过精心选择的“正确”函数中，我们只需调用path对象上的方法，该对象本质上包含您想要的语义路径字符串中的大多数重要功能。使用可以检查的IDE，我们也可以更轻松地获得自动完成功能。我们可以使用open内置Path函数来完成相同的任务，但是在编写程序时有很多好处供程序员使用该对象。
file_byte_iterator比我在此页面上尝试的所有方法都快得多。恭喜您！
总结一下chrispy，Skurmedel，Ben Hoyt和Peter Hansen的所有要点，这将是一次处理一个字节的二进制文件的最佳解决方案：
with open("myfile", "rb") as f:
while True:
byte = f.read(1)
if not byte:
break
do_stuff_with(ord(byte))对于python 2.6及更高版本，因为：
或使用JF Sebastians解决方案提高速度
from functools import partial
with open(filename, 'rb') as file:
for byte in iter(partial(file.read, 1), b''):
# Do stuff with byte或者，如果您希望将其用作生成器功能（如codeape所示）：
def bytes_from_file(filename):
with open(filename, "rb") as f:
while True:
byte = f.read(1)
if not byte:
break
yield(ord(byte))
# example:
for b in bytes_from_file('filename'):
do_stuff_with(b)经过上述所有尝试并使用@Aaron Hall的回答后，在运行Window 10、8 Gb RAM和32位Python 3.5的计算机上，我收到了约90 Mb文件的内存错误。我被同事推荐使用numpy改用它，这样效果会很好。
到目前为止，读取整个二进制文件（我已经测试过）的最快方法是：
import numpy as np
file = "binary_file.bin"
data = np.fromfile(file, 'u1')到目前为止，速度比任何其他方法都要快。希望它能对某人有所帮助！
这篇文章本身不是该问题的直接答案。相反，它是一个数据驱动的可扩展基准，可用于比较已发布到此问题的许多答案（以及在以后的更现代的Python版本中使用的新功能的变体），因此应该有助于确定哪个具有最佳性能。
在某些情况下，我已经修改了参考答案中的代码，以使其与基准框架兼容。
首先，以下是当前最新版本的Python 2和3的结果：
Fastest to slowest execution speeds with 32-bit Python 2.7.16
numpy version 1.16.5
Test file size: 1,024 KiB
100 executions, best of 3 repetitions
1 Tcll (array.array) : 3.8943 secs, rel speed 1.00x, 0.00% slower (262.95 KiB/sec)
2 Vinay Sajip (read all into memory) : 4.1164 secs, rel speed 1.06x, 5.71% slower (248.76 KiB/sec)
3 codeape + iter + partial : 4.1616 secs, rel speed 1.07x, 6.87% slower (246.06 KiB/sec)
4 codeape : 4.1889 secs, rel speed 1.08x, 7.57% slower (244.46 KiB/sec)
5 Vinay Sajip (chunked) : 4.1977 secs, rel speed 1.08x, 7.79% slower (243.94 KiB/sec)
6 Aaron Hall (Py 2 version) : 4.2417 secs, rel speed 1.09x, 8.92% slower (241.41 KiB/sec)
7 gerrit (struct) : 4.2561 secs, rel speed 1.09x, 9.29% slower (240.59 KiB/sec)
8 Rick M. (numpy) : 8.1398 secs, rel speed 2.09x, 109.02% slower (125.80 KiB/sec)
9 Skurmedel : 31.3264 secs, rel speed 8.04x, 704.42% slower ( 32.69 KiB/sec)
Benchmark runtime (min:sec) - 03:26Fastest to slowest execution speeds with 32-bit Python 3.8.0
numpy version 1.17.4
Test file size: 1,024 KiB
100 executions, best of 3 repetitions
1 Vinay Sajip + "yield from" + "walrus operator" : 3.5235 secs, rel speed 1.00x, 0.00% slower (290.62 KiB/sec)
2 Aaron Hall + "yield from" : 3.5284 secs, rel speed 1.00x, 0.14% slower (290.22 KiB/sec)
3 codeape + iter + partial + "yield from" : 3.5303 secs, rel speed 1.00x, 0.19% slower (290.06 KiB/sec)
4 Vinay Sajip + "yield from" : 3.5312 secs, rel speed 1.00x, 0.22% slower (289.99 KiB/sec)
5 codeape + "yield from" + "walrus operator" : 3.5370 secs, rel speed 1.00x, 0.38% slower (289.51 KiB/sec)
6 codeape + "yield from" : 3.5390 secs, rel speed 1.00x, 0.44% slower (289.35 KiB/sec)
7 jfs (mmap) : 4.0612 secs, rel speed 1.15x, 15.26% slower (252.14 KiB/sec)
8 Vinay Sajip (read all into memory) : 4.5948 secs, rel speed 1.30x, 30.40% slower (222.86 KiB/sec)
9 codeape + iter + partial : 4.5994 secs, rel speed 1.31x, 30.54% slower (222.64 KiB/sec)
10 codeape : 4.5995 secs, rel speed 1.31x, 30.54% slower (222.63 KiB/sec)
11 Vinay Sajip (chunked) : 4.6110 secs, rel speed 1.31x, 30.87% slower (222.08 KiB/sec)
12 Aaron Hall (Py 2 version) : 4.6292 secs, rel speed 1.31x, 31.38% slower (221.20 KiB/sec)
13 Tcll (array.array) : 4.8627 secs, rel speed 1.38x, 38.01% slower (210.58 KiB/sec)
14 gerrit (struct) : 5.0816 secs, rel speed 1.44x, 44.22% slower (201.51 KiB/sec)
15 Rick M. (numpy) + "yield from" : 11.8084 secs, rel speed 3.35x, 235.13% slower ( 86.72 KiB/sec)
16 Skurmedel : 11.8806 secs, rel speed 3.37x, 237.18% slower ( 86.19 KiB/sec)
17 Rick M. (numpy) : 13.3860 secs, rel speed 3.80x, 279.91% slower ( 76.50 KiB/sec)
Benchmark runtime (min:sec) - 04:47我还使用更大的10 MiB测试文件（运行了将近一个小时）运行了它，并获得了与上面显示的结果相当的性能结果。
这是用于进行基准测试的代码：
from __future__ import print_function
import array
import atexit
from collections import deque, namedtuple
import io
from mmap import ACCESS_READ, mmap
import numpy as np
from operator import attrgetter
import os
import random
import struct
import sys
import tempfile
from textwrap import dedent
import time
import timeit
import traceback
try:
xrange
except NameError: # Python 3
xrange = range
class KiB(int):
""" KibiBytes - multiples of the byte units for quantities of information. """
def __new__(self, value=0):
return 1024*value
BIG_TEST_FILE = 1 # MiBs or 0 for a small file.
SML_TEST_FILE = KiB(64)
EXECUTIONS = 100 # Number of times each "algorithm" is executed per timing run.
TIMINGS = 3 # Number of timing runs.
CHUNK_SIZE = KiB(8)
if BIG_TEST_FILE:
FILE_SIZE = KiB(1024) * BIG_TEST_FILE
else:
FILE_SIZE = SML_TEST_FILE # For quicker testing.
# Common setup for all algorithms -- prefixed to each algorithm's setup.
COMMON_SETUP = dedent("""
# Make accessible in algorithms.
from __main__ import array, deque, get_buffer_size, mmap, np, struct
from __main__ import ACCESS_READ, CHUNK_SIZE, FILE_SIZE, TEMP_FILENAME
from functools import partial
try:
xrange
except NameError: # Python 3
xrange = range
""")
def get_buffer_size(path):
""" Determine optimal buffer size for reading files. """
st = os.stat(path)
try:
bufsize = st.st_blksize # Available on some Unix systems (like Linux)
except AttributeError:
bufsize = io.DEFAULT_BUFFER_SIZE
return bufsize
# Utility primarily for use when embedding additional algorithms into benchmark.
VERIFY_NUM_READ = """
# Verify generator reads correct number of bytes (assumes values are correct).
bytes_read = sum(1 for _ in file_byte_iterator(TEMP_FILENAME))
assert bytes_read == FILE_SIZE, \
'Wrong number of bytes generated: got {:,} instead of {:,}'.format(
bytes_read, FILE_SIZE)
"""
TIMING = namedtuple('TIMING', 'label, exec_time')
class Algorithm(namedtuple('CodeFragments', 'setup, test')):
# Default timeit "stmt" code fragment.
_TEST = """
#for b in file_byte_iterator(TEMP_FILENAME): # Loop over every byte.
