{"id":958962,"date":"2024-12-27T03:36:35","date_gmt":"2024-12-26T19:36:35","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/958962.html"},"modified":"2024-12-27T03:36:37","modified_gmt":"2024-12-26T19:36:37","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e6%8a%98%e5%8f%a0%e8%be%93%e5%87%ba","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/958962.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u6298\u53e0\u8f93\u51fa"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u6298\u53e0\u8f93\u51fa\"<\/p>\n

\u5728Python\u4e2d\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u4f8b\u5982\u4f7f\u7528\u5217\u8868\u6216\u5b57\u5178\u7ed3\u6784\u8fdb\u884c\u6570\u636e\u7684\u5206\u7ec4\u3001\u4f7f\u7528\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u6cd5\u6216\u8005\u4f7f\u7528\u6761\u4ef6\u548c\u5faa\u73af\u63a7\u5236\u7ed3\u6784\u6765\u63a7\u5236\u8f93\u51fa\u7684\u5185\u5bb9\u3002\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u3001\u6761\u4ef6\u8bed\u53e5\u3001\u5faa\u73af\u63a7\u5236\u7ed3\u6784<\/strong>\u7b49\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u548c\u6298\u53e0\u8f93\u51fa\uff0c\u5176\u4e2d\uff0c\u4f7f\u7528itertools.groupby<\/code>\u51fd\u6570\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u65b9\u5f0f\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f<\/h3>\n<\/p>\n

\u5217\u8868\u63a8\u5bfc\u5f0f\u662f\u4e00\u79cd\u7b80\u6d01\u7684\u65b9\u5f0f\u6765\u521b\u5efa\u5217\u8868\uff0c\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u7b80\u5355\u7684\u6298\u53e0\u8f93\u51fa\u3002\u901a\u8fc7\u6761\u4ef6\u8868\u8fbe\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u548c\u6298\u53e0\u3002<\/p>\n<\/p>\n

1.1 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n

data = [1, 2, 2, 3, 3, 3, 4]<\/p>\n
folded_output = [x for i, x in enumerate(data) if i == 0 or x != data[i-1]]<\/p>\n
print(folded_output)  # \u8f93\u51fa: [1, 2, 3, 4]<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u8be6\u7ec6\u63cf\u8ff0<\/h4>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u904d\u5386\u6570\u636e\u5217\u8868\uff0c\u7136\u540e\u4f7f\u7528\u6761\u4ef6\u8868\u8fbe\u5f0f\u68c0\u67e5\u5f53\u524d\u5143\u7d20\u662f\u5426\u4e0e\u524d\u4e00\u4e2a\u5143\u7d20\u4e0d\u540c\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u6709\u6548\u5730\u53bb\u9664\u8fde\u7eed\u91cd\u590d\u7684\u5143\u7d20\uff0c\u4ece\u800c\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528itertools.groupby<\/code><\/h3>\n<\/p>\n
Python\u7684itertools<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86groupby<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u5206\u7ec4\u8fde\u7eed\u91cd\u590d\u7684\u5143\u7d20\uff0c\u8fd9\u662f\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u7684\u53e6\u4e00\u79cd\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
2.1 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
from itertools import groupby<\/p>\n
data = [1, 2, 2, 3, 3, 3, 4]<\/p>\n
folded_output = [key for key, group in groupby(data)]<\/p>\n
print(folded_output)  # \u8f93\u51fa: [1, 2, 3, 4]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u8be6\u7ec6\u63cf\u8ff0<\/h4>\n<\/p>\n
groupby<\/code>\u51fd\u6570\u4f1a\u904d\u5386\u8f93\u5165\u6570\u636e\uff0c\u5e76\u5728\u9047\u5230\u65b0\u7684\u5143\u7d20\u65f6\u4ea7\u751f\u4e00\u4e2a\u65b0\u7684\u5206\u7ec4\u3002\u901a\u8fc7\u4ec5\u4fdd\u7559\u6bcf\u4e2a\u5206\u7ec4\u7684\u7b2c\u4e00\u4e2a\u5143\u7d20\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cgroupby<\/code>\u51fd\u6570\u8981\u6c42\u8f93\u5165\u6570\u636e\u662f\u5df2\u6392\u5e8f\u7684\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u5b57\u5178\u8fdb\u884c\u5206\u7ec4<\/h3>\n<\/p>\n
