{"id":1148337,"date":"2025-01-13T16:35:57","date_gmt":"2025-01-13T08:35:57","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1148337.html"},"modified":"2025-01-13T16:36:00","modified_gmt":"2025-01-13T08:36:00","slug":"dw%e5%a6%82%e4%bd%95%e4%bf%9d%e5%ad%98python%e6%a0%bc%e5%bc%8f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1148337.html","title":{"rendered":"dw\u5982\u4f55\u4fdd\u5b58python\u683c\u5f0f"},"content":{"rendered":"

\"dw\u5982\u4f55\u4fdd\u5b58python\u683c\u5f0f\"<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a<\/p>\n

dw\u4fdd\u5b58python\u683c\u5f0f\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528pandas\u5e93\u7684to_csv\u3001to_excel\u51fd\u6570\uff0c\u4f7f\u7528pickle\u5e93\u4fdd\u5b58\u5bf9\u8c61\uff0c\u4f7f\u7528joblib\u5e93\u4fdd\u5b58\u6a21\u578b\uff0c\u4f7f\u7528json\u5e93\u4fdd\u5b58\u6570\u636e<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528pandas\u5e93\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\uff0c\u5176to_csv\u51fd\u6570\u53ef\u4ee5\u5c06DataFrame\u5bf9\u8c61\u8f7b\u677e\u4fdd\u5b58\u4e3aCSV\u683c\u5f0f\u6587\u4ef6\u3002CSV\u6587\u4ef6\u662f\u4e00\u79cd\u901a\u7528\u7684\u8868\u683c\u6570\u636e\u683c\u5f0f\uff0c\u5177\u6709\u5f88\u597d\u7684\u53ef\u8bfb\u6027\u548c\u517c\u5bb9\u6027\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u4e0e\u5176\u4ed6\u8f6f\u4ef6\u548c\u5de5\u5177\u8fdb\u884c\u6570\u636e\u4ea4\u6362\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528pandas\u5e93\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6<\/p>\n<\/p>\n

pandas\u5e93\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60<\/a>\u9886\u57df\u3002\u4f7f\u7528pandas\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3aCSV\u683c\u5f0f\u6587\u4ef6\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6<\/strong><\/h2>\n
df.to_csv('data.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u3001\u5e74\u9f84\u548c\u57ce\u5e02\u4fe1\u606f\u7684DataFrame\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528to_csv\u51fd\u6570\u5c06\u5176\u4fdd\u5b58\u4e3a\u540d\u4e3a"data.csv"\u7684CSV\u6587\u4ef6\u3002index=False<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u4e0d\u4fdd\u5b58\u884c\u7d22\u5f15\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528pandas\u5e93\u4fdd\u5b58\u4e3aExcel\u6587\u4ef6<\/p>\n<\/p>\n
\u9664\u4e86CSV\u683c\u5f0f\uff0cpandas\u5e93\u8fd8\u652f\u6301\u5c06\u6570\u636e\u4fdd\u5b58\u4e3aExcel\u683c\u5f0f\u6587\u4ef6\u3002\u4f7f\u7528to_excel\u51fd\u6570\u53ef\u4ee5\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3aExcel\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4fdd\u5b58\u4e3aExcel\u6587\u4ef6<\/strong><\/h2>\n
df.to_excel('data.xlsx', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528to_excel\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3a\u540d\u4e3a"data.xlsx"\u7684Excel\u6587\u4ef6\u3002index=False<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u4e0d\u4fdd\u5b58\u884c\u7d22\u5f15\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528pickle\u5e93\u4fdd\u5b58\u5bf9\u8c61<\/p>\n<\/p>\n
pickle\u5e93\u662fPython\u5185\u7f6e\u7684\u4e00\u4e2a\u5e8f\u5217\u5316\u5e93\uff0c\u53ef\u4ee5\u5c06Python\u5bf9\u8c61\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u4e5f\u53ef\u4ee5\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7dPython\u5bf9\u8c61\u3002\u4f7f\u7528pickle\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u4fdd\u5b58\u548c\u52a0\u8f7d\u5404\u79cdPython\u5bf9\u8c61\uff0c\u5305\u62ec\u5217\u8868\u3001\u5b57\u5178\u3001\u7c7b\u5b9e\u4f8b\u7b49\u3002<\/p>\n<\/p>\n