# pass # Do stuff with byte...
deque(file_byte_iterator(TEMP_FILENAME), maxlen=0) # Data sink.
"""
# Must overload __new__ because (named)tuples are immutable.
def __new__(cls, setup, test=None):
""" Dedent (unindent) code fragment string arguments.
Args:
`setup` -- Code fragment that defines things used by `test` code.
In this case it should define a generator function named
`file_byte_iterator()` that will be passed that name of a test file
of binary data. This code is not timed.
`test` -- Code fragment that uses things defined in `setup` code.
Defaults to _TEST. This is the code that's timed.
"""
test = cls._TEST if test is None else test # Use default unless one is provided.
# Uncomment to replace all performance tests with one that verifies the correct
# number of bytes values are being generated by the file_byte_iterator function.
#test = VERIFY_NUM_READ
return tuple.__new__(cls, (dedent(setup), dedent(test)))
algorithms = {
'Aaron Hall (Py 2 version)': Algorithm("""
def file_byte_iterator(path):
with open(path, "rb") as file:
callable = partial(file.read, 1024)
sentinel = bytes() # or b''
for chunk in iter(callable, sentinel):
for byte in chunk:
yield byte
"""),
"codeape": Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while True:
chunk = f.read(chunksize)
if chunk:
for b in chunk:
yield b
else:
break
"""),
"codeape + iter + partial": Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
for chunk in iter(partial(f.read, chunksize), b''):
for b in chunk:
yield b
"""),
"gerrit (struct)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
fmt = '{}B'.format(FILE_SIZE) # Reads entire file at once.
for b in struct.unpack(fmt, f.read()):
yield b
"""),
'Rick M. (numpy)': Algorithm("""
def file_byte_iterator(filename):
for byte in np.fromfile(filename, 'u1'):
yield byte
"""),
"Skurmedel": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
byte = f.read(1)
while byte:
yield byte
byte = f.read(1)
"""),
"Tcll (array.array)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
arr = array.array('B')
arr.fromfile(f, FILE_SIZE) # Reads entire file at once.
for b in arr:
yield b
"""),
"Vinay Sajip (read all into memory)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
bytes_read = f.read() # Reads entire file at once.
for b in bytes_read:
yield b
"""),
"Vinay Sajip (chunked)": Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
chunk = f.read(chunksize)
while chunk:
for b in chunk:
yield b
chunk = f.read(chunksize)
"""),
} # End algorithms
#
# Versions of algorithms that will only work in certain releases (or better) of Python.
#
if sys.version_info >= (3, 3):
algorithms.update({
'codeape + iter + partial + "yield from"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
for chunk in iter(partial(f.read, chunksize), b''):
yield from chunk
"""),
'codeape + "yield from"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while True:
chunk = f.read(chunksize)
if chunk:
yield from chunk
else:
break
"""),
"jfs (mmap)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f, \
mmap(f.fileno(), 0, access=ACCESS_READ) as s:
yield from s
"""),
'Rick M. (numpy) + "yield from"': Algorithm("""
def file_byte_iterator(filename):
# data = np.fromfile(filename, 'u1')
yield from np.fromfile(filename, 'u1')
"""),
'Vinay Sajip + "yield from"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
chunk = f.read(chunksize)
while chunk:
yield from chunk # Added in Py 3.3
chunk = f.read(chunksize)
"""),
}) # End Python 3.3 update.
if sys.version_info >= (3, 5):
algorithms.update({
'Aaron Hall + "yield from"': Algorithm("""
from pathlib import Path
def file_byte_iterator(path):
''' Given a path, return an iterator over the file
that lazily loads the file.
