From a7676909fad0757266f3fdb3022a5ab54337f61e Mon Sep 17 00:00:00 2001 From: kefandaoren Date: Sun, 17 Mar 2024 18:58:39 +0800 Subject: [PATCH] vault backup: 2024-03-17 18:58:39 --- Extras/Omnivore/数据处理过程.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/Extras/Omnivore/数据处理过程.md b/Extras/Omnivore/数据处理过程.md index 6407f957..451d5204 100644 --- a/Extras/Omnivore/数据处理过程.md +++ b/Extras/Omnivore/数据处理过程.md @@ -82,8 +82,6 @@ plt.show() ## 平均客单价 -为避免极端值影响，先按月份将所有数据分组，剔除前 1%和后 1%的订单后再计算平均客单价 - ```python # Attempting the analysis again with additional checks import pandas as pd @@ -337,8 +335,6 @@ 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'] @@ -370,8 +366,6 @@ 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，能够较为客观地反映现在的情况 - ```python # Filter for immediate departures after December 2022 immediate_departures_after_dec2022 = immediate_departures[immediate_departures['YearMonth'] > '2022-12']