{"id":1145813,"date":"2025-01-08T23:11:29","date_gmt":"2025-01-08T15:11:29","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1145813.html"},"modified":"2025-01-08T23:11:32","modified_gmt":"2025-01-08T15:11:32","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e5%a4%9a%e4%b8%aa%e5%ad%97%e5%85%b8%e8%bd%ac%e5%8c%96%e6%88%90%e6%95%b0%e6%8d%ae%e6%a1%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1145813.html","title":{"rendered":"python\u5982\u4f55\u5c06\u591a\u4e2a\u5b57\u5178\u8f6c\u5316\u6210\u6570\u636e\u6846"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u591a\u4e2a\u5b57\u5178\u8f6c\u5316\u6210\u6570\u636e\u6846\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5c06\u591a\u4e2a\u5b57\u5178\u8f6c\u6362\u6210\u6570\u636e\u6846\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528pandas\u5e93\u3001\u5229\u7528from_dict\u65b9\u6cd5\u3001\u4ee5\u53ca\u7ed3\u5408\u5217\u8868\u63a8\u5bfc\u7b49\u3002\u6700\u5e38\u89c1\u548c\u4fbf\u6377\u7684\u65b9\u6cd5\u662f\u4f7f\u7528pandas\u5e93\u7684DataFrame\u6784\u9020\u51fd\u6570\u3002<\/strong> \u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528pandas\u5e93\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n

\u5b89\u88c5\u548c\u5bfc\u5165Pandas\u5e93<\/h4>\n<\/p>\n

\u8981\u4f7f\u7528pandas\u5e93\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u5b83\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u521b\u5efa\u5b57\u5178\u5217\u8868<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u591a\u4e2a\u5b57\u5178\uff0c\u6bcf\u4e2a\u5b57\u5178\u4ee3\u8868\u4e00\u884c\u6570\u636e\uff1a<\/p>\n<\/p>\n
dict1 = {'Name': 'Alice', 'Age': 25, 'City': 'New York'}<\/p>\n
dict2 = {'Name': 'Bob', 'Age': 30, 'City': 'Los Angeles'}<\/p>\n
dict3 = {'Name': 'Charlie', 'Age': 35, 'City': 'Chicago'}<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5c06\u5b57\u5178\u5217\u8868\u8f6c\u6362\u4e3a\u6570\u636e\u6846<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528pd.DataFrame<\/code>\u65b9\u6cd5\u5c06\u5b57\u5178\u5217\u8868\u8f6c\u6362\u4e3a\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
data = [dict1, dict2, dict3]<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
      Name  Age         City<\/p>\n
0    Alice   25    New York<\/p>\n
1      Bob   30  Los Angeles<\/p>\n
2  Charlie   35     Chicago<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5229\u7528from_dict<\/code>\u65b9\u6cd5<\/h3>\n<\/p>\n
\u4f7f\u7528\u5b57\u5178\u7684\u5217\u8868<\/h4>\n<\/p>\n
\u4e0e\u7b2c\u4e00\u79cd\u65b9\u6cd5\u7c7b\u4f3c\uff0c\u6211\u4eec\u4f7f\u7528\u5b57\u5178\u7684\u5217\u8868\uff1a<\/p>\n<\/p>\n
data = [dict1, dict2, dict3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8c03\u7528from_dict<\/code>\u65b9\u6cd5<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pandas\u7684from_dict<\/code>\u65b9\u6cd5\u6765\u521b\u5efa\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
df = pd.DataFrame.from_dict(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u4e0e\u7b2c\u4e00\u79cd\u65b9\u6cd5\u76f8\u540c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7ed3\u5408\u5217\u8868\u63a8\u5bfc<\/h3>\n<\/p>\n
\u521b\u5efa\u5b57\u5178\u5217\u8868<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u591a\u4e2a\u5b57\u5178\uff1a<\/p>\n<\/p>\n
dict1 = {'Name': 'Alice', 'Age': 25, 'City': 'New York'}<\/p>\n
