{"id":1165281,"date":"2025-01-15T15:18:28","date_gmt":"2025-01-15T07:18:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1165281.html"},"modified":"2025-01-15T15:18:30","modified_gmt":"2025-01-15T07:18:30","slug":"python%e5%a6%82%e4%bd%95%e6%a0%b9%e6%8d%ae%e9%a2%91%e7%8e%87%e5%8f%8d%e6%98%a0%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1165281.html","title":{"rendered":"python\u5982\u4f55\u6839\u636e\u9891\u7387\u53cd\u6620\u503c"},"content":{"rendered":"

\"python\u5982\u4f55\u6839\u636e\u9891\u7387\u53cd\u6620\u503c\"<\/p>\n

Python\u6839\u636e\u9891\u7387\u53cd\u6620\u503c\u7684\u65b9\u6cd5\u6709\u5f88\u591a<\/strong>\uff0c\u5305\u62ec\u4f7f\u7528Counter\u6a21\u5757\u3001\u5229\u7528Pandas\u5e93\u3001\u4ee5\u53ca\u4f7f\u7528Numpy\u7b49\u5de5\u5177\u3002<\/strong>\u5728\u672c\u6587\u4e2d\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff0c\u5177\u4f53\u662f\u901a\u8fc7\u4f7f\u7528Counter\u6a21\u5757\u6765\u5b9e\u73b0\u7684\u3002<\/p>\n<\/p>\n

\u4e00\u3001Counter\u6a21\u5757<\/h3>\n<\/p>\n

Counter\u662fPython\u5185\u7f6ecollections\u6a21\u5757\u4e2d\u7684\u4e00\u4e2a\u7c7b\uff0c\u7528\u4e8e\u7edf\u8ba1\u53ef\u54c8\u5e0c\u5bf9\u8c61\u7684\u9891\u7387\u3002\u5b83\u975e\u5e38\u9002\u5408\u7528\u4e8e\u8ba1\u7b97\u5143\u7d20\u51fa\u73b0\u7684\u6b21\u6570\uff0c\u5e76\u4e14\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u8f6c\u6362\u6210\u5b57\u5178\u683c\u5f0f\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

