CODEWITHSUNDEEP
pythonpandaspivot-table
Bode
Bode

Reputation: 529

Pivoting a groupby (month and hour ) datetime column

I have a dataframe,df after converting DateTimeC column to a datetime type such as:

  Index     DateTimeC                       eventName  
    0      2017-08-20 01:11:24.210000        ABC           
    1      2017-08-20 01:11:30.224000        CDE         
    2      2017-08-20 02:16:30.210000        CDE       
    3      2017-08-20 02:27:30.211000        CDE       
    2      2017-09-10 01:30:40.212000        DEF            
    3      2017-09-11 01:35:23.122000        CDE            
    4      2017-09-11 02:22:22.145000        CDE            
    5      2017-09-16 02:26:11.222000        DEF

I intend to groupby month and hour and count the count of events in eventName within the grouped object.So applying this code:

 df2=df.groupby([df['DateTimeC'].dt.month,df['DateTimeC'].dt.hour])['EventName'].count()

I get:

   Index   EventName                       
    8,1     2          
    8,2     2         
    9,1     2           
    9,2     2

However, i want to use the pivot_table () on the resulting series as month as the index and hour as the column while the value parameter should be the frequency. So the resulting dataframe should be:

   Index    0  1  2  3  4  5  6  7  8... 24                
    8       0  2  2  0  0  0  0  0  0... 0
    9       0  2  2  0  0  0  0  0  0... 0

So what will be the corresponding argument for the parameters of the pivot_table () since the date and time are in the same column: DateTimeC

I tried to add the rename_index to rename the column for the frequency/count result so I can pass the new name to the 'value' parameter in the pivot_table () using this code:

 df2=df.groupby([df['DateTimeC'].dt.month,df['DateTimeC'].dt.hour])['EventName'].count().reset_index(name='frequency')

but i get this error:

ValueError: cannot insert DateTimeC, already exists

Also, get the mean per hour for each month and transform the number associated with a specific month to its word equivalent

    Index      averagePerHour                       
    August       0.17          
    September    0.17

Upvotes: 1

Views: 977

Answers (1)

jezrael
jezrael

Reputation: 862841

I think need unstack for reshape and reindex if necessary add missing hours:

df2=(df.groupby([df['DateTimeC'].dt.month.rename('month'),
                df['DateTimeC'].dt.hour.rename('hour')])
      .size()
      .unstack(fill_value=0)
      .reindex(columns=np.arange(24), fill_value=0))
print (df2)
hour   0   1   2   3   4   5   6   7   8   9  ...  14  15  16  17  18  19  20  \
month                                         ...                               
8       0   2   2   0   0   0   0   0   0   0 ...   0   0   0   0   0   0   0   
9       0   2   2   0   0   0   0   0   0   0 ...   0   0   0   0   0   0   0   

hour   21  22  23  
month              
8       0   0   0  
9       0   0   0  

[2 rows x 24 columns]

And then create dictionary for renamewith mean:

L = ['Jan', 'Feb', 'Mar', 'Apr','May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
d = dict(enumerate(L, 1))
df3 = df2.mean(axis=1).rename(d).to_frame('averagePerHour')
print (df3)
       averagePerHour
month                
Aug          0.166667
Sep          0.166667

If omit reindex and missing some hours then mean is different:

df2=(df.groupby([df['DateTimeC'].dt.month.rename('month'),
                df['DateTimeC'].dt.hour.rename('hour')])
      .size()
      .unstack(fill_value=0)
      )
print (df2)
hour   1  2
month      
8      2  2
9      2  2

L = ['Jan', 'Feb', 'Mar', 'Apr','May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
d = dict(enumerate(L, 1))
df3 = df2.mean(axis=1).rename(d).to_frame('averagePerHour')
print (df3)
       averagePerHour
month                
Aug               2.0
Sep               2.0

EDIT: If want convert months number to stings use dt.strftime, check also http://strftime.org/:

df2=(df.groupby([df['DateTimeC'].dt.strftime('%B').rename('month'),
                df['DateTimeC'].dt.hour.rename('hour')])
      .size()
      .unstack(fill_value=0)
      )
print (df2)
hour       1  2
month          
August     2  2
September  2  2

df3 = df2.mean(axis=1).to_frame('averagePerHour')
print (df3)
           averagePerHour
month                    
August                2.0
September             2.0

Upvotes: 1

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