熊猫：如何按日期将数据分组在一起，并对分组数据应用多种功能？

Question

In my code i have a pandas dataframe with a column for the day and a column called value. I would like to group the dataframe by day and find the minimum and maximum value for that day, average the min and max and then subtract that average from the value column in the dataframe.

The closest thing i have been able to do has been:

temp_max = var.groupby(['day']).max()
temp_min = var.groupby(['day']).min()

answer = var.groupby(['day'])['value'].apply(lambda x : x - (temp_max['value'] - temp_min['value']) / 2 )

input:

    Unnamed: 0  hrs                   vt                   rt      value
0       119899    1  2017-03-01 07:00:00  2017-03-01 06:00:00  67.910011
1       119900    2  2017-03-01 08:00:00  2017-03-01 06:00:00  52.970033
2       119901    3  2017-03-01 09:00:00  2017-03-01 06:00:00  49.010011
3       119902    4  2017-03-01 10:00:00  2017-03-01 06:00:00  47.030000
4       119903    5  2017-03-01 11:00:00  2017-03-01 06:00:00  45.949989
5       119904    6  2017-03-01 12:00:00  2017-03-01 06:00:00  45.949989

output:

1    0           NaN
 1     41.540022
 2     31.549989
 3     29.570005
 4     36.949989
 5     38.030000
 6     40.010011
 7     33.980000
 8     47.030000
 9           NaN
 10          NaN
 11          NaN
 12          NaN
 13          NaN
 14          NaN
 15          NaN
 16          NaN
2    1           NaN
     2           NaN
     3           NaN
     4           NaN
     5           NaN
     6           NaN
     7           NaN
     8           NaN
     17          NaN
     18          NaN
     19          NaN
     20          NaN
     21          NaN
             ...    
6    4           NaN
     5           NaN
     6           NaN
     7           NaN
     8           NaN
     53          NaN
     54          NaN
     55          NaN
     56          NaN
7    1           NaN
     2           NaN
     3           NaN
     4           NaN
     5           NaN
     6           NaN
     7           NaN
     8           NaN
     57          NaN
     58          NaN
     59          NaN
     60          NaN
8    1           NaN
     2           NaN
     3           NaN
     4           NaN
     5           NaN
     6           NaN
     7           NaN
     8           NaN
     61          NaN

The values appear to be correct but i was hoping to keep my original dataframe and just update the values in place. Is there a different way i should be approaching this? Thx in advance!

Answer 1

How about something like this?

new_frame = pd.DataFrame(columns=var.columns)

for day,frame in var.groupby('day'):

    frame.loc[:,'value'] = frame['value'].apply(lambda x: x - (frame.value.max() + frame.value.min())/2)

    new_frame = new_frame.append(frame)

You could do it in one line using a list comprehension and groupby but it looks a bit ugly

var.loc[:,'value'] = pd.concat([frm.value.apply(lambda x:x-(frm.value.min() + frm.value.max())/2) for d,frm in var.groupby('day')])

I believe that would accomplish what you're trying to do, albeit not being particularly readable!