{"id":1099999,"date":"2025-01-08T15:36:02","date_gmt":"2025-01-08T07:36:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1099999.html"},"modified":"2025-01-08T15:36:05","modified_gmt":"2025-01-08T07:36:05","slug":"python%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e5%9b%9b%e5%88%97%e5%b7%a6%e5%af%b9%e9%bd%90-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1099999.html","title":{"rendered":"python\u5982\u4f55\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50"},"content":{"rendered":"

\"python\u5982\u4f55\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\"<\/p>\n

\u901a\u8fc7\u4f7f\u7528Python\u7f16\u7a0b\u8bed\u8a00\uff0c\u53ef\u4ee5\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\u4f7f\u7528\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u3001pandas\u5e93\u3001tabulate\u5e93\u7b49\u3002\u5176\u4e2d\uff0c\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u6cd5\u6700\u4e3a\u76f4\u63a5\u548c\u5e38\u7528\uff0c\u672c\u6587\u5c06\u91cd\u70b9\u4ecb\u7ecd\u3002<\/strong>\u901a\u8fc7\u4f7f\u7528\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\uff0c\u53ef\u4ee5\u7075\u6d3b\u5730\u63a7\u5236\u5217\u7684\u5bbd\u5ea6\u548c\u5bf9\u9f50\u65b9\u5f0f\uff0c\u975e\u5e38\u9002\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u5bf9\u9f50\u4efb\u52a1\u3002<\/p>\n<\/p>\n

\u4f7f\u7528\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50<\/strong><\/p>\n<\/p>\n

\u8981\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\uff0c\u6700\u7b80\u5355\u7684\u65b9\u6cd5\u662f\u4f7f\u7528Python\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u529f\u80fd\u3002\u8fd9\u91cc\u4e3b\u8981\u4ecb\u7ecd\u4e09\u79cd\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u5f0f\uff1a\u4f7f\u7528\u767e\u5206\u53f7\uff08%\uff09\u3001str.format()\u65b9\u6cd5\u548cf-string\uff08\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u5b57\u9762\u91cf\uff09\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u767e\u5206\u53f7\uff08%\uff09\u683c\u5f0f\u5316<\/h3>\n<\/p>\n

\u767e\u5206\u53f7\u683c\u5f0f\u5316\u662fPython\u4e2d\u6700\u65e9\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u5f0f\uff0c\u5b83\u7c7b\u4f3c\u4e8eC\u8bed\u8a00\u4e2d\u7684printf\u51fd\u6570\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

data = [<\/p>\n
    ("Alice", "Engineer", "New York", "10001"),<\/p>\n
    ("Bob", "Doctor", "Los Angeles", "90001"),<\/p>\n
    ("Charlie", "Teacher", "Chicago", "60601"),<\/p>\n
    ("David", "Artist", "San Francisco", "94101")<\/p>\n
]<\/p>\n
for row in data:<\/p>\n
    print("%-10s %-10s %-15s %-10s" % row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c%-10s<\/code>\u8868\u793a\u5de6\u5bf9\u9f50\uff0c\u5bbd\u5ea6\u4e3a10\uff0c%-15s<\/code>\u8868\u793a\u5de6\u5bf9\u9f50\uff0c\u5bbd\u5ea6\u4e3a15\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6570\u636e\u6309\u7167\u6307\u5b9a\u7684\u5bbd\u5ea6\u8fdb\u884c\u5de6\u5bf9\u9f50\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528str.format()\u65b9\u6cd5<\/h3>\n<\/p>\n
str.format()\u65b9\u6cd5\u662fPython 2.7\u548c3.0\u4e2d\u5f15\u5165\u7684\u4e00\u79cd\u66f4\u73b0\u4ee3\u548c\u5f3a\u5927\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u5f0f\u3002\u4e0b\u9762\u662f\u4f7f\u7528str.format()\u65b9\u6cd5\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
data = [<\/p>\n
    ("Alice", "Engineer", "New York", "10001"),<\/p>\n
    ("Bob", "Doctor", "Los Angeles", "90001"),<\/p>\n
    ("Charlie", "Teacher", "Chicago", "60601"),<\/p>\n
    ("David", "Artist", "San Francisco", "94101")<\/p>\n
]<\/p>\n
for row in data:<\/p>\n
    print("{:<10} {:<10} {:<15} {:<10}".format(*row))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c{:<10}<\/code>\u8868\u793a\u5de6\u5bf9\u9f50\uff0c\u5bbd\u5ea6\u4e3a10\uff0c{:<15}<\/code>\u8868\u793a\u5de6\u5bf9\u9f50\uff0c\u5bbd\u5ea6\u4e3a15\u3002str.format()\u65b9\u6cd5\u63d0\u4f9b\u4e86\u66f4\u76f4\u89c2\u548c\u53ef\u8bfb\u6027\u66f4\u9ad8\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u5f0f\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528f-string\uff08\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u5b57\u9762\u91cf\uff09<\/h3>\n<\/p>\n
