Python - Calculate the minimum of column values of a Pandas DataFrame

To find the minimum value in a Pandas DataFrame column, use the min() function. This method works on individual columns or across the entire DataFrame.

Basic Syntax

The basic syntax for finding minimum values is ?

# For a single column
df['column_name'].min()

# For all numeric columns
df.min()

Example 1: Finding Minimum in a Single Column

Let's create a DataFrame and find the minimum value in the "Units" column ?

import pandas as pd

# Create DataFrame1
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

print("DataFrame1:")
print(dataFrame1)

# Finding minimum value of a single column "Units"
print("\nMinimum Units from DataFrame1 =", dataFrame1['Units'].min())

DataFrame1:
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3    Tesla     80
4  Bentley    110
5   Jaguar     90

Minimum Units from DataFrame1 = 80

Example 2: Finding Minimum Across Multiple Columns

Here's how to find minimum values in different DataFrames and columns ?

import pandas as pd

# Create DataFrame2
dataFrame2 = pd.DataFrame({
    "Product": ['TV', 'PenDrive', 'HeadPhone', 'EarPhone', 'HDD', 'SSD'],
    "Price": [8000, 500, 3000, 1500, 3000, 4000]
})

print("DataFrame2:")
print(dataFrame2)

# Finding minimum value of a single column "Price"
print("\nMinimum Price from DataFrame2 =", dataFrame2['Price'].min())

# Finding minimum across all numeric columns
print("\nMinimum values for all numeric columns:")
print(dataFrame2.min(numeric_only=True))

DataFrame2:
   Product  Price
0       TV   8000
1  PenDrive    500
2 HeadPhone   3000
3  EarPhone   1500
4      HDD   3000
5      SSD   4000

Minimum Price from DataFrame2 = 500

Minimum values for all numeric columns:
Price    500
dtype: int64

Additional Options

The min() function supports several useful parameters ?

import pandas as pd

# DataFrame with missing values
df = pd.DataFrame({
    'A': [1, 2, None, 4],
    'B': [5, None, 7, 8],
    'C': [9, 10, 11, 12]
})

print("DataFrame with missing values:")
print(df)

# Skip NaN values (default behavior)
print("\nMinimum values (skipping NaN):")
print(df.min())

# Include NaN values in calculation
print("\nMinimum values (including NaN):")
print(df.min(skipna=False))

DataFrame with missing values:
     A    B   C
0  1.0  5.0   9
1  2.0  NaN  10
2  NaN  7.0  11
3  4.0  8.0  12

Minimum values (skipping NaN):
A     1.0
B     5.0
C     9.0
dtype: float64

Minimum values (including NaN):
A   NaN
B   NaN
C   9.0
dtype: float64

Conclusion

Use df['column'].min() to find the minimum value in a specific column, or df.min() for all numeric columns. The function automatically handles missing values by default, making it reliable for real-world data analysis.