Good Python tutorial collection
Python 入门指南
12. 虚拟环境和包
Python教程
Python encoding and decoding
Unicode can be implemented by different character encodings. The most commonly used encodings are UTF-8, UTF-16 and the now-obsolete UCS-2. UTF-8 uses one byte for any ASCII character, all of which have the same code values in both UTF-8 and ASCII encoding, and up to four bytes for other characters. UCS-2 uses a 16-bit code unit (two 8-bit bytes) for each character but cannot encode every character in the current Unicode standard. UTF-16 extends UCS-2, using one 16-bit unit for the characters that were representable in UCS-2 and two 16-bit units (4 × 8 bits) to handle each of the additional characters.
Python process large file
Python: Fastest way to process large file
http://stackoverflow.com/questions/30294146/python-fastest-way-to-process-large-file
Question: I have multiple 3GB tab delimited files. There are 20 million rows in each file. All the rows have to be independently processed, no relation between any two rows. My question is, what will be faster A. Reading line by line using
with open() as infile:
for line in infile:
Or B. Reading the file into memory in chunks and processing it, say 250 MB at a time?
It sounds like your code is I/O bound. This means that multiprocessing isn't going to help—if you spend 90% of your time reading from disk, having an extra 7 processes waiting on the next read isn't going to help anything.
And, while using a CSV reading module (whether the stdlib's csv or something like NumPy or Pandas) may be a good idea for simplicity, it's unlikely to make much different to performance.
Still, it's worth checking that you really are I/O bound, instead of just guessing. Run your program and see whether your CPU usage is close to 0% or close to 100% or a core. Do what Amadan suggested in a comment, and run your program with just pass for the processing and see whether that cuts off 5% of the time or 70%. You may even want to try comparing with a loop over os.open and os.read(1024*1024) or something and see if that's any faster.
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Since your using Python 2.x, Python is relying on the C stdio library to guess how much to buffer at a time, so it might be worth forcing it to buffer more. The simplest way to do that is to use readlines(bufsize) for some large bufsize. (You can try different numbers and measure them to see where the peak is. In my experience, usually anything from 64K-8MB is about the same, but depending on your system that may be different—especially if you're, e.g., reading off a network filesystem with great throughput but horrible latency that swamps the throughput-vs.-latency of the actual physical drive and the caching the OS does.)
So, for example:
bufsize = 65536
with open(path) as infile:
while True:
lines = infile.readlines(bufsize)
if not lines:
break
for line in lines:
process(line)
Meanwhile, assuming you're on a 64-bit system, you may want to try using mmap instead of reading the file in the first place. This certainly isn't guaranteed to be better, but it may be better, depending on your system. For example:
with open(path) as infile:
m = mmap.mmap(infile, 0, access=mmap.ACCESS_READ)
A Python mmap is sort of a weird object—it acts like a str and like a file at the same time, so you can, e.g., manually iterate scanning for newlines, or you can call readline on it as if it were a file. Both of those will take more processing from Python than iterating the file as lines or doing batch readlines (because a loop that would be in C is now in pure Python… although maybe you can get around that with re, or with a simple Cython extension?)… but the I/O advantage of the OS knowing what you're doing with the mapping may swamp the CPU disadvantage.
Unfortunately, Python doesn't expose the madvise call that you'd use to tweak things in an attempt to optimize this in C (e.g., explicitly setting MADV_SEQUENTIAL instead of making the kernel guess, or forcing transparent huge pages)—but you can actually ctypes the function out of libc.
Excellent post: http://codereview.stackexchange.com/questions/88885/efficiently-filter-a-large-100gb-csv-file-v3