dict2 = {'Name': 'Bob', 'Age': 30, 'City': 'Los Angeles'}<\/p>\n
dict3 = {'Name': 'Charlie', 'Age': 35, 'City': 'Chicago'}<\/p>\n
dict_list = [dict1, dict2, dict3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc<\/h4>\n<\/p>\n
\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5c06\u5b57\u5178\u5217\u8868\u8f6c\u6362\u4e3a\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
df = pd.DataFrame([dict(i) for i in dict_list])<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u4e0e\u524d\u4e24\u79cd\u65b9\u6cd5\u76f8\u540c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n
\u793a\u4f8b\u5b57\u5178<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u5b57\u5178\u7f3a\u5c11\u67d0\u4e9b\u952e\u7684\u60c5\u51b5\uff1a<\/p>\n<\/p>\n
dict1 = {'Name': 'Alice', 'Age': 25}<\/p>\n
dict2 = {'Name': 'Bob', 'City': 'Los Angeles'}<\/p>\n
dict3 = {'Name': 'Charlie', 'Age': 35, 'City': 'Chicago'}<\/p>\n
data = [dict1, dict2, dict3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f6c\u6362\u4e3a\u6570\u636e\u6846<\/h4>\n<\/p>\n
\u5f53\u5b57\u5178\u7684\u952e\u4e0d\u5b8c\u5168\u76f8\u540c\u65f6\uff0cpandas\u4f1a\u81ea\u52a8\u586b\u5145\u7f3a\u5931\u503c\u4e3aNaN\uff1a<\/p>\n<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
      Name   Age         City<\/p>\n
0    Alice  25.0          NaN<\/p>\n
1      Bob   NaN  Los Angeles<\/p>\n
2  Charlie  35.0      Chicago<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528\u591a\u5c42\u5d4c\u5957\u5b57\u5178<\/h3>\n<\/p>\n
\u793a\u4f8b\u591a\u5c42\u5d4c\u5957\u5b57\u5178<\/h4>\n<\/p>\n
\u6709\u65f6\u5b57\u5178\u53ef\u80fd\u5305\u542b\u5d4c\u5957\u5b57\u5178\uff1a<\/p>\n<\/p>\n
dict1 = {'Name': 'Alice', 'DetAI<\/a>ls': {'Age': 25, 'City': 'New York'}}<\/p>\n
dict2 = {'Name': 'Bob', 'Details': {'Age': 30, 'City': 'Los Angeles'}}<\/p>\n
dict3 = {'Name': 'Charlie', 'Details': {'Age': 35, 'City': 'Chicago'}}<\/p>\n
data = [dict1, dict2, dict3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f6c\u6362\u4e3a\u6570\u636e\u6846<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528json_normalize<\/code>\u65b9\u6cd5\u5c06\u5d4c\u5957\u5b57\u5178\u8f6c\u6362\u4e3a\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
from pandas import json_normalize<\/p>\n
df = json_normalize(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
      Name  Details.Age  Details.City<\/p>\n
0    Alice           25    New York<\/p>\n
1      Bob           30  Los Angeles<\/p>\n
2  Charlie           35     Chicago<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e<\/h3>\n<\/p>\n
\u5206\u5757\u8bfb\u53d6<\/h4>\n<\/p>\n
\u5982\u679c\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\uff0c\u53ef\u4ee5\u8003\u8651\u5206\u5757\u8bfb\u53d6\u548c\u5904\u7406\uff1a<\/p>\n<\/p>\n
chunk_size = 1000  # \u8bbe\u7f6e\u6bcf\u5757\u7684\u5927\u5c0f<\/p>\n
data_chunks = pd.read_json('large_file.json', lines=True, chunksize=chunk_size)<\/p>\n
for chunk in data_chunks:<\/p>\n
