{"id":1090495,"date":"2025-01-08T14:03:14","date_gmt":"2025-01-08T06:03:14","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1090495.html"},"modified":"2025-01-08T14:03:16","modified_gmt":"2025-01-08T06:03:16","slug":"%e5%a6%82%e4%bd%95%e6%8a%8a%e8%82%a1%e7%a5%a8%e6%95%b0%e6%8d%ae%e5%af%bc%e5%85%a5python-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1090495.html","title":{"rendered":"\u5982\u4f55\u628a\u80a1\u7968\u6570\u636e\u5bfc\u5165Python"},"content":{"rendered":"

\"\u5982\u4f55\u628a\u80a1\u7968\u6570\u636e\u5bfc\u5165Python\"<\/p>\n

\u5982\u4f55\u628a\u80a1\u7968\u6570\u636e\u5bfc\u5165Python<\/strong><\/p>\n<\/p>\n

\u628a\u80a1\u7968\u6570\u636e\u5bfc\u5165Python\u7684\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u5229\u7528Yahoo Finance API\u3001\u901a\u8fc7Alpha Vantage API\u3001\u501f\u52a9Quandl\u5e73\u53f0\u3001\u4f7f\u7528yfinance\u5e93\u7b49\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u52a3\uff0c\u9002\u7528\u4e8e\u4e0d\u540c\u9700\u6c42\u3002\u5229\u7528Pandas\u5e93\u3001\u901a\u8fc7API\u83b7\u53d6\u3001\u4f7f\u7528Quandl\u5e73\u53f0\u3001\u7ed3\u5408yfinance\u5e93\u3001\u5bfc\u5165\u672c\u5730CSV\u6587\u4ef6<\/strong>\uff0c\u8fd9\u4e9b\u90fd\u662f\u5e38\u89c1\u4e14\u6709\u6548\u7684\u65b9\u6cd5\u3002\u5176\u4e2d\uff0c\u4f7f\u7528yfinance\u5e93\u662f\u4e00\u79cd\u975e\u5e38\u4fbf\u6377\u4e14\u529f\u80fd\u5f3a\u5927\u7684\u65b9\u6cd5\uff0c\u5b83\u5141\u8bb8\u7528\u6237\u5feb\u901f\u83b7\u53d6\u80a1\u7968\u6570\u636e\u5e76\u76f4\u63a5\u5bfc\u5165\u5230Pandas\u6570\u636e\u6846\u4e2d\u3002<\/p>\n<\/p>\n

\u5177\u4f53\u6765\u8bf4\uff0cyfinance\u5e93\u662f\u57fa\u4e8eYahoo Finance API\u7684\u4e00\u4e2aPython\u5e93\uff0c\u80fd\u591f\u8f7b\u677e\u83b7\u53d6\u80a1\u7968\u3001\u671f\u6743\u3001\u6307\u6570\u7b49\u91d1\u878d\u6570\u636e\u3002\u5b83\u652f\u6301\u591a\u79cd\u6570\u636e\u7c7b\u578b\uff0c\u5305\u62ec\u5386\u53f2\u4ef7\u683c\u6570\u636e\u3001\u5b9e\u65f6\u4ef7\u683c\u6570\u636e\u3001\u80a1\u606f\u4fe1\u606f\u7b49\u3002\u6b64\u5916\uff0cyfinance\u5e93\u8fd8\u63d0\u4f9b\u4e86\u53cb\u597d\u7684API\u63a5\u53e3\uff0c\u4fbf\u4e8e\u7528\u6237\u5feb\u901f\u4e0a\u624b\u5e76\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u5c06\u80a1\u7968\u6570\u636e\u5bfc\u5165Python\uff0c\u5e76\u7ed3\u5408\u5b9e\u9645\u6848\u4f8b\u8fdb\u884c\u6f14\u793a\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93\u5bfc\u5165\u80a1\u7968\u6570\u636e<\/h3>\n<\/p>\n

Pandas\u662fPython\u4e2d\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u5404\u79cd\u6570\u636e\u683c\u5f0f\u3002\u8981\u4f7f\u7528Pandas\u5bfc\u5165\u80a1\u7968\u6570\u636e\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bfb\u53d6CSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u6216\u8005\u5176\u4ed6\u683c\u5f0f\u7684\u6587\u4ef6\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n

