python之路(1)_重要函数使用
2024-04-12 11:57:31
1、dataframe.apply
DataFrame.apply(func, axis=0, broadcast=False, raw=False,reduce=None, args=(), **kwds)
axis=0
默认是处理一列数据
- 1、这个func函数需要自己实现，函数的传入参数根据axis来定，
- 2、比如axis = 1，就会把一行数据作为Series的数据 结构传入给自己实现的函数中，
我们在函数中实现对Series不同属性之间的计算，返回一个结果， - 3、apply函数 会自动遍历每一行DataFrame的数据，最后将所有结果组合成一个Series数据结构并返回。
- 4、apply的返回值就是函数func函数的返回值
import numpy as np
import pandas as pdf = lambda x: x.max() - x.min()df = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['utah', 'ohio', 'texas', 'oregon'])
print(df)# 默认按列处理数据,最后列标签变为行索引
t1 = df.apply(f)
print(t1)# axis=1表示按行处理数据，返回一个series类型数据，行索引还是不变
t2 = df.apply(f, axis=1)
print(t2)print(list('bde')) b d e
utah -1.536470 -0.275776 -0.753388
ohio 1.399516 0.422876 0.709520
texas -0.368937 -0.743115 0.319832
oregon 1.437989 -1.828255 0.689356
b 2.974459
d 2.251131
e 1.462909
dtype: float64
utah 1.260694
ohio 0.976640
texas 1.062948
oregon 3.266244
dtype: float64
['b', 'd', 'e']
2、jieba.cut
def cut(self, sentence, cut_all=False, HMM=True):
jieba分词主函数，返回generator
参数:
- sentence: 待切分文本.
- cut_all: 切分模式. True 全模式, False 精确模式.
- HMM: 是否使用隐式马尔科夫.jieba.cut 和 jieba.lcut 接受 3 个参数：
- 1、需要分词的字符串（unicode 或 UTF-8 字符串、GBK 字符串）
- 2、cut_all 参数：是否使用全模式，默认值为 False
- 3、HMM 参数：用来控制是否使用 HMM 模型，默认值为 True
# 导入 jieba
import jieba
import jieba.posseg as pseg #词性标注
import jieba.analyse as anls #关键词提取# 全模式
seg_list = jieba.cut("他来到上海交通大学", cut_all=True)
print("【全模式】：" + "/ ".join(seg_list))
print(type(seg_list))# 精确模式
# 返回的是生成器
seg_list1 = jieba.cut("他来到上海交通大学", cut_all=False)
print("【精确模式】：" + "/ ".join(seg_list1))
print(type(seg_list1))# 返回的是列表
seg_list2 = jieba.lcut("他来到上海交通大学", cut_all=True)
print("【返回列表】：{0}".format(seg_list2))
print(type(seg_list2))Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\xgxg\AppData\Local\Temp\jieba.cache
Loading model cost 0.664 seconds.
Prefix dict has been built successfully.【全模式】：他/ 来到/ 上海/ 上海交通大学/ 交通/ 大学
<class 'generator'>
【精确模式】：他/ 来到/ 上海交通大学
<class 'generator'>
【返回列表】：['他', '来到', '上海', '上海交通大学', '交通', '大学']
<class 'list'>
3、collections.defaultdict
class collections.defaultdict([default_factory[, …]])defaultdict类返回一个类似于的字典对象，第一个参数给default_factory属性赋值，其它的参数都传递给dict构造器。
通俗来说就是defaultdict类的初始化函数接收一个
类型作为参数，当访问的键不存在，实例化一个值作为默认值。
from collections import defaultdictdict1 = defaultdict(int)
dict2 = defaultdict(set)
dict3 = defaultdict(str)
dict4 = defaultdict(list)
dict1[2] ='two'print(dict1[1])
print(dict2[1])
print(dict3[1])
print(dict4[1])0
set()[]
4、enumerate
4.1、enumerate介绍
enumerate(iterable, start=0)Return an enumerate object. iterable must be a sequence, an iterator, or some other object which supports iteration. The next() method of the iterator returned by enumerate() returns a tuple containing a count (from start which defaults to 0) and the values obtained from iterating over iterable.
