{"id":1034017,"date":"2024-12-31T11:46:48","date_gmt":"2024-12-31T03:46:48","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1034017.html"},"modified":"2024-12-31T11:46:50","modified_gmt":"2024-12-31T03:46:50","slug":"python%e5%ad%97%e7%ac%a6%e4%b8%b2%e5%a6%82%e4%bd%95%e5%86%99csv","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1034017.html","title":{"rendered":"python\u5b57\u7b26\u4e32\u5982\u4f55\u5199csv"},"content":{"rendered":"

\"python\u5b57\u7b26\u4e32\u5982\u4f55\u5199csv\"<\/p>\n

Python\u5b57\u7b26\u4e32\u5199\u5165CSV\u7684\u65b9\u5f0f\u6709\u5f88\u591a\u79cd\uff0c\u4f7f\u7528\u5185\u7f6e\u7684csv\u6a21\u5757\u3001pandas\u5e93\u3001\u6216\u8005\u624b\u52a8\u5904\u7406\u5b57\u7b26\u4e32\u5185\u5bb9\u7b49\u65b9\u6cd5\u3002\u63a8\u8350\u7684\u65b9\u6cd5\u5305\u62ec\uff1acsv\u6a21\u5757\u7684writer\u5bf9\u8c61\u3001pandas\u7684to_csv\u65b9\u6cd5\u3001\u624b\u52a8\u5904\u7406\u3002<\/strong>\u5176\u4e2d\uff0ccsv\u6a21\u5757\u7684writer\u5bf9\u8c61\u6700\u4e3a\u5e38\u7528\uff0c\u65b9\u4fbf\u4e14\u9ad8\u6548\u3002\u4e0b\u9762\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u7528\u8fd9\u51e0\u79cd\u65b9\u6cd5\u5c06Python\u5b57\u7b26\u4e32\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528csv\u6a21\u5757<\/h3>\n<\/p>\n

Python\u5185\u7f6e\u7684csv\u6a21\u5757\u63d0\u4f9b\u4e86\u5904\u7406CSV\u6587\u4ef6\u7684\u529f\u80fd\uff0c\u5305\u542b\u8bfb\u5199CSV\u6587\u4ef6\u7684\u7b80\u4fbf\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528csv\u6a21\u5757\u5c06\u5b57\u7b26\u4e32\u5199\u5165CSV\u6587\u4ef6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

1\u3001\u521b\u5efaCSV\u6587\u4ef6\u5e76\u5199\u5165\u6570\u636e<\/h4>\n<\/p>\n

csv\u6a21\u5757\u4e2d\u7684writer<\/code>\u5bf9\u8c61\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u5c06\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002\u9996\u5148\uff0c\u9700\u8981\u5bfc\u5165csv\u6a21\u5757\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2aCSV\u6587\u4ef6\u5e76\u5199\u5165\u6570\u636e\u3002<\/p>\n<\/p>\n

import csv<\/p>\n
\u5b9a\u4e49\u8981\u5199\u5165\u7684\u5b57\u7b26\u4e32\u6570\u636e<\/strong><\/h2>\n
data = [<\/p>\n
    ["Name", "Age", "City"],<\/p>\n
    ["Alice", "30", "New York"],<\/p>\n
    ["Bob", "25", "Los Angeles"],<\/p>\n
    ["Charlie", "35", "Chicago"]<\/p>\n
]<\/p>\n
\u521b\u5efa\u5e76\u6253\u5f00CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('output.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.writer(file)<\/p>\n
    # \u5199\u5165\u6570\u636e<\/p>\n
    for row in data:<\/p>\n
        writer.writerow(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5199\u5165\u5b57\u5178\u6570\u636e<\/h4>\n<\/p>\n
\u9664\u4e86\u5199\u5165\u5217\u8868\u6570\u636e\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528DictWriter<\/code>\u5bf9\u8c61\u5c06\u5b57\u5178\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import csv<\/p>\n
\u5b9a\u4e49\u8981\u5199\u5165\u7684\u5b57\u5178\u6570\u636e<\/strong><\/h2>\n
data = [<\/p>\n
    {"Name": "Alice", "Age": "30", "City": "New York"},<\/p>\n
    {"Name": "Bob", "Age": "25", "City": "Los Angeles"},<\/p>\n
    {"Name": "Charlie", "Age": "35", "City": "Chicago"}<\/p>\n
]<\/p>\n
