{"id":1127091,"date":"2025-01-08T20:07:12","date_gmt":"2025-01-08T12:07:12","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1127091.html"},"modified":"2025-01-08T20:07:15","modified_gmt":"2025-01-08T12:07:15","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e6%97%a5%e6%9c%9f%e8%bd%ac%e5%8c%96%e4%b8%ba%e6%95%b0%e5%ad%97%e4%bf%a1%e5%8f%b7","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1127091.html","title":{"rendered":"python\u5982\u4f55\u5c06\u65e5\u671f\u8f6c\u5316\u4e3a\u6570\u5b57\u4fe1\u53f7"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u65e5\u671f\u8f6c\u5316\u4e3a\u6570\u5b57\u4fe1\u53f7\"<\/p>\n

Python\u5c06\u65e5\u671f\u8f6c\u5316\u4e3a\u6570\u5b57\u4fe1\u53f7\u7684\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u65f6\u95f4\u6233\u3001\u8f6c\u6362\u4e3a\u5e8f\u5217\u683c\u5f0f\u3001\u63d0\u53d6\u65e5\u671f\u7279\u5f81\u3002<\/strong>\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65f6\u95f4\u6233\u6765\u7b80\u5316\u5904\u7406\u65f6\u95f4\u7684\u8fc7\u7a0b\uff0c\u4f8b\u5982\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60<\/a>\uff1b\u6211\u4eec\u4e5f\u53ef\u4ee5\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u5e8f\u5217\u683c\u5f0f\uff0c\u5982\u5e74-\u6708-\u65e5\uff0c\u8fd9\u6837\u4fbf\u4e8e\u6392\u5e8f\u548c\u7d22\u5f15\uff1b\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u63d0\u53d6\u65e5\u671f\u7279\u5f81\uff0c\u5982\u5e74\u3001\u6708\u3001\u65e5\u3001\u5468\u51e0\u7b49\uff0c\u4fbf\u4e8e\u8fdb\u884c\u7279\u5f81\u5de5\u7a0b\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u65f6\u95f4\u6233<\/h3>\n<\/p>\n

\u65f6\u95f4\u6233\u662f\u8868\u793a\u65f6\u95f4\u7684\u4e00\u79cd\u65b9\u5f0f\uff0c\u662f\u81ea1970\u5e741\u67081\u65e5\uff08\u79f0\u4e3aUnix\u7eaa\u5143\uff09\u4ee5\u6765\u7684\u79d2\u6570\u3002Python\u4e2d\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65f6\u95f4\u6233\u3002<\/p>\n<\/p>\n

1.1 \u4f7f\u7528datetime<\/code>\u6a21\u5757<\/h4>\n<\/p>\n

import datetime<\/p>\n
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65f6\u95f4\u6233<\/strong><\/h2>\n
def date_to_timestamp(date_str, date_format="%Y-%m-%d"):<\/p>\n
    dt = datetime.datetime.strptime(date_str, date_format)<\/p>\n
    timestamp = dt.timestamp()<\/p>\n
    return timestamp<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
date_str = "2023-10-01"<\/p>\n
timestamp = date_to_timestamp(date_str)<\/p>\n
print(f"\u65e5\u671f {date_str} \u7684\u65f6\u95f4\u6233\u4e3a\uff1a{timestamp}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u4f7f\u7528pandas<\/code>\u6a21\u5757<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65f6\u95f4\u6233<\/strong><\/h2>\n
def date_to_timestamp_pandas(date_str):<\/p>\n
    timestamp = pd.to_datetime(date_str).timestamp()<\/p>\n
    return timestamp<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
date_str = "2023-10-01"<\/p>\n
timestamp = date_to_timestamp_pandas(date_str)<\/p>\n
print(f"\u65e5\u671f {date_str} \u7684\u65f6\u95f4\u6233\u4e3a\uff1a{timestamp}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8f6c\u6362\u4e3a\u5e8f\u5217\u683c\u5f0f<\/h3>\n<\/p>\n
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u5e74\u3001\u6708\u3001\u65e5\u7b49\u7ec4\u6210\u7684\u5e8f\u5217\u683c\u5f0f\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6392\u5e8f\u548c\u7d22\u5f15\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
2.1 \u4f7f\u7528datetime<\/code>\u6a21\u5757<\/h4>\n<\/p>\n
import datetime<\/p>\n
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u5e74\u3001\u6708\u3001\u65e5\u7b49\u683c\u5f0f<\/strong><\/h2>\n
def date_to_sequence(date_str, date_format="%Y-%m-%d"):<\/p>\n
