读取二进制文件并遍历每个字节

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 语句在低于 2.5 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，这不是必需的。

蟒蛇 3

在 Python 3 中，它有点不同。我们将不再以字节模式从流中获取原始字符，而是字节对象，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)

或者正如本霍伊特所说，跳过不等于并利用 b"" 评估为假的事实。这使得代码在 2.6 和 3.x 之间兼容，无需任何更改。如果您从字节模式转到文本或相反，它还可以使您免于更改条件。

with open("myfile", "rb") as f:
    byte = f.read(1)
    while byte:
        # Do stuff with byte.
        byte = f.read(1)

蟒蛇3.8

从现在开始，感谢 := 运算符，上面的代码可以用更短的方式编写。

with open("myfile", "rb") as f:
    while (byte := f.read(1)):
        # Do stuff with byte.

---

此生成器从文件中生成字节，以块的形式读取文件：

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)

有关 iterators 和 generators 的信息，请参阅 Python 文档。

---

如果文件不是太大，那么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 表示您要对传入的字节执行的一些操作。

如果你想一次处理一个块：

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)

with 语句在 Python 2.5 及更高版本中可用。

---

要读取文件 — 一次一个字节（忽略缓冲） — 您可以使用 two-argument iter(callable, sentinel) built-in function：

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) 直到它不返回任何内容 b''（空字节串）。对于大文件，内存不会无限增长。您可以将 buffering=0 传递给 open()，以禁用缓冲——它保证每次迭代只读取一个字节（慢）。

with-语句自动关闭文件 — 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，但一次处理一个字节仍然是低效的。例如，下面是 blackhole.py 实用程序，它吃掉给定的所有东西：

#!/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)

例子：

$ dd if=/dev/zero bs=1M count=1000 | python3 blackhole.py

它在我的机器上 chunksize == 32768 时处理 ~1.5 GB/s，而在 chunksize == 1 时只处理 ~7.5 MB/s。也就是说，一次读取一个字节要慢 200 倍。如果您可以重写处理以一次使用多个字节并且如果您需要性能，请考虑到这一点。

mmap 允许您同时将文件视为 bytearray 和文件对象。如果您需要访问这两个接口，它可以作为将整个文件加载到内存中的替代方法。特别是，您可以只使用普通的 for 循环一次在内存映射文件上迭代一个字节：

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 byte

mmap 支持切片表示法。例如，mm[i:i+len] 从位置 i 开始的文件返回 len 个字节。 Python 3.2 之前不支持上下文管理器协议；在这种情况下，您需要显式调用 mm.close()。使用 mmap 迭代每个字节比 file.read(1) 消耗更多的内存，但 mmap 快一个数量级。

---

在 Python 中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，它有一个方便的方法专门将文件作为字节读取，允许我们迭代字节。我认为这是一个不错的（如果又快又脏）答案：

import pathlib

for byte in pathlib.Path(path).read_bytes():
    print(byte)

有趣的是，这是提到 pathlib 的唯一答案。

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)

如果文件可能太大而无法在内存中进行迭代，您可以习惯性地将其分块，使用带有 callable, sentinel 签名的 iter 函数 - Python 2 版本：

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。）

大文件或缓冲/交互式阅读的最佳实践

让我们创建一个函数来执行此操作，包括 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，直到它获得它或 EOF 请求的所有字节。 file.read1 允许我们避免阻塞，因此它可以更快地返回。没有其他答案也提到这一点。

最佳实践用法演示：

让我们用一兆字节（实际上是兆字节）的伪随机数据创建一个文件：

import random
import pathlib
path = 'pseudorandom_bytes'
pathobj = pathlib.Path(path)

pathobj.write_bytes(
  bytes(random.randint(0, 255) for _ in range(2**20)))

现在让我们遍历它并在内存中实现它：

>>> 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]

不要逐行迭代二进制文件

不要执行以下操作 - 这会拉取任意大小的块，直到它到达换行符 - 当块太小并且可能太大时太慢：

    with open(path, 'rb') as file:
        for chunk in file: # text newline iteration - not for bytes
            yield from chunk