\u83b7\u53d6\u5b57\u6bb5\u540d<\/strong><\/h2>\n
fieldnames = data[0].keys()<\/p>\n
\u521b\u5efa\u5e76\u6253\u5f00CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('output_dict.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.DictWriter(file, fieldnames=fieldnames)<\/p>\n
    # \u5199\u5165\u5b57\u6bb5\u540d<\/p>\n
    writer.writeheader()<\/p>\n
    # \u5199\u5165\u6570\u636e<\/p>\n
    for row in data:<\/p>\n
        writer.writerow(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528pandas\u5e93<\/h3>\n<\/p>\n
pandas\u5e93\u662fPython\u4e2d\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\uff0c\u80fd\u8f7b\u677e\u5904\u7406CSV\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528pandas\u5e93\u5c06\u5b57\u7b26\u4e32\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
1\u3001\u521b\u5efaDataFrame\u5e76\u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u5b9a\u4e49\u8981\u5199\u5165\u7684\u5b57\u7b26\u4e32\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    "Name": ["Alice", "Bob", "Charlie"],<\/p>\n
    "Age": [30, 25, 35],<\/p>\n
    "City": ["New York", "Los Angeles", "Chicago"]<\/p>\n
}<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(data)<\/p>\n
\u5c06DataFrame\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
df.to_csv('output_pandas.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4ece\u5b57\u7b26\u4e32\u8bfb\u53d6\u6570\u636e\u5e76\u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u6709\u65f6\uff0c\u6570\u636e\u53ef\u80fd\u4ee5\u5b57\u7b26\u4e32\u5f62\u5f0f\u5b58\u5728\uff0c\u53ef\u4ee5\u5148\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3aDataFrame\uff0c\u518d\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
from io import StringIO<\/p>\n
\u5b9a\u4e49\u8981\u5199\u5165\u7684\u5b57\u7b26\u4e32\u6570\u636e<\/strong><\/h2>\n
data = """Name,Age,City<\/p>\n
Alice,30,New York<\/p>\n
Bob,25,Los Angeles<\/p>\n
Charlie,35,Chicago"""<\/p>\n
\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
df = pd.read_csv(StringIO(data))<\/p>\n
\u5c06DataFrame\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
df.to_csv('output_from_string.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u624b\u52a8\u5904\u7406\u5b57\u7b26\u4e32<\/h3>\n<\/p>\n
\u5982\u679c\u4e0d\u4f7f\u7528csv\u6a21\u5757\u6216\u8005pandas\u5e93\uff0c\u4e5f\u53ef\u4ee5\u624b\u52a8\u5904\u7406\u5b57\u7b26\u4e32\u6570\u636e\u5e76\u5199\u5165CSV\u6587\u4ef6\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u7b80\u5355\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
1\u3001\u624b\u52a8\u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
# \u5b9a\u4e49\u8981\u5199\u5165\u7684\u5b57\u7b26\u4e32\u6570\u636e<\/p>\n
data = """Name,Age,City<\/p>\n
Alice,30,New York<\/p>\n
Bob,25,Los Angeles<\/p>\n
Charlie,35,Chicago"""<\/p>\n
\u5c06\u5b57\u7b26\u4e32\u6570\u636e\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('output_manual.csv', mode='w') as file:<\/p>\n
    file.write(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u5e76\u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
# \u5b9a\u4e49\u8981\u5199\u5165\u7684\u6570\u636e<\/p>\n
header = ["Name", "Age", "City"]<\/p>\n
rows = [<\/p>\n
