<\/span><\/h2>\n\n\n\n\u9996\u5148\uff0c\u4e86\u89e3\u5982\u4f55\u8bfb\u53d6\u548c\u5199\u5165\u6587\u4ef6\u662f\u57fa\u7840\u3002<\/p>\n\n\n\n
# \u8bfb\u53d6\u6587\u4ef6<\/em>
with open('file1.txt', 'r') as file1:
data1 = file1.readlines()
# \u5199\u5165\u6587\u4ef6<\/em>
with open('merged.txt', 'w') as merged_file:
for line in data1:
merged_file.write(line)<\/code><\/pre>\n\n\n\n<\/span>2. \u6587\u4ef6\u5185\u5bb9\u5bf9\u6bd4<\/strong><\/span><\/h2>\n\n\n\n\u4f7f\u7528difflib<\/code>\u5e93\u6765\u5bf9\u6bd4\u4e24\u4e2a\u6587\u4ef6\u7684\u5dee\u5f02\u3002<\/p>\n\n\n\nimport difflib
with open('file1.txt', 'r') as file1, open('file2.txt', 'r') as file2:
diff = difflib.unified_diff(file1.readlines(), file2.readlines())
print('\\n'.join(diff))<\/code><\/pre>\n\n\n\n<\/span>3. \u57fa\u4e8e\u884c\u7684\u5408\u5e76<\/strong><\/span><\/h2>\n\n\n\n\u5f53\u6587\u4ef6\u57fa\u4e8e\u76f8\u540c\u884c\u7ed3\u6784\u5408\u5e76\u65f6\uff0c\u53ef\u4ee5\u76f4\u63a5\u904d\u5386\u8ffd\u52a0\u3002<\/p>\n\n\n\n
data = []
for filename in ['file1.txt', 'file2.txt']:
with open(filename, 'r') as file:
data.extend(file.readlines())
with open('merged.txt', 'w') as merged_file:
for line in data:
merged_file.write(line)<\/code><\/pre>\n\n\n\n<\/span>4. \u53bb\u91cd\u5408\u5e76<\/strong><\/span><\/h2>\n\n\n\n\u5229\u7528\u96c6\u5408\u53bb\u9664\u91cd\u590d\u884c\u540e\u5408\u5e76\u3002<\/p>\n\n\n\n
unique_lines = set()
for filename in ['file1.txt', 'file2.txt']:
with open(filename, 'r') as file:
unique_lines.update(file.readlines())
with open('merged_unique.txt', 'w') as merged_file:
for line in sorted(unique_lines): # \u6392\u5e8f\u786e\u4fdd\u4e00\u81f4\u7684\u8f93\u51fa\u987a\u5e8f<\/em>
merged_file.write(line)<\/code><\/pre>\n\n\n\n<\/span>5. CSV\u6587\u4ef6\u5408\u5e76<\/strong><\/span><\/h2>\n\n\n\n\u5bf9\u4e8eCSV\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u3002<\/p>\n\n\n\nimport pandas as pd
df1 = pd.read_csv('file1.csv')
df2 = pd.read_csv('file2.csv')
# \u5047\u8bbe\u5408\u5e76\u4f9d\u636e\u4e3a\u76f8\u540c\u7684\u5217\u540d<\/em>
merged_df = pd.concat([df1, df2], ignore_index=True)
merged_df.to_csv('merged.csv', index=False)<\/code><\/pre>\n\n\n\n<\/span>6. \u6309\u5217\u5408\u5e76CSV<\/strong><\/span><\/h2>\n\n\n\n\u7279\u5b9a\u5217\u7684\u5408\u5e76\uff0c\u4f8b\u5982\u901a\u8fc7\u5171\u540c\u952e\u8fde\u63a5\u3002<\/p>\n\n\n\n
merged_df = pd.merge(df1, df2, on='common_key', how='outer')
merged_df.to_csv('merged_by_key.csv', index=False)<\/code><\/pre>\n\n\n\n<\/span>7. \u5927\u6587\u4ef6\u9ad8\u6548\u5bf9\u6bd4<\/strong><\/span><\/h2>\n\n\n\n\u5bf9\u4e8e\u5927\u6587\u4ef6\uff0c\u9010\u884c\u8bfb\u53d6\u5bf9\u6bd4\u4ee5\u8282\u7701\u5185\u5b58\u3002<\/p>\n\n\n\n
