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I have a DataFrame and I would like to drop the last column of it. Until now I had just been dropping what I believed to be the final column with
if len(fish_frame.columns) == 4:
del fish_frame[3].
Right before this command, however, I drop all columns of NaNs. So that removes column [3] because it is filled with NaNs, so it fails.
I would like to say just drop the final column of the entire DataFrame. I feel like that would work perfectly.
I tried fish_frame([:-1], axis=1) but that's invalid syntax.
Any help would be appreciated thanks.
The 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
602
Use drop() to remove last column of pandas dataframe. Use del keyword to drop last column of pandas dataframe. Use pop() to drop last column of pandas dataframe.
You can also use DataFrame. drop() method to delete the last n columns. Use axis=1 to specify the columns and inplace=True to apply the change on the existing DataFrame.
Pandas Drop Multiple Columns By Index You can use df. columns[[index1, index2, indexn]] to identify the list of column names in that index position and pass that list to the drop method. Note that an index is 0 based. Use 0 to delete the first column and 1 to delete the second column and so on.
Use iloc[] to select last N columns of pandas dataframe. Use [] to select last N columns of pandas dataframe. Use tail() to select last N columns of pandas dataframe.
Use iloc and list indexing
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
If you want to drop the last column
df = df.iloc[:,:-1]
if particular column needs to be dropped with index
df = df.drop(df.columns[column_index],axis=1)
with column name
df = df.drop(['column_name'],axis =1)
Use drop with the columns index:
fish_frame = fish_frame.drop(fish_frame.columns[-1],axis=1)
Output:
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
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