如何在此 Pandas 数据透视表中重新排序星期几？

Question

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
    
df = pd.read_excel("Baltimore Towing Division.xlsx",sheet_name="TowingData")

df['Month'] = pd.DatetimeIndex(df['TowedDate']).strftime("%b")
df['Week day'] = pd.DatetimeIndex(df['TowedDate']).strftime("%a")


monthOrder = ['Jan', 'Feb', 'Mar', 'Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
dayOrder = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']


Pivotdf = pd.pivot_table(df, index=['Month'],
                        values=['TowedDate'],
                        columns=['Week day'],
                        fill_value=0,
                        aggfunc='count').reindex(monthOrder,axis=0).reindex(dayOrder,axis=1)

print(df)

我在数据透视表末尾使用 .reindex 函数来重新索引月份和“工作日”列，它在结果中返回 NaN。

在 axis=1 中使用 .reindex

不在数据透视表为我带来结果的日期列上执行 .reindex，但一周中的每一天都杂乱无章。我需要它们按如下顺序出现在表中：周一、周二、周三、周四、周五、周六、周日

Whitout 在 axis=1 中使用 .reindex

Answer 1

也许使用 loc：

# with values=['TowedDate'] -> MultiIndex
Pivotdf = pd.pivot_table(df, index=['Month'],
                        values=['TowedDate'],
                        columns=['Week day'],
                        fill_value=0,
                        aggfunc='count').loc[monthOrder, (slice(None), dayOrder)]

# OR

# with values='TowedDate' -> Index
Pivotdf = pd.pivot_table(df, index=['Month'],
                        values='TowedDate',
                        columns=['Week day'],
                        fill_value=0,
                        aggfunc='count').loc[monthOrder, dayOrder)]

输出：

>>> Pivotdf
         TowedDate                        
Week day       Mon Tue Wed Thu Fri Sat Sun
Month                                     
Jan              1   0   1   0   1   0   0
Feb              2   0   1   0   1   0   0
Mar              1   0   0   0   0   0   0
Apr              0   0   0   1   0   1   0
May              0   1   1   3   1   1   2
Jun              1   0   0   0   0   1   2
Jul              0   1   0   0   2   0   0
Aug              3   0   0   0   1   2   1
Sep              0   0   1   1   0   1   0
Oct              3   0   0   0   1   0   1
Nov              1   0   0   0   1   2   3
Dec              0   1   1   0   0   0   0

Answer 2

Corralien的方法解决了这个问题。

# with values=['TowedDate'] -> MultiIndex
Pivotdf = pd.pivot_table(df, index=['Month'],
                        values=['TowedDate'],
                        columns=['Week day'],
                        fill_value=0,
                        aggfunc='count').loc[monthOrder, (slice(None), dayOrder)]

非常感谢所有在这个问题上回答和帮助我的会员