Normalizing numerical column in Pandas DataFrame with Min-Max scaling

Last update on December 21 2024 07:43:17 (UTC/GMT +8 hours)

Normalize a numerical column in a Pandas DataFrame.

Sample Solution:

Python Code:

import pandas as pd
# Create a sample DataFrame with a numerical column
data = {'Values': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)

# Define a function to perform Min-Max scaling
def min_max_scaling(column):
    min_val = column.min()
    max_val = column.max()
    scaled_column = (column - min_val) / (max_val - min_val)
    return scaled_column

# Apply Min-Max scaling to the 'Values' column
df['Normalized_Values'] = min_max_scaling(df['Values'])

# Display the normalized DataFrame
print(df)

Output:

   Values  Normalized_Values
0      10               0.00
1      20               0.25
2      30               0.50
3      40               0.75
4      50               1.00

Explanation:

In the exerciser above,

• First we create a sample DataFrame (df) with a numerical column 'Values'.
• The min_max_scaling function performs Min-Max scaling on a given column, scaling the values to the range [0, 1].
• Next we apply the min_max_scaling function to the 'Values' column and create a new column 'Normalized_Values' in the DataFrame.
• The resulting DataFrame (df) contains the original 'Values' column and the normalized 'Normalized_Values' column.

Flowchart:

Python Code Editor:

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