Pandas row order by time date specified by external order

Question

So, I have problems ordering my Dataframe, I tried using this question, but I was not able to get it to work. I have a Dataframe nudf, for example:

                     date  level_1      0 
0     2016-10-01 00:00:00      0.0  74.00    
1     2016-10-01 00:30:00      0.5     72    
2     2016-10-01 01:00:00      1.0     70    
3     2016-10-01 01:30:00      1.5     64    
4     2016-10-01 02:00:00      2.0     63    
5     2016-10-01 02:30:00      2.5     60    
...                   ...      ...    ...   
19003 2017-09-31 21:30:00     21.5    129    
19004 2017-09-31 22:00:00     22.0    118    
19005 2017-09-31 22:30:00     22.5    106  
19006 2017-09-31 23:00:00     23.0     84    
19007 2017-09-31 23:30:00     23.5     76    

And what I would like to do is to arrange the lines in the following monthly order:

[4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3]

This is the last 12 months from last month. I would like to ignore the year and order a row block every month in accordance with the above procedure.

For example, given the following lines:

0     2016-02-01 00:00:00      0.0  74.00    
1     2016-02-01 00:30:00      0.5     72    
2     2016-03-01 01:00:00      1.0     70    
3     2016-03-01 01:30:00      1.5     64    
4     2017-04-01 02:00:00      2.0     63    
5     2017-04-01 02:30:00      2.5     60  

The result should be:

4     2017-04-01 02:00:00      2.0     63    
5     2017-04-01 02:30:00      2.5     60
0     2016-02-01 00:00:00      0.0  74.00    
1     2016-02-01 00:30:00      0.5     72    
2     2016-03-01 01:00:00      1.0     70    
3     2016-03-01 01:30:00      1.5     64      

I tried:

nudf['month'] = nudf.apply(lambda row: row.date.month, axis=1)
nudf.month = nudf.month.astype("category")
nudf.month.cat.set_categories([x.month for x in reversed(_get_last_x_months(12))], inplace=True)

nudf.sort_values(["month"], inplace=True)

But the order of the day and hour is not supported.

Answer 1

categorical, argsort iloc
, , kind='mergesort', mergesort "" .

mcats = [4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3]
nudf.iloc[pd.Categorical(nudf.date.dt.month, mcats, True).argsort(kind='mergesort')]

                 date  level_1     0
4 2017-04-01 02:00:00      2.0  63.0
5 2017-04-01 02:30:00      2.5  60.0
0 2016-02-01 00:00:00      0.0  74.0
1 2016-02-01 00:30:00      0.5  72.0
2 2016-03-01 01:00:00      1.0  70.0
3 2016-03-01 01:30:00      1.5  64.0

---

mcats = [4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3]
nudf = nudf.assign(month=pd.Categorical(nudf.date.dt.month, mcats, True))
nudf.sort_values('month', kind='mergesort')

                 date  level_1     0 month
4 2017-04-01 02:00:00      2.0  63.0     4
5 2017-04-01 02:30:00      2.5  60.0     4
0 2016-02-01 00:00:00      0.0  74.0     2
1 2016-02-01 00:30:00      0.5  72.0     2
2 2016-03-01 01:00:00      1.0  70.0     3
3 2016-03-01 01:30:00      1.5  64.0     3

---

, ,

mcats = [4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3]
nudf = nudf.assign(month=pd.Categorical(nudf.date.dt.month, mcats, True))
nudf.sort_values(['month', 'date'])

argsort np.lexsort, , .

mcats = [4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3]
nudf.iloc[np.lexsort(
    [nudf.date, pd.Categorical(nudf.date.dt.month, mcats, True)]
)]

Answer 2

, %. , .

nudf.sort_values(by='date', inplace=True)

mcats = [x.month for x in reversed(_get_last_x_months(12))]
#[4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3]
nudf['m_fake'] = (nudf.date.dt.month+(12-mcats[0]))%12
nudf.sort_values(by='m_fake')

#                 date  val1  val2  m_fake
#4 2017-04-01 02:00:00   2.0    63       0
#5 2017-04-01 02:30:00   2.5    60       0
#0 2016-02-01 00:00:00   0.0    74       10
#1 2016-02-01 00:30:00   0.5    72       10
#2 2016-03-01 01:00:00   1.0    70       11
#3 2016-03-01 01:30:00   1.5    64       11

Answer 3

map,

# creates an int value based on the date using .dt.month (must be a date type)
df['month_value'] = df['date'].dt.month

# creates a dictionary that will remap the values
new_order = {4:1, 5:2, 6:3, 7:4, 8:5, 9:6, 10:7, 11:8, 12:9, 1:10, 2:11, 3:12}

# creates a new column based on the mapping
df['new_value'] = df['month_value'].map(new_order)

# sorts the values based on the new column
df.sort_values(by='new_value')

           date  month_value  new_value
4 2017-04-01            4          1
5 2017-04-01            4          1
0 2016-02-01            2         11
1 2016-02-01            2         11
2 2016-03-01            3         12
3 2016-03-01            3         12