101 lines
4.3 KiB
Python
101 lines
4.3 KiB
Python
import pandas as pd
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import random
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import math
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work_order_excel="data/excel/6万工单记录.xlsx"
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soft_row_data={
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"博微配网计价通D3":{"基本功能":[], "高级功能":[]},
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"储能C1软件":{"基本功能":[], "高级功能":[]},
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"西藏计价通Z1":{"基本功能":[], "高级功能":[]},
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"技改检修工程计价通T1":{"基本功能":[], "高级功能":[]},
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"检修清单计价通T1":{"基本功能":[], "高级功能":[]},
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"电力建设计价通软件":{"基本功能":[], "高级功能":[]},
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}
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df = pd.read_excel(work_order_excel)
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for idx, row in df.iterrows():
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if pd.isna(row["产品线"]):
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continue
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if "博微配网计价通D3" in row["产品线"]:
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soft_row_data["博微配网计价通D3"][row["问题类型"]].append((idx, row))
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elif "博微电力建设计价通软件" in row["产品线"]:
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soft_row_data["电力建设计价通软件"][row["问题类型"]].append((idx, row))
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elif "新能源系列" in row["产品线"] and "博微新型储能电站建设计价通C1软件" in row["产品名称"]:
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soft_row_data["储能C1软件"][row["问题类型"]].append((idx, row))
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elif "博微西藏计价通Z1" in row["产品线"]:
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soft_row_data["西藏计价通Z1"][row["问题类型"]].append((idx, row))
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elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-概预算" in row["产品名称"]:
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soft_row_data["技改检修工程计价通T1"][row["问题类型"]].append((idx, row))
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elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-清单" in row["产品名称"]:
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soft_row_data["检修清单计价通T1"][row["问题类型"]].append((idx, row))
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# 计算每个软件和功能类型的数据量
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total_count = 0
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counts = {}
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for software, types in soft_row_data.items():
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counts[software] = {}
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for type_name, rows in types.items():
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counts[software][type_name] = len(rows)
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total_count += len(rows)
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print(f"原始数据总量: {total_count}条")
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for software, types in counts.items():
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print(f"{software}: 基本功能 {types['基本功能']}条, 高级功能 {types['高级功能']}条")
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# 计算均衡提取的数量
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total_target = 2000
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categories_count = sum(len(types) for types in soft_row_data.values())
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per_category_target = math.ceil(total_target / categories_count)
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# 均衡提取数据
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balanced_data = []
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extracted_counts = {}
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extracted_indices = set() # 使用集合存储已提取数据的索引
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for software, types in soft_row_data.items():
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extracted_counts[software] = {}
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for type_name, rows in types.items():
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# 如果数据量不足，全部提取；否则随机抽取目标数量
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if len(rows) <= per_category_target:
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extracted = rows
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else:
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extracted = random.sample(rows, per_category_target)
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extracted_counts[software][type_name] = len(extracted)
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for idx, row in extracted:
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extracted_indices.add(idx) # 记录已提取数据的索引
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balanced_data.append(row)
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# 数据量不足2000时，从剩余数据中补充
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remaining_target = total_target - len(balanced_data)
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if remaining_target > 0:
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# 收集所有未被选中的数据
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remaining_data = []
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for software, types in soft_row_data.items():
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for type_name, rows in types.items():
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# 添加未被选中的数据
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for idx, row in rows:
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if idx not in extracted_indices:
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remaining_data.append(row)
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# 如果剩余数据足够，随机抽取补充
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if len(remaining_data) >= remaining_target:
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additional_data = random.sample(remaining_data, remaining_target)
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else:
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additional_data = remaining_data
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balanced_data.extend(additional_data)
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# 输出结果
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print(f"\n均衡提取后数据总量: {len(balanced_data)}条")
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for software, types in extracted_counts.items():
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print(f"{software}: 基本功能 {types['基本功能']}条, 高级功能 {types['高级功能']}条")
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# 将均衡提取的数据转换为DataFrame并保存
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balanced_df = pd.DataFrame(balanced_data)
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balanced_df.to_excel("data/excel/均衡提取2000条工单.xlsx", index=False)
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print(f"\n已将均衡提取的{len(balanced_data)}条数据保存至'data/excel/均衡提取2000条工单.xlsx'") |