1\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u7684CSV\u6587\u4ef6\u5305\u542b\u4e86\u80a1\u7968\u6570\u636e\uff0c\u901a\u5e38\u5305\u62ec\u65e5\u671f\u3001\u5f00\u76d8\u4ef7\u3001\u6536\u76d8\u4ef7\u3001\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u548c\u6210\u4ea4\u91cf\u7b49\u4fe1\u606f\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('path_to_your_file.csv')<\/p>\n
\u6253\u5370\u6570\u636e\u6846\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6Excel\u6587\u4ef6<\/h4>\n<\/p>\n
\u7c7b\u4f3c\u5730\uff0cPandas\u4e5f\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6Excel\u6587\u4ef6\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6Excel\u6587\u4ef6<\/p>\n
df = pd.read_excel('path_to_your_file.xlsx')<\/p>\n
\u6253\u5370\u6570\u636e\u6846\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5229\u7528Yahoo Finance API\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h3>\n<\/p>\n
Yahoo Finance API\u662f\u4e00\u4e2a\u63d0\u4f9b\u91d1\u878d\u6570\u636e\u7684API\uff0c\u53ef\u4ee5\u7528\u4e8e\u83b7\u53d6\u5b9e\u65f6\u548c\u5386\u53f2\u80a1\u7968\u6570\u636e\u3002\u867d\u7136Yahoo Finance API\u5b98\u65b9\u5df2\u7ecf\u505c\u6b62\u7ef4\u62a4\uff0c\u4f46\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e9b\u7b2c\u4e09\u65b9\u5e93\u6765\u8bbf\u95ee\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5yfinance\u5e93<\/h4>\n<\/p>\n
yfinance\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u7b2c\u4e09\u65b9\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u83b7\u53d6Yahoo Finance\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
pip install yfinance<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528yfinance\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h4>\n<\/p>\n
import yfinance as yf<\/p>\n
\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
ticker = yf.Ticker("AAPL")<\/p>\n
\u83b7\u53d6\u5386\u53f2\u6570\u636e<\/strong><\/h2>\n
hist = ticker.history(period="1mo")<\/p>\n
\u6253\u5370\u6570\u636e\u6846\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(hist.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u901a\u8fc7Alpha Vantage API\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h3>\n<\/p>\n
Alpha Vantage\u662f\u53e6\u4e00\u4e2a\u63d0\u4f9b\u91d1\u878d\u6570\u636e\u7684API\uff0c\u7528\u6237\u9700\u8981\u6ce8\u518c\u83b7\u53d6\u4e00\u4e2aAPI\u5bc6\u94a5\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5alpha_vantage\u5e93<\/h4>\n<\/p>\n
pip install alpha_vantage<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Alpha Vantage\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h4>\n<\/p>\n
from alpha_vantage.timeseries import TimeSeries<\/p>\n
\u8f93\u5165\u4f60\u7684API\u5bc6\u94a5<\/strong><\/h2>\n
api_key = 'your_api_key'<\/p>\n
\u521d\u59cb\u5316TimeSeries\u5bf9\u8c61<\/strong><\/h2>\n
ts = TimeSeries(key=api_key, output_format='pandas')<\/p>\n
\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
data, meta_data = ts.get_intraday(symbol='AAPL', interval='1min', outputsize='full')<\/p>\n
\u6253\u5370\u6570\u636e\u6846\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u501f\u52a9Quandl\u5e73\u53f0\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h3>\n<\/p>\n
Quandl\u662f\u4e00\u4e2a\u63d0\u4f9b\u5404\u79cd\u91d1\u878d\u6570\u636e\u7684\u5e73\u53f0\uff0c\u7528\u6237\u9700\u8981\u6ce8\u518c\u5e76\u83b7\u53d6\u4e00\u4e2aAPI\u5bc6\u94a5\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Quandl\u5e93<\/h4>\n<\/p>\n
pip install quandl<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Quandl\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h4>\n<\/p>\n
import quandl<\/p>\n
\u8f93\u5165\u4f60\u7684API\u5bc6\u94a5<\/strong><\/h2>\n
quandl.ApiConfig.api_key = 'your_api_key'<\/p>\n
\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
data = quandl.get('WIKI\/AAPL')<\/p>\n
\u6253\u5370\u6570\u636e\u6846\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5bfc\u5165\u672c\u5730CSV\u6587\u4ef6<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u6709\u672c\u5730\u7684CSV\u6587\u4ef6\uff0c\u5305\u542b\u4e86\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\uff0c\u53ef\u4ee5\u76f4\u63a5\u7528Pandas\u8bfb\u53d6\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('path_to_your_file.csv')<\/p>\n
