vault backup: 2024-03-16 22:40:04
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@@ -175,6 +175,96 @@ plt.show()
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## 业务类型占比
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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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# Define the categorization function based on the provided criteria
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def categorize_business_type(x):
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if any(keyword in x for keyword in ['保障转运', '密接', '回城', '发热', '送样', '民政任务']):
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return '疫情'
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elif any(keyword in x for keyword in ['高铁', '航空', '机场', '救护车']):
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return '转运'
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elif '保障' in x:
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return '保障'
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else:
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return '其他'
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# Apply the categorization function to the '类型' column to create a new 'Business Category' column
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data['Business Category'] = data['类型'].apply(categorize_business_type)
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# Calculate the percentage of business for each category
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business_percentage = data['Business Category'].value_counts(normalize=True) * 100
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# Calculate the revenue share for each business category
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revenue_share = data.groupby('Business Category')['总成交价'].sum()
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revenue_share_percentage = (revenue_share / revenue_share.sum()) * 100
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# Plotting the business percentage pie chart
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plt.figure(figsize=(8, 8))
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plt.pie(business_percentage, labels=business_percentage.index, autopct='%1.1f%%', startangle=140)
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plt.title('业务类型占比')
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plt.show()
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# Plotting the revenue share percentage pie chart
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plt.figure(figsize=(8, 8))
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plt.pie(revenue_share_percentage, labels=revenue_share_percentage.index, autopct='%1.1f%%', startangle=140)
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plt.title('业务营收占比')
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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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# Next, group by the new regional category and sum the revenues
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revenue_by_category = data.groupby('Regional Category')['总成交价'].sum()
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# For the pie chart, we can directly use the revenue_by_category Series
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# The index of this Series will be the labels, and the values will be the sizes for each pie slice
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plt.figure(figsize=(8, 8))
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plt.pie(revenue_by_category, labels=revenue_by_category.index, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange'])
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plt.title('营收贡献占比')
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plt.show()
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```
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## 业务时段分布
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```python
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@@ -280,7 +370,7 @@ plt.show()
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更进一步，剔除掉疫情期间的所有业务，能够
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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
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```python
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# Filter for immediate departures after December 2022
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@@ -312,43 +402,3 @@ plt.show()
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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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# Next, group by the new regional category and sum the revenues
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revenue_by_category = data.groupby('Regional Category')['总成交价'].sum()
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# For the pie chart, we can directly use the revenue_by_category Series
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# The index of this Series will be the labels, and the values will be the sizes for each pie slice
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plt.figure(figsize=(8, 8))
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plt.pie(revenue_by_category, labels=revenue_by_category.index, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange'])
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plt.title('营收贡献占比')
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plt.show()
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```
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