<\/span><\/h2>\n\n\n\ndata = \" Hello World! \"
cleaned_data = data.strip() # \u795e\u5947\u7684\u4e00\u884c\uff0c\u5de6\u53f3\u7a7a\u683c\u62dc\u62dc<\/em><\/code><\/pre>\n\n\n\n\n- \u89e3\u8bfb<\/strong>\uff1a
strip()<\/code>\u65b9\u6cd5\u53bb\u6389\u5b57\u7b26\u4e32\u9996\u5c3e\u7684\u7a7a\u767d\u5b57\u7b26\uff0c\u7b80\u5355\u9ad8\u6548\u3002<\/li>\n<\/ul>\n\n\n\n<\/span>2. \u8f6c\u6362\u6570\u636e\u7c7b\u578b<\/strong><\/span><\/h2>\n\n\n\nnum_str = \"123\"
num_int = int(num_str) # \u5b57\u7b26\u4e32\u8f6c\u6574\u6570\uff0c\u5c31\u662f\u8fd9\u4e48\u76f4\u63a5<\/em><\/code><\/pre>\n\n\n\n\n- \u6ce8\u610f<\/strong>\uff1a\u8f6c\u6362\u65f6\u8981\u786e\u4fdd\u6570\u636e\u683c\u5f0f\u6b63\u786e\uff0c\u5426\u5219\u4f1a\u62a5\u9519\u3002<\/li>\n<\/ul>\n\n\n\n
<\/span>3. \u5927\u5c0f\u5199\u8f6c\u6362<\/strong><\/span><\/h2>\n\n\n\ntext = \"Python is Awesome\"
lower_text = text.lower() # \u5168\u90e8\u53d8\u5c0f\u5199\uff0c\u4fbf\u4e8e\u7edf\u4e00\u5904\u7406<\/em>
upper_text = text.upper() # \u6216\u8005\u5168\u90e8\u5927\u5199\uff0c\u968f\u4f60\u5fc3\u60c5<\/em><\/code><\/pre>\n\n\n\n<\/span>4. \u79fb\u9664\u5217\u8868\u4e2d\u7684\u91cd\u590d\u5143\u7d20<\/strong><\/span><\/h2>\n\n\n\nmy_list = [1, 2, 2, 3, 4, 4]
unique_list = list(set(my_list)) # \u96c6\u5408\u7279\u6027\uff0c\u53bb\u91cd\u65e0\u538b\u529b<\/em><\/code><\/pre>\n\n\n\n\n- \u5c0f\u8d34\u58eb<\/strong>\uff1a\u8fd9\u62db\u867d\u597d\uff0c\u4f46\u6539\u53d8\u4e86\u539f\u5217\u8868\u987a\u5e8f\u54e6\u3002<\/li>\n<\/ul>\n\n\n\n
<\/span>5. \u5feb\u901f\u7edf\u8ba1\u5143\u7d20\u51fa\u73b0\u6b21\u6570<\/strong><\/span><\/h2>\n\n\n\nfrom collections import Counter
data = ['apple', 'banana', 'apple', 'orange']
counts = dict(Counter(data)) # \u60f3\u8981\u77e5\u9053\u8c01\u6700\u53d7\u6b22\u8fce\uff1f<\/em><\/code><\/pre>\n\n\n\n\n- \u89e3\u8bfb<\/strong>\uff1a
Counter<\/code>\u662f\u7edf\u8ba1\u795e\u5668\uff0c\u8f7b\u677e\u83b7\u53d6\u9891\u7387\u3002<\/li>\n<\/ul>\n\n\n\n<\/span>6. \u5b57\u7b26\u4e32\u5206\u5272\u6210\u5217\u8868<\/strong><\/span><\/h2>\n\n\n\nsentence = \"Hello world\"
words = sentence.split(\" \") # \u5206\u5272\u7b26\u9ed8\u8ba4\u4e3a\u7a7a\u683c\uff0c\u4e00\u53e5\u8bdd\u53d8\u5355\u8bcd\u5217\u8868<\/em><\/code><\/pre>\n\n\n\n<\/span>7. \u5217\u8868\u5408\u5e76<\/strong><\/span><\/h2>\n\n\n\nlist1 = [1, 2, 3]
list2 = [4, 5, 6]
merged_list = list1 + list2 # \u5408\u5e76\u5217\u8868\uff0c\u5c31\u8fd9\u4e48\u7b80\u5355<\/em><\/code><\/pre>\n\n\n\n<\/span>8. \u6570\u636e\u586b\u5145<\/strong><\/span><\/h2>\n\n\n\nmy_list = [1, 2]
filled_list = my_list * 3 # \u91cd\u590d\u4e09\u6b21\uff0c\u5feb\u901f\u586b\u5145\u5217\u8868<\/em><\/code><\/pre>\n\n\n\n<\/span>9. \u63d0\u53d6\u65e5\u671f\u65f6\u95f4<\/strong><\/span><\/h2>\n\n\n\nfrom datetime import datetime
date_str = \"2023-04-01\"
date_obj = datetime.strptime(date_str, \"%Y-%m-%d\") # \u65e5\u671f\u5b57\u7b26\u4e32\u53d8\u5bf9\u8c61<\/em><\/code><\/pre>\n\n\n\n\n- \u5173\u952e\u70b9<\/strong>\uff1a