import pickle<\/p>\n
\u521b\u5efa\u4e00\u4e2aPython\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
\u4fdd\u5b58\u5bf9\u8c61\u5230\u6587\u4ef6<\/strong><\/h2>\n
with open('data.pkl', 'wb') as file:<\/p>\n
    pickle.dump(data, file)<\/p>\n
\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u5bf9\u8c61<\/strong><\/h2>\n
with open('data.pkl', 'rb') as file:<\/p>\n
    loaded_data = pickle.load(file)<\/p>\n
print(loaded_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u3001\u5e74\u9f84\u548c\u57ce\u5e02\u4fe1\u606f\u7684\u5b57\u5178\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528pickle.dump\u51fd\u6570\u5c06\u5176\u4fdd\u5b58\u5230\u540d\u4e3a"data.pkl"\u7684\u6587\u4ef6\u4e2d\u3002\u4e4b\u540e\uff0c\u6211\u4eec\u4f7f\u7528pickle.load\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u8be5\u5bf9\u8c61\uff0c\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528joblib\u5e93\u4fdd\u5b58\u6a21\u578b<\/p>\n<\/p>\n
joblib\u5e93\u662f\u4e00\u4e2a\u7528\u4e8e\u9ad8\u6548\u4fdd\u5b58\u548c\u52a0\u8f7d\u5927\u578bPython\u5bf9\u8c61\u7684\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u4fdd\u5b58\u548c\u52a0\u8f7d\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002joblib\u5e93\u53ef\u4ee5\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u548c\u590d\u6742\u6a21\u578b\uff0c\u5e76\u4e14\u5728\u6027\u80fd\u548c\u5b58\u50a8\u6548\u7387\u65b9\u9762\u8868\u73b0\u4f18\u5f02\u3002<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
from joblib import dump, load<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
X = [[1, 1], [1, 2], [2, 2], [2, 3]]<\/p>\n
y = [0, 1, 2, 3]<\/p>\n
model.fit(X, y)<\/p>\n
\u4fdd\u5b58\u6a21\u578b\u5230\u6587\u4ef6<\/strong><\/h2>\n
dump(model, 'model.joblib')<\/p>\n
\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6a21\u578b<\/strong><\/h2>\n
loaded_model = load('model.joblib')<\/p>\n
print(loaded_model.predict([[1, 1]]))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u5e76\u5bf9\u5176\u8fdb\u884c\u4e86\u8bad\u7ec3\uff0c\u7136\u540e\u4f7f\u7528joblib.dump\u51fd\u6570\u5c06\u6a21\u578b\u4fdd\u5b58\u5230\u540d\u4e3a"model.joblib"\u7684\u6587\u4ef6\u4e2d\u3002\u4e4b\u540e\uff0c\u6211\u4eec\u4f7f\u7528joblib.load\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u8be5\u6a21\u578b\uff0c\u5e76\u4f7f\u7528\u52a0\u8f7d\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528json\u5e93\u4fdd\u5b58\u6570\u636e<\/p>\n<\/p>\n
json\u5e93\u662fPython\u5185\u7f6e\u7684\u4e00\u4e2a\u7528\u4e8e\u5904\u7406JSON\u6570\u636e\u7684\u5e93\u3002JSON\uff08JavaScript Object Notation\uff09\u662f\u4e00\u79cd\u8f7b\u91cf\u7ea7\u7684\u6570\u636e\u4ea4\u6362\u683c\u5f0f\uff0c\u5e7f\u6cdb\u7528\u4e8eWeb\u5e94\u7528\u548cAPI\u3002\u4f7f\u7528json\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06Python\u5bf9\u8c61\u4fdd\u5b58\u4e3aJSON\u683c\u5f0f\u6587\u4ef6\uff0c\u4e5f\u53ef\u4ee5\u4eceJSON\u683c\u5f0f\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6570\u636e\u3002<\/p>\n<\/p>\n
import json<\/p>\n
\u521b\u5efa\u4e00\u4e2aPython\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