with open('large_file1.txt', 'r') as f1, open('large_file2.txt', 'r') as f2:
for line1, line2 in zip(f1, f2):
if line1 != line2:
print(\"Difference found!\")
break<\/code><\/pre>\n\n\n\n<\/span>8. \u6587\u672c\u6587\u4ef6\u7684\u4e8c\u8fdb\u5236\u5bf9\u6bd4<\/strong><\/span><\/h2>\n\n\n\n\u4f7f\u7528filecmp<\/code>\u6a21\u5757\u6bd4\u8f83\u6587\u4ef6\u7684\u4e8c\u8fdb\u5236\u5185\u5bb9\u3002<\/p>\n\n\n\nimport filecmp
if filecmp.cmp('file1.txt', 'file2.txt'):
print(\"Files are identical.\")
else:
print(\"Files differ.\")<\/code><\/pre>\n\n\n\n<\/span>9. \u52a8\u6001\u5408\u5e76\u591a\u4e2a\u6587\u4ef6<\/strong><\/span><\/h2>\n\n\n\n\u4f7f\u7528\u5faa\u73af\u52a8\u6001\u5408\u5e76\u591a\u4e2a\u6587\u4ef6\u8def\u5f84\u5217\u8868\u4e2d\u7684\u6587\u4ef6\u3002<\/p>\n\n\n\n
file_paths = ['file{}.txt'.format(i) for i in range(1, 4)] # \u5047\u8bbe\u6709file1.txt\u5230file3.txt<\/em>
with open('merged_all.txt', 'w') as merged:
for path in file_paths:
with open(path, 'r') as file:
merged.write(file.read() + '\\n') # \u6dfb\u52a0\u6362\u884c\u7b26\u533a\u5206\u4e0d\u540c\u6587\u4ef6\u7684\u5185\u5bb9<\/em><\/code><\/pre>\n\n\n\n<\/span>10. \u9ad8\u7ea7\u5408\u5e76\u7b56\u7565\uff1a\u667a\u80fd\u5408\u5e76<\/strong><\/span><\/h2>\n\n\n\n\u5982\u679c\u5408\u5e76\u4f9d\u636e\u66f4\u590d\u6742\uff0c\u5982\u6309\u65e5\u671f\u6216ID\u6392\u5e8f\u5408\u5e76\uff0c\u53ef\u4ee5\u5148\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\u5904\u7406\u3002<\/p>\n\n\n\n
# \u5047\u8bbe\u662fCSV\u4e14\u6309\u65e5\u671f\u5217\u6392\u5e8f\u5408\u5e76<\/em>
dfs = [pd.read_csv(f) for f in ['file1.csv', 'file2.csv']]
sorted_df = pd.concat(dfs).sort_values(by='date_column') # \u5047\u5b9a'date_column'\u662f\u65e5\u671f\u5217<\/em>
sorted_df.to_csv('smart_merged.csv', index=False)<\/code><\/pre>\n\n\n\n
\n\n\n\n<\/span>\u8fdb\u9636\u6280\u5de7\u548c\u573a\u666f<\/strong><\/span><\/h2>\n\n\n\n<\/span>11. \u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u8fdb\u884c\u590d\u6742\u6587\u672c\u5904\u7406<\/strong><\/span><\/h3>\n\n\n\n\u5728\u5408\u5e76\u6216\u5bf9\u6bd4\u524d\uff0c\u53ef\u80fd\u9700\u8981\u5bf9\u6587\u4ef6\u5185\u5bb9\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4f8b\u5982\u63d0\u53d6\u7279\u5b9a\u6a21\u5f0f\u7684\u6570\u636e\u3002<\/p>\n\n\n\n
import re
pattern = r'(\\d{4}-\\d{2}-\\d{2})' # \u5047\u8bbe\u63d0\u53d6\u65e5\u671f\u6a21\u5f0f<\/em>
lines_with_dates = []
with open('source.txt', 'r') as file:
for line in file:
match = re.search(pattern, line)
if match:
lines_with_dates.append(match.group(0))
# \u5047\u8bbe\u4f60\u60f3\u5c06\u63d0\u53d6\u7684\u4fe1\u606f\u5199\u5165\u65b0\u6587\u4ef6<\/em>
with open('dates_extracted.txt', 'w') as out_file:
for date in lines_with_dates:
out_file.write(date + '\\n')<\/code><\/pre>\n\n\n\n