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Extras/Omnivore/数据处理过程.md
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@@ -137,9 +137,45 @@ plt.show()
![image.png|600](https://image.kfdr.top/i/2024/03/16/65f55b0a3adbf.png)
## 业务时段分布
## 支付方式统计
查看一天二十四小时中的业务分布情况
```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')
# 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/2024/03/16/65f571d80fd2f.png)
## 业务时段分布
```python
import pandas as pd
@@ -211,6 +247,72 @@ plt.show()
![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5625b9cac2.png)
考虑到表格中的预约类型分为马上出发和预约派车两种，这意味着实际业务的发生时间往往与系统记录的订单时间不匹配，因此将预约派车类型的订单全部剔除，只研究马上出发订单的时间分布
```python
# Filter out booked departures to focus on immediate departures only
immediate_departures = data[data['预约类型'] == '马上出发']
# Calculating the ratio of day and night shifts for immediate departures only
immediate_departures_shift_ratio = immediate_departures['班次'].value_counts()
# Generate a pie chart to show the ratio of day and night shifts for immediate departures
plt.figure(figsize=(8, 8))
plt.pie(immediate_departures_shift_ratio, labels=immediate_departures_shift_ratio.index, autopct='%1.1f%%', startangle=140, colors=['lightblue', 'lightgreen'])
plt.title('白班和夜班的比例（马上出发）')
plt.show()
# Grouping immediate departures by 'YearMonth' and '班次' (Shift), then count the occurrences
immediate_departures_count_by_month_shift = immediate_departures.groupby(['YearMonth', '班次']).size().unstack(fill_value=0)
# Plotting the distribution of immediate departures by month and shift
immediate_departures_count_by_month_shift.plot(kind='bar', stacked=True, figsize=(14, 7), color=['lightblue', 'lightgreen'])
plt.title('白班和夜班的业务量（马上出发）')
plt.xlabel('月份')
plt.ylabel('业务频数')
plt.legend(title='班次', loc='upper right')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
```
![image.png|600](https://image.kfdr.top/i/2024/03/16/65f5a296cdea9.png)
![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
```python
# Filter for immediate departures after December 2022
immediate_departures_after_dec2022 = immediate_departures[immediate_departures['YearMonth'] > '2022-12']
# Calculate the ratio of day and night shifts for this filtered data
shift_ratio_after_dec2022 = immediate_departures_after_dec2022['班次'].value_counts()
# Generate a pie chart to show the ratio of day and night shifts for immediate departures after December 2022
plt.figure(figsize=(8, 8))
plt.pie(shift_ratio_after_dec2022, labels=shift_ratio_after_dec2022.index, autopct='%1.1f%%', startangle=140, colors=['lightblue', 'lightgreen'])
plt.title('2023年1月起白班和夜班的比例（马上出发）')
plt.show()
# Grouping immediate departures by 'YearMonth' and '班次' (Shift), then count the occurrences
immediate_departures_count_by_month_shift = immediate_departures_after_dec2022.groupby(['YearMonth', '班次']).size().unstack(fill_value=0)
# Plotting the distribution of immediate departures by month and shift
immediate_departures_count_by_month_shift.plot(kind='bar', stacked=True, figsize=(14, 7), color=['lightblue', 'lightgreen'])
plt.title('2023年1月起白班和夜班的业务量（马上出发）')
plt.xlabel('月份')
plt.ylabel('业务频数')
plt.legend(title='班次', loc='upper right')
plt.xticks(rotation=45)
plt.tight_layout()
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
@@ -250,40 +352,3 @@ plt.show()
![image.png|600](https://image.kfdr.top/i/2024/03/16/65f569fe39e54.png)
## 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')
# 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/2024/03/16/65f571d80fd2f.png)