![\"python\u5982\u4f55\u5b9e\u73b0\u6570\u636e\u5339\u914d\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24212650\/25d7516c-758e-4b3a-a6e7-923bb8d3f561.webp\")
<\/p>\n<\/p>\n
\u5728Python\u4e2d\u5b9e\u73b0\u6570\u636e\u5339\u914d\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5305\u62ec\u4f7f\u7528\u5b57\u5178\u3001\u96c6\u5408\u3001\u6b63\u5219\u8868\u8fbe\u5f0f\u3001Pandas\u5e93\u7b49\u3002\u5b57\u5178\u53ef\u4ee5\u7528\u4e8e\u5feb\u901f\u67e5\u627e\u548c\u5339\u914d\u952e\u503c\u5bf9\uff0c\u96c6\u5408\u9002\u7528\u4e8e\u96c6\u5408\u95f4\u7684\u4ea4\u96c6\u6216\u5dee\u96c6\u64cd\u4f5c\uff0c\u6b63\u5219\u8868\u8fbe\u5f0f\u7528\u4e8e\u590d\u6742\u6a21\u5f0f\u7684\u6587\u672c\u5339\u914d\uff0c\u800cPandas\u5e93\u5219\u9002\u5408\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u7684\u5339\u914d\u548c\u5408\u5e76\u3002<\/strong>\u5728\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\uff0cPandas\u5e93\u56e0\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u800c\u5e7f\u53d7\u6b22\u8fce\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u53ef\u4ee5\u901a\u8fc7merge\u51fd\u6570\u8f7b\u677e\u5b9e\u73b0\u4e24\u4e2a\u6570\u636e\u6846\u7684\u5339\u914d\u548c\u5408\u5e76\u3002merge\u51fd\u6570\u80fd\u591f\u6839\u636e\u4e00\u4e2a\u6216\u591a\u4e2a\u952e\u5c06\u4e24\u4e2a\u6570\u636e\u6846\u5408\u5e76\u5728\u4e00\u8d77\uff0c\u975e\u5e38\u9002\u5408\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n \u4e00\u3001\u5b57\u5178\u548c\u96c6\u5408\u5339\u914d<\/p>\n<\/p>\n \u5b57\u5178\u548c\u96c6\u5408\u662fPython\u4e2d\u5185\u7f6e\u7684\u6570\u636e\u7ed3\u6784\uff0c\u9002\u5408\u7528\u4e8e\u5feb\u901f\u67e5\u627e\u548c\u5339\u914d\u64cd\u4f5c\u3002<\/p>\n<\/p>\n \u5b57\u5178\u662f\u4e00\u79cd\u952e\u503c\u5bf9\u6570\u636e\u7ed3\u6784\uff0c\u67e5\u627e\u901f\u5ea6\u975e\u5e38\u5feb\u3002\u53ef\u4ee5\u901a\u8fc7\u952e\u6765\u83b7\u53d6\u5bf9\u5e94\u7684\u503c\uff0c\u4ece\u800c\u5b9e\u73b0\u6570\u636e\u5339\u914d\u3002<\/p>\n<\/p>\n key_to_match = 'banana'<\/p>\n if key_to_match in data_dict:<\/p>\n print(f"Found: {key_to_match} with value {data_dict[key_to_match]}")<\/p>\n else:<\/p>\n print("Not found")<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u68c0\u67e5\u5b57\u5178\u4e2d\u662f\u5426\u5b58\u5728\u67d0\u4e2a\u952e\u6765\u5b9e\u73b0\u5339\u914d\u64cd\u4f5c\u3002<\/p>\n<\/p>\n \u96c6\u5408\u662f\u53e6\u4e00\u79cd\u5185\u7f6e\u6570\u636e\u7ed3\u6784\uff0c\u9002\u5408\u7528\u4e8e\u96c6\u5408\u4e4b\u95f4\u7684\u4ea4\u96c6\u3001\u5e76\u96c6\u548c\u5dee\u96c6\u64cd\u4f5c\u3002<\/p>\n<\/p>\n set_b = {3, 4, 5, 6}<\/p>\n intersection = set_a & set_b<\/p>\n print(f"Intersection: {intersection}")<\/p>\n <\/code><\/pre>\n<\/p>\n \u8fd9\u4e2a\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u96c6\u5408\u64cd\u4f5c\u6765\u5b9e\u73b0\u6570\u636e\u5339\u914d\uff0c\u627e\u51fa\u4e24\u4e2a\u96c6\u5408\u7684\u4ea4\u96c6\u5143\u7d20\u3002<\/p>\n<\/p>\n \u4e8c\u3001\u6b63\u5219\u8868\u8fbe\u5f0f\u5339\u914d<\/p>\n<\/p>\n \u6b63\u5219\u8868\u8fbe\u5f0f\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u5b57\u7b26\u4e32\u6a21\u5f0f\u5339\u914d\u5de5\u5177\uff0c\u9002\u5408\u7528\u4e8e\u590d\u6742\u7684\u6587\u672c\u5339\u914d\u3002<\/p>\n<\/p>\n Python\u7684 pattern = r'\\b\\d{3}-\\d{2}-\\d{4}\\b'<\/p>\n text = "My phone number is 123-45-6789."<\/p>\n match = re.search(pattern, text)<\/p>\n if match:<\/p>\n print(f"Matched: {match.group()}")<\/p>\n else:<\/p>\n print("No match found")<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u5339\u914d\u7f8e\u56fd\u793e\u4f1a\u5b89\u5168\u53f7\u7801\u683c\u5f0f\u7684\u5b57\u7b26\u4e32\u3002<\/p>\n<\/p>\n \u6b63\u5219\u8868\u8fbe\u5f0f\u8fd8\u652f\u6301\u5206\u7ec4\u3001\u66ff\u6362\u3001\u6355\u83b7\u7b49\u9ad8\u7ea7\u7279\u6027\uff0c\u53ef\u4ee5\u6ee1\u8db3\u590d\u6742\u7684\u5339\u914d\u9700\u6c42\u3002<\/p>\n<\/p>\n match = re.match(pattern, text)<\/p>\n if match:<\/p>\n area, group, serial = match.groups()<\/p>\n print(f"Area: {area}, Group: {group}, Serial: {serial}")<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u5206\u7ec4\u6355\u83b7\uff0c\u53ef\u4ee5\u63d0\u53d6\u51fa\u5339\u914d\u6a21\u5f0f\u4e2d\u7684\u5b50\u90e8\u5206\u3002<\/p>\n<\/p>\n \u4e09\u3001Pandas\u6570\u636e\u5339\u914d<\/p>\n<\/p>\n Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5339\u914d\u548c\u5408\u5e76\u529f\u80fd\u3002<\/p>\n<\/p>\n Pandas\u7684 df1 = pd.DataFrame({<\/p>\n 'key': ['A', 'B', 'C'],<\/p>\n 'value1': [1, 2, 3]<\/p>\n })<\/p>\n df2 = pd.DataFrame({<\/p>\n 'key': ['A', 'B', 'D'],<\/p>\n 'value2': [4, 5, 6]<\/p>\n })<\/p>\n merged_df = pd.merge(df1, df2, on='key', how='inner')<\/p>\n print(merged_df)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4e24\u4e2a\u6570\u636e\u6846\u6839\u636e Pandas\u8fd8\u652f\u6301\u591a\u952e\u5408\u5e76\u3001\u7d22\u5f15\u5408\u5e76\u4ee5\u53ca\u6761\u4ef6\u5408\u5e76\u7b49\u590d\u6742\u64cd\u4f5c\uff0c\u9002\u5408\u7528\u4e8e\u5927\u578b\u6570\u636e\u96c6\u7684\u5904\u7406\u3002<\/p>\n<\/p>\n df1 = pd.DataFrame({<\/p>\n 'key1': ['A', 'B', 'C'],<\/p>\n 'key2': ['X', 'Y', 'Z'],<\/p>\n 'value1': [1, 2, 3]<\/p>\n })<\/p>\n df2 = pd.DataFrame({<\/p>\n 'key1': ['A', 'B', 'D'],<\/p>\n 'key2': ['X', 'Y', 'W'],<\/p>\n 'value2': [4, 5, 6]<\/p>\n })<\/p>\n merged_df = pd.merge(df1, df2, on=['key1', 'key2'], how='outer')<\/p>\n