\u6253\u5370\u6570\u636e\u6846\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/h3>\n<\/p>\n
\u83b7\u53d6\u5230\u80a1\u7968\u6570\u636e\u540e\uff0c\u53ef\u80fd\u9700\u8981\u8fdb\u884c\u4e00\u4e9b\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\uff0c\u4ee5\u4fbf\u8fdb\u884c\u540e\u7eed\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
# \u68c0\u67e5\u7f3a\u5931\u503c<\/p>\n
print(df.isnull().sum())<\/p>\n
\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/h2>\n
df = df.dropna()<\/p>\n
\u6216\u8005\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df = df.fillna(method='ffill')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h4>\n<\/p>\n
# \u5c06\u65e5\u671f\u5217\u8f6c\u6362\u4e3a\u65e5\u671f\u7c7b\u578b<\/p>\n
df['Date'] = pd.to_datetime(df['Date'])<\/p>\n
\u68c0\u67e5\u6570\u636e\u7c7b\u578b<\/strong><\/h2>\n
print(df.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u80a1\u7968\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e9b\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u5982Matplotlib\u548cSeaborn\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Matplotlib\u548cSeaborn<\/h4>\n<\/p>\n
pip install matplotlib seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u66f2\u7ebf<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u8bbe\u7f6e\u7ed8\u56fe\u98ce\u683c<\/strong><\/h2>\n
sns.set_style('whitegrid')<\/p>\n
\u7ed8\u5236\u6536\u76d8\u4ef7\u66f2\u7ebf<\/strong><\/h2>\n
plt.figure(figsize=(12, 6))<\/p>\n
plt.plot(df['Date'], df['Close'])<\/p>\n
plt.title('Stock Closing Prices')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Close Price')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u5b9e\u6218\u6848\u4f8b<\/h3>\n<\/p>\n
\u4e0b\u9762\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u5b8c\u6574\u7684\u5b9e\u6218\u6848\u4f8b\u6765\u6f14\u793a\u5982\u4f55\u5c06\u80a1\u7968\u6570\u636e\u5bfc\u5165Python\uff0c\u5e76\u8fdb\u884c\u7b80\u5355\u7684\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
1\u3001\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528yfinance\u5e93\u83b7\u53d6\u82f9\u679c\u516c\u53f8\uff08AAPL\uff09\u7684\u80a1\u7968\u6570\u636e\u3002<\/p>\n<\/p>\n
import yfinance as yf<\/p>\n
\u83b7\u53d6\u82f9\u679c\u516c\u53f8\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
ticker = yf.Ticker("AAPL")<\/p>\n
hist = ticker.history(period="1y")<\/p>\n
\u6253\u5370\u6570\u636e\u6846\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(hist.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/h4>\n<\/p>\n
# \u68c0\u67e5\u7f3a\u5931\u503c<\/p>\n
print(hist.isnull().sum())<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
hist = hist.fillna(method='ffill')<\/p>\n
\u68c0\u67e5\u6570\u636e\u7c7b\u578b<\/strong><\/h2>\n
print(hist.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u8bbe\u7f6e\u7ed8\u56fe\u98ce\u683c<\/strong><\/h2>\n
sns.set_style('whitegrid')<\/p>\n
\u7ed8\u5236\u6536\u76d8\u4ef7\u66f2\u7ebf<\/strong><\/h2>\n
plt.figure(figsize=(12, 6))<\/p>\n
plt.plot(hist.index, hist['Close'])<\/p>\n
plt.title('AAPL Stock Closing Prices')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Close Price')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5c06\u80a1\u7968\u6570\u636e\u5bfc\u5165Python\uff0c\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001\u5229\u7528Yahoo Finance API\u3001\u901a\u8fc7Alpha Vantage API\u3001\u501f\u52a9Quandl\u5e73\u53f0\u3001\u4f7f\u7528yfinance\u5e93\u7b49\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u672c\u6587\u8fd8\u4ecb\u7ecd\u4e86\u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406\u4ee5\u53ca\u6570\u636e\u53ef\u89c6\u5316\u7684\u57fa\u672c\u64cd\u4f5c\uff0c\u901a\u8fc7\u4e00\u4e2a\u5b9e\u6218\u6848\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u80a1\u7968\u6570\u636e\u5bfc\u5165Python\u5e76\u8fdb\u884c\u7b80\u5355\u7684\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u83b7\u53d6\u5b9e\u65f6\u80a1\u7968\u6570\u636e\uff1f<\/strong>
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