enter image description here的目标： 我有一个pandas数据框，如下所示，有多个列，想要得到几列的小计，并在Placement＃Name上写下“Total”。
数据帧： enter image description here
我的尝试：
**adsize_sales_second_table.loc["Grand Total"] = pd.Series(adsize_sales_second_table.loc
[:, ["Delivered Impressions",
"Clicks",
"Conversion", "Spend"]].sum(),
index=["Delivered Impressions", "Clicks", "Conversion", "Spend"]**
)
这是最后添加一行，无法确定填写小计： enter image description here
预期输出 我原本期望输出如下 enter image description here
查看已修改的代码：
adsize_sales_data = adsize_sales_second_table.loc[:, ["Placement# Name", "Adsize", "Delivered Impressions", "Clicks",
"CTR", "Conversion", "Conversion Rate", "Spend", "eCPA"]]
cols = ["Delivered Impressions", "Clicks", "Conversion", "Spend"]
adsize_sales_data['Placement# Name'] = adsize_sales_data['Placement# Name'].ffill()
grand = adsize_sales_data[cols].sum()
grand.loc['Placement# Name'] = 'Grand total'
adsize_sales_data_new = adsize_sales_data.groupby('Placement# Name')[cols].sum()
adsize_sales_data_new.index = adsize_sales_data.index.astype(str)+'____'
adsize_sales_data = (pd.concat([adsize_sales_data.set_index('Placement# Name'), adsize_sales_data_new], keys=('a', 'b')).sort_index(level=1).reset_index())
adsize_sales_data['Placement# Name'] = np.where(adsize_sales_data ['level_0'] == 'a', adsize_sales_data['Placement# Name'], 'Total')
adsize_sales_data = adsize_sales_data.drop('level_0', axis=1)
adsize_sales_data.loc[len(adsize_sales_data.index)] = grand
print (adsize_sales_data)
它现在给出了值错误。enter image description here
答案 0 :(得分：1)
使用：
#specify columns to sum
cols = ["Delivered Impressions", "Clicks", "Conversion", "Spend"]
#replace NaNs by forward filling
df['Placement# Name'] = df['Placement# Name'].ffill()
#count grand total
grand = df[cols].sum()
grand.loc['Placement# Name'] = 'Grand total'
print (grand)
#get subtotal by aggregation sum
df1 = df.groupby('Placement# Name')[cols].sum()
#change sum for correct order
df1.index = df1.index + '____'
#join to original, sort by second level of MultiIndex
df = (pd.concat([df.set_index('Placement# Name'), df1], keys=('a', 'b'))
.sort_index(level=1)
.reset_index())
#change values to total
df['Placement# Name'] = np.where(df['level_0'] == 'a', df['Placement# Name'], 'Total')
#remove column
df = df.drop('level_0', axis=1)
#add grand total
df.loc[len(df.index)] = grand
print (df)
Placement# Name Adsize CTR \
0 13.iab units (mobile only) + non-expanding adh... 320x50 0.0119683
1 Total NaN NaN
2 15.iab units (mobile only) + non-expanding adh... 320x50 0.0138741
3 15.iab units (mobile only) + non-expanding adh... 768x90 0.0271041
4 Total NaN NaN
5 18.iab units - desktop + mobile 160x600 0.00155927
6 18.iab units - desktop + mobile 300x250 0.00797965
7 18.iab units - desktop + mobile 300x600 0.00275059
8 18.iab units - desktop + mobile 728x90 0.00496391
9 Total NaN NaN
10 4.iab units (mobile only) + non-expanding adhe... 320x50 0.0141497
11 Total NaN NaN
12 5.iab units (mobile only) + non-expanding adhe... 320x50 0.0111654
13 5.iab units (mobile only) + non-expanding adhe... 768x90 0.0253428
14 Total NaN NaN
15 6.iab units - desktop + mobile 160x600 7.41895e-05
16 6.iab units - desktop + mobile 300x250 0.011838
17 6.iab units - desktop + mobile 300x600 0.000259538
18 6.iab units - desktop + mobile 728x90 0.00538178
19 Total NaN NaN
20 Grand total NaN NaN
Clicks Conversion Conversion Rate Delivered Impressions Spend eCPA
0 1888 4 2.53566e-05 157750 1126.79 281.696
1 1888 4 NaN 157750 1126.79 NaN
2 2121 17 0.000111202 152875 1091.96 64.2332
