{"id":1176241,"date":"2025-01-15T17:42:30","date_gmt":"2025-01-15T09:42:30","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1176241.html"},"modified":"2025-01-15T17:42:33","modified_gmt":"2025-01-15T09:42:33","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%88%86%e6%9e%90%e4%b8%8b%e5%bd%a9%e7%a5%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1176241.html","title":{"rendered":"\u5982\u4f55\u7528python\u5206\u6790\u4e0b\u5f69\u7968"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u5206\u6790\u4e0b\u5f69\u7968\"<\/p>\n

\u4f7f\u7528Python\u5206\u6790\u5f69\u7968\u7684\u5173\u952e\u6b65\u9aa4\u5305\u62ec\uff1a\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u8d8b\u52bf\u5206\u6790\u548c\u9884\u6d4b\u6a21\u578b\u6784\u5efa\u3002<\/strong> \u6570\u636e\u6536\u96c6\u662f\u5f69\u7968\u5206\u6790\u7684\u7b2c\u4e00\u6b65\uff0c\u6570\u636e\u6e05\u6d17\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\uff0c\u6570\u636e\u53ef\u89c6\u5316\u5e2e\u52a9\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u8d8b\u52bf\uff0c\u8d8b\u52bf\u5206\u6790\u63ed\u793a\u5386\u53f2\u6570\u636e\u4e2d\u7684\u89c4\u5f8b\uff0c\u9884\u6d4b\u6a21\u578b\u5229\u7528\u7edf\u8ba1\u548c\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u5bf9\u672a\u6765\u7684\u5f00\u5956\u8fdb\u884c\u9884\u6d4b\u3002\u4e0b\u9762\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u7684\u6570\u636e\u6536\u96c6\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u5f69\u7968\u6570\u636e\u7684\u6536\u96c6\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u4ece\u5f69\u7968\u5b98\u65b9\u7f51\u7ad9\u6216\u7b2c\u4e09\u65b9\u6570\u636e\u63d0\u4f9b\u5546\u83b7\u53d6\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684requests\u5e93\u548cBeautifulSoup\u5e93\u6765\u6293\u53d6\u7f51\u9875\u4e0a\u7684\u5f69\u7968\u6570\u636e\uff0c\u6216\u8005\u4f7f\u7528API\u63a5\u53e3\u76f4\u63a5\u83b7\u53d6\u7ed3\u6784\u5316\u7684\u6570\u636e\u3002\u786e\u4fdd\u83b7\u53d6\u7684\u6570\u636e\u5177\u6709\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u662f\u975e\u5e38\u91cd\u8981\u7684\uff0c\u56e0\u4e3a\u9519\u8bef\u6216\u4e0d\u5b8c\u6574\u7684\u6570\u636e\u4f1a\u5f71\u54cd\u540e\u7eed\u7684\u5206\u6790\u7ed3\u679c\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u6570\u636e\u6536\u96c6<\/h3>\n<\/p>\n

1\u3001\u4ece\u7f51\u7ad9\u6293\u53d6\u6570\u636e<\/h4>\n<\/p>\n

\u4f7f\u7528Python\u7684requests\u5e93\u548cBeautifulSoup\u5e93\u53ef\u4ee5\u6293\u53d6\u7f51\u9875\u4e0a\u7684\u5f69\u7968\u6570\u636e\u3002\u9996\u5148\uff0c\u53d1\u9001HTTP\u8bf7\u6c42\u83b7\u53d6\u7f51\u9875\u5185\u5bb9\uff0c\u7136\u540e\u4f7f\u7528BeautifulSoup\u89e3\u6790HTML\u6587\u6863\uff0c\u63d0\u53d6\u6240\u9700\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n

import requests<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
\u53d1\u9001HTTP\u8bf7\u6c42\u83b7\u53d6\u7f51\u9875\u5185\u5bb9<\/strong><\/h2>\n
url = 'https:\/\/example.com\/lottery'<\/p>\n
response = requests.get(url)<\/p>\n
html_content = response.content<\/p>\n
\u4f7f\u7528BeautifulSoup\u89e3\u6790HTML\u6587\u6863<\/strong><\/h2>\n
soup = BeautifulSoup(html_content, 'html.parser')<\/p>\n
\u63d0\u53d6\u5f69\u7968\u6570\u636e<\/strong><\/h2>\n
lottery_data = []<\/p>\n
for row in soup.find_all('tr'):<\/p>\n
    cols = row.find_all('td')<\/p>\n
    if len(cols) > 0:<\/p>\n
        draw_date = cols[0].text<\/p>\n
        numbers = [int(col.text) for col in cols[1:]]<\/p>\n
        lottery_data.append((draw_date, numbers))<\/p>\n
print(lottery_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528API\u63a5\u53e3\u83b7\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u8bb8\u591a\u5f69\u7968\u6570\u636e\u63d0\u4f9b\u5546\u63d0\u4f9bAPI\u63a5\u53e3\uff0c\u53ef\u4ee5\u76f4\u63a5\u83b7\u53d6\u7ed3\u6784\u5316\u7684\u6570\u636e\u3002\u4f7f\u7528requests\u5e93\u53d1\u9001HTTP\u8bf7\u6c42\uff0c\u83b7\u53d6JSON\u683c\u5f0f\u7684\u6570\u636e\uff0c\u7136\u540e\u89e3\u6790\u5e76\u5b58\u50a8\u3002<\/p>\n<\/p>\n