print(merged_df)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u6307\u5b9a\u591a\u4e2a\u952e\uff0c\u53ef\u4ee5\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u6570\u636e\u5339\u914d\u64cd\u4f5c\u3002<\/p>\n<\/p>\n \u56db\u3001NumPy\u6570\u7ec4\u5339\u914d<\/p>\n<\/p>\n NumPy\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002<\/p>\n<\/p>\n NumPy\u7684 array1 = np.array([1, 2, 3, 4])<\/p>\n array2 = np.array([3, 4, 5, 6])<\/p>\n matched = np.isin(array1, array2)<\/p>\n print(array1[matched])<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c NumPy\u8fd8\u63d0\u4f9b\u4e86\u5e7f\u64ad\u3001\u63a9\u7801\u548c\u6761\u4ef6\u9009\u62e9\u7b49\u9ad8\u7ea7\u529f\u80fd\uff0c\u53ef\u4ee5\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u5339\u914d\u64cd\u4f5c\u3002<\/p>\n<\/p>\n mask = array1 > 2<\/p>\n selected = array1[mask]<\/p>\n print(selected)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u63a9\u7801\u64cd\u4f5c\uff0c\u53ef\u4ee5\u6839\u636e\u6761\u4ef6\u9009\u62e9\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n \u5728Python\u4e2d\u5b9e\u73b0\u6570\u636e\u5339\u914d\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6570\u636e\u7ed3\u6784\u7684\u7279\u6027\u3002\u5b57\u5178\u548c\u96c6\u5408\u9002\u5408\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u7684\u5feb\u901f\u5339\u914d\uff0c\u6b63\u5219\u8868\u8fbe\u5f0f\u9002\u7528\u4e8e\u590d\u6742\u7684\u6587\u672c\u5339\u914d\uff0cPandas\u548cNumPy\u5219\u9002\u5408\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\u3002\u901a\u8fc7\u7075\u6d3b\u8fd0\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u5b8c\u6210\u5404\u79cd\u6570\u636e\u5339\u914d\u4efb\u52a1\u3002<\/p>\n<\/p>\n Python\u4e2d\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u6570\u636e\u5339\u914d\u65b9\u6cd5\uff1f<\/strong> \u5728\u5904\u7406\u6570\u636e\u65f6\uff0c\u5982\u4f55\u63d0\u9ad8\u5339\u914d\u7684\u6548\u7387\uff1f<\/strong> Python\u4e2d\u5982\u4f55\u5904\u7406\u6a21\u7cca\u5339\u914d\u7684\u9700\u6c42\uff1f<\/strong>\n
data_dict = {'apple': 2, 'banana': 5, 'orange': 3}<\/p>\n\n
set_a = {1, 2, 3, 4}<\/p>\n\n
re<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u6b63\u5219\u8868\u8fbe\u5f0f\u7684\u652f\u6301\uff0c\u4f7f\u7528re.search()<\/code>\u3001re.match()<\/code>\u3001re.findall()<\/code>\u7b49\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u4e0d\u540c\u7c7b\u578b\u7684\u5339\u914d\u3002<\/p>\n<\/p>\nimport re<\/p>\n\n
pattern = r'(\\d{3})-(\\d{2})-(\\d{4})'<\/p>\n\n
