python dataframe pandas drop column using int

I understand that to drop a column you use df.drop('column name', axis=1). Is there a way to drop a column using a numerical index instead of the column name?

Solution 1:

You can delete column on i index like this:

df.drop(df.columns[i], axis=1)

It could work strange, if you have duplicate names in columns, so to do this you can rename column you want to delete column by new name. Or you can reassign DataFrame like this:

df = df.iloc[:, [j for j, c in enumerate(df.columns) if j != i]]

Solution 2:

Drop multiple columns like this:

cols = [1,2,4,5,12]
df.drop(df.columns[cols],axis=1,inplace=True)

inplace=True is used to make the changes in the dataframe itself without doing the column dropping on a copy of the data frame. If you need to keep your original intact, use:

df_after_dropping = df.drop(df.columns[cols],axis=1)

Solution 3:

If there are multiple columns with identical names, the solutions given here so far will remove all of the columns, which may not be what one is looking for. This may be the case if one is trying to remove duplicate columns except one instance. The example below clarifies this situation:

# make a df with duplicate columns 'x'
df = pd.DataFrame({'x': range(5) , 'x':range(5), 'y':range(6, 11)}, columns = ['x', 'x', 'y']) 


df
Out[495]: 
   x  x   y
0  0  0   6
1  1  1   7
2  2  2   8
3  3  3   9
4  4  4  10

# attempting to drop the first column according to the solution offered so far     
df.drop(df.columns[0], axis = 1) 
   y
0  6
1  7
2  8
3  9
4  10

As you can see, both Xs columns were dropped. Alternative solution:

column_numbers = [x for x in range(df.shape[1])]  # list of columns' integer indices

column_numbers .remove(0) #removing column integer index 0
df.iloc[:, column_numbers] #return all columns except the 0th column

   x  y
0  0  6
1  1  7
2  2  8
3  3  9
4  4  10

As you can see, this truly removed only the 0th column (first 'x').

Solution 4:

You need to identify the columns based on their position in dataframe. For example, if you want to drop (del) column number 2,3 and 5, it will be,

df.drop(df.columns[[2,3,5]], axis = 1)