Write a program in Python to find the lowest value in a given DataFrame and store the lowest value in a new row and column

In this tutorial, we'll learn how to find the minimum values in a Pandas DataFrame and store them in both a new column and a new row. This is useful for data analysis when you need to track minimum values across rows and columns.

Creating the Sample DataFrame

Let's start by creating a sample DataFrame to work with ?

import pandas as pd

data = [[12, 13, 5], [10, 6, 4], [16, 18, 20], [11, 15, 58]]
df = pd.DataFrame(data, columns=['one', 'two', 'three'])
print("Original DataFrame:")
print(df)

Original DataFrame:
   one  two  three
0   12   13      5
1   10    6      4
2   16   18     20
3   11   15     58

Adding a Column with Row-wise Minimum Values

To find the minimum value in each row and store it as a new column, we use min(axis=1) where axis=1 operates across columns ?

import pandas as pd

data = [[12, 13, 5], [10, 6, 4], [16, 18, 20], [11, 15, 58]]
df = pd.DataFrame(data, columns=['one', 'two', 'three'])

# Add new column to store minimum value of each row
df['min_value'] = df.min(axis=1)
print("DataFrame with minimum column added:")
print(df)

DataFrame with minimum column added:
   one  two  three  min_value
0   12   13      5          5
1   10    6      4          4
2   16   18     20         16
3   11   15     58         11

Adding a Row with Column-wise Minimum Values

To add a new row containing the minimum value of each column, we use min(axis=0) where axis=0 operates across rows ?

import pandas as pd

data = [[12, 13, 5], [10, 6, 4], [16, 18, 20], [11, 15, 58]]
df = pd.DataFrame(data, columns=['one', 'two', 'three'])

# Add column with row-wise minimums
df['min_value'] = df.min(axis=1)

# Add new row with column-wise minimums
df.loc[len(df)] = df.min(axis=0)
print("Final DataFrame with both minimum column and row:")
print(df)

Final DataFrame with both minimum column and row:
   one  two  three  min_value
0   12   13      5          5
1   10    6      4          4
2   16   18     20         16
3   11   15     58         11
4   10    6      4          4

How It Works

The solution uses two key Pandas operations:

• df.min(axis=1) − Finds minimum values across columns (row-wise)

• df.min(axis=0) − Finds minimum values across rows (column-wise)

• df.loc[len(df)] − Adds a new row at the end of the DataFrame

Complete Example

import pandas as pd

# Create sample DataFrame
data = [[12, 13, 5], [10, 6, 4], [16, 18, 20], [11, 15, 58]]
df = pd.DataFrame(data, columns=['one', 'two', 'three'])

print("Step 1: Original DataFrame")
print(df)
print()

print("Step 2: Add column with row-wise minimums")
df['min_value'] = df.min(axis=1)
print(df)
print()

print("Step 3: Add row with column-wise minimums")
df.loc[len(df)] = df.min(axis=0)
print(df)

Step 1: Original DataFrame
   one  two  three
0   12   13      5
1   10    6      4
2   16   18     20
3   11   15     58

Step 2: Add column with row-wise minimums
   one  two  three  min_value
0   12   13      5          5
1   10    6      4          4
2   16   18     20         16
3   11   15     58         11

Step 3: Add row with column-wise minimums
   one  two  three  min_value
0   12   13      5          5
1   10    6      4          4
2   16   18     20         16
3   11   15     58         11
4   10    6      4          4

Conclusion

Use df.min(axis=1) to find row-wise minimums and df.min(axis=0) for column-wise minimums. The loc method allows you to add new rows with calculated values to your DataFrame.