    ["Alice", "30", "New York"],<\/p>\n
    ["Bob", "25", "Los Angeles"],<\/p>\n
    ["Charlie", "35", "Chicago"]<\/p>\n
]<\/p>\n
\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u6570\u636e<\/strong><\/h2>\n
data = ",".join(header) + "\\n" + "\\n".join([",".join(row) for row in rows])<\/p>\n
\u5c06\u683c\u5f0f\u5316\u7684\u5b57\u7b26\u4e32\u6570\u636e\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('output_formatted.csv', mode='w') as file:<\/p>\n
    file.write(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5904\u7406\u590d\u6742\u6570\u636e\u7ed3\u6784<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5982\u5d4c\u5957\u5217\u8868\u6216\u5d4c\u5957\u5b57\u5178\u3002\u5904\u7406\u8fd9\u7c7b\u6570\u636e\u65f6\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u63d0\u53d6\u6709\u7528\u7684\u4fe1\u606f\u5e76\u683c\u5f0f\u5316\u4e3aCSV\u683c\u5f0f\u3002<\/p>\n<\/p>\n
1\u3001\u5904\u7406\u5d4c\u5957\u5217\u8868<\/h4>\n<\/p>\n
import csv<\/p>\n
\u5b9a\u4e49\u5d4c\u5957\u5217\u8868\u6570\u636e<\/strong><\/h2>\n
data = [<\/p>\n
    ["Name", "Age", "Address", ["City", "State"]],<\/p>\n
    ["Alice", "30", "123 MAI<\/a>n St", ["New York", "NY"]],<\/p>\n
    ["Bob", "25", "456 Maple Ave", ["Los Angeles", "CA"]],<\/p>\n
    ["Charlie", "35", "789 Oak Dr", ["Chicago", "IL"]]<\/p>\n
]<\/p>\n
\u6241\u5e73\u5316\u6570\u636e\u5e76\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('output_nested_list.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.writer(file)<\/p>\n
    for row in data:<\/p>\n
        flat_row = row[:3] + row[3]<\/p>\n
        writer.writerow(flat_row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u5d4c\u5957\u5b57\u5178<\/h4>\n<\/p>\n
import csv<\/p>\n
\u5b9a\u4e49\u5d4c\u5957\u5b57\u5178\u6570\u636e<\/strong><\/h2>\n
data = [<\/p>\n
    {"Name": "Alice", "Age": "30", "Address": {"Street": "123 Main St", "City": "New York", "State": "NY"}},<\/p>\n
    {"Name": "Bob", "Age": "25", "Address": {"Street": "456 Maple Ave", "City": "Los Angeles", "State": "CA"}},<\/p>\n
    {"Name": "Charlie", "Age": "35", "Address": {"Street": "789 Oak Dr", "City": "Chicago", "State": "IL"}}<\/p>\n
]<\/p>\n
\u63d0\u53d6\u5b57\u6bb5\u540d<\/strong><\/h2>\n
fieldnames = ["Name", "Age", "Street", "City", "State"]<\/p>\n
\u6241\u5e73\u5316\u6570\u636e\u5e76\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('output_nested_dict.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.DictWriter(file, fieldnames=fieldnames)<\/p>\n
    writer.writeheader()<\/p>\n
    for row in data:<\/p>\n
        flat_row = {<\/p>\n
            "Name": row["Name"],<\/p>\n
            "Age": row["Age"],<\/p>\n
            "Street": row["Address"]["Street"],<\/p>\n
            "City": row["Address"]["City"],<\/p>\n
            "State": row["Address"]["State"]<\/p>\n
        }<\/p>\n
        writer.writerow(flat_row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5904\u7406\u5927\u6570\u636e\u91cf<\/h3>\n<\/p>\n
\u5904\u7406\u5927\u6570\u636e\u91cf\u65f6\uff0c\u9700\u8981\u8003\u8651\u5185\u5b58\u548c\u6548\u7387\u3002\u4ee5\u4e0b\u662f\u5904\u7406\u5927\u6570\u636e\u91cf\u7684\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
1\u3001\u5206\u5757\u5904\u7406\u6570\u636e<\/h4>\n<\/p>\n
