From 2998e1c2b2d4eb6028199cfe880fcd3f9352e099 Mon Sep 17 00:00:00 2001 From: "song.haoyang" Date: Sun, 11 Feb 2024 18:41:45 +0800 Subject: [PATCH 1/3] practice --- .gitignore | 1 + .vscode/settings.json | 3 + samples/commonlib/use_datetime.py | 15 + samples/commonlib/use_datetime_copy.py | 15 + samples/db/do_mysql copy.py | 171 ++ samples/db/do_mysql.py | 62 +- samples/pdf/megerPdf.py | 56 + samples/pdf/read_from_pdf.py | 100 + samples/pyWxDumpUtils/cloud1.png | Bin 0 -> 451457 bytes samples/pyWxDumpUtils/cloud2.png | Bin 0 -> 497584 bytes samples/pyWxDumpUtils/cloud3.png | Bin 0 -> 506244 bytes samples/pyWxDumpUtils/cloud4.png | Bin 0 -> 478625 bytes samples/pyWxDumpUtils/cloud5.png | Bin 0 -> 496237 bytes samples/pyWxDumpUtils/me/cloud01.png | Bin 0 -> 428002 bytes samples/pyWxDumpUtils/me/cloud02.png | Bin 0 -> 431100 bytes samples/pyWxDumpUtils/me/cloud03.png | Bin 0 -> 471042 bytes samples/pyWxDumpUtils/me/cloud04.png | Bin 0 -> 462969 bytes samples/pyWxDumpUtils/me/cloud05.png | Bin 0 -> 477010 bytes samples/pyWxDumpUtils/muban3.png | Bin 0 -> 494480 bytes samples/pyWxDumpUtils/stopwords.dat | 1893 +++++++++++++++++ samples/pyWxDumpUtils/test.py | 96 + samples/pyWxDumpUtils/testwordcloud copy.py | 95 + samples/pyWxDumpUtils/testwordcloud.py | 95 + samples/pyWxDumpUtils/testwordcloudgeneral.py | 96 + 24 files changed, 2684 insertions(+), 14 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 samples/commonlib/use_datetime_copy.py create mode 100644 samples/db/do_mysql copy.py create mode 100644 samples/pdf/megerPdf.py create mode 100644 samples/pdf/read_from_pdf.py create mode 100644 samples/pyWxDumpUtils/cloud1.png create mode 100644 samples/pyWxDumpUtils/cloud2.png create mode 100644 samples/pyWxDumpUtils/cloud3.png create mode 100644 samples/pyWxDumpUtils/cloud4.png create mode 100644 samples/pyWxDumpUtils/cloud5.png create mode 100644 samples/pyWxDumpUtils/me/cloud01.png create mode 100644 samples/pyWxDumpUtils/me/cloud02.png create mode 100644 samples/pyWxDumpUtils/me/cloud03.png create mode 100644 samples/pyWxDumpUtils/me/cloud04.png create mode 100644 samples/pyWxDumpUtils/me/cloud05.png create mode 100644 samples/pyWxDumpUtils/muban3.png create mode 100644 samples/pyWxDumpUtils/stopwords.dat create mode 100644 samples/pyWxDumpUtils/test.py create mode 100644 samples/pyWxDumpUtils/testwordcloud copy.py create mode 100644 samples/pyWxDumpUtils/testwordcloud.py create mode 100644 samples/pyWxDumpUtils/testwordcloudgeneral.py diff --git a/.gitignore b/.gitignore index 2cc2704f..27958c2d 100644 --- a/.gitignore +++ b/.gitignore @@ -52,3 +52,4 @@ MANIFEST .mr.developer.cfg .project .pydevproject +*.txt \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..b881eff7 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.analysis.autoImportCompletions": true +} \ No newline at end of file diff --git a/samples/commonlib/use_datetime.py b/samples/commonlib/use_datetime.py index 625fad85..7ae5f27f 100755 --- a/samples/commonlib/use_datetime.py +++ b/samples/commonlib/use_datetime.py @@ -38,3 +38,18 @@ utc8_dt = utc_dt.astimezone(timezone(timedelta(hours=8))) print('UTC+0:00 now =', utc_dt) print('UTC+8:00 now =', utc8_dt) + +import datetime + +target_date = datetime.datetime(2024, 2, 2, 18, 0, 0) # 目标日期和时间 +current_datetime = datetime.datetime.now() # 当前日期和时间 + +time_left = target_date - current_datetime # 计算剩余时间 +time_left = max(time_left, datetime.timedelta(0)) # 确保剩余时间不为负值 + +# 提取剩余时间的小时、分钟和秒 +hours, remainder = divmod(time_left.total_seconds(), 3600) +minutes, seconds = divmod(remainder, 60) + +# 输出倒计时信息 +print(f"当前时间与2024-02-02 18:00:00之间相隔 {int(hours)} 小时 {int(minutes)} 分钟 {int(seconds)} 秒") diff --git a/samples/commonlib/use_datetime_copy.py b/samples/commonlib/use_datetime_copy.py new file mode 100644 index 00000000..f5d7b563 --- /dev/null +++ b/samples/commonlib/use_datetime_copy.py @@ -0,0 +1,15 @@ + +import datetime + +target_date = datetime.datetime(2024, 2, 2, 18, 0, 0) # 目标日期和时间 +current_datetime = datetime.datetime.now() # 当前日期和时间 + +time_left = target_date - current_datetime # 计算剩余时间 +time_left = max(time_left, datetime.timedelta(0)) # 确保剩余时间不为负值 + +# 提取剩余时间的小时、分钟和秒 +hours, remainder = divmod(time_left.total_seconds(), 3600) +minutes, seconds = divmod(remainder, 60) + +# 输出倒计时信息 +print(f"当前时间与2024-02-02 18:00:00之间相隔 {int(hours)} 小时 {int(minutes)} 分钟 {int(seconds)} 秒") diff --git a/samples/db/do_mysql copy.py b/samples/db/do_mysql copy.py new file mode 100644 index 00000000..088dc91e --- /dev/null +++ b/samples/db/do_mysql copy.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +########## prepare ########## + +# install mysql-connector-python: +# pip3 install mysql-connector-python --allow-external mysql-connector-python + +import mysql.connector +import pandas as pd +import numpy as np +import re +from fractions import Fraction + +# change root password to yours: +conn = mysql.connector.connect(user='root', password='root', database='sys') + +# 运行查询: +cursor = conn.cursor() +cursor.execute('SELECT a.