From baeed8b3429c60d562f295b9d3d860e7359b10a4 Mon Sep 17 00:00:00 2001 From: kefandaoren Date: Sat, 16 Mar 2024 22:40:04 +0800 Subject: [PATCH] vault backup: 2024-03-16 22:40:04 --- Extras/Omnivore/数据处理过程.md | 132 ++++++++++++++++++++++---------- 1 file changed, 91 insertions(+), 41 deletions(-) diff --git a/Extras/Omnivore/数据处理过程.md b/Extras/Omnivore/数据处理过程.md index 5f523f66..6407f957 100644 --- a/Extras/Omnivore/数据处理过程.md +++ b/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) -