以上内容仅适用于您应该在没有 'b'旗帜。

---

总结 chrispy、Skurmedel、Ben Hoyt 和 Peter Hansen 的所有亮点，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：

python缓冲区内部 - 无需读取块

DRY原则——不重复读行

with 语句确保一个干净的文件关闭

当没有更多字节时（而不是当字节为零时），'byte' 评估为 false

或使用 JF Sebastians 解决方案来提高速度

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，如果您希望它作为生成器函数，如 codeape 演示的那样：

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)

---

这篇文章本身并不是这个问题的直接答案。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（以及利用后来更现代的 Python 版本中添加的新功能的变体）——因此应该有助于确定哪个具有最佳性能。

在少数情况下，我修改了引用答案中的代码，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) - 03:26

Fastest 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

我还使用一个更大的 10 MiB 测试文件（运行了将近一个小时）运行它，并获得了与上面显示的性能相当的结果。

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()

---

Python 3，一次读取所有文件：

with open("filename", "rb") as binary_file:
    # Read the whole file at once
    data = binary_file.read()
    print(data)

您可以使用 data 变量迭代任何您想要的内容。

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在尝试了以上所有方法并使用@Aaron Hall 的答案后，我在运行 Window 10、8 Gb RAM 和 Python 3.5 32 位的计算机上遇到了 ~90 Mb 文件的内存错误。一位同事建议我改用 numpy，效果非常好。

到目前为止，读取整个二进制文件（我已经测试过）的最快速度是：

import numpy as np

file = "binary_file.bin"
data = np.fromfile(file, 'u1')

Reference

迄今为止，比任何其他方法都快。希望它可以帮助某人！

---

如果您有大量二进制数据要读取，您可能需要考虑 struct module。它被记录为“在 C 和 Python 类型之间”进行转换，但是当然，字节就是字节，而这些是否被创建为 C 类型并不重要。例如，如果您的二进制数据包含两个 2 字节整数和一个 4 字节整数，您可以按如下方式读取它们（示例取自 struct 文档）：

>>> struct.unpack('hhl', b'\x00\x01\x00\x02\x00\x00\x00\x03')
(1, 2, 3)

您可能会发现这比显式循环文件内容更方便、更快，或两者兼而有之。

---

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)

如果你想迭代字符而不是整数，你可以简单地使用 data = file.read()，它应该是 py3 中的 bytes() 对象。

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对于大尺寸，我认为使用生成器不会不好，这个答案是为了读取文件之类的东西，尽管@codeapp 有一个类似的答案，我认为删除内部循环会更有意义。

def read_chunk(file_object, chunk_size=125):
    while True:
        file =  file_object.read(chunk_size)
        if not file:
            break
        yield file


#sample use 
buffer = io.BytesIO()
file = open('myfile', 'r')
for chunk in read_chunk(file):
    buffer.write(chunk)
buffer.seek(0)
// save the file or do whatever you want here

您仍然可以将其用作普通列表，我认为这没有任何用处，但是

file_list = list(read_chunk(file, chunk_size=10000))
for i in file_list:
    # do something

并获取每个块的索引

for index, chunk in enumurate(read_chunk(file, chunk_size=10000)):
    #use the index as a number index
    # you can try and get the size of each chunk with this 
    length = len(chunk)

请注意，注意文件的大小，并且 chunk_size 始终以字节为单位。

---

这是使用 Numpy fromfile 读取网络字节序数据的示例，该示例寻址上述@Nirmal 注释：

dtheader= np.dtype([('Start Name','b', (4,)),
                ('Message Type', np.int32, (1,)),
                ('Instance', np.int32, (1,)),
                ('NumItems', np.int32, (1,)),
                ('Length', np.int32, (1,)),
                ('ComplexArray', np.int32, (1,))])
dtheader=dtheader.newbyteorder('>')

headerinfo = np.fromfile(iqfile, dtype=dtheader, count=1)

print(raw['Start Name'])

我希望这有帮助。问题是 fromfile 不能识别 EOF 并允许优雅地跳出任意大小的文件的循环。

---