    dt = datetime.datetime.strptime(date_str, date_format)<\/p>\n
    return dt.year, dt.month, dt.day<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
date_str = "2023-10-01"<\/p>\n
year, month, day = date_to_sequence(date_str)<\/p>\n
print(f"\u65e5\u671f {date_str} \u8f6c\u6362\u4e3a\u5e8f\u5217\u683c\u5f0f\uff1a\u5e74={year}, \u6708={month}, \u65e5={day}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u4f7f\u7528pandas<\/code>\u6a21\u5757<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u5e74\u3001\u6708\u3001\u65e5\u7b49\u683c\u5f0f<\/strong><\/h2>\n
def date_to_sequence_pandas(date_str):<\/p>\n
    dt = pd.to_datetime(date_str)<\/p>\n
    return dt.year, dt.month, dt.day<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
date_str = "2023-10-01"<\/p>\n
year, month, day = date_to_sequence_pandas(date_str)<\/p>\n
print(f"\u65e5\u671f {date_str} \u8f6c\u6362\u4e3a\u5e8f\u5217\u683c\u5f0f\uff1a\u5e74={year}, \u6708={month}, \u65e5={day}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u63d0\u53d6\u65e5\u671f\u7279\u5f81<\/h3>\n<\/p>\n
\u5728\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u5206\u6790\u65f6\uff0c\u63d0\u53d6\u65e5\u671f\u7279\u5f81\u662f\u5e38\u7528\u7684\u7279\u5f81\u5de5\u7a0b\u624b\u6bb5\u3002\u5e38\u89c1\u7684\u65e5\u671f\u7279\u5f81\u6709\u5e74\u3001\u6708\u3001\u65e5\u3001\u5468\u51e0\u3001\u5b63\u5ea6\u7b49\u3002<\/p>\n<\/p>\n
3.1 \u4f7f\u7528datetime<\/code>\u6a21\u5757<\/h4>\n<\/p>\n
import datetime<\/p>\n
\u63d0\u53d6\u65e5\u671f\u7279\u5f81<\/strong><\/h2>\n
def extract_date_features(date_str, date_format="%Y-%m-%d"):<\/p>\n
    dt = datetime.datetime.strptime(date_str, date_format)<\/p>\n
    features = {<\/p>\n
        "year": dt.year,<\/p>\n
        "month": dt.month,<\/p>\n
        "day": dt.day,<\/p>\n
        "weekday": dt.weekday(),  # \u5468\u51e0\uff080\u8868\u793a\u5468\u4e00\uff0c6\u8868\u793a\u5468\u65e5\uff09<\/p>\n
        "quarter": (dt.month - 1) \/\/ 3 + 1  # \u5b63\u5ea6<\/p>\n
    }<\/p>\n
    return features<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
date_str = "2023-10-01"<\/p>\n
features = extract_date_features(date_str)<\/p>\n
print(f"\u65e5\u671f {date_str} \u7684\u7279\u5f81\u4e3a\uff1a{features}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u4f7f\u7528pandas<\/code>\u6a21\u5757<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u63d0\u53d6\u65e5\u671f\u7279\u5f81<\/strong><\/h2>\n
def extract_date_features_pandas(date_str):<\/p>\n
    dt = pd.to_datetime(date_str)<\/p>\n
    features = {<\/p>\n
        "year": dt.year,<\/p>\n
        "month": dt.month,<\/p>\n
        "day": dt.day,<\/p>\n
        "weekday": dt.weekday(),  # \u5468\u51e0\uff080\u8868\u793a\u5468\u4e00\uff0c6\u8868\u793a\u5468\u65e5\uff09<\/p>\n
        "quarter": dt.quarter  # \u5b63\u5ea6<\/p>\n
    }<\/p>\n
    return features<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
date_str = "2023-10-01"<\/p>\n
features = extract_date_features_pandas(date_str)<\/p>\n
print(f"\u65e5\u671f {date_str} \u7684\u7279\u5f81\u4e3a\uff1a{features}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5b9e\u9645\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u6570\u5b57\u4fe1\u53f7\u53ef\u4ee5\u7b80\u5316\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5de5\u4f5c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
4.1 \u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5206\u6790\u662f\u91d1\u878d\u3001\u6c14\u8c61\u7b49\u9886\u57df\u7684\u91cd\u8981\u5e94\u7528\u3002\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65f6\u95f4\u6233\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u5b58\u50a8\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