\u5b57\u5178\u662f\u4e00\u79cd\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u7528\u4e8e\u5c06\u6570\u636e\u6309\u7167\u67d0\u79cd\u89c4\u5219\u8fdb\u884c\u5206\u7ec4\u3002\u901a\u8fc7\u5c06\u76f8\u540c\u7684\u5143\u7d20\u5f52\u4e3a\u4e00\u7ec4\uff0c\u53ef\u4ee5\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u3002<\/p>\n<\/p>\n
3.1 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
data = [1, 2, 2, 3, 3, 3, 4]<\/p>\n
folded_output = list(dict.fromkeys(data))<\/p>\n
print(folded_output)  # \u8f93\u51fa: [1, 2, 3, 4]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u8be6\u7ec6\u63cf\u8ff0<\/h4>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528dict.fromkeys()<\/code>\u65b9\u6cd5\u5c06\u5217\u8868\u8f6c\u6362\u4e3a\u5b57\u5178\uff0c\u7136\u540e\u518d\u5c06\u5b57\u5178\u952e\u8f6c\u6362\u56de\u5217\u8868\u3002\u7531\u4e8e\u5b57\u5178\u952e\u662f\u552f\u4e00\u7684\uff0c\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u6709\u6548\u53bb\u9664\u91cd\u590d\u5143\u7d20\uff0c\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u5faa\u73af\u63a7\u5236\u7ed3\u6784<\/h3>\n<\/p>\n
\u5faa\u73af\u63a7\u5236\u7ed3\u6784\u53ef\u4ee5\u7528\u4e8e\u624b\u52a8\u5b9e\u73b0\u6570\u636e\u7684\u6298\u53e0\u8f93\u51fa\uff0c\u901a\u8fc7\u904d\u5386\u6570\u636e\u5e76\u4f7f\u7528\u6761\u4ef6\u8bed\u53e5\u63a7\u5236\u8f93\u51fa\u5185\u5bb9\u3002<\/p>\n<\/p>\n
4.1 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
data = [1, 2, 2, 3, 3, 3, 4]<\/p>\n
folded_output = []<\/p>\n
for item in data:<\/p>\n
    if not folded_output or folded_output[-1] != item:<\/p>\n
        folded_output.append(item)<\/p>\n
print(folded_output)  # \u8f93\u51fa: [1, 2, 3, 4]<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u8be6\u7ec6\u63cf\u8ff0<\/h4>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e00\u4e2a\u5faa\u73af\u6765\u904d\u5386\u6570\u636e\uff0c\u5e76\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\u68c0\u67e5\u5f53\u524d\u5143\u7d20\u662f\u5426\u4e0e\u8f93\u51fa\u5217\u8868\u4e2d\u7684\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u76f8\u540c\u3002\u53ea\u6709\u5f53\u5b83\u4eec\u4e0d\u76f8\u540c\u65f6\uff0c\u624d\u4f1a\u5c06\u5f53\u524d\u5143\u7d20\u6dfb\u52a0\u5230\u8f93\u51fa\u5217\u8868\u4e2d\uff0c\u4ece\u800c\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528\u81ea\u5b9a\u4e49\u51fd\u6570<\/h3>\n<\/p>\n
\u81ea\u5b9a\u4e49\u51fd\u6570\u53ef\u4ee5\u5c01\u88c5\u4ee5\u4e0a\u903b\u8f91\uff0c\u4f7f\u5f97\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u7684\u4ee3\u7801\u66f4\u52a0\u6a21\u5757\u5316\u548c\u53ef\u590d\u7528\u3002<\/p>\n<\/p>\n
5.1 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
def fold_output(data):<\/p>\n
    folded_output = []<\/p>\n
    for item in data:<\/p>\n
        if not folded_output or folded_output[-1] != item:<\/p>\n
            folded_output.append(item)<\/p>\n
    return folded_output<\/p>\n
data = [1, 2, 2, 3, 3, 3, 4]<\/p>\n
print(fold_output(data))  # \u8f93\u51fa: [1, 2, 3, 4]<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u8be6\u7ec6\u63cf\u8ff0<\/h4>\n<\/p>\n
\u901a\u8fc7\u5b9a\u4e49fold_output<\/code>\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u6298\u53e0\u8f93\u51fa\u7684\u903b\u8f91\u5c01\u88c5\u8d77\u6765\u3002\u8fd9\u4f7f\u5f97\u4ee3\u7801\u66f4\u52a0\u6e05\u6670\uff0c\u5e76\u4e14\u51fd\u6570\u53ef\u4ee5\u5728\u5176\u4ed6\u5730\u65b9\u590d\u7528\uff0c\u4ece\u800c\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u7ef4\u62a4\u6027\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f7f\u7528Numpy\u5e93\u5b9e\u73b0<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\uff0cnumpy<\/code>\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u65b9\u6cd5\u6765\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\uff0c\u5305\u62ec\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u3002<\/p>\n<\/p>\n