患者编号,姓名,性别,年龄,就诊日期,既往史,并发症,临床表现,舌质,中药组成,中药,克数 FROM Sheet4 a LEFT JOIN Sheet4_detail b ON a.`患者编号`=b.`患者编号`') +values = cursor.fetchall() + +# 获取查询结果的字段名 +columns = [i[0] for i in cursor.description] + +# 关闭 Cursor 和 Connection +cursor.close() +conn.close() + +# 创建 DataFrame,并指定列名 +df = pd.DataFrame(values, columns=columns) +# print(df) + + + +## 1. 远志、石菖蒲、川芎的用量及用量的频次、频率,平均用量 +print("1. 远志、石菖蒲、川芎的用量及用量的频次、频率,平均用量") +# 将克数转换为数字 +df['克数'] = df['克数'].str.extract('(\d+)').astype(float) + +# 筛选出远志、石菖蒲、川芎的数据 +selected_herbs = ['远志', '石菖蒲', '川芎'] +selected_df = df[df['中药'].isin(selected_herbs)] + +# 获取用量及用量的频次 +usage_info = selected_df.groupby('中药')['克数'].agg(['sum', 'count']) + +# 计算平均用量 +usage_info['平均用量'] = usage_info['sum'] / usage_info['count'] + +# 重命名列名 +usage_info = usage_info.rename(columns={'sum': '用量总和', 'count': '用量频次'}) + +# print(usage_info) + +# 验证 1 MySQL,验证通过 +# SELECT +# COUNT(1) AS total_count, +# SUM(CAST(REPLACE(c.克数, 'g', '') AS SIGNED)) AS sum_of_numbers +# FROM +# ( +# SELECT a.患者编号,姓名,性别,年龄,就诊日期,既往史,并发症,临床表现,舌质,中药组成,中药,克数 +# FROM Sheet4 a +# LEFT JOIN Sheet4_detail b ON a.`患者编号` = b.`患者编号` +# ) AS c +# WHERE +# c.`中药` = '石菖蒲'; + + +# + +## 2. 远志与石菖蒲、远志与川芎用量比例及比例的频次、频率 +print(" 2. 远志与石菖蒲、远志与川芎用量比例及比例的频次、频率 ") + + +# 为远志与石菖蒲、远志与川芎创建透视表 +# pivot_table = df.pivot_table(index='患者编号', columns='中药', values='克数', aggfunc='sum', fill_value=0) +# 是用来指定在执行 unstack 操作时,对于缺失值(NaN)的填充值 +pivot_table = df.groupby(['患者编号', '中药'])['克数'].sum().unstack(fill_value=0) + +# 计算远志与石菖蒲的用量比例 +pivot_table['远志:石菖蒲比例'] = pivot_table['远志'] / pivot_table['石菖蒲'] +pivot_table['远志:石菖蒲比例'] = pivot_table['远志:石菖蒲比例'].replace([np.inf, -np.inf, np.nan], 0).round(2) + + +# 获取远志与石菖蒲比例的频次和频率 +pivot_table_prop1 = pivot_table['远志:石菖蒲比例'] +count_vc1 = pivot_table['远志:石菖蒲比例'].value_counts().sort_index() +freq_vc1 = count_vc1 / count_vc1.sum() * 100 + +# 将 pivot_table_prop1 转换为 DataFrame +df_prop1 = pivot_table_prop1.reset_index() +df_prop1.columns = ['患者编号', '远志:石菖蒲比例'] + +# 合并 df_prop1、count_vc1 和 freq_vc1 +result_df = pd.merge(df_prop1.drop(columns='患者编号'), pd.DataFrame({'远志:石菖蒲比例频次': count_vc1}), + left_on='远志:石菖蒲比例', right_index=True, how='left') + +result_df = pd.merge(result_df, pd.DataFrame({'远志:石菖蒲比例频率(%)': freq_vc1}), + left_on='远志:石菖蒲比例', right_index=True, how='left') + +# 去除患者编号这一列 +result_df = result_df[['远志:石菖蒲比例', '远志:石菖蒲比例频次', '远志:石菖蒲比例频率(%)']] + +# 格式化 '远志:石菖蒲比例' 列 +result_df['远志:石菖蒲比例'] = result_df['远志:石菖蒲比例'].apply( + lambda x: f"{Fraction(x).limit_denominator()}" if not pd.isna(x) and x != float('inf') else 'Infinity') + + +# 基于 '远志:石菖蒲比例' 列分组,取每组的第一个值 +grouped_result_df = result_df.groupby('远志:石菖蒲比例').first() + +# 输出结果 +print(grouped_result_df) + +# 计算远志与川芎的用量比例 +pivot_table['远志:川芎比例'] = pivot_table['远志'] / pivot_table['川芎'] +pivot_table['远志:川芎比例'] = pivot_table['远志:川芎比例'].replace([np.inf, -np.inf, np.nan], 0).round(2) +# 获取远志与石菖蒲比例的频次和频率 +pivot_table_prop2 = pivot_table['远志:川芎比例'] +# 获取远志与川芎比例的频次和频率 +count_vc2 = pivot_table['远志:川芎比例'].value_counts().sort_index() +freq_vc2 = count_vc2 / count_vc2.sum() * 100 + +# 将 pivot_table_prop2 转换为 DataFrame +df_prop2 = pivot_table_prop2.reset_index() +df_prop2.columns = ['患者编号', '远志:川芎比例'] + +# 合并 df_prop2、count_vc2 和 freq_vc1 +result_df2 = pd.merge(df_prop2.drop(columns='患者编号'), pd.DataFrame({'远志:川芎比例频次': count_vc2}), + left_on='远志:川芎比例', right_index=True, how='left') + +result_df2 = pd.merge(result_df2, pd.DataFrame({'远志:川芎比例频率(%)': freq_vc2}), + left_on='远志:川芎比例', right_index=True, how='left') + + +result_df2['远志:川芎比例'] = result_df2['远志:川芎比例'].apply(lambda x: f"{Fraction(x).limit_denominator()}" if not pd.isna(x) and x != float('inf') else 'Infinity') + +# 基于 '远志:石菖蒲比例' 列分组,取每组的第一个值 +grouped_result_df2 = result_df2.groupby('远志:川芎比例').first() +# 输出结果 +print(grouped_result_df2) + + +## 3. 远志与石菖蒲、远志与川芎用量比例归纳表 +print(" 3. 远志与石菖蒲、远志与川芎用量比例归纳表 ") + +# 选取远志、石菖蒲和川芎的数据 +selected_herbs = ['远志', '石菖蒲', '川芎'] +selected_df = df[df['中药'].isin(selected_herbs)] + +# 计算远志与石菖蒲的用量比例 +yunzhi_shichangpu = selected_df[selected_df['中药'].isin(['远志', '石菖蒲'])].groupby('患者编号')['克数'].agg('sum') +yunzhi_shichangpu_ratio = yunzhi_shichangpu.div(yunzhi_shichangpu.sum()) + +# 计算远志与川芎的用量比例 +yunzhi_chuanxiong = selected_df[selected_df['中药'].isin(['远志', '川芎'])].groupby('患者编号')['克数'].agg('sum') +yunzhi_chuanxiong_ratio = yunzhi_chuanxiong.div(yunzhi_chuanxiong.sum()) + +# 创建归纳表 +summary_table = pd.DataFrame(index=['<1', '=1', '>1']) + +# 添加远志与石菖蒲的用量比例信息 +summary_table['远志:石菖蒲 频次'] = pd.cut(yunzhi_shichangpu_ratio, bins=[0, 0.1, 0.2, 1], labels=['<1', '=1', '>1']).value_counts().sort_index() +summary_table['远志:石菖蒲 