%Y-%m-%d<\/code>\u662f\u65e5\u671f\u683c\u5f0f\uff0c\u6309\u9700\u8c03\u6574\u3002<\/li>\n<\/ul>\n\n\n\n<\/span>10. \u5b57\u7b26\u4e32\u66ff\u6362<\/strong><\/span><\/h2>\n\n\n\nold_string = \"Python is fun.\"
new_string = old_string.replace(\"fun\", \"awesome\") # \u6539\u5934\u6362\u9762\uff0c\u4e00\u8a00\u65e2\u51fa<\/em><\/code><\/pre>\n\n\n\n<\/span>11. \u5feb\u901f\u6392\u5e8f<\/strong><\/span><\/h2>\n\n\n\nnumbers = [5, 2, 9, 1, 5]
sorted_numbers = sorted(numbers) # \u81ea\u7136\u6392\u5e8f\uff0c\u5347\u5e8f\u9ed8\u8ba4<\/em><\/code><\/pre>\n\n\n\n\n- \u8fdb\u9636<\/strong>\uff1a
reverse=True<\/code>\u53ef\u964d\u5e8f\u6392\u5217\u3002<\/li>\n<\/ul>\n\n\n\n<\/span>12. \u63d0\u53d6\u6570\u5b57<\/strong><\/span><\/h2>\n\n\n\nmixed_str = \"The year is 2023\"
nums = ''.join(filter(str.isdigit, mixed_str)) # \u53ea\u7559\u4e0b\u6570\u5b57\uff0c\u5176\u4f59\u8d70\u5f00<\/em><\/code><\/pre>\n\n\n\n\n- \u89e3\u5bc6<\/strong>\uff1a
filter<\/code>\u51fd\u6570\u914d\u5408isdigit<\/code>\uff0c\u53ea\u4fdd\u7559\u6570\u5b57\u5b57\u7b26\u3002<\/li>\n<\/ul>\n\n\n\n<\/span>13. \u7a7a\u503c\u5904\u7406\uff08\u5047\u8bbe\u662f\u5217\u8868\uff09<\/strong><\/span><\/h2>\n\n\n\ndata_list = [None, 1, 2, None, 3]
filtered_list = [x for x in data_list if x is not None] # \u62d2\u7edd\u7a7a\u503c\uff0c\u5e72\u51c0\u5229\u843d<\/em><\/code><\/pre>\n\n\n\n\n- \u8bed\u6cd5\u7cd6<\/strong>\uff1a\u5217\u8868\u63a8\u5bfc\u5f0f\uff0c\u7b80\u6d01\u4f18\u96c5\u3002<\/li>\n<\/ul>\n\n\n\n
<\/span>14. \u5b57\u5178\u952e\u503c\u5bf9\u4e92\u6362<\/strong><\/span><\/h2>\n\n\n\nmy_dict = {\"key1\": \"value1\", \"key2\": \"value2\"}
swapped_dict = {v: k for k, v in my_dict.items()} # \u7ffb\u8f6c\u4e7e\u5764\uff0c\u952e\u53d8\u503c\uff0c\u503c\u53d8\u952e<\/em><\/code><\/pre>\n\n\n\n<\/span>15. \u5e73\u5747\u503c\u8ba1\u7b97<\/strong><\/span><\/h2>\n\n\n\nnumbers = [10, 20, 30, 40]
average = sum(numbers) \/ len(numbers) # \u5e73\u5747\u6570\uff0c\u4e00\u6b65\u5230\u4f4d<\/em><\/code><\/pre>\n\n\n\n<\/span>16. \u5b57\u7b26\u4e32\u5206\u7ec4<\/strong><\/span><\/h2>\n\n\n\ns = \"abcdef\"
grouped = [s[i:i+2] for i in range(0, len(s), 2)] # \u6bcf\u4e24\u4e2a\u4e00\u7ec4\uff0c\u5206\u5272\u6709\u9053<\/em><\/code><\/pre>\n\n\n\n\n- \u5e94\u7528<\/strong>\uff1a\u9002\u7528\u4e8e\u4efb\u4f55\u9700\u8981\u5206\u7ec4\u7684\u573a\u666f\u3002<\/li>\n<\/ul>\n\n\n\n
<\/span>17. \u6570\u636e\u6807\u51c6\u5316<\/strong><\/span><\/h2>\n\n\n\nimport numpy as np
data = np.array([1, 2, 3])
normalized_data = (data - data.mean()) \/ data.std() # \u6570\u5b66\u4e4b\u7f8e\uff0c\u6807\u51c6\u5206\u5e03<\/em><\/code><\/pre>\n\n\n\n\n- \u80cc\u666f<\/strong>\uff1a\u6570\u636e\u5206\u6790\u5fc5\u5907\uff0c\u8ba9\u6570\u636e\u7b26\u5408\u6807\u51c6\u6b63\u6001\u5206\u5e03\u3002<\/li>\n<\/ul>\n\n\n\n