from collections import Counter<\/p>\n
data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']<\/p>\n
counter = Counter(data)<\/p>\n
print(counter)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cCounter\u4f1a\u7edf\u8ba1\u51fa\u6bcf\u4e2a\u6c34\u679c\u51fa\u73b0\u7684\u6b21\u6570\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u5b57\u5178\uff0c\u952e\u4e3a\u5143\u7d20\uff0c\u503c\u4e3a\u9891\u7387\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u64cd\u4f5c<\/h4>\n<\/p>\n
Counter\u5bf9\u8c61\u652f\u6301\u8bb8\u591a\u6709\u7528\u7684\u65b9\u6cd5\u548c\u64cd\u4f5c\uff0c\u6bd4\u5982\uff1a<\/p>\n<\/p>\n
# \u83b7\u53d6\u51fa\u73b0\u9891\u7387\u6700\u9ad8\u7684\u5143\u7d20<\/p>\n
most_common_element = counter.most_common(1)<\/p>\n
print(most_common_element)<\/p>\n
\u5c06Counter\u5bf9\u8c61\u8f6c\u6362\u4e3a\u666e\u901a\u5b57\u5178<\/strong><\/h2>\n
counter_dict = dict(counter)<\/p>\n
print(counter_dict)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9ad8\u7ea7\u7528\u6cd5<\/h4>\n<\/p>\n
Counter\u6a21\u5757\u8fd8\u652f\u6301\u4e00\u4e9b\u9ad8\u7ea7\u7528\u6cd5\uff0c\u6bd4\u5982\u4e0e\u5176\u4ed6Counter\u5bf9\u8c61\u7684\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
counter1 = Counter(['apple', 'banana', 'apple'])<\/p>\n
counter2 = Counter(['banana', 'orange'])<\/p>\n
\u5408\u5e76\u4e24\u4e2aCounter\u5bf9\u8c61<\/strong><\/h2>\n
combined_counter = counter1 + counter2<\/p>\n
print(combined_counter)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5b83\u7684value_counts<\/code>\u65b9\u6cd5\u6765\u7edf\u8ba1\u9891\u7387\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']<\/p>\n
series = pd.Series(data)<\/p>\n
frequency = series.value_counts()<\/p>\n
print(frequency)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1\u3001\u6570\u636e\u5904\u7406<\/h4>\n<\/p>\n
Pandas\u4e0d\u4ec5\u53ef\u4ee5\u7edf\u8ba1\u9891\u7387\uff0c\u8fd8\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5404\u79cd\u5904\u7406\u548c\u5206\u6790\uff0c\u6bd4\u5982\u6392\u5e8f\u3001\u8fc7\u6ee4\u7b49\u3002<\/p>\n<\/p>\n
# \u6309\u9891\u7387\u6392\u5e8f<\/p>\n
sorted_frequency = frequency.sort_values(ascending=False)<\/p>\n
print(sorted_frequency)<\/p>\n
\u8fc7\u6ee4\u9891\u7387\u5927\u4e8e1\u7684\u5143\u7d20<\/strong><\/h2>\n
filtered_frequency = frequency[frequency > 1]<\/p>\n
print(filtered_frequency)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u56fe<\/h4>\n<\/p>\n
Pandas\u8fd8\u652f\u6301\u4e0e\u5176\u4ed6\u53ef\u89c6\u5316\u5e93\uff08\u5982Matplotlib\uff09\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u7ed8\u5236\u9891\u7387\u5206\u5e03\u56fe\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
frequency.plot(kind='bar')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Numpy\u5e93<\/h3>\n<\/p>\n
Numpy\u662f\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u65b9\u6cd5\u3002\u867d\u7136Numpy\u672c\u8eab\u4e0d\u76f4\u63a5\u63d0\u4f9b\u9891\u7387\u7edf\u8ba1\u7684\u65b9\u6cd5\uff0c\u4f46\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408\u5176\u4ed6\u6a21\u5757\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array(['apple', 'banana', 'apple', 'orange', 'banana', 'apple'])<\/p>\n
unique, counts = np.unique(data, return_counts=True)<\/p>\n
frequency = dict(zip(unique, counts))<\/p>\n
print(frequency)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1\u3001\u6570\u7ec4\u64cd\u4f5c<\/h4>\n<\/p>\n
Numpy\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u5176\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\uff0c\u8fd9\u4f7f\u5f97\u5b83\u975e\u5e38\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u83b7\u53d6\u51fa\u73b0\u9891\u7387\u6700\u9ad8\u7684\u5143\u7d20<\/p>\n
max_freq_element = unique[np.argmax(counts)]<\/p>\n
print(max_freq_element)<\/p>\n
\u5c06\u9891\u7387\u8f6c\u6362\u4e3a\u767e\u5206\u6bd4<\/strong><\/h2>\n
percentages = counts \/ counts.sum() * 100<\/p>\n
print(dict(zip(unique, percentages)))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4e0e\u5176\u4ed6\u5e93\u7ed3\u5408<\/h4>\n<\/p>\n
Numpy\u53ef\u4ee5\u4e0e\u5176\u4ed6\u6570\u636e\u5206\u6790\u5e93\uff08\u5982Pandas\uff09\u7ed3\u5408\u4f7f\u7528\uff0c\u63d0\u4f9b\u66f4\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u5c06Numpy\u6570\u7ec4\u8f6c\u6362\u4e3aPandas Series<\/strong><\/h2>\n
series = pd.Series(counts, index=unique)<\/p>\n
print(series)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
\u6839\u636e\u9891\u7387\u53cd\u6620\u503c\u7684\u65b9\u6cd5\u5728\u8bb8\u591a\u5b9e\u9645\u5e94\u7528\u4e2d\u90fd\u975e\u5e38\u6709\u7528\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
1\u3001\u6587\u672c\u5206\u6790<\/h4>\n<\/p>\n
\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u4e2d\uff0c\u8bcd\u9891\u7edf\u8ba1\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u4efb\u52a1\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e0a\u8ff0\u65b9\u6cd5\u6765\u7edf\u8ba1\u6587\u672c\u4e2d\u5404\u4e2a\u8bcd\u8bed\u7684\u51fa\u73b0\u9891\u7387\uff0c\u4ece\u800c\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u3002<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
text = "this is a simple text with simple words"<\/p>\n
words = text.split()<\/p>\n