enumerate是枚举的意思，顾名思义，enumerate()函数用来将一个可迭代序列生成一个enumerate对象，该enumerate对象的每个元素是由可迭代对象的索引编号和对应的元素组成的元组
s='hello's1 = enumerate(s)
print(s1)
print(type(s1))for i in s1:print(i)<enumerate object at 0x00000211E1BFF3F0>
<class 'enumerate'>
(0, 'h')
(1, 'e')
(2, 'l')
(3, 'l')
(4, 'o')
4.2、enumerate应用
- 当需要遍历一个对象，并为每个元素指定一个编号时，用enumerate生成元祖序列
然后解包遍历一下就实现了 - 默认的，索引编号从0开始，但是多数时候可能想要从1开始。enumerate函数提供了可选参数，用start就可以指定开始编号了
s2 = enumerate(s,start=1)for i in s2:print(i)(1, 'h')
(2, 'e')
(3, 'l')
(4, 'l')
(5, 'o')
5、torch.utils.data
torch.utils.data官方手册
torch.utils.data主要包括以下三个类:- class torch.utils.data.Dataset
- class torch.utils.data.sampler.Sampler(data_source)
- class torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None)
主要介绍class torch.utils.data.DataLoader和class torch.utils.data.Dataset
class torch.utils.data.Dataset- 按行索引进行打包张量数据，把张量数据和对应的张量标签进行打包，然后把数据送入到DataLoader
class torch.utils.data.DataLoader- 负责对Dataset打包好的数据进行分批次，便于进行batch循环和模型的训练
DataLoader源代码
class torch.utils.data.DataLoader- dataset (Dataset): 加载数据的数据集
- batch_size (int, optional): 每批加载多少个样本
- shuffle (bool, optional): 设置为“真”时,在每个epoch对数据打乱.（默认：False）
- sampler (Sampler, optional): 定义从数据集中提取样本的策略,返回一个样本
- batch_sampler (Sampler, optional): like sampler, but returns a batch of indices at a time 返回一批样本. 与atch_size, shuffle, sampler和 drop_last互斥.
- num_workers (int, optional): 用于加载数据的子进程数。0表示数据将在主进程中加载。（默认：0）
- collate_fn (callable, optional): 合并样本列表以形成一个 mini-batch. # callable可调用对象
- pin_memory (bool, optional): 如果为 True, 数据加载器会将张量复制到 CUDA 固定内存中,然后再返回它们.
- drop_last (bool, optional): 设定为 True 如果数据集大小不能被批量大小整除的时候, 将丢掉最后一个不完整的batch,(默认：False).
- timeout (numeric, optional): 如果为正值，则为从工作人员收集批次的超时值。应始终是非负的。（默认：0）
- worker_init_fn (callable, optional): If not None, this will be called on each worker subprocess with the worker id (an int in
[0, num_workers - 1]) as input, after seeding and before data loading. (default: None)．
class torch.utils.data.Dataset- 作用: (1) 创建数据集,有__getitem__(self, index)函数来根据索引序号获取样本数据和标签, 有__len__(self)函数来获取数据集的长度.
class TensorDataset(Dataset):"""Dataset wrapping data and target tensors.Each sample will be retrieved by indexing both tensors along the firstdimension.Arguments:data_tensor (Tensor): contains sample data.target_tensor (Tensor): contains sample targets (labels)."""def __init__(self, data_tensor, target_tensor):assert data_tensor.size(0) == target_tensor.size(0)self.data_tensor = data_tensorself.target_tensor = target_tensordef __getitem__(self, index):return self.data_tensor[index], self.target_tensor[index]def __len__(self):return self.data_tensor.size(0)from torch.utils.data import TensorDataset
import torch
from torch.utils.data import DataLoadera = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = torch.tensor([44, 55, 66, 44, 55, 66, 44, 55, 66, 44, 55, 66])# 按行索引进行打包张量数据，把数据和对应的标签进行打包，然后把数据送入到DataLoader
train_ids = TensorDataset(a, b)
print(type(train_ids))
# 切片输出，train_ids切片得到的是元组，可以同时取出两列元素
print(train_ids[0:2])
print('=' * 80)# 循环取数据
for x_train, y_label in train_ids:print(x_train, y_label)<class 'torch.utils.data.dataset.TensorDataset'>
(tensor([[1, 2, 3],[4, 5, 6]]), tensor([44, 55]))
================================================================================
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
