pip install textcase # 推薦使用Python 3.6+

from textcase import convert
 
# 基礎(chǔ)轉(zhuǎn)換
print(convert("hello_world", "camelCase"))    # helloWorld
print(convert("HelloWorld", "snake_case"))    # hello_world
print(convert("hello-world", "CONSTANT_CASE")) # HELLO_WORLD
 
# 智能處理縮寫
print(convert("parseXML", "kebab-case"))      # parse-xml
print(convert("MyHTMLParser", "snake_case"))  # my_html_parser
 
# 特殊字符處理
print(convert("data@123", "PascalCase"))      # Data123
print(convert("user-name", "sentence_case"))  # User name

# 將自定義分隔符轉(zhuǎn)換為標(biāo)準(zhǔn)格式
print(convert("user|name|age", "snake_case", delimiter="|"))  # user_name_age

from textcase import batch_convert
 
# 批量轉(zhuǎn)換整個(gè)目錄
batch_convert(
    input_dir="./variables",
    output_dir="./formatted",
    target_case="camelCase",
    file_pattern="*.py"
)

from textcase import regex_convert
 
# 僅轉(zhuǎn)換特定模式的字符串
text = "ID: user_id123, Name: user-name"
print(regex_convert(r"\b\w+\b", text, "PascalCase")) 
# ID: UserId123, Name: UserName

from textcase import StreamingConverter
 
# 流式處理大文件
with open("large_file.txt", "r") as f:
    converter = StreamingConverter("camelCase")
    for line in f:
        processed = converter.convert(line)
        # 實(shí)時(shí)處理或?qū)懭胄挛募?

from concurrent.futures import ThreadPoolExecutor
 
def process_chunk(chunk):
    return convert(chunk, "snake_case")
 
# 分塊并行處理
with ThreadPoolExecutor() as executor:
    results = list(executor.map(process_chunk, large_text.split("\n")))

def generate_class(name, fields):
    properties = "\n".join([
        f"private {convert(field, 'camelCase')} {field.upper()};"
        for field in fields
    ])
    return f"""
    public class {convert(name, 'PascalCase')} {{
        {properties}
    }}
    """
 
print(generate_class("user_profile", ["user_id", "full_name"]))

import pandas as pd
 
def clean_dataframe(df):
    return df.applymap(lambda x: convert(x, "snake_case") if isinstance(x, str) else x)
 
# 處理包含混合大小寫的CSV數(shù)據(jù)
df = pd.read_csv("dirty_data.csv")
clean_df = clean_dataframe(df)

from flask import jsonify
 
@app.route("/users")
def get_users():
    users = fetch_users()
    formatted = [{
        "userId": convert(user["id"], "camelCase"),
        "userName": convert(user["name"], "camelCase")
    } for user in users]
    return jsonify(formatted)

from textcase import TextCaseError
 
try:
    convert("invalid@input", "camelCase")
except TextCaseError as e:
    print(f"轉(zhuǎn)換失敗: {e}")

import time
 
start = time.perf_counter()
result = convert(large_text, "snake_case")
print(f"處理時(shí)間: {time.perf_counter() - start:.4f}秒")