\u4fdd\u5b58\u5bf9\u8c61\u5230JSON\u6587\u4ef6<\/strong><\/h2>\n
with open('data.json', 'w') as file:<\/p>\n
    json.dump(data, file)<\/p>\n
\u4eceJSON\u6587\u4ef6\u4e2d\u52a0\u8f7d\u5bf9\u8c61<\/strong><\/h2>\n
with open('data.json', 'r') as file:<\/p>\n
    loaded_data = json.load(file)<\/p>\n
print(loaded_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u3001\u5e74\u9f84\u548c\u57ce\u5e02\u4fe1\u606f\u7684\u5b57\u5178\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528json.dump\u51fd\u6570\u5c06\u5176\u4fdd\u5b58\u5230\u540d\u4e3a"data.json"\u7684JSON\u6587\u4ef6\u4e2d\u3002\u4e4b\u540e\uff0c\u6211\u4eec\u4f7f\u7528json.load\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u8be5\u5bf9\u8c61\uff0c\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/p>\n<\/p>\n
\u4e0a\u8ff0\u65b9\u6cd5\u5206\u522b\u4ecb\u7ecd\u4e86\u4f7f\u7528pandas\u5e93\u3001pickle\u5e93\u3001joblib\u5e93\u548cjson\u5e93\u4fdd\u5b58Python\u683c\u5f0f\u6570\u636e\u7684\u5177\u4f53\u6b65\u9aa4\u548c\u793a\u4f8b\u3002\u4f7f\u7528pandas\u5e93\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u5177\u6709\u5f88\u597d\u7684\u53ef\u8bfb\u6027\u548c\u517c\u5bb9\u6027<\/strong>\uff0c\u9002\u5408\u65e5\u5e38\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5de5\u4f5c\u3002\u800cpickle\u5e93\u548cjoblib\u5e93\u5219\u9002\u7528\u4e8e\u9700\u8981\u4fdd\u5b58\u548c\u52a0\u8f7d\u590d\u6742Python\u5bf9\u8c61\u548c\u6a21\u578b\u7684\u573a\u666f\u3002json\u5e93\u5219\u9002\u7528\u4e8e\u9700\u8981\u4e0eWeb\u5e94\u7528\u548cAPI\u8fdb\u884c\u6570\u636e\u4ea4\u6362\u7684\u60c5\u51b5\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u4fdd\u5b58Python\u683c\u5f0f\u6570\u636e\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f7f\u7528HDF5\u683c\u5f0f\u4fdd\u5b58\u6570\u636e<\/p>\n<\/p>\n
HDF5\uff08Hierarchical Data Format version 5\uff09\u662f\u4e00\u79cd\u7528\u4e8e\u5b58\u50a8\u548c\u7ba1\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u6587\u4ef6\u683c\u5f0f\u3002\u5b83\u652f\u6301\u9ad8\u6548\u7684\u8bfb\u5199\u64cd\u4f5c\u548c\u6570\u636e\u538b\u7f29\uff0c\u975e\u5e38\u9002\u5408\u7528\u4e8e\u4fdd\u5b58\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u7684to_hdf\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3aHDF5\u683c\u5f0f\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4fdd\u5b58\u4e3aHDF5\u6587\u4ef6<\/strong><\/h2>\n
df.to_hdf('data.h5', key='df', mode='w')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528to_hdf\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3a\u540d\u4e3a"data.h5"\u7684HDF5\u6587\u4ef6\u3002key='df'<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u6570\u636e\u96c6\u7684\u540d\u79f0\uff0cmode='w'<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u5199\u5165\u6a21\u5f0f\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u4f7f\u7528SQLite\u6570\u636e\u5e93\u4fdd\u5b58\u6570\u636e<\/p>\n<\/p>\n
SQLite\u662f\u4e00\u79cd\u8f7b\u91cf\u7ea7\u7684\u5173\u7cfb\u578b\u6570\u636e\u5e93\u7ba1\u7406\u7cfb\u7edf\uff0c\u9002\u7528\u4e8e\u5d4c\u5165\u5f0f\u7cfb\u7edf\u548c\u5c0f\u578b\u5e94\u7528\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u7684to_sql\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u5230SQLite\u6570\u636e\u5e93\u4e2d\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import sqlite3<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u8fde\u63a5SQLite\u6570\u636e\u5e93<\/strong><\/h2>\n
