vault backup: 2024-03-16 18:09:01

Extras/Omnivore/数据处理过程.md

@@ -253,5 +253,40 @@ plt.show()
 ## 支付方式统计
 
 ```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')  
+  
+# Group data by 'YearMonth'  
+grouped = data.groupby('YearMonth')  
+  
+# Assuming 'data' is your DataFrame and '支付方式' is the column for Payment Methods  
+data['Payment Category'] = data['支付方式'].apply(lambda x: '挂账' if '挂账' in str(x).lower() else '现付')  
+  
+# Group by 'YearMonth' and 'Payment Category', then count the occurrences  
+monthly_payment_category_counts = data.groupby(['YearMonth', 'Payment Category']).size().unstack(fill_value=0)  
+  
+# Calculate the percentage of 'Pending' and 'Other' categories for each month  
+monthly_payment_category_percentage = (monthly_payment_category_counts.div(monthly_payment_category_counts.sum(axis=1), axis=0) * 100)  
+  
+# Plotting the results - A stacked bar chart would be suitable to show percentages month-by-month  
+monthly_payment_category_percentage.plot(kind='bar', stacked=True, figsize=(14, 7), color=['tomato', 'lightblue'])  
+plt.title('支付方式占比')  
+plt.xlabel('月份')  
+plt.ylabel('比例')  
+plt.legend(title='支付方式')  
+plt.xticks(rotation=45)  
+plt.tight_layout()  
+plt.show()
 ```
+
+![image.png|600](https://image.kfdr.top/i/2024/03/16/65f56e94b7e9e.png)