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 libraryimport pandas as pd # list of Tuplesdata = [ (20, 16, 23), (30, None, 11), (40, 34, 11), (50, 35, None), (60, 40, 13) ] # creating a DataFrame objectdf = 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 dataframeminvalue_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 libraryimport pandas as pd # list of Tuplesdata = [ (20, 16, 23), (30, None, 11), (40, 34, 11), (50, 35, None), (60, 40, 13) ] # creating a DataFrame objectdf = 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 dataframeminvalue_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 libraryimport pandas as pd # list of Tuplesdata = [ (20, 16, 23), (30, None, 11), (40, 34, 11), (50, 35, None), (60, 40, 13) ] # creating a DataFrame objectdf = 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 valueminvalue_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 libraryimport pandas as pd # list of Tuplesdata = [ (20, 16, 23), (30, None, 11), (40, 34, 11), (50, 35, None), (60, 40, 13) ] # creating a DataFrame objectdf = 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 libraryimport pandas as pd # list of Tuplesdata = [ (20, 16, 23), (30, None, 11), (40, 34, 11), (50, 35, None), (60, 40, 13) ] # creating a DataFrame objectdf = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], columns = ['x', 'y', 'z']) # get the index position\label of# minimum values in every columnminvalueIndexLabel = 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 libraryimport pandas as pd # list of Tuplesdata = [ (20, 16, 23), (30, None, 11), (40, 34, 11), (50, 35, None), (60, 40, 13) ] # creating a DataFrame objectdf = pd.DataFrame(data, index = ['a', 'b', 'c', 'd', 'e'], columns = ['x', 'y', 'z']) # get the index position\label of# minimum values in every rowminvalueIndexLabel = df.idxmin(axis = 1) minvalueIndexLabel |
Output