import requests<\/p>\n
\u53d1\u9001HTTP\u8bf7\u6c42\u83b7\u53d6\u5f69\u7968\u6570\u636e<\/strong><\/h2>\n
api_url = 'https:\/\/api.example.com\/lottery'<\/p>\n
response = requests.get(api_url)<\/p>\n
data = response.json()<\/p>\n
\u89e3\u6790\u5e76\u5b58\u50a8\u5f69\u7968\u6570\u636e<\/strong><\/h2>\n
lottery_data = []<\/p>\n
for item in data['results']:<\/p>\n
    draw_date = item['date']<\/p>\n
    numbers = item['numbers']<\/p>\n
    lottery_data.append((draw_date, numbers))<\/p>\n
print(lottery_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
1\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u5f69\u7968\u6570\u636e\u53ef\u80fd\u5305\u542b\u7f3a\u5931\u503c\u6216\u9519\u8bef\u503c\uff0c\u9700\u8981\u8fdb\u884c\u5904\u7406\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u5e76\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(lottery_data, columns=['date', 'numbers'])<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
\u786e\u4fdd\u6570\u636e\u4e00\u81f4\u6027<\/strong><\/h2>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
df['numbers'] = df['numbers'].apply(lambda x: [int(n) for n in x])<\/p>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u8f6c\u6362<\/h4>\n<\/p>\n
\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u9002\u5408\u5206\u6790\u7684\u683c\u5f0f\u3002\u4f8b\u5982\uff0c\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\uff0c\u5c06\u5f69\u7968\u53f7\u7801\u8f6c\u6362\u4e3a\u6574\u6570\u5217\u8868\u3002<\/p>\n<\/p>\n
# \u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f<\/p>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
\u5c06\u5f69\u7968\u53f7\u7801\u8f6c\u6362\u4e3a\u6574\u6570\u5217\u8868<\/strong><\/h2>\n
df['numbers'] = df['numbers'].apply(lambda x: [int(n) for n in x])<\/p>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
1\u3001\u7ed8\u5236\u5386\u53f2\u5f00\u5956\u8d70\u52bf\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\u7ed8\u5236\u5386\u53f2\u5f00\u5956\u8d70\u52bf\u56fe\uff0c\u5e2e\u52a9\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u5386\u53f2\u5f00\u5956\u8d70\u52bf\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
sns.lineplot(x='date', y='numbers', data=df.explode('numbers'))<\/p>\n
plt.title('Lottery Numbers Over Time')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Numbers')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u53f7\u7801\u5206\u5e03\u56fe<\/h4>\n<\/p>\n
\u7ed8\u5236\u53f7\u7801\u5206\u5e03\u56fe\uff0c\u5c55\u793a\u5404\u4e2a\u53f7\u7801\u7684\u51fa\u73b0\u9891\u7387\uff0c\u5e2e\u52a9\u8bc6\u522b\u54ea\u4e9b\u53f7\u7801\u66f4\u5e38\u51fa\u73b0\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u53f7\u7801\u51fa\u73b0\u9891\u7387<\/p>\n
number_counts = df.explode('numbers')['numbers'].value_counts()<\/p>\n
\u7ed8\u5236\u53f7\u7801\u5206\u5e03\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
sns.barplot(x=number_counts.index, y=number_counts.values)<\/p>\n
plt.title('Number Distribution')<\/p>\n
plt.xlabel('Number')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8d8b\u52bf\u5206\u6790<\/h3>\n<\/p>\n
1\u3001\u79fb\u52a8\u5e73\u5747<\/h4>\n<\/p>\n
\u4f7f\u7528\u79fb\u52a8\u5e73\u5747\u65b9\u6cd5\u5e73\u6ed1\u6570\u636e\uff0c\u8bc6\u522b\u957f\u671f\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747<\/p>\n
df['moving_average'] = df['numbers'].apply(lambda x: sum(x) \/ len(x)).rolling(window=10).mean()<\/p>\n
\u7ed8\u5236\u79fb\u52a8\u5e73\u5747\u8d70\u52bf\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
sns.lineplot(x='date', y='moving_average', data=df)<\/p>\n
