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Streamlining Code: The Power of Map, Filter, and Reduce in Functional Programming - Python



Map, Filter, and Reduce are key paradigms in functional programming. 
They enable the creation of concise and efficient code, reducing the need for explicit loops and branching. 

1. MAP:

Map applies a function to all items in an input list.

 

# Example: Converting temperatures from Celsius to Fahrenheit

celsius = [0, 10, 20, 30, 40]

fahrenheit = list(map(lambda c: (c * 9/5) + 32, celsius))

print(fahrenheit)  

# Output: [32.0, 50.0, 68.0, 86.0, 104.0]

 

In data science, you might use map to apply a transformation to each element in a dataset.

 

2. FILTER:

Filter creates a list of elements for which a function returns true.

 

# Example: Filtering out even numbers

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

odd_numbers = list(filter(lambda x: x % 2 != 0, numbers))

print(odd_numbers)  

# Output: [1, 3, 5, 7, 9]

 

In data analysis, you could use filter to select data points that meet certain criteria.

 

3. REDUCE:

Reduce applies a rolling computation to sequential pairs of values in a list.

 

from functools import reduce

 

# Example: Calculating the product of all numbers in a list

numbers = [1, 2, 3, 4, 5]

product = reduce(lambda x, y: x * y, numbers)

print(product)  

# Output: 120

 

In data science, reduce can be used for aggregations like sum, product, or more complex rolling computations.

 

Here's a more data science-oriented example combining all three:

 

import pandas as pd

from functools

 

# Sample dataset

data = pd.DataFrame({

    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],

    'age': [25, 30, 35, 40, 45],

    'salary': [50000, 60000, 75000, 90000, 100000]

})

 

# MAP: Increase all salaries by 10%

data['new_salary'] = list(map(lambda x: x * 1.1, data['salary']))

 

# FILTER: Select employees older than 30

older_employees = list(filter(lambda row: row['age'] > 30, data.to_dict('records')))

 

# REDUCE: Calculate total salary of older employees

total_salary = functools.reduce(lambda x, y: x + y['new_salary'], older_employees, 0)

 

print(data)

print("\nOlder employees:\n", older_employees)

print("\nTotal salary of older employees:\n", total_salary)


# Output:

      name  age  salary  new_salary

0    Alice   25   50000     55000.0

1      Bob   30   60000     66000.0

2  Charlie   35   75000     82500.0

3    David   40   90000     99000.0

4      Eve   45  100000    110000.0


Older employees:

 [{'name': 'Charlie', 'age': 35, 'salary': 75000, 'new_salary': 82500.0}, {'name': 'David', 'age': 40, 'salary': 90000, 'new_salary': 99000.00000000001}, {'name': 'Eve', 'age': 45, 'salary': 100000, 'new_salary': 110000.00000000001}]


Total salary of older employees:

 291500.0

 

This example demonstrates:

- Using MAP to apply a salary increase

- Using FILTER to select older employees

- Using REDUCE to calculate the total salary of filtered employees

 

These functions can significantly simplify data manipulation tasks and are often more readable and efficient than traditional for loops.

 

Happy Computing!

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