\u5206\u5757\u5904\u7406\u6570\u636e\u53ef\u4ee5\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u5927\u91cf\u6570\u636e\u5230\u5185\u5b58\u4e2d\u3002\u4f7f\u7528pandas\u7684chunksize<\/code>\u53c2\u6570\u53ef\u4ee5\u5206\u5757\u8bfb\u53d6\u548c\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u5b9a\u4e49\u8981\u5199\u5165\u7684\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    "Name": ["Alice", "Bob", "Charlie"] * 100000,<\/p>\n
    "Age": [30, 25, 35] * 100000,<\/p>\n
    "City": ["New York", "Los Angeles", "Chicago"] * 100000<\/p>\n
}<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(data)<\/p>\n
\u5206\u5757\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
df.to_csv('output_large.csv', index=False, chunksize=10000)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528\u751f\u6210\u5668\u5904\u7406\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528\u751f\u6210\u5668\u53ef\u4ee5\u9010\u884c\u751f\u6210\u6570\u636e\uff0c\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u5927\u91cf\u6570\u636e\u5230\u5185\u5b58\u4e2d\u3002<\/p>\n<\/p>\n
import csv<\/p>\n
\u5b9a\u4e49\u751f\u6210\u5668\u51fd\u6570<\/strong><\/h2>\n
def data_generator():<\/p>\n
    yield ["Name", "Age", "City"]<\/p>\n
    for i in range(1000000):<\/p>\n
        yield ["Alice", str(30 + i % 10), "New York"]<\/p>\n
        yield ["Bob", str(25 + i % 5), "Los Angeles"]<\/p>\n
        yield ["Charlie", str(35 + i % 7), "Chicago"]<\/p>\n
\u4f7f\u7528\u751f\u6210\u5668\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('output_generator.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.writer(file)<\/p>\n
    for row in data_generator():<\/p>\n
        writer.writerow(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7ed3\u8bba<\/h3>\n<\/p>\n
\u5c06Python\u5b57\u7b26\u4e32\u5199\u5165CSV\u6587\u4ef6\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u4f7f\u7528csv\u6a21\u5757\u3001pandas\u5e93\u3001\u6216\u8005\u624b\u52a8\u5904\u7406\u5b57\u7b26\u4e32\u6570\u636e\uff0c\u90fd\u662f\u53ef\u884c\u7684\u65b9\u5f0f<\/strong>\u3002\u5bf9\u4e8e\u590d\u6742\u6570\u636e\u7ed3\u6784\uff0c\u9700\u8981\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5c06\u6570\u636e\u6241\u5e73\u5316\u4e3aCSV\u683c\u5f0f\u3002\u5904\u7406\u5927\u6570\u636e\u91cf\u65f6\uff0c\u5efa\u8bae\u5206\u5757\u5904\u7406\u6216\u4f7f\u7528\u751f\u6210\u5668\uff0c\u4ee5\u63d0\u9ad8\u5185\u5b58\u548c\u6548\u7387\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u7406\u89e3\u548c\u638c\u63e1Python\u5b57\u7b26\u4e32\u5199\u5165CSV\u6587\u4ef6\u7684\u65b9\u6cd5\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u5c06\u5b57\u7b26\u4e32\u5199\u5165CSV\u6587\u4ef6\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684csv<\/code>\u6a21\u5757\u6765\u5c06\u5b57\u7b26\u4e32\u5199\u5165CSV\u6587\u4ef6\u3002\u9996\u5148\uff0c\u6253\u5f00\u4e00\u4e2a\u6587\u4ef6\u5e76\u521b\u5efa\u4e00\u4e2acsv.writer<\/code>\u5bf9\u8c61\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528writerow()<\/code>\u6216writerows()<\/code>\u65b9\u6cd5\u5c06\u5b57\u7b26\u4e32\u4f5c\u4e3a\u884c\u5199\u5165CSV\u6587\u4ef6\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a  <\/p>\n
