如何根据 python 中的其他列值创建另一列？

Question

I have the following dataframe with the following code:

for i in range(int(tower1_base),int(tower1_top)):
     if i not in tower1_not_included_int :
         df = pd.concat([df, pd.DataFrame({"Tower": 1, "Floor": i, "Unit": list("ABCDEFG")})],ignore_index=True)

Result:
   Tower    Floor   Unit
0   1       1.0      A
1   1       1.0      B
2   1       1.0      C
3   1       1.0      D
4   1       1.0      E
5   1       1.0      F
6   1       1.0      G

How can I create another Index column like this?

   Tower    Floor   Unit     Index
0   1       1.0      A       1A1
1   1       2.0      B       1B2
2   1       3.0      C       1C3
3   1       4.0      D       1D4 
4   1       5.0      E       1E5
5   1       6.0      F       1F6
6   1       7.0      G       1G7

Answer 1

You can simply add the columns:

df['Index'] = df['Tower'].astype(str)+df['Unit']+df['Floor'].astype(int).astype(str)

Outputs this for the first version of your dataframe:

   Tower  Floor Unit Index
0      1    1.0    A   1A1
1      1    1.0    B   1B1
2      1    1.0    C   1C1
3      1    1.0    D   1D1
4      1    1.0    E   1E1
5      1    1.0    F   1F1
6      1    1.0    G   1G1

Answer 2

Another approach. I've created a copy of the dataframe and reordered the columns, to make the "melting" easier.

dfAl = df.reindex(columns=['Tower','Unit','Floor'])

to_load = []  #list to load the new column
vals    = pd.DataFrame.to_numpy(dfAl)   #All values extracted

for sublist in vals:
    combs = ''.join([str(i).strip('.0') for i in sublist]) #melting values
    to_load.append(combs)

df['Index'] = to_load

If you really want the 'Index' column to be a real index, the last step:

df = df.set_index('Index')

print(df)

        Tower  Floor Unit
Index                   
1A1        1    1.0    A
1B2        1    2.0    B
1C3        1    3.0    C
1D4        1    4.0    D
1E5        1    5.0    E
1F6        1    6.0    F
1G7        1    7.0    G