conn = sqlite3.connect('data.db')<\/p>\n
\u4fdd\u5b58\u6570\u636e\u5230SQLite\u6570\u636e\u5e93<\/strong><\/h2>\n
df.to_sql('people', conn, if_exists='replace', index=False)<\/p>\n
\u4eceSQLite\u6570\u636e\u5e93\u4e2d\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
df_loaded = pd.read_sql('SELECT * FROM people', conn)<\/p>\n
print(df_loaded)<\/p>\n
\u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5<\/strong><\/h2>\n
conn.close()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u3001\u5e74\u9f84\u548c\u57ce\u5e02\u4fe1\u606f\u7684DataFrame\u5bf9\u8c61\uff0c\u7136\u540e\u8fde\u63a5\u5230\u540d\u4e3a"data.db"\u7684SQLite\u6570\u636e\u5e93\uff0c\u5e76\u4f7f\u7528to_sql\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u5230\u540d\u4e3a"people"\u7684\u8868\u4e2d\u3002\u4e4b\u540e\uff0c\u6211\u4eec\u4f7f\u7528read_sql\u51fd\u6570\u4eceSQLite\u6570\u636e\u5e93\u4e2d\u52a0\u8f7d\u6570\u636e\uff0c\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u516b\u3001\u4f7f\u7528Parquet\u683c\u5f0f\u4fdd\u5b58\u6570\u636e<\/p>\n<\/p>\n
Parquet\u662f\u4e00\u79cd\u5217\u5f0f\u5b58\u50a8\u683c\u5f0f\uff0c\u5177\u6709\u9ad8\u6548\u7684\u538b\u7f29\u548c\u7f16\u7801\u6027\u80fd\uff0c\u9002\u5408\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u7684to_parquet\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3aParquet\u683c\u5f0f\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4fdd\u5b58\u4e3aParquet\u6587\u4ef6<\/strong><\/h2>\n
df.to_parquet('data.parquet', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528to_parquet\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3a\u540d\u4e3a"data.parquet"\u7684Parquet\u6587\u4ef6\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u4f7f\u7528Feather\u683c\u5f0f\u4fdd\u5b58\u6570\u636e<\/p>\n<\/p>\n
Feather\u662f\u4e00\u79cd\u9ad8\u6027\u80fd\u7684\u4e8c\u8fdb\u5236\u6570\u636e\u683c\u5f0f\uff0c\u4e13\u4e3a\u5feb\u901f\u8bfb\u5199DataFrame\u5bf9\u8c61\u800c\u8bbe\u8ba1\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u7684to_feather\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3aFeather\u683c\u5f0f\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4fdd\u5b58\u4e3aFeather\u6587\u4ef6<\/strong><\/h2>\n
df.to_feather('data.feather')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528to_feather\u51fd\u6570\u5c06DataFrame\u5bf9\u8c61\u4fdd\u5b58\u4e3a\u540d\u4e3a"data.feather"\u7684Feather\u6587\u4ef6\u3002<\/p>\n<\/p>\n
\u5341\u3001\u4f7f\u7528YAML\u683c\u5f0f\u4fdd\u5b58\u6570\u636e<\/p>\n<\/p>\n
YAML\uff08YAML AI<\/a>n't Markup Language\uff09\u662f\u4e00\u79cd\u4eba\u7c7b\u53ef\u8bfb\u7684\u6570\u636e\u5e8f\u5217\u5316\u683c\u5f0f\uff0c\u5e7f\u6cdb\u7528\u4e8e\u914d\u7f6e\u6587\u4ef6\u548c\u6570\u636e\u4ea4\u6362\u3002\u53ef\u4ee5\u4f7f\u7528PyYAML\u5e93\u5c06Python\u5bf9\u8c61\u4fdd\u5b58\u4e3aYAML\u683c\u5f0f\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import yaml<\/p>\n
\u521b\u5efa\u4e00\u4e2aPython\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
\u4fdd\u5b58\u5bf9\u8c61\u5230YAML\u6587\u4ef6<\/strong><\/h2>\n
with open('data.yaml', 'w') as file:<\/p>\n
    yaml.dump(data, file)<\/p>\n
\u4eceYAML\u6587\u4ef6\u4e2d\u52a0\u8f7d\u5bf9\u8c61<\/strong><\/h2>\n
with open('data.yaml', 'r') as file:<\/p>\n
