### 使用 Python 绘制 PMP 燃尽图 为了实现这一目标，可以利用 `matplotlib` 和 `pandas` 库来处理数据并创建图表。这些工具能够有效地展示项目的进度情况，特别是燃尽图这种直观反映项目进展的图形。 #### 安装所需库 首先安装必要的 Python 库： ```bash pip install matplotlib pandas numpy ``` #### 数据准备 假设有一个简单的 CSV 文件 (`project_data.csv`) 来存储每日的工作量记录，其中包含日期、计划完成的任务数、实际已完成的任务数等字段。 | Date | Planned Work (hours) | Actual Completed Work (hours) | |--------|-------------------------------| | 2023-10-01 | 8 | 7 | | ... | ... | ... | #### 创建绘图函数 下面是一个完整的例子，展示了如何读取上述格式的数据文件，并绘制出带有三条线的实际剩余工作、理想剩余工作和预测剩余工作的燃尽图。 ```python import pandas as pd import matplotlib.pyplot as plt from datetime import timedelta, datetime def plot_burn_down_chart(file_path): df = pd.read_csv(file_path) # 计算总工时和其他必要变量 total_work_hours = sum(df['Planned Work (hours)']) ideal_remaining_work = [] actual_remaining_work = list(total_work_hours - df['Actual Completed Work (hours)'].cumsum()) forecasted_remaining_work = [] start_date = min(pd.to_datetime(df['Date'])) end_date = max(pd.to_datetime(df['Date'])) + timedelta(days=1) num_days = (end_date - start_date).days daily_ideal_completion_rate = total_work_hours / num_days current_day = start_date while current_day <= end_date: days_passed = (current_day - start_date).days if not ideal_remaining_work or days_passed >= len(ideal_remaining_work): remaining_work = max(0, total_work_hours - daily_ideal_completion_rate * days_passed) ideal_remaining_work.append(remaining_work) date_str = current_day.strftime('%Y-%m-%d') if date_str in df['Date'].values: index = df[df['Date'] == date_str].index[0] completed_today = df.loc[index]['Actual Completed Work (hours)'] last_forecast_value = forecasted_remaining_work[-1] if forecasted_remaining_work else total_work_hours new_forecast = max(0, last_forecast_value - completed_today) forecasted_remaining_work.append(new_forecast) elif forecasted_remaining_work: forecasted_remaining_work.append(forecasted_remaining_work[-1]) else: forecasted_remaining_work.append(total_work_hours) current_day += timedelta(days=1) dates = [(start_date + timedelta(days=i)).strftime("%b %d") for i in range(len(ideal_remaining_work))] fig, ax = plt.subplots(figsize=(10,6)) ax.plot(dates, ideal_remaining_work, label='Ideal Remaining Work', marker='o') ax.plot(dates[:len(actual_remaining_work)], actual_remaining_work, 'r-', label='Actual Remaining Work', marker='s') ax.plot(dates, forecasted_remaining_work, '--g', label='Forecasted Remaining Work') ax.set_title('Burn Down Chart of Project Progress') ax.set_xlabel('Dates') ax.set_ylabel('Remaining Work Hours') ax.legend() plt.xticks(rotation=45) plt.tight_layout() plt.show() plot_burn_down_chart('path/to/your/project_data.csv') # 替换为您的CSV路径 ``` 这段代码实现了从给定的时间序列数据中提取信息，并基于此构建了一个清晰易懂的燃尽图[^1]。