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  Question 37: Convert Continuous Column to Categorical in Pandas
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FULL QUESTION:
Write a Pandas program to convert continuous values of a column in a given
DataFrame to categorical.

Input:
{
    'Name': ['Alberto Franco', 'Gino Mcneill', 'Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],
    'Age': [18, 22, 40, 50, 80]
}

SHORT QUESTION:
Uses pd.cut to transform continuous numerical age values into discrete categorical age group bins.

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README & SETUP INSTRUCTIONS:
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# Question 37: Convert Continuous Column to Categorical in Pandas

- Prerequisites: Python 3.x, Pandas (pip install pandas).
- Setup: None.
- How to Run: python question_37.py
- Expected Behavior: Takes continuous Age values, applies pd.cut with defined bin edges and labels, and displays categorical column.

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SOURCE CODE FILES:
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1. question_37.py
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import pandas as pd

data = {
    'Name': ['Alberto Franco', 'Gino Mcneill', 'Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],
    'Age': [18, 22, 40, 50, 80]
}

df = pd.DataFrame(data)

bins = [0, 19, 39, 59, 100]
labels = ['Youth', 'Young Adult', 'Adult', 'Senior']

df['Age_Group'] = pd.cut(df['Age'], bins=bins, labels=labels)

print("Transformed DataFrame:")
print(df)
print("\nData Types:")
print(df.dtypes)
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