merge()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u4e24\u4e2a\u6570\u636e\u6846\u7684\u5339\u914d\u548c\u5408\u5e76\uff0c\u7c7b\u4f3c\u4e8eSQL\u7684JOIN\u64cd\u4f5c\u3002<\/p>\n<\/p>\nimport pandas as pd<\/p>\nkey<\/code>\u5217\u8fdb\u884c\u5339\u914d\uff0c\u5e76\u5408\u5e76\u6210\u4e00\u4e2a\u65b0\u7684\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n\n
# \u591a\u952e\u5408\u5e76<\/p>\n\n
numpy.isin()<\/code>\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u68c0\u67e5\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u662f\u5426\u5b58\u5728\u4e8e\u53e6\u4e00\u4e2a\u6570\u7ec4\u4e2d\uff0c\u7c7b\u4f3c\u4e8e\u96c6\u5408\u7684\u4ea4\u96c6\u64cd\u4f5c\u3002<\/p>\n<\/p>\nimport numpy as np<\/p>\nnumpy.isin()<\/code>\u51fd\u6570\u7528\u4e8e\u627e\u51faarray1<\/code>\u4e2d\u5b58\u5728\u4e8earray2<\/code>\u4e2d\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n\n
# \u4f7f\u7528\u63a9\u7801\u8fdb\u884c\u6761\u4ef6\u9009\u62e9<\/p>\n\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u5728Python\u4e2d\uff0c\u5b9e\u73b0\u6570\u636e\u5339\u914d\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u4e3b\u8981\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u7684\u6570\u636e\u7ed3\u6784\uff08\u5982\u5217\u8868\u3001\u5b57\u5178\u548c\u96c6\u5408\uff09\u8fdb\u884c\u5339\u914d\u3001\u5229\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u5339\u914d\u3001\u4ee5\u53ca\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u8fdb\u884c\u5b57\u7b26\u4e32\u5339\u914d\u3002\u901a\u8fc7\u5217\u8868\u63a8\u5bfc\u5f0f\u3001\u8fc7\u6ee4\u51fd\u6570\u4ee5\u53ca\u5408\u5e76\u64cd\u4f5c\uff0c\u7528\u6237\u53ef\u4ee5\u9ad8\u6548\u5730\u67e5\u627e\u548c\u5339\u914d\u6570\u636e\u3002\u6b64\u5916\uff0cNumPy\u5e93\u4e5f\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\uff0c\u9002\u5408\u8fdb\u884c\u6570\u503c\u6570\u636e\u7684\u5339\u914d\u3002<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u6570\u636e\u5339\u914d\u7684\u6548\u7387\uff0c\u53ef\u4ee5\u91c7\u53d6\u4ee5\u4e0b\u51e0\u4e2a\u7b56\u7565\uff1a\u9996\u5148\uff0c\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\uff0c\u4f8b\u5982\u4f7f\u7528\u96c6\u5408\u6765\u6d88\u9664\u91cd\u590d\u9879\uff0c\u63d0\u5347\u67e5\u627e\u901f\u5ea6\uff1b\u5176\u6b21\uff0c\u5229\u7528Pandas\u7684\u5411\u91cf\u5316\u64cd\u4f5c\uff0c\u907f\u514d\u4f7f\u7528\u5faa\u73af\u6765\u5904\u7406\u6570\u636e\uff1b\u8fd8\u6709\uff0c\u8003\u8651\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4f8b\u5982\u53bb\u91cd\u3001\u6392\u5e8f\u6216\u7d22\u5f15\uff0c\u80fd\u591f\u663e\u8457\u51cf\u5c11\u5339\u914d\u65f6\u7684\u8ba1\u7b97\u91cf\u3002\u8fd9\u4e9b\u65b9\u6cd5\u7ed3\u5408\u4f7f\u7528\uff0c\u5c06\u5927\u5927\u63d0\u9ad8\u5339\u914d\u7684\u901f\u5ea6\u548c\u6548\u7387\u3002<\/p>\n
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