How to sort columns except index column in a data frame in python after pivot-CodePudding

So I have a data frame

testdf = pd.DataFrame({"loc" : ["ab12","bc12","cd12","ab12","bc13","cd12"], "months" : 
         ["Jun21","Jun21","July21","July21","Aug21","Aug21"], "dept" : 
         ["dep1","dep2","dep3","dep2","dep1","dep3"], "count": [15, 16, 15, 92, 90, 2]})

That looks like this:

When I pivot it,

df =  pd.pivot_table(testdf, values = ['count'], index = ['loc','dept'], columns = ['months'], aggfunc=np.sum).reset_index()
df.columns = df.columns.droplevel(0)
df

it looks like this:

I am looking for a sort function which will sort only the months columns in sequence and not the first 2 columns i.e loc & dept.

when I try this:

df.sort_values(by = ['Jun21'],ascending = False, inplace = True, axis = 1, ignore_index=True)[2:]

it gives me error.

I want the columns to be in sequence Jun21, Jul21, Aug21

I am looking for something which will make it dynamic and I wont need to manually change the sequence when the month changes.

Any hint will be really appreciated.

CodePudding user response：

We can start by converting the column months in datetime like so :

>>> testdf.months = (pd.to_datetime(testdf.months, format="%b%y", errors='coerce'))
>>> testdf
    loc     months      dept    count
0   ab12    2021-06-01  dep1    15
1   bc12    2021-06-01  dep2    16
2   cd12    2021-07-01  dep3    15
3   ab12    2021-07-01  dep2    92
4   bc13    2021-08-01  dep1    90
5   cd12    2021-08-01  dep3    2

Then, we apply your code to get the pivot :

>>> df =  pd.pivot_table(testdf, values = ['count'], index = ['loc','dept'], columns = ['months'], aggfunc=np.sum).reset_index()
>>> df.columns = df.columns.droplevel(0)
>>> df
months  NaT     NaT     2021-06-01  2021-07-01  2021-08-01
0       ab12    dep1    15.0        NaN         NaN
1       ab12    dep2    NaN         92.0        NaN
2       bc12    dep2    16.0        NaN         NaN
3       bc13    dep1    NaN         NaN         90.0
4       cd12    dep3    NaN         15.0        2.0

And to finish we can reformat the column names using strftime to get the expected result :

>>> df.columns = df.columns.map(lambda t: t.strftime('%b%y') if pd.notnull(t) else '')
>>> df
months                  Jun21   Jul21   Aug21
0       ab12    dep1    15.0    NaN     NaN
1       ab12    dep2    NaN     92.0    NaN
2       bc12    dep2    16.0    NaN     NaN
3       bc13    dep1    NaN     NaN     90.0
4       cd12    dep3    NaN     15.0    2.0

CodePudding user response：

It is quite simple if you do using groupby

df = testdf.groupby(['loc', 'dept', 'months']).sum().unstack(level=2)
df = df.reindex(['Jun21', 'July21', 'Aug21'], axis=1, level=1)

Output

          count             
months    Jun21 July21 Aug21
loc  dept                   
ab12 dep1  15.0    NaN   NaN
     dep2   NaN   92.0   NaN
bc12 dep2  16.0    NaN   NaN
bc13 dep1   NaN    NaN  90.0
cd12 dep3   NaN   15.0   2.0