<\/span>18. \u6570\u636e\u8fc7\u6ee4\uff08\u57fa\u4e8e\u6761\u4ef6\uff09<\/strong><\/span><\/h2>\n\n\n\ndata = [1, 2, 3, 4, 5]
even_numbers = [x for x in data if x % 2 == 0] # \u53ea\u7559\u5076\u6570\uff0c\u6392\u9664\u5f02\u5df1<\/em><\/code><\/pre>\n\n\n\n\n- \u6280\u5de7<\/strong>\uff1a\u5217\u8868\u63a8\u5bfc\u7ed3\u5408\u6761\u4ef6\u5224\u65ad\uff0c\u9ad8\u6548\u7b5b\u9009\u3002<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n<\/span>\u8fdb\u9636\u5b9e\u8df5\u4e0e\u6280\u5de7<\/strong><\/span><\/h2>\n\n\n\n\u65e2\u7136\u4f60\u5df2\u7ecf\u638c\u63e1\u4e86\u57fa\u7840\u7684\u5341\u516b\u79cd\u65b9\u6cd5\uff0c\u63a5\u4e0b\u6765\u8ba9\u6211\u4eec\u6df1\u5165\u4e00\u4e9b\uff0c\u63a2\u8ba8\u5982\u4f55\u5c06\u8fd9\u4e9b\u6280\u5de7\u7ed3\u5408\u8d77\u6765\uff0c\u89e3\u51b3\u66f4\u590d\u6742\u7684\u6570\u636e\u6e05\u6d17\u95ee\u9898\uff0c\u5e76\u5206\u4eab\u4e00\u4e9b\u5b9e\u6218\u4e2d\u7684\u5c0f\u6280\u5de7\u3002<\/p>\n\n\n\n
<\/span>1. \u590d\u6742\u5b57\u7b26\u4e32\u5904\u7406\uff1a\u6b63\u5219\u8868\u8fbe\u5f0f<\/strong><\/span><\/h2>\n\n\n\n\u6b63\u5219\u8868\u8fbe\u5f0f\u662f\u6570\u636e\u6e05\u6d17\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\uff0c\u867d\u7136\u4e25\u683c\u6765\u8bf4\u53ef\u80fd\u8d85\u8fc7\u4e00\u884c\uff0c\u4f46\u5b83\u80fd\u9ad8\u6548\u5730\u5904\u7406\u6a21\u5f0f\u5339\u914d\u548c\u66ff\u6362\u3002<\/p>\n\n\n\n
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)<\/code><\/pre>\n\n\n\n\u8fd9\u6bb5\u4ee3\u7801\u5206\u522b\u63d0\u53d6\u4e86\u6587\u672c\u4e2d\u7684\u7535\u5b50\u90ae\u4ef6\u548c\u7535\u8bdd\u53f7\u7801\uff0c\u5c55\u793a\u4e86\u6b63\u5219\u8868\u8fbe\u5f0f\u7684\u5f3a\u5927\u3002<\/p>\n\n\n\n
<\/span>2. Pandas\u5e93\u7684\u9b54\u6cd5<\/strong><\/span><\/h2>\n\n\n\n\u5bf9\u4e8e\u6570\u636e\u5206\u6790\u548c\u6e05\u6d17\uff0cPandas\u662f\u4e0d\u4e8c\u4e4b\u9009\u3002\u867d\u7136Pandas\u7684\u547d\u4ee4\u901a\u5e38\u4e0d\u6b62\u4e00\u884c\uff0c\u4f46\u5176\u9ad8\u6548\u6027\u548c\u7b80\u6d01\u6027\u503c\u5f97\u5b66\u4e60\u3002<\/p>\n\n\n\n
import pandas as pd
df = pd.read_csv('data.csv')
# \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c<\/em>
df_clean = df.dropna()
# \u66ff\u6362\u7279\u5b9a\u503c<\/em>
df['column_name'] = df['column_name'].replace('old_value', 'new_value')<\/code><\/pre>\n\n\n\n\n- \u6ce8\u610f<\/strong>\uff1aPandas\u867d\u7136\u5f3a\u5927\uff0c\u4f46\u5bf9\u4e8e\u521d\u5b66\u8005\u53ef\u80fd\u9700\u8981\u66f4\u591a\u65f6\u95f4\u6765\u719f\u6089\u3002<\/li>\n<\/ul>\n\n\n\n
<\/span>3. \u9519\u8bef\u5904\u7406\u548c\u65e5\u5fd7\u8bb0\u5f55<\/strong><\/span><\/h2>\n\n\n\n\u5728\u5904\u7406\u5927\u91cf\u6570\u636e\u65f6\uff0c\u9519\u8bef\u51e0\u4e4e\u662f\u4e0d\u53ef\u907f\u514d\u7684\u3002\u5b66\u4f1a\u7528try-except\u7ed3\u6784\u6355\u83b7\u5f02\u5e38\uff0c\u5e76\u4f7f\u7528logging\u8bb0\u5f55\u65e5\u5fd7\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u5347\u8c03\u8bd5\u6548\u7387\u3002<\/p>\n\n\n\n