频率(%)'] = (summary_table['远志:石菖蒲 频次'] / summary_table['远志:石菖蒲 频次'].sum() * 100).round(2) + +# # 添加远志与川芎的用量比例信息 +# summary_table['远志:川芎 频次'] = pd.cut(yunzhi_chuanxiong_ratio, bins=[0, 0.1, 0.2, 1], labels=['<1', '=1', '>1']).value_counts().sort_index() +# summary_table['远志:川芎 频率(%)'] = (summary_table['远志:川芎 频次'] / summary_table['远志:川芎 频次'].sum() * 100).round(2) + +# print(summary_table) diff --git a/samples/db/do_mysql.py b/samples/db/do_mysql.py index d1d1db8d..7e3a2b19 100755 --- a/samples/db/do_mysql.py +++ b/samples/db/do_mysql.py @@ -7,25 +7,59 @@ # pip3 install mysql-connector-python --allow-external mysql-connector-python import mysql.connector +import pandas as pd +import numpy as np +import re # change root password to yours: -conn = mysql.connector.connect(user='root', password='password', database='test') - -cursor = conn.cursor() -# 创建user表: -cursor.execute('create table user (id varchar(20) primary key, name varchar(20))') -# 插入一行记录,注意MySQL的占位符是%s: -cursor.execute('insert into user (id, name) values (%s, %s)', ('1', 'Michael')) -print('rowcount =', cursor.rowcount) -# 提交事务: -conn.commit() -cursor.close() +conn = mysql.connector.connect(user='root', password='root', database='sys') # 运行查询: cursor = conn.cursor() -cursor.execute('select * from user where id = %s', ('1',)) +cursor.execute('select 患者编号,中药组成 from Sheet4 ') values = cursor.fetchall() -print(values) -# 关闭Cursor和Connection: + +# 获取查询结果的字段名 +columns = [i[0] for i in cursor.description] + +# 关闭 Cursor 和 Connection cursor.close() conn.close() + +# 创建 DataFrame,并指定列名 +df = pd.DataFrame(values, columns=columns) + +# 提取中文和数字字母的正则表达式 +pattern = re.compile(r'([\u4e00-\u9fa5]+)(\d+\w+)') +result = df['中药组成'].str.extractall(pattern) + + +# 保存原始的患者编号列 +patient_ids = df['患者编号'].iloc[result.index.get_level_values(0)].reset_index(drop=True) + +# 重置索引 +result.reset_index(drop=True, inplace=True) + + +# 将患者编号加回结果中 +result['患者编号'] = patient_ids + +# 重新排列列的顺序 +result = result[['患者编号', 0, 1]] +# 重命名列名 +result.columns = ['患者编号', '草药', '克数'] +# 打印 result 的所有内容 +print(result) + +# 将数据插入新表 +connInsert = mysql.connector.connect(user='root', password='root', database='sys') +cursorInsert = connInsert.cursor() + +# 使用 iterrows() 迭代 DataFrame 中的行 +for _, row in result.iterrows(): + cursorInsert.execute('INSERT INTO Sheet4_detail (患者编号, 中药, 克数) VALUES (%s, %s, %s)', (row['患者编号'], row['草药'], row['克数'])) + +# 提交更改并关闭连接 +connInsert.commit() +cursorInsert.close() +connInsert.close() diff --git a/samples/pdf/megerPdf.py b/samples/pdf/megerPdf.py new file mode 100644 index 00000000..2b73c0f2 --- /dev/null +++ b/samples/pdf/megerPdf.py @@ -0,0 +1,56 @@ +import os +import fitz # PyMuPDF + +def merge_pdfs(folder_path, output_path): + # 创建 PyMuPDF 的文档对象 + merger = fitz.open() + + # 递归处理文件夹内的所有 PDF 文件 + for root, dirs, files in os.walk(folder_path): + for pdf_file in files: + if pdf_file.endswith('.pdf'): + pdf_path = os.path.join(root, pdf_file) + + # 打印文件名,用于调试 + print(f'Merging: {pdf_file}') + + # 尝试打开 PDF 文件 + try: + pdf_document = fitz.open(pdf_path) + + # 打印输入 PDF 文件的页面数量 + print(f'Input PDF pages: {pdf_document.page_count}') + + # 检查页面数量,只有当有页面时才合并 + if pdf_document.page_count > 0: + merger.insert_pdf(pdf_document) + + pdf_document.close() + except Exception as e: + print(f'Error merging {pdf_file}: {e}') + import traceback + traceback.print_exc() + + # 打印合并后的文档的页面数量 + print(f'Merged PDF pages: {merger.page_count}') + + # 保存合并后的 PDF + try: + # 只有当合并后的文档有页面时才保存 + if merger.page_count > 0: + merger.save(output_path) + else: + print('No pages to save. Merged PDF will not be created.') + except Exception as e: + print(f'Error saving merged PDF: {e}') + + merger.close() + +# 指定文件夹路径和输出文件路径 +folder_path = r'D:\personSpace\Math' +output_path = os.path.join(folder_path, 'merged.pdf') + +# 调用合并函数 +merge_pdfs(folder_path, output_path) + +print(f'合并完成,输出文件保存在: {output_path}') diff --git a/samples/pdf/read_from_pdf.py b/samples/pdf/read_from_pdf.py new file mode 100644 index 00000000..6079c26d --- /dev/null +++ b/samples/pdf/read_from_pdf.py @@ -0,0 +1,100 @@ +from pdfminer.pdfparser import PDFParser, PDFDocument +from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter +from pdfminer.converter import PDFPageAggregator +from pdfminer.layout import LAParams, LTTextBox +from pdfminer.pdfinterp import PDFTextExtractionNotAllowed +from collections import defaultdict, deque +from itertools import count + +def aho_corasick(): + G = defaultdict(count(1).