f-string\u662fPython 3.6\u4e2d\u5f15\u5165\u7684\u4e00\u79cd\u65b0\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u5f0f\uff0c\u5b83\u66f4\u52a0\u7b80\u6d01\u548c\u9ad8\u6548\u3002\u4e0b\u9762\u662f\u4f7f\u7528f-string\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
data = [<\/p>\n
    ("Alice", "Engineer", "New York", "10001"),<\/p>\n
    ("Bob", "Doctor", "Los Angeles", "90001"),<\/p>\n
    ("Charlie", "Teacher", "Chicago", "60601"),<\/p>\n
    ("David", "Artist", "San Francisco", "94101")<\/p>\n
]<\/p>\n
for row in data:<\/p>\n
    name, profession, city, zipcode = row<\/p>\n
    print(f"{name:<10} {profession:<10} {city:<15} {zipcode:<10}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c{name:<10}<\/code>\u8868\u793a\u5de6\u5bf9\u9f50\uff0c\u5bbd\u5ea6\u4e3a10\uff0c{city:<15}<\/code>\u8868\u793a\u5de6\u5bf9\u9f50\uff0c\u5bbd\u5ea6\u4e3a15\u3002f-string\u63d0\u4f9b\u4e86\u4e00\u79cd\u66f4\u52a0\u7b80\u6d01\u548c\u9ad8\u6548\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u5f0f\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528pandas\u5e93<\/h3>\n<\/p>\n
pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u9002\u7528\u4e8e\u5904\u7406\u548c\u5c55\u793a\u6570\u636e\u3002\u867d\u7136pandas\u5e76\u4e0d\u662f\u4e13\u95e8\u7528\u4e8e\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\uff0c\u4f46\u5b83\u63d0\u4f9b\u4e86\u975e\u5e38\u65b9\u4fbf\u7684\u6570\u636e\u5c55\u793a\u529f\u80fd\u3002\u4e0b\u9762\u662f\u4f7f\u7528pandas\u5e93\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = [<\/p>\n
    ("Alice", "Engineer", "New York", "10001"),<\/p>\n
    ("Bob", "Doctor", "Los Angeles", "90001"),<\/p>\n
    ("Charlie", "Teacher", "Chicago", "60601"),<\/p>\n
    ("David", "Artist", "San Francisco", "94101")<\/p>\n
]<\/p>\n
df = pd.DataFrame(data, columns=["Name", "Profession", "City", "Zipcode"])<\/p>\n
print(df.to_string(index=False))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528pandas\u5e93\u521b\u5efa\u4e86\u4e00\u4e2aDataFrame\uff0c\u5e76\u901a\u8fc7to_string<\/code>\u65b9\u6cd5\u8f93\u51fa\u6570\u636e\u3002pandas\u4f1a\u81ea\u52a8\u8c03\u6574\u5217\u5bbd\uff0c\u5e76\u9ed8\u8ba4\u5de6\u5bf9\u9f50\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528tabulate\u5e93<\/h3>\n<\/p>\n
tabulate\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u7f8e\u89c2\u8868\u683c\u7684Python\u5e93\uff0c\u9002\u7528\u4e8e\u5c55\u793a\u7ed3\u6784\u5316\u6570\u636e\u3002\u4e0b\u9762\u662f\u4f7f\u7528tabulate\u5e93\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from tabulate import tabulate<\/p>\n
data = [<\/p>\n
    ("Alice", "Engineer", "New York", "10001"),<\/p>\n
    ("Bob", "Doctor", "Los Angeles", "90001"),<\/p>\n
    ("Charlie", "Teacher", "Chicago", "60601"),<\/p>\n
    ("David", "Artist", "San Francisco", "94101")<\/p>\n
]<\/p>\n
headers = ["Name", "Profession", "City", "Zipcode"]<\/p>\n
print(tabulate(data, headers, tablefmt="plAI<\/a>n", stralign="left"))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528tabulate\u5e93\u521b\u5efa\u4e86\u4e00\u4e2a\u7f8e\u89c2\u7684\u8868\u683c\uff0c\u5e76\u901a\u8fc7stralign="left"<\/code>\u53c2\u6570\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002tabulate\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u8868\u683c\u6837\u5f0f\u548c\u5bf9\u9f50\u65b9\u5f0f\uff0c\u975e\u5e38\u7075\u6d3b\u548c\u6613\u7528\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f7f\u7528PrettyTable\u5e93<\/h3>\n<\/p>\n
PrettyTable\u662f\u53e6\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u7f8e\u89c2\u8868\u683c\u7684Python\u5e93\uff0c\u9002\u7528\u4e8e\u5c55\u793a\u7ed3\u6784\u5316\u6570\u636e\u3002\u4e0b\u9762\u662f\u4f7f\u7528PrettyTable\u5e93\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from prettytable import PrettyTable<\/p>\n
