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?

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]]

Drop multiple columns like this:

cols = [1,2,4,5,12]

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)

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']) 

   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) 
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’).

If you have two columns with the same name. One simple way is to manually rename the columns like this:-

df.columns = ['column1', 'column2', 'column3']

Then you can drop via column index as you requested, like this:-

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

df.column[1] will drop index 1.

Remember axis 1 = columns and axis 0 = rows.

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)

if you really want to do it with integers (but why?), then you could build a dictionary.

col_dict = {x: col for x, col in enumerate(df.columns)}

then df = df.drop(col_dict[0], 1) will work as desired

edit: you can put it in a function that does that for you, though this way it creates the dictionary every time you call it

def drop_col_n(df, col_n_to_drop):
    col_dict = {x: col for x, col in enumerate(df.columns)}
    return df.drop(col_dict[col_n_to_drop], 1)

df = drop_col_n(df, 2)

You can simply supply columns parameter to df.drop command so you don’t to specify axis in that case, like so

columns_list = [1, 2, 4] # index numbers of columns you want to delete
df = df.drop(columns=df.columns[columns_list])

For reference see columns parameter here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html?highlight=drop#pandas.DataFrame.drop

You can use the following line to drop the first two columns (or any column you don’t need):

df.drop([df.columns[0], df.columns[1]], axis=1)


Good way to get the columns you want (doesn’t matter duplicate names).

For example you have the column indices you want to drop contained in a list-like variable

unnecessary_cols = [1, 4, 5, 6]


import numpy as np
df.iloc[:, np.setdiff1d(np.arange(len(df.columns)), unnecessary_cols)]

Since there can be multiple columns with same name , we should first rename the columns.
Here is code for the solution.

df.drop(columns=[1,2])#drop second and third columns