word_freq = Counter(words)<\/p>\n
print(word_freq)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u9884\u5904\u7406\u4e2d\uff0c\u9891\u7387\u7edf\u8ba1\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u548c\u5904\u7406\u5f02\u5e38\u503c\u3002\u4f8b\u5982\uff0c\u7edf\u8ba1\u67d0\u5217\u4e2d\u5404\u4e2a\u503c\u7684\u9891\u7387\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53d1\u73b0\u5e76\u5904\u7406\u5f02\u5e38\u6570\u636e\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'column1': ['a', 'b', 'a', 'c', 'b', 'a']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
frequency = df['column1'].value_counts()<\/p>\n
print(frequency)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u63a8\u8350\u7cfb\u7edf<\/h4>\n<\/p>\n
\u5728\u63a8\u8350\u7cfb\u7edf\u4e2d\uff0c\u9891\u7387\u7edf\u8ba1\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u7269\u54c1\u7684\u6d41\u884c\u5ea6\uff0c\u4ece\u800c\u4e3a\u7528\u6237\u63a8\u8350\u70ed\u95e8\u7269\u54c1\u3002<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
user_interactions = [<\/p>\n
    ['item1', 'item2', 'item1'],<\/p>\n
    ['item2', 'item3'],<\/p>\n
    ['item1', 'item3', 'item2']<\/p>\n
]<\/p>\n
flat_list = [item for sublist in user_interactions for item in sublist]<\/p>\n
item_freq = Counter(flat_list)<\/p>\n
print(item_freq)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f18\u5316\u4e0e\u6027\u80fd<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u6027\u80fd\u5f80\u5f80\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u8003\u8651\u56e0\u7d20\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e9b\u4f18\u5316\u6280\u5de7\u6765\u63d0\u9ad8\u9891\u7387\u7edf\u8ba1\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528\u751f\u6210\u5668<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u91cf\u8f83\u5927\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u751f\u6210\u5668\u6765\u8282\u7701\u5185\u5b58\u3002<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
def data_generator():<\/p>\n
    for i in range(1000000):<\/p>\n
        yield 'item' + str(i % 10)<\/p>\n
counter = Counter(data_generator())<\/p>\n
print(counter)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u591a\u7ebf\u7a0b\u4e0e\u591a\u8fdb\u7a0b<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u8ba1\u7b97\u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u6765\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
from multiprocessing import Pool<\/p>\n
def count_chunk(chunk):<\/p>\n
    return Counter(chunk)<\/p>\n
data = ['item' + str(i % 10) for i in range(1000000)]<\/p>\n
chunks = [data[i:i + 100000] for i in range(0, len(data), 100000)]<\/p>\n
with Pool(4) as p:<\/p>\n
    counters = p.map(count_chunk, chunks)<\/p>\n
total_counter = sum(counters, Counter())<\/p>\n
print(total_counter)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Counter\u6a21\u5757\u3001Pandas\u5e93\u3001\u4ee5\u53caNumpy\u7b49\u5de5\u5177\uff0c\u6211\u4eec\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u6839\u636e\u9891\u7387\u53cd\u6620\u503c\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u52a3\uff0c\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u5e76\u7ed3\u5408\u4f18\u5316\u6280\u5de7\u6765\u63d0\u9ad8\u6027\u80fd\u3002\u65e0\u8bba\u662f\u6587\u672c\u5206\u6790\u3001\u6570\u636e\u6e05\u6d17\u8fd8\u662f\u63a8\u8350\u7cfb\u7edf\uff0c\u9891\u7387\u7edf\u8ba1\u90fd\u662f\u4e00\u4e2a\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\u3002<\/strong><\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 1. \u5982\u4f55\u901a\u8fc7Python\u5b9e\u73b0\u9891\u7387\u4e0e\u503c\u7684\u6620\u5c04\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5b57\u5178\uff08dict\uff09\u6765\u5b9e\u73b0\u9891\u7387\u4e0e\u503c\u7684\u6620\u5c04\u3002\u9996\u5148\uff0c\u901a\u8fc7\u6570\u636e\u96c6\u5408\uff08\u5982\u5217\u8868\uff09\u7edf\u8ba1\u5404\u4e2a\u503c\u7684\u51fa\u73b0\u9891\u7387\uff0c\u7136\u540e\u5c06\u9891\u7387\u4f5c\u4e3a\u952e\uff0c\u503c\u4f5c\u4e3a\u503c\u5b58\u50a8\u5728\u5b57\u5178\u4e2d\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528collections.Counter<\/code>\u7c7b\u5feb\u901f\u7edf\u8ba1\u9891\u7387\uff0c\u4e4b\u540e\u5c06\u7ed3\u679c\u6574\u7406\u6210\u5b57\u5178\u5f62\u5f0f\u3002<\/p>\n
2. \u4f7f\u7528Python\u8fdb\u884c\u9891\u7387\u5206\u6790\u7684\u5e38\u7528\u5e93\u6709\u54ea\u4e9b\uff1f<\/strong>
\u8fdb\u884c\u9891\u7387\u5206\u6790\u65f6\uff0cpandas<\/code>\u548cnumpy<\/code>\u662f\u4e24\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u5e93\u3002pandas<\/code>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u9891\u7387\u7edf\u8ba1\u548c\u6570\u636e\u53ef\u89c6\u5316\uff1bnumpy<\/code>\u5219\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u7ec4\uff0c\u5e76\u63d0\u4f9b\u591a\u79cd\u6570\u5b66\u51fd\u6570\u6765\u8fdb\u884c\u9891\u7387\u8ba1\u7b97\u3002\u6b64\u5916\uff0ccollections<\/code>\u6a21\u5757\u4e2d\u7684Counter<\/code>\u7c7b\u4e5f\u975e\u5e38\u9002\u5408\u4e8e\u5feb\u901f\u7edf\u8ba1\u9891\u7387\u3002<\/p>\n
3. \u5982\u4f55\u53ef\u89c6\u5316\u9891\u7387\u6570\u636e\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u5176\u5206\u5e03\uff1f<\/strong>
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