data = [<\/p>\n
    ("Alice", "Engineer", "New York", "10001"),<\/p>\n
    ("Bob", "Doctor", "Los Angeles", "90001"),<\/p>\n
    ("Charlie", "Teacher", "Chicago", "60601"),<\/p>\n
    ("David", "Artist", "San Francisco", "94101")<\/p>\n
]<\/p>\n
table = PrettyTable()<\/p>\n
table.field_names = ["Name", "Profession", "City", "Zipcode"]<\/p>\n
for row in data:<\/p>\n
    table.add_row(row)<\/p>\n
table.align = "l"<\/p>\n
print(table)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528PrettyTable\u5e93\u521b\u5efa\u4e86\u4e00\u4e2a\u7f8e\u89c2\u7684\u8868\u683c\uff0c\u5e76\u901a\u8fc7align<\/code>\u5c5e\u6027\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002PrettyTable\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u8868\u683c\u6837\u5f0f\u548c\u5bf9\u9f50\u65b9\u5f0f\uff0c\u975e\u5e38\u9002\u5408\u5c55\u793a\u7ed3\u6784\u5316\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u4f7f\u7528Texttable\u5e93<\/h3>\n<\/p>\n
Texttable\u662f\u4e00\u4e2a\u7528\u4e8e\u5728\u7ec8\u7aef\u4e2d\u521b\u5efa\u8868\u683c\u7684Python\u5e93\uff0c\u9002\u7528\u4e8e\u5c55\u793a\u7ed3\u6784\u5316\u6570\u636e\u3002\u4e0b\u9762\u662f\u4f7f\u7528Texttable\u5e93\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from texttable import Texttable<\/p>\n
data = [<\/p>\n
    ("Alice", "Engineer", "New York", "10001"),<\/p>\n
    ("Bob", "Doctor", "Los Angeles", "90001"),<\/p>\n
    ("Charlie", "Teacher", "Chicago", "60601"),<\/p>\n
    ("David", "Artist", "San Francisco", "94101")<\/p>\n
]<\/p>\n
table = Texttable()<\/p>\n
table.add_rows([["Name", "Profession", "City", "Zipcode"]] + data)<\/p>\n
table.set_cols_align(["l", "l", "l", "l"])<\/p>\n
print(table.draw())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528Texttable\u5e93\u521b\u5efa\u4e86\u4e00\u4e2a\u7f8e\u89c2\u7684\u8868\u683c\uff0c\u5e76\u901a\u8fc7set_cols_align<\/code>\u65b9\u6cd5\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002Texttable\u5e93\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u8868\u683c\u521b\u5efa\u548c\u5bf9\u9f50\u65b9\u5f0f\uff0c\u975e\u5e38\u9002\u5408\u5728\u7ec8\u7aef\u4e2d\u5c55\u793a\u6570\u636e\u3002<\/p>\n<\/p>\n
\u516b\u3001\u4f7f\u7528NumPy\u5e93<\/h3>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684Python\u5e93\uff0c\u867d\u7136NumPy\u5e76\u4e0d\u662f\u4e13\u95e8\u7528\u4e8e\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7NumPy\u6570\u7ec4\u5b9e\u73b0\u6570\u636e\u7684\u5de6\u5bf9\u9f50\u3002\u4e0b\u9762\u662f\u4f7f\u7528NumPy\u5e93\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([<\/p>\n
    ["Alice", "Engineer", "New York", "10001"],<\/p>\n
    ["Bob", "Doctor", "Los Angeles", "90001"],<\/p>\n
    ["Charlie", "Teacher", "Chicago", "60601"],<\/p>\n
    ["David", "Artist", "San Francisco", "94101"]<\/p>\n
])<\/p>\n
for row in data:<\/p>\n
    print("{:<10} {:<10} {:<15} {:<10}".format(*row))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528NumPy\u6570\u7ec4\u5b58\u50a8\u6570\u636e\uff0c\u5e76\u901a\u8fc7\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002NumPy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u5b58\u50a8\u548c\u5904\u7406\u529f\u80fd\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u4f7f\u7528CSV\u6587\u4ef6<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u6570\u636e\u53ef\u80fd\u5b58\u50a8\u5728CSV\u6587\u4ef6\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u4f7f\u7528\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002\u4e0b\u9762\u662f\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import csv<\/p>\n
with open("data.csv", newline="") as csvfile:<\/p>\n
    reader = csv.reader(csvfile)<\/p>\n
    for row in reader:<\/p>\n