__next__) # transitions + W = defaultdict(set) # alphabet + F = defaultdict(lambda: 0) # fallbacks + O = defaultdict(set) # outputs + + # automaton + return G, W, F, O + +def add_word(word, G, W, F, O): + state = 0 + # add transitions between states + for w in word: + W[state].add(w) + state = G[state, w] + + # add output + O[state].add(word) + +def build_fsa(G, W, F, O): + # initial states + queue = deque(G[0, w] for w in W[0]) + + while queue: + state = queue.popleft() + + # for each letter in alphabet + for w in W[state]: + # find fallback state + t = F[state] + while t and (t, w) not in G: + t = F[t] + + # for next state define its fallback and output + s = G[state, w] + F[s] = G[t, w] if (t, w) in G else 0 + O[s] |= O[F[s]] + + queue.append(s) + +def search_in_pdf(text, G, W, F, O): + state = 0 + + for i, t in enumerate(text): + # fallback + while state and (state, t) not in G: + state = F[state] + + # transition + state = G[state, t] if (state, t) in G else 0 + + # output + if O[state]: + print('@', i, O[state]) + +# PDF文档解析 +path = r"D:\Documents\WeChat Files\wxid_lsootbrkhf4x22\FileStorage\File\2024-01\录入文献\录入文献\袁梦石以天地转气汤为主治疗中重度阿尔茨海默病经验总结_罗慧.pdf" + +praser = PDFParser(open(path, 'rb')) +doc = PDFDocument() +praser.set_document(doc) +doc.set_parser(praser) +doc.initialize() + +if not doc.is_extractable: + raise PDFTextExtractionNotAllowed +else: + rsrcmgr = PDFResourceManager() + laparams = LAParams() + device = PDFPageAggregator(rsrcmgr, laparams=laparams) + interpreter = PDFPageInterpreter(rsrcmgr, device) + + # 获取PDF中的文本内容 + pdf_text = "" + for page in doc.get_pages(): + interpreter.process_page(page) + layout = device.get_result() + for x in layout: + if isinstance(x, LTTextBox): + pdf_text += x.get_text() + + # 示例中药名称搜索 + G, W, F, O = aho_corasick() + add_word("远志", G, W, F, O) + add_word("石菖蒲", G, W, F, O) + add_word("川芎", G, W, F, O) + + build_fsa(G, W, F, O) + + # 在PDF文本中搜索中药名称 + search_in_pdf(pdf_text, G, W, F, O) + print(pdf_text) diff --git a/samples/pyWxDumpUtils/cloud1.png b/samples/pyWxDumpUtils/cloud1.png new file mode 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"version_list_path": "微信版本偏移文件路径" # 微信版本偏移文件路径(可选) +} +bias_addr = BiasAddr(args["account"], args["mobile"], args["name"], args["key"], args["db_path"])## +result = bias_addr.run(True) +# ************************************************************************************************ # +# 获取微信信息 +wx_info = read_info(VERSION_LIST, True) + +# 获取微信文件夹路径 +args = { + "mode": "db_path", + "require_list": "all", # 需要的数据库名称(可选) + "wx_files": "WeChat Files", # 'WeChat Files'路径(可选) + "wxid": "wxid_", # wxid_,用于确认用户文件夹(可选) +} +user_dirs = get_wechat_db(args["require_list"], args["wx_files"], args["wxid"], True) +# ************************************************************************************************ # +# 解密微信数据库 +args = { + "mode": "decrypt", + "key": "dc04f757625448c08b71f96dc03270c6ffa5e8dcc3ad49e58e3e8e91edc18242", # 密钥 + "db_path": "D:\\Documents\\MuMu\\EnMicroMsg.db", # 数据库路径 + "out_path": "/path/to/decrypted" # 输出路径(必须是目录)[默认为当前路径下decrypted文件夹] +} +result = batch_decrypt(args["key"], args["db_path"], args["out_path"], True) + +from pywxdump import VERSION_LIST_PATH, VERSION_LIST +from pywxdump import batch_decrypt +key = "dc04f75" # 解密密钥 +db_path = "D:\\Documents\\MuMu\\EnMicroMsg.db" # 数据库路径(文件or文件list) +out_path = "D:\\Documents\\MuMu" # 输出路径(目录) + +result = batch_decrypt(key, db_path, out_path, True) +# ************************************************************************************************ # +# 查看聊天记录 +args = { + "mode": "dbshow", + "msg_path": "解密后的 MSG.db 的路径", # 解密后的 MSG.db 的路径 + "micro_path": "解密后的 MicroMsg.db 的路径", # 解密后的 MicroMsg.db 的路径 + "media_path": "解密后的 MediaMSG.db 的路径", # 解密后的 MediaMSG.db 的路径 + "filestorage_path": "文件夹FileStorage的路径" # 文件夹 FileStorage 的路径(用于显示图片) +} +from flask import Flask, request, jsonify, render_template, g +import logging + +app = Flask(__name__, template_folder='./show_chat/templates') +app.logger.setLevel(logging.ERROR) + + +@app.before_request +def before_request(): + g.MSG_ALL_db_path = args["msg_path"] + g.MicroMsg_db_path = args["micro_path"] + g.MediaMSG_all_db_path = args["media_path"] + g.FileStorage_path = args["filestorage_path"] + g.USER_LIST = get_user_list(args["msg_path"], args["micro_path"]) + + +app.register_blueprint(app_show_chat) +print("[+] 请使用浏览器访问 http://127.0.0.1:5000/ 查看聊天记录") +app.run(debug=False) +# ************************************************************************************************ # +# 导出聊天记录为 HTML +args = { + "mode": "export", + "username": "luomi1998", # 微信账号(聊天对象账号) + "outpath": "/path/to/export", # 导出路径 + "msg_path": "D:/Documents/WeChat Files/MSG", # 解密后的 MSG.db 的路径 + "micro_path": "D:/Documents/WeChat Files/MicroMsg", # 解密后的 MicroMsg.db 的路径 + "media_path": "D:/Documents/WeChat Files/MediaMSG" # 解密后的 MediaMSG.db 的路径 + ,"filestorage_path": "D:/Documents/WeChat Files/FileStorage" # 文件夹 FileStorage 的路径(用于显示图片) +} +{ + "test": "", + "msg_path": "C:\\Users\\junio\\wxdump_tmp\\decrypted\\wxid_lsootbrkhf4x22\\merge_all.db", + "micro_path": "C:\\Users\\junio\\wxdump_tmp\\decrypted\\wxid_lsootbrkhf4x22\\merge_all.db", + "media_path": "C:\\Users\\junio\\wxdump_tmp\\decrypted\\wxid_lsootbrkhf4x22\\merge_all.db", + "wx_path": "D:\\Documents\\WeChat