# DataLoader进行数据封装
# 经过DataLoader之后,返回一个可迭代对象，进而可以用for遍历，批量载入数据
train_loader = DataLoader(dataset=train_ids, batch_size=4, shuffle=True)print("train_loader is:",train_loader)
print(type(train_loader))
print('=' * 80)# enumerate会返回一个元组,data是一个列表，由2列元素，第1列是数据，第2列是数据所对应的标签
for i, data in enumerate(train_loader, 1): # 注意enumerate返回值有两个,一个是batch批次的序号，一个是数据（包含训练数据和标签）print("data is:",data)x_data, label = dataprint(' batch:{0} x_data:{1} label: {2}'.format(i, x_data, label))train_loader is: <torch.utils.data.dataloader.DataLoader object at 0x00000211E5B19F28>
<class 'torch.utils.data.dataloader.DataLoader'>
================================================================================
data is: [tensor([[1, 2, 3],[7, 8, 9],[1, 2, 3],[4, 5, 6]]), tensor([44, 66, 44, 55])]batch:1 x_data:tensor([[1, 2, 3],[7, 8, 9],[1, 2, 3],[4, 5, 6]]) label: tensor([44, 66, 44, 55])
data is: [tensor([[7, 8, 9],[4, 5, 6],[7, 8, 9],[4, 5, 6]]), tensor([66, 55, 66, 55])]batch:2 x_data:tensor([[7, 8, 9],[4, 5, 6],[7, 8, 9],[4, 5, 6]]) label: tensor([66, 55, 66, 55])
data is: [tensor([[7, 8, 9],[1, 2, 3],[4, 5, 6],[1, 2, 3]]), tensor([66, 44, 55, 44])]batch:3 x_data:tensor([[7, 8, 9],[1, 2, 3],[4, 5, 6],[1, 2, 3]]) label: tensor([66, 44, 55, 44])
a1 = [1,2]
b1,b2 = a1
print(b1)
print(b2)1
2
6、sorted
6.1、sorted介绍
- Both list.sort() and sorted() have a key parameter to specify a function (or other callable) to be called on each list element prior to making comparisons.
- list.sort（）和sorted（）都有一个关键参数，用于指定在进行比较之前在每个列表元素上要调用的函数（或其他可调用函数）
在比较之前，对于sorted中的key参数的使用，就是先将列表的每个元素单独取出来并且作用于指定的key函数操作，然后把操作后的结果进行比较
y1 = "This is a test string from Andrew".split()
print(y1)# key=str.lower,先对列表中的每一个字符串变为小写，然后再按首字母进行升序排列
x1 = sorted(y1, key=str.lower)
print(x1)['This', 'is', 'a', 'test', 'string', 'from', 'Andrew']
['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
a = [2, 3, 5, 4, 22, 45, 99, 16]
# 先对列表的每个元素进行计算，然后比较结果大小，按升序排列
b = sorted(a, key=lambda x: x*x-8*x+16)
print(b)
#结果：[4, 3, 5, 2, 16, 22, 45, 99]，这里的函数在x=4时取最小值。students = [('john', 'A', 15), ('jane', 'B', 12),('dave', 'B', 10)]
print(students[2][1])[4, 3, 5, 2, 16, 22, 45, 99]
B
6.2、sorted应用
sorted(list1, key=lambda x: x[1], reverse=False)- 对于key=lambda x: x[1]的理解，将list1中每个元素单独取出来，比如(‘the’, 136)这个元素，把这个元素（元组）当成x，使用x[1]就取出了136
- 依次类推，对每个元素都这样操作，将所得到的结果进行排序
list1 = [('the', 136), ('and', 111), ('of', 82), ('to', 71), ('our', 68), ('we', 59), ('that', 49), ('a', 46), ('is', 36), ('in', 26), ('this', 24), ('for', 23), ('are', 22), ('but', 20)]
print(list1)
print('=' * 80)word_sort = sorted(list1, key=lambda x: x[1], reverse=False) # 根据词频降序排序
print(word_sort)[('the', 136), ('and', 111), ('of', 82), ('to', 71), ('our', 68), ('we', 59), ('that', 49), ('a', 46), ('is', 36), ('in', 26), ('this', 24), ('for', 23), ('are', 22), ('but', 20)]
================================================================================
[('but', 20), ('are', 22), ('for', 23), ('this', 24), ('in', 26), ('is', 36), ('a', 46), ('that', 49), ('we', 59), ('our', 68), ('to', 71), ('of', 82), ('and', 111), ('the', 136)]
7、词频统计实战
import time
from collections import defaultdict, Counter
from jieba import analyse
from functools import wraps
import pandas as pd
from nltk import FreqDisttext = """My fellow citizens: I stand here today humbled by the task before us, grateful for the trust you've bestowed, mindful of the sacrifices borne by our ancestors.