3 152 2 0.000356633 5608 40.0571 20.0286
4 2273 19 NaN 158483 1132.02 NaN
5 37 21 0.000884993 23729 132.204 6.29545
6 684 58 0.000676637 85718 477.572 8.234
7 34 13 0.00105169 12361 68.8684 5.29757
8 403 80 0.000985392 81186 452.322 5.65403
9 1158 172 NaN 202994 1130.97 NaN
10 3840 23 8.47511e-05 271383 1938.45 84.2804
11 3840 23 NaN 271383 1938.45 NaN
12 1127 4 3.96287e-05 100937 720.979 180.245
13 183 0 0 7221 51.5786 0
14 1310 4 NaN 108158 772.557 NaN
15 1 0 0 13479 75.0973 0
16 792 0 0 66903 372.745 0
17 1 0 0 3853 21.4667 0
18 266 0 0 49426 275.373 0
19 1060 0 NaN 133661 744.683 NaN
20 11529 222 NaN 1.03243e+06 6845.46 NaN
#if necessary write to file
df.to_excel('file.xlsx', index=False)
EDIT1：
cols = ["Delivered Impressions", "Clicks", "Conversion", "Spend"]
df['Placement# Name'] = df['Placement# Name'].ffill()
grand = df[cols].sum()
grand.loc['Placement# Name'] = 'Grand total'
print (grand)
df1 = df.groupby('Placement# Name')[cols].sum()
df1.index = df1.index + '____'
#create empty DataFrame
df2 = pd.DataFrame(index=df1.index + '__')
df = pd.concat([df.set_index('Placement# Name'), df1, df2], keys=('a', 'b', 'c')).sort_index(level=1).reset_index()
#get output by 2 conditions
m1 = df['level_0'] == 'a'
m2 = df['level_0'] == 'c'
df['Placement# Name'] = np.select([m1, m2], [df['Placement# Name'], np.nan], default='Total')
df = df.drop('level_0', axis=1)
df.loc[len(df.index)] = grand
print (df)
0 13.iab units (mobile only) + non-expanding adh... 320x50 0.0119683
1 Total NaN NaN
2 NaN NaN NaN
3 15.iab units (mobile only) + non-expanding adh... 320x50 0.0138741
4 15.iab units (mobile only) + non-expanding adh... 768x90 0.0271041
5 Total NaN NaN
6 NaN NaN NaN
7 18.iab units - desktop + mobile 160x600 0.00155927
8 18.iab units - desktop + mobile 300x250 0.00797965
9 18.iab units - desktop + mobile 300x600 0.00275059
10 18.iab units - desktop + mobile 728x90 0.00496391
11 Total NaN NaN
12 NaN NaN NaN
13 4.iab units (mobile only) + non-expanding adhe... 320x50 0.0141497
14 Total NaN NaN
15 NaN NaN NaN
16 5.iab units (mobile only) + non-expanding adhe... 320x50 0.0111654
17 5.iab units (mobile only) + non-expanding adhe... 768x90 0.0253428
18 Total NaN NaN
19 NaN NaN NaN
20 6.iab units - desktop + mobile 160x600 7.41895e-05
21 6.iab units - desktop + mobile 300x250 0.011838
22 6.iab units - desktop + mobile 300x600 0.000259538
23 6.iab units - desktop + mobile 728x90 0.00538178
24 Total NaN NaN
25 NaN NaN NaN
26 Grand total NaN NaN
Clicks Conversion Conversion Rate Delivered Impressions Spend eCPA
0 1888 4 2.53566e-05 157750 1126.79 281.696
1 1888 4 NaN 157750 1126.79 NaN
2 NaN NaN NaN NaN NaN NaN
3 2121 17 0.000111202 152875 1091.96 64.2332
4 152 2 0.000356633 5608 40.0571 20.0286
5 2273 19 NaN 158483 1132.02 NaN
6 NaN NaN NaN NaN NaN NaN
7 37 21 0.000884993 23729 132.204 6.29545
8 684 58 0.000676637 85718 477.572 8.234
9 34 13 0.00105169 12361 68.8684 5.29757
10 403 80 0.000985392 81186 452.322 5.65403
11 1158 172 NaN 202994 1130.97 NaN
12 NaN NaN NaN NaN NaN NaN
13 3840 23 8.47511e-05 271383 1938.45 84.2804
14 3840 23 NaN 271383 1938.45 NaN
15 NaN NaN NaN NaN NaN NaN
16 1127 4 3.96287e-05 100937 720.979 180.245
17 183 0 0 7221 51.5786 0
18 1310 4 NaN 108158 772.557 NaN
19 NaN NaN NaN NaN NaN NaN
20 1 0 0 13479 75.0973 0
21 792 0 0 66903 372.745 0
22 1 0 0 3853 21.4667 0
23 266 0 0 49426 275.373 0
24 1060 0 NaN 133661 744.683 NaN
25 NaN NaN NaN NaN NaN NaN
26 11529 222 NaN 1.03243e+06 6845.46 NaN