Files\\wxid_lsootbrkhf4x22", + "key": "dc04f757625448c08b71f96dc03270c6ffa5e8dcc3ad49e58e3e8e91edc18242", + "my_wxid": "wxid_lsootbrkhf4x22" +} +export(args["username"], args["outpath"], args["msg_path"], args["micro_path"], args["media_path"] + ,args["filestorage_path"]) \ No newline at end of file diff --git a/samples/pyWxDumpUtils/testwordcloud copy.py b/samples/pyWxDumpUtils/testwordcloud copy.py new file mode 100644 index 00000000..18927031 --- /dev/null +++ b/samples/pyWxDumpUtils/testwordcloud copy.py @@ -0,0 +1,95 @@ +from collections import Counter +import pandas as pd +import re +import pymysql +import numpy as np +import jieba +from wordcloud import WordCloud +from PIL import Image + + +def read_file(file_name): + with open(file_name, "r", encoding="utf-8") as fp: + lines = fp.readlines() + lines = [line.rstrip("\n") for line in lines] + return lines + + +# 从message表提取原始聊天记录并保存 +def extract(): + # 连接到MySQL数据库 + conn = pymysql.connect(host='localhost', port=3308, user='root', password='root', database='demo') + + cursor = conn.cursor() + cursor.execute('SELECT JSON_EXTRACT(content, "$.msg") AS msg_value FROM wxid_p34mlvra9brc22_0_20230207_20240201 WHERE talker = "我";') + contents = cursor.fetchall() + + with open('原始聊天记录me.txt', 'w+', encoding='utf-8') as file: + for content in contents: + file.write(content[0] + '\n') + + cursor.close() + conn.close() + + +# 对聊天记录进行处理 +def process(): + emoj_regx = re.compile(r"\[[^\]]+\]") # 匹配表情图片 + wxid_regx = re.compile(r"wxid.*") + english_regx = re.compile(r"[a-zA-Z]+") # 匹配英文字符 + chinese_regx = re.compile(r"表情|图片|未知|时长|逼|秒|撤回|没意思|翻译结果|用户上传的GIF表情|卡片式链接|带有引用的文本消息") # 匹配中文字符“表情”和“图片” + space_regx = re.compile(r"\s+") # 匹配空格 + digit_regx = re.compile(r"\d+") # 匹配数字 + content_lines = read_file('原始聊天记录me.txt') + for i in range(len(content_lines)): + content_lines[i] = emoj_regx.sub(r"", content_lines[i]) # 去除表情图片 + content_lines[i] = wxid_regx.sub(r"", content_lines[i]) + content_lines[i] = english_regx.sub(r"", content_lines[i]) # 去除英文字符 + content_lines[i] = chinese_regx.sub(r"", content_lines[i]) # 去除中文字符“表情”和“图片” + content_lines[i] = space_regx.sub(r"", content_lines[i]) # 去除空格 + content_lines[i] = digit_regx.sub(r"", content_lines[i]) # 去除数字 + content_lines = [line for line in content_lines if line != ''] + + return content_lines + + +def cut(content_lines): + jieba.load_userdict('./原始聊天记录me.txt') + stopwords = read_file('./stopwords.dat') + all_words = [] + for line in content_lines: + all_words += [word for word in jieba.cut(line) if word not in stopwords] + # 使用Counter计算所有词频 + all_word_freq = Counter(all_words) + # 过滤掉出现次数小于等于10的词 + dict_words = {word: count for word, count in all_word_freq.items() if count > 10} + return dict_words + +def get_cloud(sorted_words, num): + mask_image = np.array(Image.open('muban3.png')) + word_counts = Counter(dict(sorted_words)) + chunk_size = len(sorted_words) // num # 总词数除以词云图数量 + + for i in range(num): + start = i * chunk_size + end = (i + 1) * chunk_size if i < num - 1 else len(sorted_words) # 确保最后一个chunk包含所有剩余的词 + chunk = {word: count for word, count in sorted_words[start:end]} + + wordcloud = WordCloud(background_color='white', mask=mask_image, font_path='simhei.ttf') + wordcloud.generate_from_frequencies(chunk) + wordcloud.to_file('./me/cloud0{}.png'.format(i + 1)) # 保存词云图,文件名按1开始编号 + +if __name__ == '__main__': + # 提取聊天记录 + extract() + # 聊天记录处理 + content_lines = process() + # 分词和停用词去除 + dict_words = cut(content_lines) + # 降序排序,并过滤掉出现次数小于等于10的词 + sorted_words = sorted(dict_words.items(), key=lambda item: item[1], reverse=True) + print(sorted_words) + + # 词云生成 + # 确保按照词频超过十次的词来构成五张词云图 + get_cloud(sorted_words, 5) \ No newline at end of file diff --git a/samples/pyWxDumpUtils/testwordcloud.py b/samples/pyWxDumpUtils/testwordcloud.py new file mode 100644 index 00000000..c5c3f780 --- /dev/null +++ b/samples/pyWxDumpUtils/testwordcloud.py @@ -0,0 +1,95 @@ +from collections import Counter +import pandas as pd +import re +import pymysql +import numpy as np +import jieba +from wordcloud import WordCloud +from PIL import Image + + +def read_file(file_name): + with open(file_name, "r", encoding="utf-8") as fp: + lines = fp.readlines() + lines = [line.rstrip("\n") for line in lines] + return lines + + +# 从message表提取原始聊天记录并保存 +def extract(): + # 连接到MySQL数据库 + conn = pymysql.connect(host='localhost', port=3308, user='root', password='root', database='demo') + + cursor = conn.cursor() + cursor.execute('SELECT JSON_EXTRACT(content, "$.msg") AS msg_value FROM wxid_p34mlvra9brc22_0_20230207_20240201 WHERE talker = "wxid_p34mlvra9brc22";') + contents = cursor.fetchall() + + with open('原始聊天记录ta.txt', 'w+', encoding='utf-8') as file: + for content in contents: + file.write(content[0] + '\n') + + cursor.close() + conn.close() + + +# 对聊天记录进行处理 +def process(): + emoj_regx = re.compile(r"\[[^\]]+\]") # 匹配表情图片 + wxid_regx = re.compile(r"wxid.