Removing Columns in a DataFrame

Let's say you have a DataFrame with a bunch of columns, but some of them are unnecessary for your analysis. To make the data less cluttered, you can remove a column from your DataFrame using pandas.

The Movie Dataset

To demonstrate this, we'll use a DataFrame of five different movies, including information about their release date, how much money they made in US dollars, and a personal rating out of 10.

import pandas as pd\n&nbsp;\n#Creates a DataFrame of "movie", "release date", "domestic gross", "worldwide gross", "personal rating", and "international box office" columns\ndf = pd.DataFrame([\n  {"movie": "The Truman Show", "release date": "1996-06-05", "domestic box office": 125618201, "worldwide box office": 264118201, "personal rating": 10, "international box office": 138500000},\n  {"movie": "Rogue One: A Star Wars Story", "release date": "2016-12-16", "domestic box office": 532177324, "worldwide box office": 1055135598, "personal rating": 9, "international box office": 522958274},\n  {"movie": "Iron Man", "release date": "2008-05-02", "domestic box office": 318604126, "worldwide box office": 585171547, "personal rating": 7, "international box office": 266567421},\n  {"movie": "Blade Runner", "release date": "1982-06-25", "domestic box office": 32656328, "worldwide box office": 39535837, "personal rating": 8, "international box office": 6879509},\n  {"movie": "Breakfast at Tiffany's", "release date": "1961-10-05", "domestic box office": 9551904, "worldwide box office": 9794721, "personal rating": 7, "international box office": 242817}\n  ])\ndf

Reset Code Run All to Here Python Output:


  
    
      
      movie
      release date
      domestic box office
      worldwide box office
      personal rating
      international box office
    
  
  
    
      0
      The Truman Show
      1996-06-05
      125618201
      264118201
      10
      138500000
    
    
      1
      Rogue One: A Star Wars Story
      2016-12-16
      532177324
      1055135598
      9
      522958274
    
    
      2
      Iron Man
      2008-05-02
      318604126
      585171547
      7
      266567421
    
    
      3
      Blade Runner
      1982-06-25
      32656328
      39535837
      8
      6879509
    
    
      4
      Breakfast at Tiffany's
      1961-10-05
      9551904
      9794721
      7
      242817

	movie	release date	domestic box office	worldwide box office	personal rating	international box office
0	The Truman Show	1996-06-05	125618201	264118201	10	138500000
1	Rogue One: A Star Wars Story	2016-12-16	532177324	1055135598	9	522958274
2	Iron Man	2008-05-02	318604126	585171547	7	266567421
3	Blade Runner	1982-06-25	32656328	39535837	8	6879509
4	Breakfast at Tiffany's	1961-10-05	9551904	9794721	7	242817

Removing One Column from a DataFrame

We can remove columns with one simple function: df.drop(). All you need to do is type the name of the column you want to get rid of in the parenthesis. Remember to put quotes around the column name and store the results in a new variable to save your changes.

import pandas as pd\n&nbsp;\ndf = pd.DataFrame([\n  {"movie": "The Truman Show", "release date": "1996-06-05", "domestic box office": 125618201, "worldwide box office": 264118201, "personal rating": 10, "international box office": 138500000},\n  {"movie": "Rogue One: A Star Wars Story", "release date": "2016-12-16", "domestic box office": 532177324, "worldwide box office": 1055135598, "personal rating": 9, "international box office": 522958274},\n  {"movie": "Iron Man", "release date": "2008-05-02", "domestic box office": 318604126, "worldwide box office": 585171547, "personal rating": 7, "international box office": 266567421},\n  {"movie": "Blade Runner", "release date": "1982-06-25", "domestic box office": 32656328, "worldwide box office": 39535837, "personal rating": 8, "international box office": 6879509},\n  {"movie": "Breakfast at Tiffany's", "release date": "1961-10-05", "domestic box office": 9551904, "worldwide box office": 9794721, "personal rating": 7, "international box office": 242817}\n  ])\ndf_dropped = df.drop(columns = "personal rating")\ndf_dropped

Reset Code Run All to Here Python Output:


  
    
      
      movie
      release date
      domestic box office
      worldwide box office
      international box office
    
  
  
    
      0
      The Truman Show
      1996-06-05
      125618201
      264118201
      138500000
    
    
      1
      Rogue One: A Star Wars Story
      2016-12-16
      532177324
      1055135598
      522958274
    
    
      2
      Iron Man
      2008-05-02
      318604126
      585171547
      266567421
    
    
      3
      Blade Runner
      1982-06-25
      32656328
      39535837
      6879509
    
    
      4
      Breakfast at Tiffany's
      1961-10-05
      9551904
      9794721
      242817

	movie	release date	domestic box office	worldwide box office	international box office
0	The Truman Show	1996-06-05	125618201	264118201	138500000
1	Rogue One: A Star Wars Story	2016-12-16	532177324	1055135598	522958274
2	Iron Man	2008-05-02	318604126	585171547	266567421
3	Blade Runner	1982-06-25	32656328	39535837	6879509
4	Breakfast at Tiffany's	1961-10-05	9551904	9794721	242817

Removing Multiple Columns from a DataFrame

When dropping multiple columns, make sure to put the columns in a list with brackets and separate them with commas.

import pandas as pd\n&nbsp;\ndf = pd.DataFrame([\n  {"movie": "The Truman Show", "release date": "1996-06-05", "domestic box office": 125618201, "worldwide box office": 264118201, "personal rating": 10, "international box office": 138500000},\n  {"movie": "Rogue One: A Star Wars Story", "release date": "2016-12-16", "domestic box office": 532177324, "worldwide box office": 1055135598, "personal rating": 9, "international box office": 522958274},\n  {"movie": "Iron Man", "release date": "2008-05-02", "domestic box office": 318604126, "worldwide box office": 585171547, "personal rating": 7, "international box office": 266567421},\n  {"movie": "Blade Runner", "release date": "1982-06-25", "domestic box office": 32656328, "worldwide box office": 39535837, "personal rating": 8, "international box office": 6879509},\n  {"movie": "Breakfast at Tiffany's", "release date": "1961-10-05", "domestic box office": 9551904, "worldwide box office": 9794721, "personal rating": 7, "international box office": 242817}\n  ])\ndf_dropped_multi = df.drop(columns = ["personal rating", "release date"])\ndf_dropped_multi

Reset Code Run All to Here Python Output:


  
    
      
      movie
      domestic box office
      worldwide box office
      international box office
    
  
  
    
      0
      The Truman Show
      125618201
      264118201
      138500000
    
    
      1
      Rogue One: A Star Wars Story
      532177324
      1055135598
      522958274
    
    
      2
      Iron Man
      318604126
      585171547
      266567421
    
    
      3
      Blade Runner
      32656328
      39535837
      6879509
    
    
      4
      Breakfast at Tiffany's
      9551904
      9794721
      242817