就餐人数数据可视化,用python,输出完整代码: 姓名 工种 开始时间 结束时间 就餐情况 备注...

बनाया गया: 13 जुलाई 2025

का उपयोग करके उत्तर दिया गया O3 द्वारा Chat01

प्रश्न

就餐人数数据可视化(包括中餐/晚餐就餐总人数,中餐/晚餐打包人数),用python,输出完整代码
姓名 工种 开始时间 结束时间 就餐情况 备注
1 刘向全 支护工 2025.6.14 2025.07.09 全天堂食 已退场
2 吴成林 支护工 2025.7.1 2025.07.09 全天堂食 已退场
3 郑绍林 支护工 2025.6.11 2025.07.12 全天堂食 请假回家
1 白红春 反井钻 2025.6.12 中午打包
2 罗正开 反井钻 2025.6.12 中午打包
3 陈波 反井钻 2025.6.12 中午打包
4 梁磊 挖机 2025.6.11 全天堂食
5 李肖肖 随车吊司机 2025.6.11 全天堂食
6 李艳鹏 挖机 2025.6.11 全天堂食
7 赵浪 支护工 2025.6.11 全天堂食
8 牛定德 支护工 2025.6.11 全天堂食
9 王明才 支护工 2025.6.12 全天堂食
10 陈勇 支护工 2025.7.1 全天堂食
11 周光贤 支护工 2025.7.1 全天堂食
12 朱国华 支护工 2025.7.12 全天堂食

विचार

Generating code for dining data visualization

The user wants to visualize dining data (e.g., lunch/dinner headcounts and packed meals) using Python. I'll need to generate code that handles data visualization for these variables, likely involving libraries like pandas for data handling and matplotlib or seaborn for creating the graphs. I'll aim for clear visualizations that represent total counts and packed meals. Let me figure out the best way to structure this code for easy understanding and use.

Visualizing dining data counts

The user wants to visualize counts of lunch and dinner dine-in vs. packed meals. I'll parse the data, categorize it by lunch/dinner and whether it's dine-in or packed. For lunch, I'll consolidate people into "lunch dine-in" and "lunch packed". For dinner, I'll count "dine-in" based on "全天堂食" and assume no packed dinner data. Then I'll generate bar charts with Python for these categories. I'll make sure the counts are clear while considering the active records based on start and end dates.

Thought for 41 seconds

Python

python
import pandas as pd import matplotlib.pyplot as plt # 输入数据 data = [ {"序号": 1, "姓名": "刘向全", "工种": "支护工", "开始时间": "2025-06-14", "结束时间": "2025-07-09", "就餐情况": "全天堂食"}, {"序号": 2, "姓名": "吴成林", "工种": "支护工", "开始时间": "2025-07-01", "结束时间": "2025-07-09", "就餐情况": "全天堂食"}, {"序号": 3, "姓名": "郑绍林", "工种": "支护工", "开始时间": "2025-06-11", "结束时间": "2025-07-12", "就餐情况": "全天堂食"}, {"序号": 1, "姓名": "白红春", "工种": "反井钻", "开始时间": "2025-06-12", "结束时间": None, "就餐情况": "中午打包"}, {"序号": 2, "姓名": "罗正开", "工种": "反井钻", "开始时间": "2025-06-12", "结束时间": None, "就餐情况": "中午打包"}, {"序号": 3, "姓名": "陈波", "工种": "反井钻", "开始时间": "2025-06-12", "结束时间": None, "就餐情况": "中午打包"}, {"序号": 4, "姓名": "梁磊", "工种": "挖机", "开始时间": "2025-06-11", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 5, "姓名": "李肖肖", "工种": "随车吊司机", "开始时间": "2025-06-11", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 6, "姓名": "李艳鹏", "工种": "挖机", "开始时间": "2025-06-11", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 7, "姓名": "赵浪", "工种": "支护工", "开始时间": "2025-06-11", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 8, "姓名": "牛定德", "工种": "支护工", "开始时间": "2025-06-11", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 9, "姓名": "王明才", "工种": "支护工", "开始时间": "2025-06-12", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 10, "姓名": "陈勇", "工种": "支护工", "开始时间": "2025-07-01", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 11, "姓名": "周光贤", "工种": "支护工", "开始时间": "2025-07-01", "结束时间": None, "就餐情况": "全天堂食"}, {"序号": 12, "姓名": "朱国华", "工种": "支护工", "开始时间": "2025-07-12", "结束时间": None, "就餐情况": "全天堂食"}, ] df = pd.DataFrame(data) # 生成各类就餐指标 df["午餐就餐"] = df["就餐情况"].apply(lambda x: 1 if x in ["全天堂食", "中午打包"] else 0) df["午餐打包"] = df["就餐情况"].apply(lambda x: 1 if x == "中午打包" else 0) df["晚餐就餐"] = df["就餐情况"].apply(lambda x: 1 if x == "全天堂食" else 0) df["晚餐打包"] = df["就餐情况"].apply(lambda x: 1 if x == "晚上打包" else 0) # 数据中暂无 counts = { "午餐就餐总人数": df["午餐就餐"].sum(), "午餐打包人数": df["午餐打包"].sum(), "晚餐就餐总人数": df["晚餐就餐"].sum(), "晚餐打包人数": df["晚餐打包"].sum(), } # 绘图 plt.figure(figsize=(8, 6)) bars = plt.bar(counts.keys(), counts.values()) plt.title("就餐人数数据统计") plt.ylabel("人数") # 在柱状图上标注数值 for bar in bars: height = bar.get_height() plt.text(bar.get_x() + bar.get_width() / 2, height + 0.15, f"{int(height)}", ha='center', va='bottom') plt.tight_layout() plt.show()

उत्तर

已为您生成了完整的 Python 脚本并实时绘制了柱状图,可直接复制运行或根据需要调整数据/样式:

  • 午餐就餐总人数 = 15
  • 午餐打包人数 = 3
  • 晚餐就餐总人数 = 12
  • 晚餐打包人数 = 0

图中每个柱子的数值已标注,方便直观比较。如需进一步拆分到每日/按工种统计或换成其他图形(饼图、堆叠柱状图等),告诉我即可!

इस प्रश्नोत्तर को साझा करें