*") + english_regx = re.compile(r"[a-zA-Z]+") # 匹配英文字符 + chinese_regx = re.compile(r"表情|图片|未知|时长|逼|秒|没意思|翻译结果|卡片式链接|带有引用的文本消息") # 匹配中文字符“表情”和“图片” + space_regx = re.compile(r"\s+") # 匹配空格 + digit_regx = re.compile(r"\d+") # 匹配数字 + content_lines = read_file('原始聊天记录ta.txt') + for i in range(len(content_lines)): + content_lines[i] = emoj_regx.sub(r"", content_lines[i]) # 去除表情图片 + content_lines[i] = wxid_regx.sub(r"", content_lines[i]) + content_lines[i] = english_regx.sub(r"", content_lines[i]) # 去除英文字符 + content_lines[i] = chinese_regx.sub(r"", content_lines[i]) # 去除中文字符“表情”和“图片” + content_lines[i] = space_regx.sub(r"", content_lines[i]) # 去除空格 + content_lines[i] = digit_regx.sub(r"", content_lines[i]) # 去除数字 + content_lines = [line for line in content_lines if line != ''] + + return content_lines + + +def cut(content_lines): + jieba.load_userdict('./原始聊天记录ta.txt') + stopwords = read_file('stopwords.dat') + all_words = [] + for line in content_lines: + all_words += [word for word in jieba.cut(line) if word not in stopwords] + # 使用Counter计算所有词频 + all_word_freq = Counter(all_words) + # 过滤掉出现次数小于等于10的词 + dict_words = {word: count for word, count in all_word_freq.items() if count > 10} + return dict_words + +def get_cloud(sorted_words, num): + mask_image = np.array(Image.open('muban3.png')) + word_counts = Counter(dict(sorted_words)) + chunk_size = len(sorted_words) // num # 总词数除以词云图数量 + + for i in range(num): + start = i * chunk_size + end = (i + 1) * chunk_size if i < num - 1 else len(sorted_words) # 确保最后一个chunk包含所有剩余的词 + chunk = {word: count for word, count in sorted_words[start:end]} + + wordcloud = WordCloud(background_color='white', mask=mask_image, font_path='simhei.ttf') + wordcloud.generate_from_frequencies(chunk) + wordcloud.to_file('cloud{}.png'.format(i + 1)) # 保存词云图,文件名按1开始编号 + +if __name__ == '__main__': + # 提取聊天记录 + # extract() + # 聊天记录处理 + content_lines = process() + # 分词和停用词去除 + dict_words = cut(content_lines) + # 降序排序,并过滤掉出现次数小于等于10的词 + sorted_words = sorted(dict_words.items(), key=lambda item: item[1], reverse=True) + print(sorted_words) + + # 词云生成 + # 确保按照词频超过十次的词来构成五张词云图 + get_cloud(sorted_words, 5) \ No newline at end of file diff --git a/samples/pyWxDumpUtils/testwordcloudgeneral.py b/samples/pyWxDumpUtils/testwordcloudgeneral.py new file mode 100644 index 00000000..7f2acc31 --- /dev/null +++ b/samples/pyWxDumpUtils/testwordcloudgeneral.py @@ -0,0 +1,96 @@ +from collections import Counter +import pandas as pd +import re +import pymysql +import numpy as np +import jieba +from wordcloud import WordCloud +from PIL import Image +import re +import numpy as np +from PIL import Image +from wordcloud import WordCloud, STOPWORDS +import matplotlib.pyplot as plt +from collections import Counter + +def read_file(file_name): + with open(file_name, "r", encoding="utf-8") as fp: + lines = fp.readlines() + lines = [line.rstrip("\n") for line in lines] + return lines + + +def extract(): + # 读取json文件 + content_lines = read_file('D:/Documents/WeChat Files/wxid_lsootbrkhf4x22/FileStorage/File/2024-02/38988759337@chatroom_0_5539.json') + + # 将聊天记录保存到txt文件中 + with open('原始聊天记录chatroom_0_5539.txt', 'w+', encoding='utf-8') as file: + for line in content_lines: + file.write(line + '\n') + + +# 对聊天记录进行处理 + + +def process(): + emoj_regx = re.compile(r"\[[^\]]+\]") # 匹配表情图片 + wxid_regx = re.compile(r"wxid.*") + english_regx = re.compile(r"[a-zA-Z]+") # 匹配英文字符 + chinese_regx = re.compile(r"[^\u4e00-\u9fa5]+") # 匹配非中文字符 + chinese_regx_txt = re.compile(r"表情|图片|未知|文本|时长|逼|秒|撤回|没意思|翻译结果|用户上传的GIF表情|卡片式链接|带有引用的文本消息") # 匹配中文字符“表情”和“图片” + + space_regx = re.compile(r"\s+") # 匹配空格 + digit_regx = re.compile(r"\d+") # 匹配数字 + content_lines = read_file('.\原始聊天记录chatroom_0_5539.txt') + for i in range(len(content_lines)): + content_lines[i] = emoj_regx.sub(r"", content_lines[i]) # 去除表情图片 + content_lines[i] = wxid_regx.sub(r"", content_lines[i]) + content_lines[i] = english_regx.sub(r"", content_lines[i]) # 去除英文字符 + content_lines[i] = chinese_regx.sub(r"", content_lines[i]) # 去除非中文字符 + content_lines[i] = chinese_regx_txt.sub(r"", content_lines[i]) # 去除非中文字符 + content_lines[i] = space_regx.sub(r"", content_lines[i]) # 去除空格 + content_lines[i] = digit_regx.sub(r"", content_lines[i]) # 去除数字 + content_lines = [line for line in content_lines if line != ''] + + return content_lines + + + +def cut(content_lines): + jieba.load_userdict('.