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The Pandas DataFrame - min() function returns the minimum of the values over the specified axis. The syntax for using this function is mentioned below:

Syntax

DataFrame.min(axis=None, skipna=None, level=None, numeric_only=None)

Parameters

axis Optional. Specify {0 or 'index', 1 or 'columns'}. If 0 or 'index' minimum of the values are generated for each column. If 1 or 'columns' minimum of the values are generated for each row. Default: 0
skipna Optional. Specify True to exclude NA/null values when computing the result. Default is True.
level Optional. Specify level (int or str). If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series. A str specifies the level name.
numeric_only Optional. Specify True to include only float, int or boolean data. Default: False

Return Value

Returns minimum of the values of Series or DataFrame if a level is specified.

Example: Using min() column-wise on whole DataFrame

In the example below, a DataFrame df is created. The min() function is used to get the minimum of values for each column.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#minimum of values of all entries column-wise
print("\ndf.min() returns:")
print(df.min())

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.min() returns:
Bonus      2
Salary    59
dtype: int64

Example: Using min() row-wise on whole DataFrame

To get the row-wise sum, the axis parameter can set to 1.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#minimum of values of all entries row-wise
print("\ndf.min(axis=1) returns:")
print(df.min(axis=1))

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.min(axis=1) returns:
John     5
Marry    3
Sam      2
Jo       4
dtype: int64

Example: Using min() on selected column

Instead of whole data frame, the min() function can be applied on selected columns. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Last Salary": [58, 60, 63, 57],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#minimum of values of single column
print("\ndf['Salary'].min() returns:")
print(df["Salary"].min())

#minimum of values of multiple columns
print("\ndf[['Salary', 'Bonus']].min() returns:")
print(df[["Salary", "Bonus"]].min())

The output of the above code will be:

The DataFrame is:
       Bonus  Last Salary  Salary
John       5           58      60
Marry      3           60      62
Sam        2           63      65
Jo         4           57      59

df['Salary'].min() returns:
59

df[['Salary', 'Bonus']].min() returns:
Salary    59
Bonus      2
dtype: int64

❮ Pandas DataFrame - Functions

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