Almost certainly an X/Y problem: you're trying hard to pad-interleave NaN values when you shouldn't be doing that at all, and the premise for your problem assumes a structure for your dataframe that you shouldn't use.

Think about the "per"s in your frame description: per time, per KPI, per SKU, you start off with some unidentified floating-point value (maybe a price; who knows). Whenever you write "per", that should become a level in a multi-level index. This reduces your dataframe to one column, "Price".

Thinking about your "pad" operation, you're really just adding a second column (of even more unknown purpose):

import pandas as pd

# Our df for this example
dict_df = {
    "Time": [1,1,1,1,2,2,2,2,3,3,3,3],
    "KPI":  ["A","B","C","D","A","B","C","D","A","B","C","D"],
    "SKU1": [10,1,0.1,0.01,40,4,0.4,0.04,90,9,0.9,0.09],
    "SKU2": [20,2,0.2,0.02,50,5,0.5,0.05,100,10,1,0.1],
    "SKU3": [30,3,0.3,0.03,60,6,0.6,0.06,110,11,1.1,0.11],
    "SKU4": [70,7,0.7,0.07,80,8,0.8,0.08,120,12,1.2,0.12]
}
df_test = pd.DataFrame(dict_df)

# Reshape data to be in stacked, multi-index form
df_test.set_index(['Time', 'KPI'], inplace=True)
df_test.columns = df_test.columns.str.slice(3).astype(int)
df_test = df_test.stack()
df_test.index.set_names('SKU', level=-1, inplace=True)
df_test = pd.DataFrame({'Price': df_test})

filter_kpi = {"A", "B", "C"}
df_test = df_test[df_test.index.get_level_values('KPI').isin(filter_kpi)]

df_test['Mystery'] = pd.NA
print(df_test)

              Price Mystery
Time KPI SKU               
1    A   1     10.0    <NA>
         2     20.0    <NA>
         3     30.0    <NA>
         4     70.0    <NA>
     B   1      1.0    <NA>
         2      2.0    <NA>
         3      3.0    <NA>
         4      7.0    <NA>
     C   1      0.1    <NA>
         2      0.2    <NA>
         3      0.3    <NA>
         4      0.7    <NA>
2    A   1     40.0    <NA>
         2     50.0    <NA>
         3     60.0    <NA>
         4     80.0    <NA>
     B   1      4.0    <NA>
         2      5.0    <NA>
         3      6.0    <NA>
         4      8.0    <NA>
     C   1      0.4    <NA>
         2      0.5    <NA>
         3      0.6    <NA>
         4      0.8    <NA>
3    A   1     90.0    <NA>
         2    100.0    <NA>
         3    110.0    <NA>
         4    120.0    <NA>
     B   1      9.0    <NA>
         2     10.0    <NA>
         3     11.0    <NA>
         4     12.0    <NA>
     C   1      0.9    <NA>
         2      1.0    <NA>
         3      1.1    <NA>
         4      1.2    <NA>