Pandas Series.min() Function

The pandas.Series.min() function is used to find the minimum value in a Pandas Series. This function returns the smallest value in the Series, which can be useful for a variety of tasks, such as finding the lowest measurement or determining the smallest element in a data set.

In this article, I will explain the syntax of Series.min() function, its parameters, and how to find the minimum value of a given Series object.

Key Points –

• The Series.min() function returns the minimum value in a Pandas Series.
• By default, min() excludes NaN values when calculating the minimum value, unless skipna=False is specified.
• It returns the minimum value found in the Series, which is of the same data type as the elements in the Series.
• The skipna parameter controls whether NaN values are excluded (default is True).
• When used with non-numerical data types, the minimum value is determined based on lexicographical (alphabetical) order.
• If skipna=False, and the Series contains any NaN values, the result will be NaN.

Syntax of Series.min() Function

Following is the syntax for creating Series.min() method.

# Syntax of Series.min() function
Series.min(axis=0, skipna=True, *args, **kwargs)

Parameters of the Series min() Function

Following are the parameters of the min() function.

• axis – Default is 0. This parameter is mainly used for compatibility with DataFrame, but for Series, it has no effect as Series is a one-dimensional structure.
• skipna – Boolean, default is True. If True, it will exclude NaN values from the calculation of the minimum value. If False, the result will be NaN if there are any NaN values in the Series.
• args, kwargs – Additional arguments or keyword arguments that are passed to other functions (not commonly used for this specific function).

Usage of Series.min() Function

The min() function returns the minimum value from a Pandas Series. It finds the smallest numerical or lexicographical value depending on the data type in the Series.

Now, let’s create a pandas series using a list of values and use the min() function.

import pandas as pd
import numpy as np

# Create a Series
series = pd.Series([25, 44, 66, 30, 12, 55, 20])
print("Original Series:\n",series)

Yields below output.

Here’s an example demonstrating the basic use of the pandas.Series.min() function.

# Use Series.min() function 
# To find the minimum value
min_value = series.min()
print("Minimum value in the Series:", min_value)

Here,

• The pandas.Series.min() function is used to identify the smallest value in a Series.
• The function scans through the Series and returns the minimum value it finds. In the example, the minimum value is 12.

Series with Negative Numbers

The pandas.Series.min() function efficiently determines the smallest value in a Series, even when it includes negative numbers.

import pandas as pd

# Create a Pandas Series with negative and positive numbers
series = pd.Series([-7, 15, -3, 22, -12, 8, 0])

# Use Series.min() function 
# To find the minimum value
result = series.min()
print("Minimum value in the Series with Negative Numbers:", result)

# Output:
# Minimum value in the Series with Negative Numbers: -12

Here,

• A Pandas Series is created with a mix of negative and positive numbers: [-7, 15, -3, 22, -12, 8, 0].
• The series.min() function is used to find the minimum value in the Series. The smallest number in the Series is -12, so the result is -12.

Series With NaN Values (Default skipna=True)

By default, the min() function in Pandas Series uses skipna=True, which automatically excludes NaN (Not a Number) values when calculating the minimum. This ensures that if a Series contains NaN values, they are ignored, and the minimum value is determined based on the non-NaN elements.

import pandas as pd
import numpy as np

# Create a Pandas Series with NaN values
series_with_nan = pd.Series([7, 15, np.nan, 3, 12, np.nan, 9])

# Use Series.min() function
# To find the minimum value (skipping NaN values by default)
result = series_with_nan.min()
print("Minimum value in the Series with NaN values (skipna=True):", result)

# Output:
# Minimum value in the Series with NaN values (skipna=True): 3.0

Here,

• A Pandas Series is created with some NaN values: [10, 25, NaN, 6, 18, NaN, 45].
• The series_with_nan.min() function is called to find the minimum value.
• By default, skipna=True, so NaN values are excluded from the calculation, and the function returns 3.0 as the smallest value in the Series, ignoring the NaN values.

Series With skipna=False to Include NaN

When using the min() function in Pandas Series, setting skipna=False ensures that NaN values are included in the calculation. If the Series contains NaN values, the result will be NaN as well. It’s important to remember that NaN represents a missing value in Python. For more information on handling NaN values, refer to the handling missing values in Pandas.

# Use Series.min() function 
# To find the minimum value (including NaN values)
result = series_with_nan.min(skipna=False)
print("Minimum value in the Series with NaN values (skipna=False):", result)

# Output:
# Minimum value in the Series with NaN values (skipna=False): nan

Here,

• A Pandas Series is created with values that include NaN: [7, 15, NaN, 3, 12, NaN, 9].
• The series_with_nan.min(skipna=False) function is used to find the minimum value, specifying skipna=False.
• Since skipna=False is set, the NaN values are included in the calculation. As a result, if any NaN values are present in the Series, the function will return NaN.
• The output is nan, indicating that the calculation includes NaN and doesn’t skip them.

FAQ on Pandas Series.min() Function

Conclusion

In conclusion, the pandas.Series.min() function is a powerful and flexible tool for finding the smallest value in a Series. By default, it excludes NaN values using the skipna=True parameter, ensuring that the calculation focuses only on valid (non-NaN) data. However, by setting skipna=False, you can include NaN values in the calculation, resulting in NaN if any are present.

Happy Learning !!

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References

• https://pandas.pydata.org/docs/reference/api/pandas.Series.min.html