import csv\n\ndata = ["name", "age", "city"]\nwith open('output.csv', mode='w', newline='') as file:\n    writer = csv.writer(file)\n    writer.writerow(data)  # \u5199\u5165\u5355\u884c\n<\/code><\/pre>\n
\u786e\u4fdd\u5728\u5199\u5165\u65f6\uff0c\u6587\u4ef6\u8def\u5f84\u662f\u6b63\u786e\u7684\uff0c\u5e76\u4e14\u6709\u5199\u5165\u6743\u9650\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u5305\u542b\u7279\u6b8a\u5b57\u7b26\u7684\u5b57\u7b26\u4e32\u4ee5\u786e\u4fddCSV\u683c\u5f0f\u7684\u6b63\u786e\u6027\uff1f<\/strong>
\u5728\u5904\u7406\u5305\u542b\u7279\u6b8a\u5b57\u7b26\uff08\u4f8b\u5982\u9017\u53f7\u6216\u6362\u884c\u7b26\uff09\u7684\u5b57\u7b26\u4e32\u65f6\uff0c\u4f7f\u7528csv<\/code>\u6a21\u5757\u7684writer<\/code>\u5bf9\u8c61\u4f1a\u81ea\u52a8\u4e3a\u60a8\u5904\u7406\u8fd9\u4e9b\u5b57\u7b26\u3002csv<\/code>\u6a21\u5757\u4f1a\u5c06\u8fd9\u4e9b\u7279\u6b8a\u5b57\u7b26\u653e\u5165\u5f15\u53f7\u4e2d\uff0c\u4ece\u800c\u786e\u4fdd\u8f93\u51fa\u7684CSV\u6587\u4ef6\u683c\u5f0f\u6b63\u786e\u3002\u5982\u679c\u60a8\u624b\u52a8\u5904\u7406\u5b57\u7b26\u4e32\uff0c\u8bf7\u786e\u4fdd\u5728\u5199\u5165\u524d\u5bf9\u5176\u8fdb\u884c\u9002\u5f53\u7684\u8f6c\u4e49\u3002<\/p>\n
\u80fd\u5426\u5c06\u591a\u4e2a\u5b57\u7b26\u4e32\u5217\u8868\u5199\u5165\u540c\u4e00\u4e2aCSV\u6587\u4ef6\uff1f<\/strong>
\u5f53\u7136\u53ef\u4ee5\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528writerows()<\/code>\u65b9\u6cd5\u5c06\u591a\u4e2a\u5b57\u7b26\u4e32\u5217\u8868\u5199\u5165\u540c\u4e00\u4e2aCSV\u6587\u4ef6\u3002\u8fd9\u4e2a\u65b9\u6cd5\u63a5\u53d7\u4e00\u4e2a\u53ef\u8fed\u4ee3\u5bf9\u8c61\uff08\u4f8b\u5982\u5217\u8868\u6216\u5143\u7ec4\u7684\u5217\u8868\uff09\uff0c\u6bcf\u4e2a\u5b50\u5217\u8868\u90fd\u5c06\u4f5c\u4e3aCSV\u6587\u4ef6\u4e2d\u7684\u4e00\u884c\u5199\u5165\u3002\u4f8b\u5982\uff1a  <\/p>\n
import csv\n\nrows = [\n    ["name", "age", "city"],\n    ["Alice", "30", "New York"],\n    ["Bob", "25", "Los Angeles"]\n]\nwith open('output.csv', mode='w', newline='') as file:\n    writer = csv.writer(file)\n    writer.writerows(rows)  # \u5199\u5165\u591a\u884c\n<\/code><\/pre>\n
\u8fd9\u6837\uff0c\u60a8\u5c06\u80fd\u591f\u5728\u4e00\u4e2aCSV\u6587\u4ef6\u4e2d\u5b58\u50a8\u591a\u6761\u8bb0\u5f55\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5b57\u7b26\u4e32\u5199\u5165CSV\u7684\u65b9\u5f0f\u6709\u5f88\u591a\u79cd\uff0c\u4f7f\u7528\u5185\u7f6e\u7684csv\u6a21\u5757\u3001pandas\u5e93\u3001\u6216\u8005\u624b\u52a8\u5904\u7406\u5b57\u7b26\u4e32\u5185\u5bb9\u7b49\u65b9\u6cd5 […]","protected":false},"author":3,"featured_media":1034033,"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\/1034017"}],"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=1034017"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1034017\/revisions"}],"predecessor-version":[{"id":1034037,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1034017\/revisions\/1034037"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1034033"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1034017"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1034017"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1034017"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}