I thank President Bush for his service to our nation -- (applause) -- as well as the generosity and cooperation he has shown throughout this transition.
Forty-four Americans have now taken the presidential oath. The words have been spoken during rising tides of prosperity and the still waters of peace. Yet, every so often, the oath is taken amidst gathering clouds and raging storms. At these moments, America has carried on not simply because of the skill or vision of those in high office, but because we, the people, have remained faithful to the ideals of our forebears and true to our founding documents.
So it has been; so it must be with this generation of Americans.
That we are in the midst of crisis is now well+++ understood. Our nation is at war against a far-reaching network of violence and hatred. Our economy is badly weakened, a consequence of greed and irresponsibility on the part of some, but also our collective failure to make hard choices and prepare the nation for a new age. Homes have been lost, jobs shed, businesses shuttered. Our health care is too costly, our schools fail too many -- and each day brings further evidence that the ways we use energy strengthen our adversaries and threaten our planet.
These are the indicators of crisis, subject to data and statistics. Less measurable, but no less profound, is a sapping of confidence across our land; a nagging fear that America's decline is inevitable, that the next generation must lower its sights.
Today I say to you that the challenges we face are real. They are serious and they are many. They will not be met easily or in a short span of time. But know this America: They will be met. (Applause.)
On this day, we gather because we have chosen hope over fear, unity of purpose over conflict and discord. On this day, we come to proclaim an end to the petty grievances and false promises, the recriminations and worn-out dogmas that for far too long have strangled our politics. We remain a young nation. But in the words of Scripture, the time has come to set aside childish things. The time has come to reaffirm our enduring spirit; to choose our better history; to carry forward that precious gift, that noble idea passed on from generation to generation: the God-given promise that all are equal, all are free, and all deserve a chance to pursue their full measure of happiness. (Applause.)
In reaffirming the greatness of our nation we understand that greatness is never a given. It must be earned. Our journey has never been one of short-cuts or settling for less. It has not been the path for the faint-hearted, for those that prefer leisure over work, or seek only the pleasures of riches and fame. Rather, it has been the risk-takers, the doers, the makers of things -- some celebrated, but more often men and women obscure in their labor -- who have carried us up the long rugged path towards prosperity and freedom.
For us, they packed up their few worldly possessions and traveled across oceans in search of a new life. For us, they toiled in sweatshops, and settled the West, endured the lash of the whip, and plowed the hard earth. For us, they fought and died in places like Concord and Gettysburg, Normandy and Khe Sahn.
Time and again these men and women struggled and sacrificed and worked till their hands were raw so that we might live a better life. They saw America as bigger than the sum of our individual ambitions, greater than all the differences of birth or wealth or faction.
This is the journey we continue today. We remain the most prosperous, powerful nation on Earth. Our workers are no less productive than when this crisis began. Our minds are no less inventive, our goods and services no less needed than they were last week, or last month, or last year. Our capacity remains undiminished. But our time of standing pat, of protecting narrow interests and putting off unpleasant decisions -- that time has surely passed. Starting today, we must pick ourselves up, dust ourselves off, and begin again the work of remaking America. (Applause.)
For everywhere we look, there is work to be done. The state of our economy calls for action, bold and swift. And we will act, not only to create new jobs, but to lay a new foundation for growth. We will build the roads and bridges, the electric grids and digital lines that feed our commerce and bind us together. We'll restore science to its rightful place, and wield technology's wonders to raise health care's quality and lower its cost. We will harness the sun and the winds and the soil to fuel our cars and run our factories. And we will transform our schools and colleges and universities to meet the demands of a new age. All this we can do. All this we will do.
Now, there are some who question the scale of our ambitions, who suggest that our system cannot tolerate too many big plans. Their memories are short, for they have forgotten what this country has already done, what free men and women can achieve when imagination is joined to common purpose, and necessity to courage. What the cynics fail to understand is that the ground has shifted beneath them, that the stale political arguments that have consumed us for so long no longer apply.
The question we ask today is not whether our government is too big or too small, but whether it works -- whether it helps families find jobs at a decent wage, care they can afford, a retirement that is dignified. Where the answer is yes, we intend to move forward. Where the answer is no, programs will end. And those of us who manage the public's dollars will be held to account, to spend wisely, reform bad habits, and do our business in the light of day, because only then can we restore the vital trust between a people and their government.
