Pandas - delete the last column of the DataFrame

Question

I have a DataFrame and I would like to leave the last column. So far, I have simply dropped what I thought was the last column with

if len(fish_frame.columns) == 4: del fish_frame[3].

However, before this command, I delete all NaNs columns . Thus, the column is deleted [3]because it fills with NaNs, so it fails.

I would say that just drop the last column of the entire DataFrame. I feel this will work just fine.

I tried fish_frame([:-1], axis=1), but this syntax is not valid.

Any help would be greatly appreciated thanks.

DataFrame:

fish_frame after dropna:

                              0        1      2           4
0                         #0721      NaN    NaN         NaN
1                       GBE COD      746  $2.00   $1,492.00
2                       GBW COD   13,894  $0.50   $6,947.00
3                       GOM COD       60  $2.00     $120.00
4            GB WINTER FLOUNDER   94,158  $0.25  $23,539.50
5           GOM WINTER FLOUNDER    3,030  $0.50   $1,515.00
6                   GBE HADDOCK   18,479  $0.02     $369.58
7                   GOM HADDOCK        0  $0.02       $0.00
8                   GBW HADDOCK  110,470  $0.02   $2,209.40
9                          HAKE      259  $1.30     $336.70
10                       PLAICE    3,738  $0.40   $1,495.20
11                      POLLOCK    3,265  $0.02      $65.30
12               WITCH FLOUNDER    1,134  $1.30   $1,474.20
13                       SNE YT    1,458  $0.65     $947.70
14                        GB YT    4,499  $0.70   $3,149.30
15                      REDFISH      841  $0.02      $16.82
16  54 DAS @ $8.00/DAY = 432.00      NaN    NaN        None

Answer 1

iloc

fish_frame = fish_frame.iloc[:, :-1]

                              0        1      2
0                         #0721      NaN    NaN
1                       GBE COD      746  $2.00
2                       GBW COD   13,894  $0.50
3                       GOM COD       60  $2.00
4            GB WINTER FLOUNDER   94,158  $0.25
5           GOM WINTER FLOUNDER    3,030  $0.50
6                   GBE HADDOCK   18,479  $0.02
7                   GOM HADDOCK        0  $0.02
8                   GBW HADDOCK  110,470  $0.02
9                          HAKE      259  $1.30
10                       PLAICE    3,738  $0.40
11                      POLLOCK    3,265  $0.02
12               WITCH FLOUNDER    1,134  $1.30
13                       SNE YT    1,458  $0.65
14                        GB YT    4,499  $0.70
15                      REDFISH      841  $0.02
16  54 DAS @ $8.00/DAY = 432.00      NaN    NaN

Answer 2

drop :

fish_frame = fish_frame.drop(fish_frame.columns[-1],axis=1)

:

                              0        1      2
0                         #0721      NaN    NaN
1                       GBE COD      746  $2.00
2                       GBW COD   13,894  $0.50
3                       GOM COD       60  $2.00
4            GB WINTER FLOUNDER   94,158  $0.25
5           GOM WINTER FLOUNDER    3,030  $0.50
6                   GBE HADDOCK   18,479  $0.02
7                   GOM HADDOCK        0  $0.02
8                   GBW HADDOCK  110,470  $0.02
9                          HAKE      259  $1.30
10                       PLAICE    3,738  $0.40
11                      POLLOCK    3,265  $0.02
12               WITCH FLOUNDER    1,134  $1.30
13                       SNE YT    1,458  $0.65
14                        GB YT    4,499  $0.70
15                      REDFISH      841  $0.02
16  54 DAS @ $8.00/DAY = 432.00      NaN    NaN