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Python collections中的Counter

Counter是dict的子类，是一个简单的计数器

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from collections import Counter
c = Counter()
for ch in 'programming':
	c[ch] = c[ch] + 1
c #  Counter({'g': 2, 'm': 2, 'r': 2, 'a': 1, 'i': 1, 'o': 1, 'n': 1, 'p': 1})

counter有多高效？
counter为什么这么高效？

速度测评

以下计数，发现并未对速度做优化

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

from time import time
import tensorflow as tf
from collections import Counter


def _read_words(filename):
    with tf.gfile.GFile(filename, "r") as f:
        return f.read().decode("utf-8").replace("\n", "<eos>").split()


# 计数 + 排序
def _build_vocab(filename):
    data = _read_words(filename)

    start = time()
    # 利用counter，计数+排序。counter并不快
    counter = Counter(data)
    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))

    # 自己计数
    # word_cnt = count(data)
    # count_pairs = sorted(word_cnt.items(), key=lambda x: (-x[1], x[0]))

    stop = time()
    print("Stop: " + str(stop))
    print(str(stop - start) + "秒")


    words, _ = list(zip(*count_pairs))
    word_to_id = dict(zip(words, range(len(words))))
    return word_to_id


def count(data):
    word_cnt = {}
    for w in data:
        if w in word_cnt:
            word_cnt[w] += 1
        else:
            word_cnt[w] = 1
    return word_cnt


word_to_id = _build_vocab("ptb_data/ptb.train.txt")
print(word_to_id["no"])