plt.title('Moving Average of Lottery Numbers')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Moving Average')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5468\u671f\u6027\u5206\u6790<\/h4>\n<\/p>\n
\u4f7f\u7528\u5085\u91cc\u53f6\u53d8\u6362\u7b49\u65b9\u6cd5\u5206\u6790\u6570\u636e\u7684\u5468\u671f\u6027\uff0c\u8bc6\u522b\u5468\u671f\u6027\u6a21\u5f0f\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from scipy.fftpack import fft<\/p>\n
\u8ba1\u7b97\u5085\u91cc\u53f6\u53d8\u6362<\/strong><\/h2>\n
numbers_sum = df['numbers'].apply(lambda x: sum(x))<\/p>\n
fft_values = fft(numbers_sum)<\/p>\n
\u7ed8\u5236\u5085\u91cc\u53f6\u53d8\u6362\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(np.abs(fft_values))<\/p>\n
plt.title('FFT of Lottery Numbers')<\/p>\n
plt.xlabel('Frequency')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u9884\u6d4b\u6a21\u578b\u6784\u5efa<\/h3>\n<\/p>\n
1\u3001\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/h4>\n<\/p>\n
\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u9884\u6d4b\u672a\u6765\u7684\u5f00\u5956\u3002\u9996\u5148\uff0c\u51c6\u5907\u8bad\u7ec3\u6570\u636e\uff0c\u7136\u540e\u8bad\u7ec3\u6a21\u578b\u5e76\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u51c6\u5907\u8bad\u7ec3\u6570\u636e<\/strong><\/h2>\n
X = df.index.values.reshape(-1, 1)<\/p>\n
y = df['numbers'].apply(lambda x: sum(x))<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
\u8bad\u7ec3\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u7ed8\u5236\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.scatter(X_test, y_test, color='blue', label='Actual')<\/p>\n
plt.plot(X_test, y_pred, color='red', label='Predicted')<\/p>\n
plt.title('Linear Regression Prediction')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Numbers Sum')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u65f6\u95f4\u5e8f\u5217\u6a21\u578b<\/h4>\n<\/p>\n
\u4f7f\u7528\u65f6\u95f4\u5e8f\u5217\u6a21\u578b\uff08\u5982ARIMA\uff09\u9884\u6d4b\u672a\u6765\u7684\u5f00\u5956\u3002\u9996\u5148\uff0c\u51c6\u5907\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u7136\u540e\u8bad\u7ec3\u6a21\u578b\u5e76\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
from statsmodels.tsa.arima_model import ARIMA<\/p>\n
\u51c6\u5907\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
time_series_data = df.set_index('date')['numbers'].apply(lambda x: sum(x))<\/p>\n
\u8bad\u7ec3ARIMA\u6a21\u578b<\/strong><\/h2>\n
model = ARIMA(time_series_data, order=(5, 1, 0))<\/p>\n
model_fit = model.fit(disp=0)<\/p>\n
\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
forecast = model_fit.forecast(steps=10)[0]<\/p>\n
\u7ed8\u5236\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(time_series_data, color='blue', label='Actual')<\/p>\n
plt.plot(pd.date_range(start=time_series_data.index[-1], periods=10, freq='D'), forecast, color='red', label='Forecast')<\/p>\n
plt.title('ARIMA Forecast')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Numbers Sum')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5bf9\u5f69\u7968\u6570\u636e\u8fdb\u884c\u5168\u9762\u7684\u5206\u6790\u548c\u9884\u6d4b\u3002\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u8d8b\u52bf\u5206\u6790\u548c\u9884\u6d4b\u6a21\u578b\u6784\u5efa\u662f\u5f69\u7968\u5206\u6790\u7684\u5173\u952e\u73af\u8282\u3002\u6bcf\u4e2a\u73af\u8282\u90fd\u6709\u5176\u72ec\u7279\u7684\u65b9\u6cd5\u548c\u6280\u672f\uff0c\u5408\u7406\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u548c\u6280\u672f\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u5f69\u7968\u6570\u636e\uff0c\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u89c4\u5f8b\uff0c\u5e76\u8fdb\u884c\u6709\u6548\u7684\u9884\u6d4b\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u5f69\u7968\u6570\u636e\u5206\u6790\uff1f<\/strong>
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