diff --git a/Extras/Omnivore/数据处理过程.md b/Extras/Omnivore/数据处理过程.md index 0261690d..e07cf800 100644 --- a/Extras/Omnivore/数据处理过程.md +++ b/Extras/Omnivore/数据处理过程.md @@ -39,11 +39,174 @@ print("Files have been merged and saved as 'merged_data.xlsx'") 删去了以下列：序号、服务单号、调度单号、联系人、联系电话、患者信息、销售、介绍人、客服、调度、来源、承包组、车牌、出车成员、医护出车和任务备注 -经过确认，所有调度单状态不为已返回的订单均未产生收入，故将其全部筛选出来后将总成交价一列的数值改为 0 以免影响计算结果，按月营收额如下所示： +经过确认，所有调度单状态不为已返回的订单均未产生收入，故将其全部筛选出来后将总成交价一列的数值改为 0 以免影响计算结果，统计后月营收额如下所示： | 日期 | 2022-04 | 2022-05 | 2022-06 | 2022-07 | 2022-08 | 2022-09 | 2022-10 | 2022-11 | 2022-12 | 2023-01 | 2023-02 | 2023-03 | 2023-04 | 2023-05 | 2023-06 | 2023-07 | 2023-08 | 2023-09 | 2023-10 | 2023-11 | 2023-12 | 2024-01 | 2024-02 | | --- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | | 营收额 | 3328917.00 | 3362286.00 | 3973152.00 | 3462363.00 | 4250864.00 | 4144810.76 | 4360712.00 | 4587020.00 | 4880988.50 | 4197830.00 | 3309294.00 | 3338335.00 | 4069565.00 | 4292058.60 | 3101339.20 | 3834394.40 | 3114722.80 | 2750602.00 | 4161377.40 | 3465051.00 | 2898861.00 | 3426260.50 | 3559553.15 | -![image.png|600](https://image.kfdr.top/i/2024/03/15/65f453278157a.png) +# 数据分析 +## 月营收趋势 + +```python +import pandas as pd +import matplotlib.pyplot as plt +plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] + +# Load the Excel file +data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx') + +# Convert '日期' to datetime format and '总成交价' to numeric +data['日期'] = pd.to_datetime(data['日期']) +data['总成交价'] = pd.to_numeric(data['总成交价'], errors='coerce') + +# Add a column for the year and month for easier analysis +data['YearMonth'] = data['日期'].dt.to_period('M') + +# Summarize monthly revenue +monthly_revenue = data.groupby('YearMonth')['总成交价'].sum().reset_index() + +plt.figure(figsize=(14, 7)) +plt.plot(monthly_revenue['YearMonth'].astype(str), monthly_revenue['总成交价'], marker='o') +plt.title('月营收趋势') +plt.xlabel('月份') +plt.ylabel('收入') +plt.xticks(rotation=45) +plt.grid(visible=True) +plt.tight_layout() +plt.show() +``` + +![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5628e38f6e.png) + +## 平均客单价 + +为避免极端值影响，先按月份将所有数据分组，剔除前 1%和后 1%的订单后再计算平均客单价 + +```python +# Attempting the analysis again with additional checks +import pandas as pd +import matplotlib.pyplot as plt +plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] + +# Load the Excel file +data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx') + +# Ensure '日期' is in datetime format for grouping +data['日期'] = pd.to_datetime(data['日期']) + +# Add a 'YearMonth' column for easier analysis +data['YearMonth'] = data['日期'].dt.to_period('M') + +# Group data by 'YearMonth' +grouped = data.groupby('YearMonth') + + +# Function to remove the top 1% and bottom 1% within each group +def remove_outliers(group): + lower = group['总成交价'].quantile(0.01) + upper = group['总成交价'].quantile(0.99) + return group[(group['总成交价'] > lower) & (group['总成交价'] < upper)] + + +# Apply the function to each group +filtered_groups = grouped.apply(remove_outliers) + +# Reset index as the grouping operation might introduce a multi-level index +filtered_groups = filtered_groups.reset_index(drop=True) + +# Group by 'YearMonth' again after filtering and calculate the average price +average_price_filtered = filtered_groups.groupby('YearMonth')['总成交价'].mean().reset_index() + +# Convert 'YearMonth' to string for plotting +average_price_filtered['YearMonth'] = average_price_filtered['YearMonth'].astype(str) + +# Plotting the result +plt.figure(figsize=(14, 7)) +plt.plot(average_price_filtered['YearMonth'], average_price_filtered['总成交价'], marker='o', linestyle='-', + color='red') +plt.title('平均客单价（每月数据去除前1%和后1%）') +plt.xlabel('月份') +plt.ylabel('价格') +plt.xticks(rotation=45) +plt.grid(visible=True) +plt.tight_layout() +plt.show() +``` + +![image.png|600](https://image.kfdr.top/i/2024/03/16/65f55b0a3adbf.png) + +## 业务时段分布 + +查看一天二十四小时中的业务分布情况 + +```python +import pandas as pd +import matplotlib.pyplot as plt +plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] + +# Load the Excel file +data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx') + +# Extracting hour from the '时间' column to analyze service demand by time of day +data['Hour'] = data['时间'].str.extract('(\d+):').astype(int) + +# Analyzing service demand by hour +service_demand_by_hour = data.groupby('Hour')['日期'].count().reset_index() + +# Plotting service demand by hour +plt.figure(figsize=(12, 6)) +plt.bar(service_demand_by_hour['Hour'], service_demand_by_hour['日期'], color='orange') +plt.title('业务时段分布') +plt.xlabel('24 小时') +plt.ylabel('业务频次') +plt.xticks(range(0, 24)) +plt.grid(axis='y') +plt.show() +``` + +![image.png|600](https://image.kfdr.top/i/2024/03/16/65f56155bee8d.png) + +## 白班夜班比例 + +```python +import pandas as pd +import matplotlib.pyplot as plt +plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] + +# Load the Excel file +data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx') + +# Ensure '日期' is in datetime format for grouping +data['日期'] = pd.to_datetime(data['日期']) + +# Add a 'YearMonth' column for easier analysis +data['YearMonth'] = data['日期'].dt.to_period('M') + +# Calculate the ratio of day and night shifts +shift_ratio = data['班次'].value_counts() + +# Generate a pie chart to show the ratio of day and night shifts +plt.figure(figsize=(8, 8)) +plt.pie(shift_ratio, labels=shift_ratio.index, autopct='%1.1f%%', startangle=140, colors=['lightblue', 'lightgreen']) +plt.title('白班和夜班的比例') +plt.show() + +# Calculate the volume of day and night shifts by month +shift_volume_by_month = data.groupby(['YearMonth', '班次'])['日期'].count().unstack(fill_value=0) + +# Generate a bar chart to show the volume of day and night shifts by month +shift_volume_by_month.plot(kind='bar', stacked=True, figsize=(14, 7), color=['lightblue', 'lightgreen']) +plt.title('Volume of Day and Night Shifts by Month') +plt.xlabel('Year-Month') +plt.ylabel('Number of Shifts') +plt.xticks(rotation=45) +plt.legend(title='Shift') +plt.tight_layout() +plt.show() +``` + +![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5617bb2942.png) + +![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5625b9cac2.png)