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")  # 日期字符串變對象

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

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)]  # 每兩個(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)行失敗：{e}")

def clean_phone(phone):
    """移除電話號碼中的非數(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]