Minimum of DataFrame over an Axis in Pandas

Minimum of DataFrame over an Axis

To find the minimum of the values over the requested axis in a DataFrame in Pandas, call min() method on this DataFrame. DataFrame.min() method returns a Series with values containing the minimum values over specified axis.

In this tutorial, we will learn the syntax of DataFrame.min() method, and how to use this method to find the minimum of values over a specified axis of DataFrame.

Syntax

The syntax of pandas DataFrame.min() method is

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

where

Parameter	Value	Description
axis	{index (0), columns (1)}. default value is 0.	Axis for the function to be applied on.
skipna	bool. default value is True.	Exclude NA/null values when computing the result.
level	int or level name. default value is None.	If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only	bool. default value is None.	Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs		Additional keyword arguments to be passed to the function.

Return Value

• Series or
• DataFrame (if level specified)

Examples

Minimum in Columns of DataFrame

By default, minimum is calculated for columns in a DataFrame.

In the following program, we take a DataFrame two columns containing numerical data, and find the minimum in columns, or we can say over index axis of this DataFrame.

Example.py

import pandas as pd

df = pd.DataFrame({'a': [7, 4], 'b': [3, 2]})
result = df.min()
print(result)

Output

a    4
b    2
dtype: int64

Minimum of Rows of DataFrame

To compute the minimum values in rows of DataFrame, pass axis=1 in call to DataFrame.min() method.

Example.py

import pandas as pd

df = pd.DataFrame({'a': [7, 4], 'b': [3, 2]})
result = df.min(axis=1)
print(result)

Output

0    3
1    2
dtype: int64

Do not skip NA while finding Minimum

By default, NA values like None, np.nan, etc are ignored. But if we would like consider those values as well, pass skipna=False to DataFrame.min() method.

Example.py

import pandas as pd

df = pd.DataFrame({'a': [7, None], 'b': [3, 2]})
result = df.min(skipna=False)
print(result)

Output

a    NaN
b    2.0
dtype: float64

If any of the values is NA along the specified axis, then NaN would be considered as minimum value.

Conclusion

In this Pandas Tutorial, we learned how to find the minimum of values over specified axis in a DataFrame using pandas DataFrame.min() method.