Nor is the question before us whether the market is a force for good or ill. Its power to generate wealth and expand freedom is unmatched. But this crisis has reminded us that without a watchful eye, the market can spin out of control. The nation cannot prosper long when it favors only the prosperous. The success of our economy has always depended not just on the size of our gross domestic product, but on the reach of our prosperity, on the ability to extend opportunity to every willing heart -- not out of charity, but because it is the surest route to our common good. (Applause.)
As for our common defense, we reject as false the choice between our safety and our ideals. Our Founding Fathers -- (applause) -- our Founding Fathers, faced with perils that we can scarcely imagine, drafted a charter to assure the rule of law and the rights of man -- a charter expanded by the blood of generations. Those ideals still light the world, and we will not give them up for expedience sake. (Applause.)
And so, to all the other peoples and governments who are watching today, from the grandest capitals to the small village where my father was born, know that America is a friend of each nation, and every man, woman and child who seeks a future of peace and dignity. And we are ready to lead once more. (Applause.)
Recall that earlier generations faced down fascism and communism not just with missiles and tanks, but with the sturdy alliances and enduring convictions. They understood that our power alone cannot protect us, nor does it entitle us to do as we please. Instead they knew that our power grows through its prudent use; our security emanates from the justness of our cause, the force of our example, the tempering qualities of humility and restraint.
We are the keepers of this legacy. Guided by these principles once more we can meet those new threats that demand even greater effort, even greater cooperation and understanding between nations. We will begin to responsibly leave Iraq to its people and forge a hard-earned peace in Afghanistan. With old friends and former foes, we'll work tirelessly to lessen the nuclear threat, and roll back the specter of a warming planet.
We will not apologize for our way of life, nor will we waver in its defense. And for those who seek to advance their aims by inducing terror and slaughtering innocents, we say to you now that our spirit is stronger and cannot be broken -- you cannot outlast us, and we will defeat you. (Applause.)
For we know that our patchwork heritage is a strength, not a weakness. We are a nation of Christians and Muslims, Jews and Hindus, and non-believers. We are shaped by every language and culture, drawn from every end of this Earth; and because we have tasted the bitter swill of civil war and segregation, and emerged from that dark chapter stronger and more united, we cannot help but believe that the old hatreds shall someday pass; that the lines of tribe shall soon dissolve; that as the world grows smaller, our common humanity shall reveal itself; and that America must play its role in ushering in a new era of peace.
To the Muslim world, we seek a new way forward, based on mutual interest and mutual respect. To those leaders around the globe who seek to sow conflict, or blame their society's ills on the West, know that your people will judge you on what you can build, not what you destroy. (Applause.)
To those who cling to power through corruption and deceit and the silencing of dissent, know that you are on the wrong side of history, but that we will extend a hand if you are willing to unclench your fist. (Applause.)
To the people of poor nations, we pledge to work alongside you to make your farms flourish and let clean waters flow; to nourish starved bodies and feed hungry minds. And to those nations like ours that enjoy relative plenty, we say we can no longer afford indifference to the suffering outside our borders, nor can we consume the world's resources without regard to effect. For the world has changed, and we must change with it.
As we consider the role that unfolds before us, we remember with humble gratitude those brave Americans who at this very hour patrol far-off deserts and distant mountains. They have something to tell us, just as the fallen heroes who lie in Arlington whisper through the ages.
We honor them not only because they are the guardians of our liberty, but because they embody the spirit of service -- a willingness to find meaning in something greater than themselves.
And yet at this moment, a moment that will define a generation, it is precisely this spirit that must inhabit us all. For as much as government can do, and must do, it is ultimately the faith and determination of the American people upon which this nation relies. It is the kindness to take in a stranger when the levees break, the selflessness of workers who would rather cut their hours than see a friend lose their job which sees us through our darkest hours. It is the firefighter's courage to storm a stairway filled with smoke, but also a parent's willingness to nurture a child that finally decides our fate.
Our challenges may be new. The instruments with which we meet them may be new. But those values upon which our success depends -- honesty and hard work, courage and fair play, tolerance and curiosity, loyalty and patriotism -- these things are old. These things are true. They have been the quiet force of progress throughout our history.
What is demanded, then, is a return to these truths. What is required of us now is a new era of responsibility -- a recognition on the part of every American that we have duties to ourselves, our nation and the world; duties that we do not grudgingly accept, but rather seize gladly, firm in the knowledge that there is nothing so satisfying to the spirit, so defining of our character than giving our all to a difficult task.
