import camelot import os import tempfile import matplotlib.pyplot as plt import numpy as np #from camelot.plotting import plot_contour #单独针对三季报的资产负债表识别合并问题 import re def process_array(arr, years=['2022', '2023', '2024'], keyword='项目'): # 确保 row 有足够的列来存储分割后的数据 def ensure_columns(row, num_columns): while len(row) < num_columns: row.append('') def is_valid_header(header, years, keyword): header_text = header.lower() # 转小写以提高匹配的鲁棒性 return any(year in header_text for year in years) and keyword in header_text # 对字符串进行清理 def clean_text(text): # 去除“年”和“月”相邻的空格 text = re.sub(r'\s*(年|月)\s*', r'\1', text) # 去除“日”左侧相邻的空格 text = re.sub(r'\s*日', '日', text) return text # 将 numpy 数组转换为列表 arr = arr.tolist() if isinstance(arr, np.ndarray) else arr if len(arr[0]) == 1 and is_valid_header(arr[0][0], years, keyword): remaining_value = arr[0][0] # 清理字符串 remaining_value = clean_text(remaining_value) parts = remaining_value.split() ensure_columns(arr[0], len(parts)) for i in range(len(parts)): arr[0][i] = parts[i] header_columns = len(arr[0]) for i in range(1, len(arr)): if len(arr[i]) == 1: remaining_value = arr[i][0] parts = remaining_value.split() if len(parts) > header_columns: parts = parts[:header_columns] ensure_columns(arr[i], header_columns) for j in range(len(parts)): arr[i][j] = parts[j] # 如果分割出的值不足，填充空值 if len(parts) < header_columns: for j in range(len(parts), header_columns): arr[i][j] = '' return arr def process_array_with_annual_comparison(arr, keywords=['本报告期', '年初至报告期末', '上年同期']): def contains_all_keywords(header, keywords): return all(keyword in header for keyword in keywords) def split_and_replace_occurrences(header, target, replacement): # 找到所有 target 出现的位置 indices = [i for i, x in enumerate(header) if x == target] if len(indices) > 1: split_index = len(indices) // 2 for i in range(split_index): header[indices[i]] = replacement return header # 将 numpy 数组转换为列表 arr = arr.tolist() if isinstance(arr, np.ndarray) else arr if len(arr) > 0 and len(arr[0]) > 0: first_row = arr[0] if contains_all_keywords(first_row, keywords): # 将 "上年同期" 拆分并替换 first_row = split_and_replace_occurrences(first_row, '上年同期', '三季报中无需识别的上年同期') arr[0] = first_row return arr def process_array_with_grants(arr, keywords=['本报告期', '年初至报告期'], target='计入当期损益的政府补助', replacement='非经常性损益'): # 检查第一行是否包含所有关键词 def contains_all_keywords(header, keywords): #return all(keyword in header for keyword in keywords) return all(any(keyword in str(cell) for cell in header) for keyword in keywords) # 检查第一列中是否存在目标文本 def contains_target_in_first_column(arr, target): return any(target in str(item[0]) for item in arr) # 替换第一列中的特定值 def replace_in_first_column(arr, target, replacement): for i in range(len(arr)): if arr[i][0] == target: arr[i][0] = replacement return arr # 将 numpy 数组转换为列表 arr = arr.tolist() if isinstance(arr, np.ndarray) else arr if len(arr) > 0 and len(arr[0]) > 0: first_row = arr[0] # 检查第一行和第一列的条件 if contains_all_keywords(first_row, keywords) and contains_target_in_first_column(arr, target): # 替换第一列中的 "合计" arr = replace_in_first_column(arr, '合计', replacement) return arr temp_dir_path = "F:\\temp" # 检查并创建临时文件夹 if not os.path.exists(temp_dir_path): os.makedirs(temp_dir_path) #file_path = "F:\\11_pdf\\603636-2024-0630-0803.pdf"#"F:\11_pdf\603636-2024-0630-0803.PDF" #file_path = r"C:\Users\钱程\Downloads\天德钰：深圳天德钰科技股份有限公司2024年半年度报告.PDF" #file_path = r"C:\Users\钱程\Downloads\600239-2023-nb-nb.pdf" file_path = r"C:\Users\钱程\Downloads\航发科技：中国航发航空科技股份有限公司2023年第三季度报告.PDF" # 创建临时文件夹 temp_dir = tempfile.mkdtemp(prefix="camelot_temp_", dir=temp_dir_path) # 设置全局临时文件夹路径 os.environ["TMP"] = temp_dir os.environ["TEMP"] = temp_dir # try: # tables = camelot.read_pdf(file_path, pages=pages, strip_text=' ,\n', copy_text=['h']) # print('读取成功') # except Exception as e: # print(f'错误在{e}') #print(f'file_path的值是{file_path}') #file_path = "F:\\11_pdf\\688670-2023-nb-nb.pdf" os.environ["GHOSTSCRIPT_BINARY"] = "gswin64c" # 确保 file_path 是正确的，并且文件是可访问的 if not os.path.exists(file_path): print(f'文件路径不正确或文件不存在: {file_path}') raise FileNotFoundError(f"文件不存在：{file_path}") else: pass#(f'file_path是存在的就是{file_path}') # 读取 PDF 文件 #tables = camelot.read_pdf(file_path, pages=pages, strip_text=' ,\n')#, copy_text=['h'] #tables = camelot.read_pdf(file_path, pages=pages, flavor='lattice', strip_text=' ,\n', temp_dir=temp_dir) #tables = camelot.read_pdf(file_path,strip_text=' ,\n',pages = '1-1', copy_text=['h','v'], temp_dir=temp_dir,shift_text = [''])#line_scale=10, #tables = camelot.read_pdf(file_path, pages='1-1', strip_text=' ,\n', copy_text=['h'], temp_dir=temp_dir) #tables = camelot.read_pdf(file_path, pages='1-1', strip_text=' ,\n', copy_text=['h'], temp_dir=temp_dir) #tables = camelot.read_pdf(file_path, pages='1-1', strip_text=' ,\n', copy_text=['v','h'], temp_dir=temp_dir,shift_text = ['']) #54 tables = camelot.read_pdf(file_path, pages='1-1', strip_text=',\n', copy_text=['v','h'],shift_text = ['l']) #camelot.plot(tables[0], kind='grid').savefig('contour_plot.png') # for t in tables: # top = t._bbox[3] # buttom = t._bbox[1] # page_num = int(t.page) # table_index = int(t.order) # arr = np.array(t.data) #print(arr) for t in tables: top = t._bbox[3] buttom = t._bbox[1] page_num = int(t.page) table_index = int(t.order) arr = np.array(t.data) print('=======') print(arr) arr = process_array_with_annual_comparison(arr) arr = process_array_with_grants(arr) if len(arr[0]) == 4 and all(value == arr[0][0] for value in arr[0]) and all("项目" in arr[0][0] and "附注" in arr[0][0] for value in arr[0]): initial_value = arr[0][0].replace(' ','') project_value = "项目" note_value = "附注" remaining_value = initial_value.replace("项目", "", 1).replace("附注", "", 1) split_index = len(remaining_value) // 2 first_half = remaining_value[:split_index] second_half = remaining_value[split_index:] # 判断 "项目" 在 original_value 中的位置 if "项目" in initial_value and first_half in initial_value and second_half in initial_value : project_index = initial_value.index("项目") year_index = initial_value.index(first_half) year_index_2 = initial_value.index(second_half) print('条件满足') # 判断 "项目" 是否在 first_half 的前面 if project_index > year_index and project_index < year_index_2: first_half, second_half = second_half, first_half arr[0] = [project_value, note_value, first_half, second_half] print(arr) tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite a=0 tables[a].parsing_report tables[a].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite tables[a].df # get a pandas DataFrame! import pandas as pd # 定义 CSV 文件的路径 csv_file_path = 'foo.csv' # 替换为你的 CSV 文件路径 excel_file_path = 'foodata.xlsx' # 定义输出的 Excel 文件路径 # 读取 CSV 文件 df = pd.read_csv(csv_file_path) # 将数据写入 Excel 文件 df.to_excel(excel_file_path, index=False) print(f"CSV 文件已成功转换为 Excel 文件：{excel_file_path}")