        print("{:<10} {:<10} {:<15} {:<10}".format(*row))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528csv\u6a21\u5757\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u5e76\u901a\u8fc7\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002csv\u6a21\u5757\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684CSV\u6587\u4ef6\u8bfb\u5199\u529f\u80fd\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002<\/p>\n<\/p>\n
\u5341\u3001\u4f7f\u7528SQL\u6570\u636e\u5e93<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u6570\u636e\u53ef\u80fd\u5b58\u50a8\u5728SQL\u6570\u636e\u5e93\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u67e5\u8be2\u6570\u636e\u5e93\u5e76\u4f7f\u7528\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002\u4e0b\u9762\u662f\u67e5\u8be2SQL\u6570\u636e\u5e93\u5e76\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import sqlite3<\/p>\n
\u521b\u5efa\u8fde\u63a5<\/strong><\/h2>\n
conn = sqlite3.connect("data.db")<\/p>\n
cursor = conn.cursor()<\/p>\n
\u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
cursor.execute("SELECT Name, Profession, City, Zipcode FROM People")<\/p>\n
rows = cursor.fetchall()<\/p>\n
\u6253\u5370\u6570\u636e<\/strong><\/h2>\n
for row in rows:<\/p>\n
    print("{:<10} {:<10} {:<15} {:<10}".format(*row))<\/p>\n
\u5173\u95ed\u8fde\u63a5<\/strong><\/h2>\n
conn.close()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u4f7f\u7528sqlite3\u6a21\u5757\u67e5\u8be2SQLite\u6570\u636e\u5e93\uff0c\u5e76\u901a\u8fc7\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002sqlite3\u6a21\u5757\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u6570\u636e\u5e93\u64cd\u4f5c\u529f\u80fd\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002<\/p>\n<\/p>\n
\u7ed3\u8bba<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u3002\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u65b9\u6cd5<\/strong>\uff08\u5305\u62ec\u767e\u5206\u53f7\u683c\u5f0f\u5316\u3001str.format()\u65b9\u6cd5\u548cf-string\uff09\u662f\u6700\u4e3a\u76f4\u63a5\u548c\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u5bf9\u9f50\u4efb\u52a1\u3002pandas\u3001tabulate\u3001PrettyTable\u3001Texttable\u548cNumPy\u5e93<\/strong>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u8868\u683c\u6837\u5f0f\u548c\u5bf9\u9f50\u65b9\u5f0f\uff0c\u975e\u5e38\u9002\u5408\u5c55\u793a\u7ed3\u6784\u5316\u6570\u636e\u3002CSV\u6587\u4ef6\u548cSQL\u6570\u636e\u5e93<\/strong>\u65b9\u6cd5\u9002\u7528\u4e8e\u5904\u7406\u5b58\u50a8\u5728\u6587\u4ef6\u548c\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u6765\u6e90\uff0c\u53ef\u4ee5\u9009\u62e9\u6700\u9002\u5408\u7684\u65b9\u6cd5\u5b9e\u73b0\u6570\u636e\u5de6\u5bf9\u9f50\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u5b57\u7b26\u4e32\u7684\u56db\u5217\u5de6\u5bf9\u9f50\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5b57\u7b26\u4e32\u7684\u683c\u5f0f\u5316\u65b9\u6cd5\uff0c\u5982f-string\u3001str.format()<\/code>\u65b9\u6cd5\u6216\u8005%<\/code>\u683c\u5f0f\u5316\u6765\u5b9e\u73b0\u56db\u5217\u5de6\u5bf9\u9f50\u3002\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u5bbd\u5ea6\u5e76\u4f7f\u7528\u586b\u5145\u5b57\u7b26\u6765\u8fbe\u5230\u8fd9\u4e2a\u76ee\u7684\u3002\u4f8b\u5982\uff0c\u4f7f\u7528f"{value:<10}"<\/code>\u53ef\u4ee5\u5c06\u5b57\u7b26\u4e32\u5de6\u5bf9\u9f50\u523010\u4e2a\u5b57\u7b26\u7684\u5bbd\u5ea6\u3002<\/p>\n
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\u662f\u7684\uff0cPython\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u8868\u683c\u683c\u5f0f\u5316\uff0c\u4f8b\u5982pandas<\/code>\u548cprettytable<\/code>\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u683c\u5f0f\u5316\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u5e26\u6709\u5de6\u5bf9\u9f50\u5217\u7684\u8868\u683c\u3002\u4f7f\u7528pandas<\/code>\u7684DataFrame<\/code>\u5bf9\u8c61\u53ef\u4ee5\u8f7b\u677e\u7ba1\u7406\u6570\u636e\u5e76\u8f93\u51fa\u5de6\u5bf9\u9f50\u7684\u683c\u5f0f\u3002<\/p>\n
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