6.1 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([1, 2, 2, 3, 3, 3, 4])<\/p>\n
folded_output = np.unique(data)<\/p>\n
print(folded_output)  # \u8f93\u51fa: [1 2 3 4]<\/p>\n
<\/code><\/pre>\n<\/p>\n
6.2 \u8be6\u7ec6\u63cf\u8ff0<\/h4>\n<\/p>\n
numpy.unique<\/code>\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u6570\u7ec4\u4e2d\u7684\u552f\u4e00\u5143\u7d20\uff0c\u5e76\u6309\u6392\u5e8f\u987a\u5e8f\u8fd4\u56de\u3002\u8fd9\u5bf9\u4e8e\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u573a\u666f\u975e\u5e38\u6709\u7528\uff0c\u56e0\u4e3anumpy<\/code>\u7684\u64cd\u4f5c\u901a\u5e38\u6bd4\u7eafPython\u5faa\u73af\u8981\u5feb\u5f97\u591a\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u4f7f\u7528Pandas\u5e93\u5b9e\u73b0<\/h3>\n<\/p>\n
pandas<\/code>\u5e93\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\uff0c\u5c24\u5176\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002<\/p>\n<\/p>\n
7.1 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
data = pd.Series([1, 2, 2, 3, 3, 3, 4])<\/p>\n
folded_output = data.drop_duplicates().tolist()<\/p>\n
print(folded_output)  # \u8f93\u51fa: [1, 2, 3, 4]<\/p>\n
<\/code><\/pre>\n<\/p>\n
7.2 \u8be6\u7ec6\u63cf\u8ff0<\/h4>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528pandas.Series<\/code>\u5bf9\u8c61\u53ca\u5176drop_duplicates<\/code>\u65b9\u6cd5\u6765\u53bb\u9664\u91cd\u590d\u5143\u7d20\u3002pandas<\/code>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u8fd9\u79cd\u65b9\u6cd5\u9002\u5408\u7528\u4e8e\u9700\u8981\u590d\u6742\u6570\u636e\u5904\u7406\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u5b9e\u73b0\u6298\u53e0\u8f93\u51fa\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u9002\u7528\u7684\u573a\u666f\u3002\u5217\u8868\u63a8\u5bfc\u5f0f\u3001itertools.groupby<\/code>\u3001\u5b57\u5178\u3001\u5faa\u73af\u63a7\u5236\u7ed3\u6784<\/strong>\u7b49\u65b9\u6cd5\u9002\u5408\u7528\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u6216\u7b80\u5355\u573a\u666f\uff1b\u81ea\u5b9a\u4e49\u51fd\u6570\u3001numpy<\/code>\u3001pandas<\/code>\u5e93<\/strong>\u5219\u9002\u5408\u4e8e\u9700\u8981\u6a21\u5757\u5316\u4ee3\u7801\u6216\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u573a\u666f\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5b9e\u73b0\u6570\u636e\u7684\u6298\u53e0\u8f93\u51fa\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u8f93\u51fa\u7684\u6298\u53e0\u663e\u793a\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u6a21\u5757\u5982pprint<\/code>\u6765\u683c\u5f0f\u5316\u8f93\u51fa\uff0c\u4f7f\u5176\u66f4\u5177\u53ef\u8bfb\u6027\u3002\u901a\u8fc7\u8c03\u6574\u7f29\u8fdb\u548c\u683c\u5f0f\u5316\u9009\u9879\uff0c\u53ef\u4ee5\u5b9e\u73b0\u8f93\u51fa\u7684\u6298\u53e0\u6548\u679c\u3002\u6b64\u5916\uff0c\u5728Jupyter Notebook\u7b49\u73af\u5883\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528HTML\u548cJavaScript\u6765\u5b9e\u73b0\u53ef\u6298\u53e0\u7684\u8f93\u51fa\u533a\u57df\u3002<\/p>\n
\u6211\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u5e93\u6765\u5b9e\u73b0Python\u8f93\u51fa\u7684\u6298\u53e0\u529f\u80fd\uff1f<\/strong>
\u9664\u4e86pprint<\/code>\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528json<\/code>\u6a21\u5757\u6765\u683c\u5f0f\u5316\u8f93\u51fa\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u5b57\u5178\u548c\u5217\u8868\u65f6\u3002\u540c\u65f6\uff0crich<\/code>\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6587\u672c\u683c\u5f0f\u5316\u529f\u80fd\uff0c\u5305\u62ec\u53ef\u6298\u53e0\u7684\u8f93\u51fa\uff0c\u80fd\u591f\u6781\u5927\u5730\u63d0\u5347\u7ec8\u7aef\u8f93\u51fa\u7684\u53ef\u8bfb\u6027\u3002<\/p>\n
\u5728Python IDE\u4e2d\u5982\u4f55\u67e5\u770b\u6298\u53e0\u8f93\u51fa\u7684\u5185\u5bb9\uff1f<\/strong>
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