This is the price and the promise of citizenship. This is the source of our confidence -- the knowledge that God calls on us to shape an uncertain destiny. This is the meaning of our liberty and our creed, why men and women and children of every race and every faith can join in celebration across this magnificent mall; and why a man whose father less than 60 years ago might not have been served in a local restaurant can now stand before you to take a most sacred oath. (Applause.)
So let us mark this day with remembrance of who we are and how far we have traveled. In the year of America's birth, in the coldest of months, a small band of patriots huddled by dying campfires on the shores of an icy river. The capital was abandoned. The enemy was advancing. The snow was stained with blood. At the moment when the outcome of our revolution was most in doubt, the father of our nation ordered these words to be read to the people:
"Let it be told to the future world...that in the depth of winter, when nothing but hope and virtue could survive... that the city and the country, alarmed at one common danger, came forth to meet [it]."
America: In the face of our common dangers, in this winter of our hardship, let us remember these timeless words. With hope and virtue, let us brave once more the icy currents, and endure what storms may come. Let it be said by our children's children that when we were tested we refused to let this journey end, that we did not turn back nor did we falter; and with eyes fixed on the horizon and God's grace upon us, we carried forth that great gift of freedom and delivered it safely to future generations.
Thank you. God bless you. And God bless the United States of America. (Applause.)"""def timeit(func):@wraps(func)def wrap(*args, **kwargs):start = time.time()result = func(*args, **kwargs)print(func.__name__, time.time() - start)return resultreturn wrap@timeit
def func_dict(word_list):"""方法一：使用字典:param word_list::return:"""count_dict = {}for item in word_list:count_dict[item] = count_dict[item] + 1 if item in count_dict else 1return sorted(count_dict.items(), key=lambda x: x[1], reverse=True)@timeit
def func_defaultdict(word_list):"""方式二：使用defaultdict:param word_list::return:"""count_dict = defaultdict(lambda: 0)for item in word_list:count_dict[item] += 1return sorted(count_dict.items(), key=lambda x: x[1], reverse=True)@timeit
def func_counter(word_list):"""方法三：使用Counter:param word_list::return:"""count_result = Counter(word_list)# print(count_result) # 一个字典对象# print(count_result.keys()) # 一个字典key值# print(count_result.values()) # 一个字典value值# print(list(count_result.elements())) # 返回的是 word_list# print(count_result.most_common(3))return count_result@timeit
def func_pd(words):"""方式四：使用pandas的value_counts:param words::return:"""count_result = pd.Series(words).value_counts()return count_result.to_dict()@timeit
def func_nltk(wordlist, fmt='dict'):"""方式五：使用nltk的FreqDist:param wordlist::return:"""count_result = FreqDist(samples=wordlist)return dict(count_result.most_common()) if fmt == 'dict' else count_result.most_common()def func_jieba():"""jieba分词统计词语重要性:return:"""with open("origin_data/weibo.txt", 'r') as f:texts = f.read() # 读取整个文件作为一个字符串result = analyse.textrank(texts, withWeight=True) # textrank算法排序# result = analyse.extract_tags(sentence=texts, withWeight=True) # tfidf考虑权重print(result)def gen_data():return text.lower().split()if __name__ == '__main__':word_list = gen_data()result = func_dict(word_list)result1 = func_defaultdict(word_list)result2 = func_counter(word_list)result3 = func_pd(word_list)result4 = func_nltk(word_list)print(result)print(result1)print(result2)print(result3)print(result4)# func_jieba()
func_dict 0.0009968280792236328
func_defaultdict 0.0
func_counter 0.000997781753540039
func_pd 0.0049860477447509766
func_nltk 0.0019941329956054688
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'city': 1, 'country,': 1, 'alarmed': 1, 'danger,': 1, 'came': 1, '[it]."': 1, 'dangers,': 1, 'winter': 1, 'hardship,': 1, 'timeless': 1, 'words.': 1, 'virtue,': 1, 'currents,': 1, 'endure': 1, 'storms': 1, 'come.': 1, 'said': 1, "children's": 1, 'tested': 1, 'refused': 1, 'end,': 1, 'turn': 1, 'falter;': 1, 'eyes': 1, 'fixed': 1, 'horizon': 1, "god's": 1, 'grace': 1, 'great': 1, 'gift': 1, 'delivered': 1, 'safely': 1, 'united': 1, 'states': 1}
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