How to obtain the minimum value of a desired axis of a DataFrame

Pandas DataFrame is a two-dimensional data structure hosting data aligned in a tabular fashion. It has the following properties:

  • Columns can have different types

  • Mutable size

  • Ability to perform arithmetic operations on rows and columns

  • Axes (rows and columns) are labeled

With a pandas DataFrame, we can leverage the min() method which returns a series of values representing the minimum values over a specified axis.

The syntax of this method is as follows:

DataFrame.min(axis=0, skipna=True, numeric_only=False, **kwargs)

The min() function accepts the following parameters:

  • axis: Indicates where the function will be applied (0 represents columns and 1 represents rows).

  • skipna: Computes results without including null values.

  • numeric_only: Includes only float, int, and boolean columns. Not implemented for Series.

  • **kwargs: Additional keyword arguments to be passed to the function.

Let's try some examples to find the minimum values over a requested axis of a DataFrame.

Code example 1

The following example shows how to extract the lowest values per column along the index axis in the given DataFrame:

import pandas as pd
 
df = pd.DataFrame({
          'Name': ['Bassem','Celeste','Rachel','Angel']
        , 'Age': [45,  33 , 32 , 258]
        , 'Salary': [2500, 3000, 2000, 1000]
        , 'Grade': [76,  88 , 25 , 66]
     })
     
print("The DataFrame is:")
print(df.head(5))
print("The Lowest values per column:")
print(df.min(axis=0))
print("The Lowest values per column while considering numerical values only:")
print(df.min(numeric_only = True,axis=0))

Let's go through the above code:

  • Line 1: We import the pandas library.

  • Lines 3–8: We define a sample DataFrame containing employees' information.

  • Lines 10–11: We print out the DataFrame.

  • Line 13: We invoke the df.min() function while specifying axis=0 as a parameter, indicating that the column's values should be considered.

  • Line 14: We display the extracted result.

  • Lines 16–17: We invoke the df.min() function again while specifying an additional argument numeric_only = True in order to consider columns containing numerical values only.

Code example 2

The following code example shows how to retrieve the minimum values per row along the column axis, in the given DataFrame:

import pandas as pd
 
df = pd.DataFrame({
          'Name': ['Bassem','Celeste','Rachel','Angel']
        , 'Age': [45, None, 32, 258]
        , 'Salary': [2500, 3000, 2000, 1000]
        , 'Grade': [76, 88, 25, None]
     })
     
print("The DataFrame is:")
print(df.head(5))
print("The Lowest values per row:")
print(df.min(axis=1))

Let's go through the above code widget:

  • Line 1: We import the pandas library.

  • Lines 3–8: We define a sample DataFrame containing employees' information.

  • Lines 10–11: We print out the Data Frame.

  • Lines 13: We invoke the df.min() function while specifying axis=1 as the parameter indicating that the row's values should be considered

  • Line 14: We display the extracted result. Notice that NaN values are ignored by default but if we want to include the NaN values, we can add the following argument as a parameter to the df.min() function: skipna=False

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