vault backup: 2024-03-16 22:40:04

Extras/Omnivore/数据处理过程.md

@@ -175,6 +175,96 @@ plt.show()
 
 ![image.png|600](https://image.kfdr.top/i/2024/03/16/65f571d80fd2f.png)
 
+## 业务类型占比
+
+```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')  
+  
+  
+# Define the categorization function based on the provided criteria  
+def categorize_business_type(x):  
+    if any(keyword in x for keyword in ['保障转运', '密接', '回城', '发热', '送样', '民政任务']):  
+        return '疫情'  
+    elif any(keyword in x for keyword in ['高铁', '航空', '机场', '救护车']):  
+        return '转运'  
+    elif '保障' in x:  
+        return '保障'  
+    else:  
+        return '其他'  
+  
+  
+# Apply the categorization function to the '类型' column to create a new 'Business Category' column  
+data['Business Category'] = data['类型'].apply(categorize_business_type)  
+  
+# Calculate the percentage of business for each category  
+business_percentage = data['Business Category'].value_counts(normalize=True) * 100  
+  
+# Calculate the revenue share for each business category  
+revenue_share = data.groupby('Business Category')['总成交价'].sum()  
+revenue_share_percentage = (revenue_share / revenue_share.sum()) * 100  
+  
+# Plotting the business percentage pie chart  
+plt.figure(figsize=(8, 8))  
+plt.pie(business_percentage, labels=business_percentage.index, autopct='%1.1f%%', startangle=140)  
+plt.title('业务类型占比')  
+plt.show()  
+  
+# Plotting the revenue share percentage pie chart  
+plt.figure(figsize=(8, 8))  
+plt.pie(revenue_share_percentage, labels=revenue_share_percentage.index, autopct='%1.1f%%', startangle=140)  
+plt.title('业务营收占比')  
+plt.show()
+```
+
+![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5adc87992d.png)
+
+![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5adcfc6e65.png)
+
+## 业务区域分布
+
+```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')  
+  
+# Correcting the approach based on the updated description for the '区域' column  
+  
+# Update the DataFrame to reflect the correct column name and values for categorization  
+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'})  
+  
+# Calculate the distribution of the new categories  
+regional_category_distribution = data['Regional Category'].value_counts()  
+  
+# Generate a pie chart to show the updated regional distribution of the business  
+plt.figure(figsize=(8, 8))  
+plt.pie(regional_category_distribution, labels=regional_category_distribution.index, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange'])  
+plt.title('业务区域分布')  
+plt.show()  
+  
+# Next, group by the new regional category and sum the revenues  
+revenue_by_category = data.groupby('Regional Category')['总成交价'].sum()  
+  
+# For the pie chart, we can directly use the revenue_by_category Series  
+# The index of this Series will be the labels, and the values will be the sizes for each pie slice  
+plt.figure(figsize=(8, 8))  
+plt.pie(revenue_by_category, labels=revenue_by_category.index, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange'])  
+plt.title('营收贡献占比')  
+plt.show()
+```
+
+![image.png|600](https://image.kfdr.top/i/2024/03/16/65f568159bb5c.png)
+
+![image.png|600](https://image.kfdr.top/i/2024/03/16/65f569fe39e54.png)
+
 ## 业务时段分布
 
 ```python
@@ -280,7 +370,7 @@ plt.show()
 
 ![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5a29dc8dd5.png)
 
-更进一步，剔除掉疫情期间的所有业务，能够
+更进一步，剔除掉疫情期间的所有业务，能够较为客观地反映现在的情况
 
 ```python
 # Filter for immediate departures after December 2022  
@@ -312,43 +402,3 @@ plt.show()
 ![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5a30c1d2c8.png)
 
 ![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5a315764a6.png)
-
-## 业务区域分布
-
-```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')  
-  
-# Correcting the approach based on the updated description for the '区域' column  
-  
-# Update the DataFrame to reflect the correct column name and values for categorization  
-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'})  
-  
-# Calculate the distribution of the new categories  
-regional_category_distribution = data['Regional Category'].value_counts()  
-  
-# Generate a pie chart to show the updated regional distribution of the business  
-plt.figure(figsize=(8, 8))  
-plt.pie(regional_category_distribution, labels=regional_category_distribution.index, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange'])  
-plt.title('业务区域分布')  
-plt.show()  
-  
-# Next, group by the new regional category and sum the revenues  
-revenue_by_category = data.groupby('Regional Category')['总成交价'].sum()  
-  
-# For the pie chart, we can directly use the revenue_by_category Series  
-# The index of this Series will be the labels, and the values will be the sizes for each pie slice  
-plt.figure(figsize=(8, 8))  
-plt.pie(revenue_by_category, labels=revenue_by_category.index, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange'])  
-plt.title('营收贡献占比')  
-plt.show()
-```
-
-![image.png|600](https://image.kfdr.top/i/2024/03/16/65f568159bb5c.png)
-
-![image.png|600](https://image.kfdr.top/i/2024/03/16/65f569fe39e54.png)
-