\原始聊天记录chatroom_0_5539.txt') + stopwords = read_file('stopwords.dat') + all_words = [] + for line in content_lines: + all_words += [word for word in jieba.cut(line) if word not in stopwords] + # 使用Counter计算所有词频 + all_word_freq = Counter(all_words) + # 过滤掉出现次数小于等于10的词 + dict_words = {word: count for word, count in all_word_freq.items() if count > 10} + return dict_words + + +def get_cloud(sorted_words): + word_counts = Counter(dict(sorted_words)) + # 保存词云图,只生成一张图,指定长宽161.8:100 + wordcloud = WordCloud(background_color='white', mask=None, font_path='simhei.ttf', stopwords=STOPWORDS, width=809, height=500) + wordcloud.generate_from_frequencies(word_counts) + plt.figure(figsize=(8.09, 5)) + plt.imshow(wordcloud, interpolation="bilinear") + plt.axis("off") + plt.show() + + + +if __name__ == '__main__': + extract() + # 聊天记录处理 + content_lines = process() + # 分词和停用词去除 + dict_words = cut(content_lines) + # 降序排序,并过滤掉出现次数小于等于10的词 + sorted_words = sorted(dict_words.items(), key=lambda item: item[1], reverse=True) + print(sorted_words) + + # 词云生成 + get_cloud(sorted_words) \ No newline at end of file From dff8f2b64570d6fd0280f0fe9bcc1a736a28ef98 Mon Sep 17 00:00:00 2001 From: Acclerate Date: Thu, 29 Feb 2024 16:57:31 +0800 Subject: [PATCH 2/3] update --- .gitignore | 3 +- samples/matplotlib/matplotlibPd copy 2.py | 49 +++++++++++++++ samples/matplotlib/matplotlibPd copy.py | 45 ++++++++++++++ samples/matplotlib/matplotlibPd.py | 45 ++++++++++++++ samples/matplotlib/output.png | Bin 0 -> 52686 bytes samples/{db => pandas}/do_mysql copy.py | 0 samples/pyWxDumpUtils/wodclodmedicine.py | 56 ++++++++++++++++++ samples/pyWxDumpUtils/wodclodmedicineByCsv.py | 23 +++++++ samples/regex/regexTestDemo.py | 15 +++++ 9 files changed, 235 insertions(+), 1 deletion(-) create mode 100644 samples/matplotlib/matplotlibPd copy 2.py create mode 100644 samples/matplotlib/matplotlibPd copy.py create mode 100644 samples/matplotlib/matplotlibPd.py create mode 100644 samples/matplotlib/output.png rename samples/{db => pandas}/do_mysql copy.py (100%) create mode 100644 samples/pyWxDumpUtils/wodclodmedicine.py create mode 100644 samples/pyWxDumpUtils/wodclodmedicineByCsv.py create mode 100644 samples/regex/regexTestDemo.py diff --git a/.gitignore b/.gitignore index 27958c2d..8b1f8a85 100644 --- a/.gitignore +++ b/.gitignore @@ -52,4 +52,5 @@ MANIFEST .mr.developer.cfg .project .pydevproject -*.txt \ No newline at end of file +*.txt +*.csv \ No newline at end of file diff --git a/samples/matplotlib/matplotlibPd copy 2.py b/samples/matplotlib/matplotlibPd copy 2.py new file mode 100644 index 00000000..58cd525e --- /dev/null +++ b/samples/matplotlib/matplotlibPd copy 2.py @@ -0,0 +1,49 @@ +import pandas as pd +import matplotlib.pyplot as plt +from matplotlib.ticker import MultipleLocator + +# 读取CSV文件数据 +df = pd.read_csv(r'D:\pyspace\learn-python3\samples\matplotlib\含远至功效.csv') + +# 设置中文显示的字体 +plt.rcParams['font.sans-serif'] = ['SimHei'] +plt.rcParams['axes.unicode_minus'] = False + +# 若百分比数据为字符串形式,需要将其转换为数值形式 +# 此行代码假设百分比列的值包含%符号,如'22.42%' +df['百分比'] = df['百分比'].str.rstrip('%').astype('float') + +# 绘制图表 +fig, ax = plt.subplots(figsize=(12, 6)) + +# 以天蓝色作为颜色绘制频次柱状图 +ax.bar(df['功效统计分析'], df['频次'], color='skyblue') + +# 设置坐标轴标签 +# ax.set_xlabel('功效统计分析') +ax.set_ylabel('频次') + +# 以红色圆圈绘制百分比折线图 +ax2 = ax.twinx() +ax2.plot(df['功效统计分析'], df['百分比'], 'ro-') +ax2.set_ylabel('百分比 (%)') + +# Set Y axis major ticks to a multiple of 1 +ax2.yaxis.set_major_locator(MultipleLocator(1)) + +# 去掉网格线 +ax.grid(False) + +# 在每个柱子上方标识频次数量值 +for bar in ax.patches: + ax.text(bar.get_x() + bar.get_width()/2, + bar.get_height(), + '{:.0f}'.format(bar.get_height()), + ha='center', + va='bottom') + +# 调整整体空白,防止标签重叠 +fig.tight_layout() + +# 显示图表 +plt.show() \ No newline at end of file diff --git a/samples/matplotlib/matplotlibPd copy.py b/samples/matplotlib/matplotlibPd copy.py new file mode 100644 index 00000000..5e77eb05 --- /dev/null +++ b/samples/matplotlib/matplotlibPd copy.py @@ -0,0 +1,45 @@ +import pandas as pd +import matplotlib.pyplot as plt + +# 读取CSV文件数据 +df = pd.read_csv(r'D:\pyspace\learn-python3\samples\matplotlib\去远至功效.csv') + +# 设置中文显示的字体 +plt.rcParams['font.sans-serif'] = ['SimHei'] +plt.rcParams['axes.unicode_minus'] = False + +# 若百分比数据为字符串形式,需要将其转换为数值形式 +# 此行代码假设百分比列的值包含%符号,如'22.42%' +df['百分比'] = df['百分比'].str.rstrip('%').astype('float') + +# 绘制图表 +fig, ax = plt.subplots(figsize=(12, 6)) + +# 以天蓝色作为颜色绘制频次柱状图 +ax.bar(df['去远至功效'], df['频次'], color='skyblue') + +# 设置坐标轴标签 +# ax.set_xlabel('去远至功效') +ax.set_ylabel('频次') + +# 以红色圆圈绘制百分比折线图 +ax2 = ax.twinx() +ax2.plot(df['去远至功效'], df['百分比'], 'ro-') +ax2.set_ylabel('百分比 (%)') + +# 去掉网格线 +ax.grid(False) + +# 在每个柱子上方标识频次数量值 +for bar in ax.patches: + ax.text(bar.get_x() + bar.get_width()/2, + bar.get_height(), + '{:.0f}'.format(bar.get_height()), + ha='center', + va='bottom') + +# 调整整体空白,防止标签重叠 +fig.tight_layout() + +# 显示图表 +plt.show() diff --git a/samples/matplotlib/matplotlibPd.py b/samples/matplotlib/matplotlibPd.py