如何从熊猫数据框创建汇总新行并将其添加回仅特定列的同一数据框

Question

I have the below pandas dataframe.

d = {'id1': ['85643', '85644','8564312','8564314','85645','8564316','85646','8564318','85647','85648','85649','85655'],'ID': ['G-00001', 'G-00001','G-00002','G-00002','G-00001','G-00002','G-00001','G-00002','G-00001','G-00001','G-00001','G-00001'],'col1': [1, 2,3,4,5,60,0,0,6,3,2,4],'Goal': [np.nan, 56,np.nan,89,73,np.nan ,np.nan ,np.nan, np.nan, np.nan, 34,np.nan ], 'col2': [3, 4,32,43,55,610,0,0,16,23,72,48],'col3': [1, 22,33,44,55,60,1,5,6,3,2,4],'Name': ['aasd', 'aasd','aabsd','aabsd','aasd','aabsd','aasd','aabsd','aasd','aasd','aasd','aasd'],'Date': ['2021-06-13', '2021-06-13','2021-06-13','2021-06-14','2021-06-15','2021-06-15','2021-06-13','2021-06-16','2021-06-13','2021-06-13','2021-06-13','2021-06-16']}

dff = pd.DataFrame(data=d)
dff
     id1     ID     col1 Goal   col2    col3   Name      Date
0   85643   G-00001 1   NaN     3       1     aasd      2021-06-13
1   85644   G-00001 2   56.0000 4       22    aasd      2021-06-13
2   8564312 G-00002 3   NaN     32      33    aabsd     2021-06-13
3   8564314 G-00002 4   89.0000 43      44    aabsd     2021-06-14
4   85645   G-00001 5   73.0000 55      55    aasd      2021-06-15
5   8564316 G-00002 60  NaN     610     60    aabsd     2021-06-15
6   85646   G-00001 0   NaN     0       1     aasd      2021-06-13
7   8564318 G-00002 0   NaN     0       5     aabsd     2021-06-16
8   85647   G-00001 6   NaN     16      6     aasd      2021-06-13
9   85648   G-00001 3   NaN     23      3     aasd      2021-06-13
10  85649   G-00001 2   34.0000 72      2     aasd      2021-06-13
11  85655   G-00001 4   NaN     48      4     aasd      2021-06-16

I want to summarize some of the columns and add them back to the same datframe based on some ids in the "id1" column. Also, I want to give a new name to the "ID" column when we add that row. for example, I have some "id1" column slices.

#Based on below "id1" column ids I want to summarize only "col1","col2","col3",and "Name" columns. #Then I want to add that row back to the same dataframe and give a new id for "ID" column. 
b65 = ['85643','85645', '85655','85646']
b66 = ['85643','85645','85647','85648','85649','85644']
b67 = ['8564312','8564314','8564316','8564318']
# I want to aggregate sum for col1,col2 and If possible col3 with average. Otherwise it also with sum.
# So final dataframe look like below
     id1     ID     col1 Goal   col2    col3   Name      Date
0   85643   G-00001 1   NaN     3       1     aasd      2021-06-13
1   85644   G-00001 2   56.0000 4       22    aasd      2021-06-13
2   8564312 G-00002 3   NaN     32      33    aabsd     2021-06-13
3   8564314 G-00002 4   89.0000 43      44    aabsd     2021-06-14
4   85645   G-00001 5   73.0000 55      55    aasd      2021-06-15
5   8564316 G-00002 60  NaN     610     60    aabsd     2021-06-15
6   85646   G-00001 0   NaN     0       1     aasd      2021-06-13
7   8564318 G-00002 0   NaN     0       5     aabsd     2021-06-16
8   85647   G-00001 6   NaN     16      6     aasd      2021-06-13
9   85648   G-00001 3   NaN     23      3     aasd      2021-06-13
10  85649   G-00001 2   34.0000 72      2     aasd      2021-06-13
11  85655   G-00001 4   NaN     48      4     aasd      2021-06-16
12          b65     10          106     61    aasd
13          b66     17          169     67    aasd
14          b67     67          685     142   aabsd   

#I was tried to do it in groupby and pandas pivot table and didn't get to work. Any suggestion would be appreciated.
Thanks in advance!

Answer 1

I am not sure how you want to handle the name column but you could just add it to the agg function

b65 = ['85643','85645', '85655','85646']
b66 = ['85643','85645','85647','85648','85649','85644']
b67 = ['8564312','8564314','8564316','8564318']

# create a dictionary
d_map = {'b65': b65, 'b66': b66, 'b67': b67}
# dictionary comprehension
df = pd.DataFrame({k: dff[dff['id1'].isin(v)].agg({'col1': sum, 'col2': sum,
                                               'col3': 'mean', 'Name': min})
                   for k,v in d_map.items()}).T.reset_index()
# rename the columns
df = df.rename(columns={'index': 'ID'})
# concat the two frames
pd.concat([dff, df]).reset_index(drop=True)

        id1       ID col1  Goal col2       col3   Name        Date
0     85643  G-00001    1   NaN    3          1   aasd  2021-06-13
1     85644  G-00001    2  56.0    4         22   aasd  2021-06-13
2   8564312  G-00002    3   NaN   32         33  aabsd  2021-06-13
3   8564314  G-00002    4  89.0   43         44  aabsd  2021-06-14
4     85645  G-00001    5  73.0   55         55   aasd  2021-06-15
5   8564316  G-00002   60   NaN  610         60  aabsd  2021-06-15
6     85646  G-00001    0   NaN    0          1   aasd  2021-06-13
7   8564318  G-00002    0   NaN    0          5  aabsd  2021-06-16
8     85647  G-00001    6   NaN   16          6   aasd  2021-06-13
9     85648  G-00001    3   NaN   23          3   aasd  2021-06-13
10    85649  G-00001    2  34.0   72          2   aasd  2021-06-13
11    85655  G-00001    4   NaN   48          4   aasd  2021-06-16
12      NaN      b65   10   NaN  106      15.25   aasd         NaN
13      NaN      b66   19   NaN  173  14.833333   aasd         NaN
14      NaN      b67   67   NaN  685       35.5  aabsd         NaN

This is where the magic happens:

df = pd.DataFrame({k: dff[dff['id1'].isin(v)].agg({'col1': sum, 'col2': sum,
                                                   'col3': 'mean', 'Name': min})
                   for k,v in d_map.items()}).T.reset_index()

dff[dff['id1'].isin(v)] is called boolean indexing which filters your frame where id1 is in v or the value for each key in the dict. The dictonary comprehension iterates through the d_map dictionary's key (k) and values (v)

.agg is a function used to aggregate data

Answer 2

you can do this:

all_lists = [b65,b66,b67]

for item in all_lists: 
    x = dff[dff.id1.isin(item)]
    y = x.sum()

    y.id1 = ''
    y.ID= ''
    y.Goal =''
    y.Name=''
    y.Date = ''

    dff = dff.append(y,ignore_index=True)
    

and this is the result: