Finding Minimum and Maximum Values in a DataFrame Column

It's often helpful to know a few specific values for each column (aka variable) in a DataFrame — mainly the highest value, lowest value, and all unique values. But sometimes, our DataFrame has so many rows, it's impossible to view the whole thing at once. Luckily, there are some pandas functions that allow us to easily locate this information.

The Movie Dataset

To demonstrate these functions, 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 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

Finding the Rows with the Largest or Smallest Value

Often, you will be interested in the rows with the smallest or largest values for a specific column. The nlargest and nsmallest functions allow us to find the rows that have the largest or smallest values for a specific columns.

For example, we may want the three movies with the largest domestic box office sales:

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.nlargest(3, 'domestic box office')

Reset Code Python Output:


  
    
      
      movie
      release date
      domestic box office
      worldwide box office
      personal rating
      international box office
    
  
  
    
      1
      Rogue One: A Star Wars Story
      2016-12-16
      532177324
      1055135598
      9
      522958274
    
    
      2
      Iron Man
      2008-05-02
      318604126
      585171547
      7
      266567421
    
    
      0
      The Truman Show
      1996-06-05
      125618201
      264118201
      10
      138500000

	movie	release date	domestic box office	worldwide box office	personal rating	international box office
1	Rogue One: A Star Wars Story	2016-12-16	532177324	1055135598	9	522958274
2	Iron Man	2008-05-02	318604126	585171547	7	266567421
0	The Truman Show	1996-06-05	125618201	264118201	10	138500000

Another option is to see the two movies that the 'personal rating' is the lowest.

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.nsmallest(2, 'personal rating')

Reset Code Python Output:


  
    
      
      movie
      release date
      domestic box office
      worldwide box office
      personal rating
      international box office
    
  
  
    
      2
      Iron Man
      2008-05-02
      318604126
      585171547
      7
      266567421
    
    
      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
2	Iron Man	2008-05-02	318604126	585171547	7	266567421
4	Breakfast at Tiffany's	1961-10-05	9551904	9794721	7	242817

Selecting specific rows based on criteria, like the minimum or maximum values, is part of the larger idea of "Row Selection" with DataFrames. View additional explanations, videos, and example problems covering row selection as part of the DISCOVERY course content found here:

Lecture: Row Selection with DataFrames

Finding the Smallest and Largest Values of Data in a Column

.max() and .min() functions allow us to find the smallest and largest numbers in a column. Make sure to specify the column in brackets before applying the function.
Note: this only works for columns of integer or float dtypes — not strings.

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["worldwide box office"].min()

Reset Code Python Output:

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["worldwide box office"].max()

Reset Code Python Output:

1055135598