new file mode 100644 index 00000000..6a8dc0d4 --- /dev/null +++ b/samples/matplotlib/matplotlibPd.py @@ -0,0 +1,45 @@ +import pandas as pd +import matplotlib.pyplot as plt + +# 读取CSV文件数据 +df = pd.read_csv(r'D:\pyspace\learn-python3\samples\matplotlib\中医症候.csv') + +# 设置中文显示的字体 +plt.rcParams['font.sans-serif'] = ['SimHei'] +plt.rcParams['axes.unicode_minus'] = False + +# 若百分比数据为字符串形式,需要将其转换为数值形式 +# 此行代码假设百分比列的值包含%符号,如'22.42%' +df['百分比'] = df['百分比'].str.rstrip('%').astype('float') + +# 绘制图表 +fig, ax = plt.subplots(figsize=(12, 6)) + +# 以天蓝色作为颜色绘制频次柱状图 +ax.bar(df['中医证候'], df['频次'], color='skyblue') + +# 设置坐标轴标签 +# ax.set_xlabel('中医证候') +ax.set_ylabel('频次') + +# 以红色圆圈绘制百分比折线图 +ax2 = ax.twinx() +ax2.plot(df['中医证候'], df['百分比'], 'ro-') +ax2.set_ylabel('百分比 (%)') + +# 去掉网格线 +ax.grid(False) + +# 在每个柱子上方标识频次数量值 +for bar in ax.patches: + ax.text(bar.get_x() + bar.get_width()/2, + bar.get_height(), + '{:.0f}'.format(bar.get_height()), + ha='center', + va='bottom') + +# 调整整体空白,防止标签重叠 +fig.tight_layout() + +# 显示图表 +plt.show() diff --git a/samples/matplotlib/output.png b/samples/matplotlib/output.png new file mode 100644 index 0000000000000000000000000000000000000000..5931b0659a6638142526aa9dcd906afef60c31c8 GIT binary patch literal 52686 zcmd43cT^K<*C!gg2N4@6ihv48lOk0>ilSoZ1OiAG1f)v|NXMfTQ2|2$>AfZNPC`=z 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b/samples/pandas/do_mysql copy.py similarity index 100% rename from samples/db/do_mysql copy.py rename to samples/pandas/do_mysql copy.py diff --git a/samples/pyWxDumpUtils/wodclodmedicine.py b/samples/pyWxDumpUtils/wodclodmedicine.py new file mode 100644 index 00000000..9c856304 --- /dev/null +++ b/samples/pyWxDumpUtils/wodclodmedicine.py @@ -0,0 +1,56 @@ +import mysql.connector +import pandas as pd +import numpy as np +import re +from fractions import Fraction + +import pandas as pd +from wordcloud import WordCloud +import matplotlib.pyplot as plt + + +# change root password to yours: +conn = mysql.connector.connect(user='root', password='root', database='sys') + +# 运行查询: +cursor = conn.cursor() +cursor.execute('SELECT * FROM `Sheet4_detail`') +values = cursor.fetchall() + +# 获取查询结果的字段名 +columns = [i[0] for i in cursor.description] + +# 关闭 Cursor 和 Connection +cursor.close() +conn.close() + +# 创建 DataFrame,并指定列名 +df = pd.DataFrame(values, columns=columns) + +# print(df) + + +# 创建中药频次字典 +freq_dict = {} +for medicine in df['中药']: + if medicine != '远志': + if medicine in freq_dict: + freq_dict[medicine] += 1 + else: + freq_dict[medicine] = 1 + +# 对字典按频次进行排序,并取前100个 +sorted_freq_dict = sorted(freq_dict.items(), key=lambda x: x[1], reverse=True)[:100] + +print(sorted_freq_dict) + +# 生成词云图时,指定字体 +wordcloud = WordCloud(width=800, height=400, background_color='white',font_path='simhei.ttf').generate_from_frequencies(dict(sorted_freq_dict)) + + + +# 显示词云图 +plt.figure(figsize=(10, 8)) +plt.imshow(wordcloud, interpolation='bilinear') +plt.axis('off') +plt.show() \ No newline at end of file diff --git a/samples/pyWxDumpUtils/wodclodmedicineByCsv.py b/samples/pyWxDumpUtils/wodclodmedicineByCsv.py new file mode 100644 index 00000000..dbf4be73 --- /dev/null +++ b/samples/pyWxDumpUtils/wodclodmedicineByCsv.py @@ -0,0 +1,23 @@ +import pandas as pd +from wordcloud import WordCloud +import matplotlib.pyplot as plt + +df = pd.read_csv(r'D:\pyspace\learn-python3\samples\pyWxDumpUtils\中药频次.csv') + +# 去除远志 +df = df[df['中药统计分析'] != '远志'] + +# 生成词云 +wordcloud = WordCloud( + background_color='white', + width=1000, + height=700, + font_path='simhei.ttf', # 使用中文字体 + colormap='YlGnBu' # 设置颜色方案 +).generate(df['中药统计分析'].to_string(index=False)) + +# 显示词云 +plt.figure(figsize=(16, 9)) +plt.imshow(wordcloud, interpolation='bilinear') +plt.axis('off') +plt.show() diff --git a/samples/regex/regexTestDemo.py b/samples/regex/regexTestDemo.py new file mode 100644 index 00000000..19357b02 --- /dev/null +++ b/samples/regex/regexTestDemo.py @@ -0,0 +1,15 @@ +import re + +# 打开文件 +# with open(r"D:\Documents\WeChat Files\wxid_lsootbrkhf4x22\FileStorage\File\2024-02\报错-药店-2.txt", "r", encoding="gbk") as f: +# data = f.read() +with open(r"D:\NeuSoftResources\海南\l_hosp_appr_info_d_药店.sql", "r", encoding="utf") as f: + data = f.read() + +# 提取数据中的 P 开头后 11 位的字符串 +pattern = r"('P[0-9]{11}')" +matches = re.findall(pattern, data) + +# 打印提取到的字符串 +for match in matches: + print(match) From 32c28966c058ea26254a6f52ad2021b822e19296 Mon Sep 17 00:00:00 2001 From: Acclerate Date: Thu, 7 Mar 2024 14:20:50 +0800 Subject: [PATCH 3/3] update --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 8b1f8a85..aa8e3410 100644 --- a/.gitignore +++ b/.gitignore @@ -53,4 +53,5 @@ MANIFEST .project .pydevproject *.txt -*.csv \ No newline at end of file +*.csv +.idea \ No newline at end of file

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