Get minimum values in rows or columns with their index position in Pandas-Dataframe

Let’s discuss how to find minimum values in rows & columns of a Dataframe and also their index position.

a) Find the minimum value among rows and columns :

Dataframe.min() : This function returns the minimum of the values in the given object. If the input is a series, the method will return a scalar which will be the minimum of the values in the series. If the input is a dataframe, then the method will return a series with a minimum of values over the specified axis in the dataframe. By default, the axis is the index axis.

1) Get minimum values of every column :
Use min() function to find the minimum value over the index axis.

Code :

# import pandas library 
import pandas as pd 
  
# list of Tuples 
data = [ 
          (20, 16, 23), 
          (30, None, 11), 
          (40, 34, 11), 
          (50, 35, None), 
          (60, 40, 13) 
        ] 
  
# creating a DataFrame object 
df = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], 
                     columns = ['x', 'y', 'z']) 
  
# getting a series object containing  
# minimum value from each column 
# of given dataframe 
minvalue_series = df.min() 
  
minvalue_series 

Output:

pandas-get-minimum-1

2) Get minimum values of every row :
Use min() function on a dataframe with ‘axis = 1’ attribute to find the minimum value over the row axis.

Code :

# import pandas library 
import pandas as pd 
  
# list of Tuples 
data = [ 
          (20, 16, 23), 
          (30, None, 11), 
          (40, 34, 11), 
          (50, 35, None), 
          (60, 40, 13) 
        ] 
  
# creating a DataFrame object 
df = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], 
                     columns = ['x', 'y', 'z']) 
  
# getting a series object containing  
# minimum value from each row 
# of given dataframe 
minvalue_series = df.min(axis = 1) 
  
minvalue_series 

Output:

pandas-get-minimum-2

3) Get minimum values of every column without skipping None Value :
Use min() function on a dataframe which has Na value with ‘skipna = False’ attribute to find the minimum value over the column axis.

Code :

# import pandas library 
import pandas as pd 
  
# list of Tuples 
data = [ 
          (20, 16, 23), 
          (30, None, 11), 
          (40, 34, 11), 
          (50, 35, None), 
          (60, 40, 13) 
        ] 
  
# creating a DataFrame object 
df = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], 
                     columns = ['x', 'y', 'z']) 
  
# getting a series object containing  
# minimum value from each column 
# of given dataframe without 
# skipping None value 
minvalue_series = df.min(skipna = False) 
  
minvalue_series 

Output:

pandas-get-minimum-3

4) Get minimum value of a single column :
Use min() function on a series to find the minimum value in the series.

Code :

# import pandas library 
import pandas as pd 
  
# list of Tuples 
data = [ 
          (20, 16, 23), 
          (30, None, 11), 
          (40, 34, 11), 
          (50, 35, None), 
          (60, 40, 13) 
        ] 
  
# creating a DataFrame object 
df = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], 
                     columns = ['x', 'y', 'z']) 
  
# getting a minimum value 
# from column 'x' 
minvalue = df['x'].min() 
  
minvalue 

Output:

b) Get row index label or position of minimum values among rows and columns :

Dataframe.idxmin() : This function returns index of first occurrence of minimum over requested axis. While finding the index of the minimum value across any index, all NA/null values are excluded.

1) Get row index label of minimum value in every column :
Use idxmin() function to find the index/label of the minimum value along the index axis.

Code :

# import pandas library 
import pandas as pd 
  
# list of Tuples 
data = [ 
          (20, 16, 23), 
          (30, None, 11), 
          (40, 34, 11), 
          (50, 35, None), 
          (60, 40, 13) 
        ] 
  
# creating a DataFrame object 
df = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], 
                     columns = ['x', 'y', 'z']) 
  
# get the index position\label of 
# minimum values in every column 
minvalueIndexLabel = df.idxmin() 
  
minvalueIndexLabel 

Output

pandas-get-minimum-5

2) Get Column names of minimum value in every row :
Use idxmin() function with ‘axis = 1’ attribute to find the index/label of the minimum value along the column axis.

Code :

# import pandas library 
import pandas as pd 
  
# list of Tuples 
data = [ 
          (20, 16, 23), 
          (30, None, 11), 
          (40, 34, 11), 
          (50, 35, None), 
          (60, 40, 13) 
        ] 
  
# creating a DataFrame object 
df = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], 
                     columns = ['x', 'y', 'z']) 
  
# get the index position\label of 
# minimum values in every row 
minvalueIndexLabel = df.idxmin(axis = 1) 
  
minvalueIndexLabel 

Output

pandas-get-minimum-4

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