    loaded_data = yaml.load(file, Loader=yaml.FullLoader)<\/p>\n
print(loaded_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u3001\u5e74\u9f84\u548c\u57ce\u5e02\u4fe1\u606f\u7684\u5b57\u5178\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528yaml.dump\u51fd\u6570\u5c06\u5176\u4fdd\u5b58\u5230\u540d\u4e3a"data.yaml"\u7684YAML\u6587\u4ef6\u4e2d\u3002\u4e4b\u540e\uff0c\u6211\u4eec\u4f7f\u7528yaml.load\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u8be5\u5bf9\u8c61\uff0c\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u5341\u4e00\u3001\u4f7f\u7528XML\u683c\u5f0f\u4fdd\u5b58\u6570\u636e<\/p>\n<\/p>\n
XML\uff08eXtensible Markup Language\uff09\u662f\u4e00\u79cd\u7528\u4e8e\u8868\u793a\u548c\u4f20\u8f93\u7ed3\u6784\u5316\u6570\u636e\u7684\u6807\u8bb0\u8bed\u8a00\uff0c\u5e7f\u6cdb\u7528\u4e8eWeb\u670d\u52a1\u548c\u6570\u636e\u4ea4\u6362\u3002\u53ef\u4ee5\u4f7f\u7528xml.etree.ElementTree\u5e93\u5c06Python\u5bf9\u8c61\u4fdd\u5b58\u4e3aXML\u683c\u5f0f\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import xml.etree.ElementTree as ET<\/p>\n
\u521b\u5efa\u4e00\u4e2aPython\u5bf9\u8c61<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
\u521b\u5efaXML\u6839\u5143\u7d20<\/strong><\/h2>\n
root = ET.Element('People')<\/p>\n
\u521b\u5efa\u5b50\u5143\u7d20\u5e76\u6dfb\u52a0\u5230\u6839\u5143\u7d20<\/strong><\/h2>\n
for i in range(len(data['Name'])):<\/p>\n
    person = ET.SubElement(root, 'Person')<\/p>\n
    name = ET.SubElement(person, 'Name')<\/p>\n
    name.text = data['Name'][i]<\/p>\n
    age = ET.SubElement(person, 'Age')<\/p>\n
    age.text = str(data['Age'][i])<\/p>\n
    city = ET.SubElement(person, 'City')<\/p>\n
    city.text = data['City'][i]<\/p>\n
\u4fdd\u5b58\u5bf9\u8c61\u5230XML\u6587\u4ef6<\/strong><\/h2>\n
tree = ET.ElementTree(root)<\/p>\n
tree.write('data.xml')<\/p>\n
\u4eceXML\u6587\u4ef6\u4e2d\u52a0\u8f7d\u5bf9\u8c61<\/strong><\/h2>\n
tree = ET.parse('data.xml')<\/p>\n
root = tree.getroot()<\/p>\n
loaded_data = {'Name': [], 'Age': [], 'City': []}<\/p>\n
for person in root.findall('Person'):<\/p>\n
    loaded_data['Name'].append(person.find('Name').text)<\/p>\n
    loaded_data['Age'].append(int(person.find('Age').text))<\/p>\n
    loaded_data['City'].append(person.find('City').text)<\/p>\n
print(loaded_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u3001\u5e74\u9f84\u548c\u57ce\u5e02\u4fe1\u606f\u7684\u5b57\u5178\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528xml.etree.ElementTree\u5e93\u5c06\u5176\u4fdd\u5b58\u5230\u540d\u4e3a"data.xml"\u7684XML\u6587\u4ef6\u4e2d\u3002\u4e4b\u540e\uff0c\u6211\u4eec\u4eceXML\u6587\u4ef6\u4e2d\u52a0\u8f7d\u8be5\u5bf9\u8c61\uff0c\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/p>\n<\/p>\n
\u4ee5\u4e0a\u4ecb\u7ecd\u4e86\u591a\u79cd\u4fdd\u5b58Python\u683c\u5f0f\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528pandas\u5e93\u4fdd\u5b58\u4e3aCSV\u3001Excel\u3001HDF5\u3001SQLite\u3001Parquet\u3001Feather\u683c\u5f0f\u6587\u4ef6\uff0c\u4f7f\u7528pickle\u5e93\u548cjoblib\u5e93\u4fdd\u5b58\u5bf9\u8c61\u548c\u6a21\u578b\uff0c\u4f7f\u7528json\u5e93\u4fdd\u5b58\u6570\u636e\uff0c\u4f7f\u7528PyYAML\u5e93\u4fdd\u5b58\u4e3aYAML\u683c\u5f0f\u6587\u4ef6\uff0c\u4f7f\u7528xml.etree.ElementTree\u5e93\u4fdd\u5b58\u4e3aXML\u683c\u5f0f\u6587\u4ef6<\/strong>\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u4fdd\u5b58Python\u683c\u5f0f\u6570\u636e\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u7f3a\u70b9\uff0c\u9009\u62e9\u65f6\u9700\u8981\u7efc\u5408\u8003\u8651\u6570\u636e\u7684\u89c4\u6a21\u3001\u683c\u5f0f\u8981\u6c42\u3001\u8bfb\u5199\u6027\u80fd\u4ee5\u53ca\u4e0e\u5176\u4ed6\u8f6f\u4ef6\u548c\u5de5\u5177\u7684\u517c\u5bb9\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Data Warehouse\u4e2d\u4fdd\u5b58Python\u683c\u5f0f\u7684\u6570\u636e\uff1f<\/strong>
