Remove duplicate columns by name in Pandas

import pandas as pd
import numpy as np

Create a dataframe

#create a dataframe
raw_data = {'name': ['Willard Morris', 'Al Jennings'],
'age': [20, 19],
'favorite_color': ['blue', 'red'],
'grade': [88, 92],
'grade': [88, 92]}
df = pd.DataFrame(raw_data, index = ['Willard Morris', 'Al Jennings'])
df

	age	favorite_color	grade	grade	name
Willard Morris	20	blue	88	88	Willard Morris
Al Jennings	19	red	92	92	Al Jennings

Remove duplicate columns (based on column name)

#preview the df
df = df.loc[:,~df.columns.duplicated()]
df

	age	favorite_color	grade	name
Willard Morris	20	blue	88	Willard Morris
Al Jennings	19	red	92	Al Jennings

df.columns.duplicated() returns a boolean array: a True or False for each column--False means the column name is unique up to that point, True means it's a duplicate

Pandas allows one to index using boolean values whereby it selects only the True values.

Since we want to keep the unduplicated columns, we need the above boolean array to be flipped (ie [True, False, True] -> ~[False,True,False])