You can use the following syntax to drop one column from a pandas DataFrame by index number:

#drop first column from DataFrame
df.drop(df.columns[0], axis=1, inplace=True)

And you can use the following syntax to drop multiple columns from a pandas DataFrame by index numbers:

#drop first, second, and fourth column from DataFrame
cols = [0, 1, 3]
df.drop(df.columns[cols], axis=1, inplace=True)

If your DataFrame has duplicate column names, you can use the following syntax to drop a column by index number:

#define list of columns
cols = [x for x in range(df.shape[1])]

#drop second column
cols.remove(1)

#view resulting DataFrame
df.iloc[:, cols]

The following examples show how to drop columns by index in practice.

Example 1: Drop One Column by Index

The following code shows how to drop the first column in a pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],
                   'first': ['Dirk', 'Kobe', 'Tim', 'Lebron'],
                   'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'],
                   'points': [26, 31, 22, 29]})

#drop first column from DataFrame
df.drop(df.columns[0], axis=1, inplace=True)

#view resulting dataFrame
df

        first	last	 points
0	Dirk	Nowitzki 26
1	Kobe	Bryant	 31
2	Tim	Duncan	 22
3	Lebron	James	 29

Example 2: Drop Multiple Columns by Index

The following code shows how to drop multiple columns in a pandas DataFrame by index:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],
                   'first': ['Dirk', 'Kobe', 'Tim', 'Lebron'],
                   'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'],
                   'points': [26, 31, 22, 29]})

#drop first, second and fourth columns from DataFrame
cols = [0, 1, 3] 
df.drop(df.columns[cols], axis=1, inplace=True)

#view resulting dataFrame
df

        last
0	Nowitzki
1	Bryant
2	Duncan
3	James

Example 3: Drop One Column by Index with Duplicates

The following code shows how to drop a column by index number in a pandas DataFrame when duplicate column names exist:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],
                   'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'],
                   'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'],
                   'points': [26, 31, 22, 29]},
                   columns=['team', 'last', 'last', 'points'])

#define list of columns range
cols = [x for x in range(df.shape[1])]

#remove second column in DataFrame
cols.remove(1)

#view resulting DataFrame
df.iloc[:, cols]

	team	last	 points
0	Mavs	Nowitzki 26
1	Lakers	Bryant	 31
2	Spurs	Duncan	 22
3	Cavs	James	 29