data = "   Hello World!   "  
cleaned_data = data.strip()  # 神奇的一行，左右空格拜拜  

num_str = "123"  
num_int = int(num_str)  # 字符串轉(zhuǎn)整數(shù)，就是這么直接  

text = "Python is Awesome"  
lower_text = text.lower()  # 全部變小寫，便于統(tǒng)一處理  
upper_text = text.upper()  # 或者全部大寫，隨你心情  

my_list = [1, 2, 2, 3, 4, 4]  
unique_list = list(set(my_list))  # 集合特性，去重?zé)o壓力  

from collections import Counter  
data = ['apple', 'banana', 'apple', 'orange']  
counts = dict(Counter(data))  # 想要知道誰(shuí)最受歡迎？  

sentence = "Hello world"  
words = sentence.split(" ")  # 分割符默認(rèn)為空格，一句話變單詞列表  

list1 = [1, 2, 3]  
list2 = [4, 5, 6]  
merged_list = list1 + list2  # 合并列表，就這么簡(jiǎn)單  

my_list = [1, 2]  
filled_list = my_list * 3  # 重復(fù)三次，快速填充列表  

from datetime import datetime  
date_str = "2023-04-01"  
date_obj = datetime.strptime(date_str, "%Y-%m-%d")  # 日期字符串變對(duì)象  

old_string = "Python is fun."  
new_string = old_string.replace("fun", "awesome")  # 改頭換面，一言既出  

numbers = [5, 2, 9, 1, 5]  
sorted_numbers = sorted(numbers)  # 自然排序，升序默認(rèn)  

mixed_str = "The year is 2023"  
nums = ''.join(filter(str.isdigit, mixed_str))  # 只留下數(shù)字，其余走開(kāi)  

data_list = [None, 1, 2, None, 3]  
filtered_list = [x for x in data_list if x is not None]  # 拒絕空值，干凈利落  

my_dict = {"key1": "value1", "key2": "value2"}  
swapped_dict = {v: k for k, v in my_dict.items()}  # 翻轉(zhuǎn)乾坤，鍵變值，值變鍵  

numbers = [10, 20, 30, 40]  
average = sum(numbers) / len(numbers)  # 平均數(shù)，一步到位  

s = "abcdef"  
grouped = [s[i:i+2] for i in range(0, len(s), 2)]  # 每?jī)蓚€(gè)一組，分割有道  

import numpy as np  
data = np.array([1, 2, 3])  
normalized_data = (data - data.mean()) / data.std()  # 數(shù)學(xué)之美，標(biāo)準(zhǔn)分布  

data = [1, 2, 3, 4, 5]  
even_numbers = [x for x in data if x % 2 == 0]  # 只留偶數(shù)，排除異己  

import re  
text = "Email: example@email.com Phone: 123-456-7890"  
emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)  
phones = re.findall(r'\b\d{3}-\d{3}-\d{4}\b', text)  

import pandas as pd  
df = pd.read_csv('data.csv')  
# 刪除含有缺失值的行  
df_clean = df.dropna()  
# 替換特定值  
df['column_name'] = df['column_name'].replace('old_value', 'new_value')  

import logging  
logging.basicConfig(level=logging.INFO)  
try:  
    result = some_function_that_might_fail()  
    logging.info(f"成功執(zhí)行！結(jié)果：{result}")  
except Exception as e:  
    logging.error(f"執(zhí)行失?。簕e}")  

def clean_phone(phone):  
    """移除電話號(hào)碼中的非數(shù)字字符"""  
    return ''.join(c for c in phone if c.isdigit())  
  
phone_numbers = ['123-456-7890', '(555) 555-5555']  
cleaned_numbers = [clean_phone(phone) for phone in phone_numbers]