'''
path = Path(path)
bufsize = get_buffer_size(path)
with path.open('rb') as file:
reader = partial(file.read1, bufsize)
for chunk in iter(reader, bytes()):
yield from chunk
"""),
}) # End Python 3.5 update.
if sys.version_info >= (3, 8, 0):
algorithms.update({
'Vinay Sajip + "yield from" + "walrus operator"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while chunk := f.read(chunksize):
yield from chunk # Added in Py 3.3
"""),
'codeape + "yield from" + "walrus operator"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while chunk := f.read(chunksize):
yield from chunk
"""),
}) # End Python 3.8.0 update.update.
#### Main ####
def main():
global TEMP_FILENAME
def cleanup():
""" Clean up after testing is completed. """
try:
os.remove(TEMP_FILENAME) # Delete the temporary file.
except Exception:
pass
atexit.register(cleanup)
# Create a named temporary binary file of pseudo-random bytes for testing.
fd, TEMP_FILENAME = tempfile.mkstemp('.bin')
with os.fdopen(fd, 'wb') as file:
os.write(fd, bytearray(random.randrange(256) for _ in range(FILE_SIZE)))
# Execute and time each algorithm, gather results.
start_time = time.time() # To determine how long testing itself takes.
timings = []
for label in algorithms:
try:
timing = TIMING(label,
min(timeit.repeat(algorithms[label].test,
setup=COMMON_SETUP + algorithms[label].setup,
repeat=TIMINGS, number=EXECUTIONS)))
except Exception as exc:
print('{} occurred timing the algorithm: "{}"\n {}'.format(
type(exc).__name__, label, exc))
traceback.print_exc(file=sys.stdout) # Redirect to stdout.
sys.exit(1)
timings.append(timing)
# Report results.
print('Fastest to slowest execution speeds with {}-bit Python {}.{}.{}'.format(
64 if sys.maxsize > 2**32 else 32, *sys.version_info[:3]))
print(' numpy version {}'.format(np.version.full_version))
print(' Test file size: {:,} KiB'.format(FILE_SIZE // KiB(1)))
print(' {:,d} executions, best of {:d} repetitions'.format(EXECUTIONS, TIMINGS))
print()
longest = max(len(timing.label) for timing in timings) # Len of longest identifier.
ranked = sorted(timings, key=attrgetter('exec_time')) # Sort so fastest is first.
fastest = ranked[0].exec_time
for rank, timing in enumerate(ranked, 1):
print('{:<2d} {:>{width}} : {:8.4f} secs, rel speed {:6.2f}x, {:6.2f}% slower '
'({:6.2f} KiB/sec)'.format(
rank,
timing.label, timing.exec_time, round(timing.exec_time/fastest, 2),
round((timing.exec_time/fastest - 1) * 100, 2),
(FILE_SIZE/timing.exec_time) / KiB(1), # per sec.
width=longest))
print()
mins, secs = divmod(time.time()-start_time, 60)
print('Benchmark runtime (min:sec) - {:02d}:{:02d}'.format(int(mins),
int(round(secs))))
main()yield from chunk不是for byte in chunk: yield byte？我想我应该以此来加强我的回答。
yield from。
enumerate因为应该将迭代理解为完成-如果没有，最后我检查了-列举在+ = 1的索引上进行簿记会产生一些开销，因此您可以选择在您的簿记中进行簿记自己的代码。甚至通过传递到双端队列maxlen=0。
enumerate。感谢您的反馈。将在我的帖子中添加没有更新的内容（尽管我认为这样做不会对结果产生太大影响）。还将添加numpy基于@Rick M.的答案。
super().，而不是tuple.你的__new__，你可以使用namedtuple属性名称，而不是指标。
如果您正在寻找快速的东西，这是我多年来一直使用的一种方法：
from array import array
with open( path, 'rb' ) as file:
data = array( 'B', file.read() ) # buffer the file
# evaluate it's data
for byte in data:
v = byte # int value
c = chr(byte)如果要迭代char而不是ints，则可以简单地使用data = file.read()，它应该是py3中的bytes（）对象。