    process(chunk)  # \u81ea\u5b9a\u4e49\u5904\u7406\u51fd\u6570<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u5c06\u591a\u4e2a\u5b57\u5178\u8f6c\u6362\u6210\u6570\u636e\u6846\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pandas\u5e93\u7684DataFrame\u6784\u9020\u51fd\u6570\u3001from_dict\u65b9\u6cd5\u3001\u4ee5\u53ca\u7ed3\u5408\u5217\u8868\u63a8\u5bfc\u3002\u5229\u7528pandas\u5e93\u662f\u6700\u4fbf\u6377\u548c\u5e38\u7528\u7684\u65b9\u6cd5<\/strong>\uff0c\u5e76\u4e14\u5b83\u8fd8\u63d0\u4f9b\u4e86\u5904\u7406\u7f3a\u5931\u503c\u548c\u5d4c\u5957\u5b57\u5178\u7684\u529f\u80fd\u3002\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\uff0c\u53ef\u4ee5\u8003\u8651\u5206\u5757\u8bfb\u53d6\u548c\u5904\u7406\u3002\u5e0c\u671b\u901a\u8fc7\u8fd9\u7bc7\u8be6\u7ec6\u7684\u4ecb\u7ecd\uff0c\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5c06\u591a\u4e2a\u5b57\u5178\u5408\u5e76\u4e3a\u4e00\u4e2a\u6570\u636e\u6846\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u7684DataFrame<\/code>\u65b9\u6cd5\uff0c\u5c06\u591a\u4e2a\u5b57\u5178\u4f5c\u4e3a\u53c2\u6570\u4f20\u5165\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u5b89\u88c5pandas<\/code>\u5e93\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u5c06\u591a\u4e2a\u5b57\u5178\u653e\u5165\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u518d\u901a\u8fc7pd.DataFrame()<\/code>\u51fd\u6570\u5c06\u5176\u8f6c\u6362\u4e3a\u6570\u636e\u6846\u3002\u4f8b\u5982\uff1adf = pd.DataFrame([dict1, dict2, dict3])<\/code>\u3002<\/p>\n
\u5728\u8f6c\u6362\u5b57\u5178\u4e3a\u6570\u636e\u6846\u65f6\uff0c\u5982\u4f55\u5904\u7406\u952e\u7684\u4e0d\u540c\uff1f<\/strong>
\u5f53\u5b57\u5178\u7684\u952e\u4e0d\u4e00\u81f4\u65f6\uff0cpandas<\/code>\u4f1a\u81ea\u52a8\u586b\u5145\u7f3a\u5931\u503c\u4e3aNaN\u3002\u53ef\u4ee5\u4f7f\u7528pd.DataFrame.from_records()<\/code>\u65b9\u6cd5\uff0c\u6307\u5b9aorient='columns'<\/code>\u6216orient='index'<\/code>\u6765\u63a7\u5236\u5982\u4f55\u5904\u7406\u6570\u636e\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u4e0d\u540c\u952e\u7684\u6570\u636e\u90fd\u80fd\u5728\u6570\u636e\u6846\u4e2d\u5f97\u5230\u4f53\u73b0\uff0c\u907f\u514d\u4e22\u5931\u4fe1\u606f\u3002<\/p>\n
\u662f\u5426\u53ef\u4ee5\u5c06\u5b57\u5178\u7684\u5217\u8868\u76f4\u63a5\u8f6c\u5316\u4e3a\u6570\u636e\u6846\uff1f<\/strong>
\u662f\u7684\uff0c\u53ef\u4ee5\u76f4\u63a5\u5c06\u5b57\u5178\u7684\u5217\u8868\u4f20\u5165pd.DataFrame()<\/code>\uff0c\u8fd9\u5bf9\u4e8e\u5904\u7406\u4e00\u7ec4\u7ed3\u6784\u76f8\u4f3c\u7684\u6570\u636e\u975e\u5e38\u65b9\u4fbf\u3002\u6570\u636e\u6846\u5c06\u6839\u636e\u5b57\u5178\u7684\u952e\u521b\u5efa\u5217\uff0c\u503c\u5219\u5bf9\u5e94\u5230\u76f8\u5e94\u7684\u884c\u4e2d\u3002\u8fd9\u79cd\u65b9\u5f0f\u7279\u522b\u9002\u5408\u7528\u4e8e\u5904\u7406\u6765\u81eaJSON\u7b49\u683c\u5f0f\u7684\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u5c06\u591a\u4e2a\u5b57\u5178\u8f6c\u6362\u6210\u6570\u636e\u6846\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528pandas\u5e93\u3001\u5229\u7528from_dict\u65b9\u6cd5\u3001\u4ee5\u53ca\u7ed3 […]","protected":false},"author":3,"featured_media":1145822,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1145813"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1145813"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1145813\/revisions"}],"predecessor-version":[{"id":1145824,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1145813\/revisions\/1145824"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1145822"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1145813"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1145813"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1145813"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}