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:

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:

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:

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:

20

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

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