\u5728Data Warehouse\u4e2d\uff0c\u4fdd\u5b58Python\u683c\u5f0f\u7684\u6570\u636e\u901a\u5e38\u9700\u8981\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u9002\u5408\u5b58\u50a8\u7684\u683c\u5f0f\uff0c\u4f8b\u5982CSV\u6216Parquet\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Python\u7684pandas\u5e93\u6765\u5b9e\u73b0\uff0c\u60a8\u53ef\u4ee5\u5c06DataFrame\u4fdd\u5b58\u4e3a\u6240\u9700\u7684\u683c\u5f0f\uff0c\u5e76\u4f7f\u7528\u9002\u5f53\u7684\u6570\u636e\u5e93\u8fde\u63a5\u5c06\u5176\u4e0a\u4f20\u5230Data Warehouse\u3002<\/p>\n
\u5728Data Warehouse\u4e2d\uff0c\u6211\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u6570\u636e\u683c\u5f0f\u6765\u4fdd\u5b58Python\u6570\u636e\uff1f<\/strong>
\u5728Data Warehouse\u4e2d\uff0c\u5e38\u7528\u7684\u6570\u636e\u683c\u5f0f\u5305\u62ecCSV\u3001Parquet\u3001ORC\u3001JSON\u7b49\u3002\u9009\u62e9\u6570\u636e\u683c\u5f0f\u65f6\uff0c\u8003\u8651\u6570\u636e\u7684\u7279\u6027\u548c\u5c06\u6765\u8bbf\u95ee\u7684\u9700\u6c42\u3002\u4f8b\u5982\uff0cParquet\u683c\u5f0f\u66f4\u9002\u5408\u5904\u7406\u5927\u6570\u636e\uff0c\u5177\u6709\u66f4\u597d\u7684\u538b\u7f29\u7387\u548c\u67e5\u8be2\u6027\u80fd\uff0c\u800cCSV\u683c\u5f0f\u5219\u66f4\u6613\u4e8e\u4eba\u7c7b\u8bfb\u53d6\u3002<\/p>\n
\u5982\u4f55\u786e\u4fdd\u5728\u4fdd\u5b58Python\u683c\u5f0f\u6570\u636e\u65f6\u4e0d\u4e22\u5931\u4fe1\u606f\uff1f<\/strong>
\u786e\u4fdd\u5728\u4fdd\u5b58Python\u683c\u5f0f\u6570\u636e\u65f6\u4e0d\u4e22\u5931\u4fe1\u606f\u7684\u5173\u952e\u662f\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u7c7b\u578b\u548c\u683c\u5f0f\u3002\u5728\u4f7f\u7528pandas\u4fdd\u5b58\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u6765\u63a7\u5236\u6570\u636e\u7684\u7cbe\u5ea6\uff0c\u4f8b\u5982\u6d6e\u70b9\u6570\u7684\u683c\u5f0f\u3002\u6b64\u5916\uff0c\u5b9a\u671f\u8fdb\u884c\u6570\u636e\u9a8c\u8bc1\u548c\u5b8c\u6574\u6027\u68c0\u67e5\u4e5f\u662f\u786e\u4fdd\u6570\u636e\u5b89\u5168\u7684\u6709\u6548\u65b9\u6cd5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1a dw\u4fdd\u5b58python\u683c\u5f0f\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528pandas\u5e93\u7684to_csv\u3001to_excel\u51fd\u6570\uff0c\u4f7f\u7528p […]","protected":false},"author":3,"featured_media":1148342,"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\/1148337"}],"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=1148337"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1148337\/revisions"}],"predecessor-version":[{"id":1148344,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1148337\/revisions\/1148344"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1148342"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1148337"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1148337"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1148337"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}