241 lines
8.9 KiB
Markdown
241 lines
8.9 KiB
Markdown
---
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aliases:
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atlas: "[[Atlas/Card|Card]]"
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created: 2024-03-15 21:36:51
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modified: 2024-03-15 21:55:16
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tags:
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title: 数据处理过程
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---
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# 数据清洗
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通过编写 Python 代码将对接人所提供的所有业务明细单整合为一个文件以便后续数据分析的进行
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```python
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import os
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import pandas as pd
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# The directory containing your Excel files
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directory = 'E:/Projects/analyse'
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# List to hold data from each file
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all_data = []
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# Loop through each file in the directory
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for filename in os.listdir(directory):
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if filename.endswith('.xlsx') or filename.endswith('.xls'):
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file_path = os.path.join(directory, filename)
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df = pd.read_excel(file_path)
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all_data.append(df)
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# Concatenate all data into a single DataFrame
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merged_data = pd.concat(all_data, ignore_index=True)
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# Save the merged DataFrame to a new Excel file
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merged_data.to_excel('merged_data.xlsx', index=False)
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print("Files have been merged and saved as 'merged_data.xlsx'")
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```
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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
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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：
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| 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 |
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| --- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |
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| 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 |
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# 数据分析
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## 月营收趋势
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
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# Load the Excel file
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data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx')
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# Convert '日期' to datetime format and '总成交价' to numeric
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data['日期'] = pd.to_datetime(data['日期'])
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data['总成交价'] = pd.to_numeric(data['总成交价'], errors='coerce')
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# Add a column for the year and month for easier analysis
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data['YearMonth'] = data['日期'].dt.to_period('M')
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# Summarize monthly revenue
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monthly_revenue = data.groupby('YearMonth')['总成交价'].sum().reset_index()
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plt.figure(figsize=(14, 7))
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plt.plot(monthly_revenue['YearMonth'].astype(str), monthly_revenue['总成交价'], marker='o')
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plt.title('月营收趋势')
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plt.xlabel('月份')
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plt.ylabel('收入')
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plt.xticks(rotation=45)
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plt.grid(visible=True)
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plt.tight_layout()
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plt.show()
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```
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## 平均客单价
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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
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```python
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# Attempting the analysis again with additional checks
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
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# Load the Excel file
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data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx')
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# Ensure '日期' is in datetime format for grouping
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data['日期'] = pd.to_datetime(data['日期'])
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# Add a 'YearMonth' column for easier analysis
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data['YearMonth'] = data['日期'].dt.to_period('M')
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# Group data by 'YearMonth'
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grouped = data.groupby('YearMonth')
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# Function to remove the top 1% and bottom 1% within each group
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def remove_outliers(group):
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lower = group['总成交价'].quantile(0.01)
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upper = group['总成交价'].quantile(0.99)
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return group[(group['总成交价'] > lower) & (group['总成交价'] < upper)]
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# Apply the function to each group
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filtered_groups = grouped.apply(remove_outliers)
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# Reset index as the grouping operation might introduce a multi-level index
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filtered_groups = filtered_groups.reset_index(drop=True)
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# Group by 'YearMonth' again after filtering and calculate the average price
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average_price_filtered = filtered_groups.groupby('YearMonth')['总成交价'].mean().reset_index()
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# Convert 'YearMonth' to string for plotting
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average_price_filtered['YearMonth'] = average_price_filtered['YearMonth'].astype(str)
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# Plotting the result
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plt.figure(figsize=(14, 7))
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plt.plot(average_price_filtered['YearMonth'], average_price_filtered['总成交价'], marker='o', linestyle='-',
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color='red')
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plt.title('平均客单价（每月数据去除前1%和后1%）')
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plt.xlabel('月份')
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plt.ylabel('价格')
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plt.xticks(rotation=45)
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plt.grid(visible=True)
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plt.tight_layout()
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plt.show()
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```
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## 业务时段分布
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查看一天二十四小时中的业务分布情况
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
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# Load the Excel file
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data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx')
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# Extracting hour from the '时间' column to analyze service demand by time of day
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data['Hour'] = data['时间'].str.extract('(\d+):').astype(int)
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# Analyzing service demand by hour
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service_demand_by_hour = data.groupby('Hour')['日期'].count().reset_index()
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# Plotting service demand by hour
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plt.figure(figsize=(12, 6))
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plt.bar(service_demand_by_hour['Hour'], service_demand_by_hour['日期'], color='orange')
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plt.title('业务时段分布')
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plt.xlabel('24 小时')
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plt.ylabel('业务频次')
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plt.xticks(range(0, 24))
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plt.grid(axis='y')
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plt.show()
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```
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## 白班夜班比例
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
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# Load the Excel file
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data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx')
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# Ensure '日期' is in datetime format for grouping
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data['日期'] = pd.to_datetime(data['日期'])
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# Add a 'YearMonth' column for easier analysis
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data['YearMonth'] = data['日期'].dt.to_period('M')
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# Calculate the ratio of day and night shifts
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shift_ratio = data['班次'].value_counts()
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# Generate a pie chart to show the ratio of day and night shifts
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plt.figure(figsize=(8, 8))
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plt.pie(shift_ratio, labels=shift_ratio.index, autopct='%1.1f%%', startangle=140, colors=['lightblue', 'lightgreen'])
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plt.title('白班和夜班的比例')
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plt.show()
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# Calculate the volume of day and night shifts by month
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shift_volume_by_month = data.groupby(['YearMonth', '班次'])['日期'].count().unstack(fill_value=0)
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# Generate a bar chart to show the volume of day and night shifts by month
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shift_volume_by_month.plot(kind='bar', stacked=True, figsize=(14, 7), color=['lightblue', 'lightgreen'])
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plt.title('Volume of Day and Night Shifts by Month')
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plt.xlabel('Year-Month')
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plt.ylabel('Number of Shifts')
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plt.xticks(rotation=45)
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plt.legend(title='Shift')
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plt.tight_layout()
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plt.show()
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```
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## 业务区域分布
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
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# Load the Excel file
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data = pd.read_excel('E:/Projects/analyse/pythonProject/merged_data.xlsx')
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# Correcting the approach based on the updated description for the '区域' column
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# Update the DataFrame to reflect the correct column name and values for categorization
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data['Regional Category'] = data['区域'].map({'市内': '省内', '广东省内': '省内', '国际': '省外', '港澳台': '省外', '广东省外': '省外'})
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# Calculate the distribution of the new categories
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regional_category_distribution = data['Regional Category'].value_counts()
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# Generate a pie chart to show the updated regional distribution of the business
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plt.figure(figsize=(8, 8))
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plt.pie(regional_category_distribution, labels=regional_category_distribution.index, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange'])
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plt.title('业务区域分布')
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plt.show()
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```
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