date_range = pd.date_range(start="2023-01-01", end="2023-12-31", freq="D")<\/p>\n
data = pd.Series(range(len(date_range)), index=date_range)<\/p>\n
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u65f6\u95f4\u6233<\/strong><\/h2>\n
data.index = data.index.map(lambda x: x.timestamp())<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u673a\u5668\u5b66\u4e60\u7279\u5f81\u5de5\u7a0b<\/h4>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u65e5\u671f\u7279\u5f81\u53ef\u4ee5\u4f5c\u4e3a\u91cd\u8981\u7684\u8f93\u5165\u7279\u5f81\u3002\u4f8b\u5982\uff0c\u5728\u9500\u552e\u9884\u6d4b\u4e2d\uff0c\u65e5\u671f\u7279\u5f81\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u6355\u6349\u5b63\u8282\u6027\u53d8\u5316\u548c\u5468\u671f\u6027\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    "date": ["2023-01-01", "2023-02-01", "2023-03-01"],<\/p>\n
    "sales": [100, 150, 200]<\/p>\n
})<\/p>\n
\u63d0\u53d6\u65e5\u671f\u7279\u5f81<\/strong><\/h2>\n
data["date"] = pd.to_datetime(data["date"])<\/p>\n
data["year"] = data["date"].dt.year<\/p>\n
data["month"] = data["date"].dt.month<\/p>\n
data["day"] = data["date"].dt.day<\/p>\n
data["weekday"] = data["date"].dt.weekday<\/p>\n
data["quarter"] = data["date"].dt.quarter<\/p>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u6570\u5b57\u4fe1\u53f7\u662f\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u9700\u6c42\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u8f6c\u6362\uff0c\u5305\u62ec\u4f7f\u7528\u65f6\u95f4\u6233\u3001\u8f6c\u6362\u4e3a\u5e8f\u5217\u683c\u5f0f\u548c\u63d0\u53d6\u65e5\u671f\u7279\u5f81\u7b49\u3002\u8fd9\u4e9b\u65b9\u6cd5\u4e0d\u4ec5\u7b80\u5316\u4e86\u6570\u636e\u5904\u7406\u7684\u8fc7\u7a0b\uff0c\u8fd8\u53ef\u4ee5\u4e3a\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u7279\u5f81\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u6570\u5b57\u683c\u5f0f\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528datetime<\/code>\u6a21\u5757\u6765\u5904\u7406\u65e5\u671f\u548c\u65f6\u95f4\u3002\u901a\u8fc7datetime<\/code>\u5bf9\u8c61\u7684timestamp()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u81ea1970\u5e741\u67081\u65e5\u4ee5\u6765\u7684\u79d2\u6570\uff0c\u4ece\u800c\u5f62\u6210\u6570\u5b57\u4fe1\u53f7\u3002\u4f8b\u5982\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165datetime<\/code>\u6a21\u5757\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2adatetime<\/code>\u5bf9\u8c61\uff0c\u6700\u540e\u8c03\u7528timestamp()<\/code>\u65b9\u6cd5\u6765\u83b7\u53d6\u5bf9\u5e94\u7684\u6570\u5b57\u4fe1\u53f7\u3002<\/p>\n
\u65e5\u671f\u8f6c\u6362\u4e3a\u6570\u5b57\u4fe1\u53f7\u7684\u5e94\u7528\u573a\u666f\u6709\u54ea\u4e9b\uff1f<\/strong>
\u5c06\u65e5\u671f\u8f6c\u6362\u4e3a\u6570\u5b57\u4fe1\u53f7\u5728\u8bb8\u591a\u5e94\u7528\u4e2d\u90fd\u975e\u5e38\u91cd\u8981\uff0c\u4f8b\u5982\u6570\u636e\u5206\u6790\u3001\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u548c\u673a\u5668\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u3002\u901a\u8fc7\u5c06\u65e5\u671f\u8f6c\u5316\u4e3a\u6570\u5b57\u683c\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u8ba1\u7b97\u673a\u66f4\u5bb9\u6613\u5904\u7406\u548c\u5206\u6790\u65f6\u95f4\u76f8\u5173\u7684\u6570\u636e\u3002\u6b64\u5916\uff0c\u8fd9\u79cd\u8f6c\u6362\u8fd8\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u7528\u6237\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n
\u5728\u8f6c\u6362\u65e5\u671f\u65f6\uff0c\u5982\u4f55\u5904\u7406\u65f6\u533a\u95ee\u9898\uff1f<\/strong>
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