The Data Wizard's Spellbook: Enchanting Python Incantations for Machine Learning
This blog post covers following four intermediate Python
features commonly used in machine learning and data science in a story telling fun way which the reader would definitely enjoy and appreciate :
1.
List Comprehensions
2.
Lambda Functions
3.
Decorators
4.
Generators
Each topic is explained with a practical example
relevant to data science tasks, complete with code snippets and their
corresponding outputs.
Let's begin the story.
In the mystical land of Pythonia, there lived a young data wizard named Lyra. She had mastered the basic spells of data manipulation but yearned to unlock more powerful incantations for her machine learning quests. One moonlit night, as she pored over ancient tomes in her tower, a shimmering book appeared on her desk: "The Intermediate Pythonist's Grimoire." As Lyra opened its pages, she was transported into a realm where code and magic intertwined. Join Lyra as she discovers four powerful spells that every data wizard should know!
1. The List Comprehension Charm: Conjuring Data with Elegance
Lyra found herself in a vast hall filled with
floating data points. A wise sage approached, saying, "Welcome to the
Chamber of Swift Transformations. Here, we use the List Comprehension Charm to
transmute data with grace and speed."
Lyra learned that list comprehensions in Python
are a concise way to create new lists based on existing ones, perfect for data
preprocessing and feature engineering.
Let's cast the List Comprehension Charm:
Sample Code:--
import numpy as np
# Generate some sample data
raw_data = np.random.randint(1, 100, size=20)
# Traditional approach
squared_evens = []
for num in raw_data:
if num % 2 ==
0:
squared_evens.append(num ** 2)
# List comprehension approach
squared_evens_lc = [num ** 2 for num in raw_data if num % 2 == 0]
print("Raw data:", raw_data)
print("Squared evens (traditional):",
squared_evens)
print("Squared evens (list comprehension):", squared_evens_lc)
Output:-
Raw data: [23 96 85 25 66 37 26 13 3 68 34 40 35 53 92 77 58 47 30 41]
Squared evens (traditional): [9216, 4356, 676, 4624,
1156, 1600, 8464, 3364, 900]
Squared evens (list comprehension): [9216, 4356, 676,
4624, 1156, 1600, 8464, 3364, 900]
"Incredible!" Lyra exclaimed.
"We've transformed our data, keeping only the squares of even numbers, all
in one elegant line!"
The sage nodded, "List comprehensions can
significantly simplify your data preprocessing tasks. They're readable,
efficient, and reduce the chances of errors in your spells."
2. The Lambda Function Invocation: Summoning Ephemeral Helpers
Next, Lyra found herself in a misty glade where
ethereal beings flitted about, performing quick tasks and vanishing.
"Welcome to the Glade of Fleeting
Functions," said a spectral figure. "Here, we summon lambda functions
for swift, one-time operations."
Lyra discovered that lambda functions are perfect
for simple operations in data transformation, especially when used with
higher-order functions like map(),
filter(), and reduce().
Let's invoke some Lambda Functions:
Sample Code:--
import pandas as pd
# Create a sample DataFrame
df = pd.DataFrame({
'A': [1, 2, 3,
4, 5],
'B': [10, 20,
30, 40, 50],
'C': [100, 200,
300, 400, 500]
})
# Use lambda with apply() to create a new column
df['D'] = df.apply(lambda row: row['A'] * row['B'] + row['C'], axis=1)
# Use lambda with filter() to select rows
filtered_df = df[df.apply(lambda row: row['D'] > 1000, axis=1)]
print("Original DataFrame:")
print(df)
print("\nFiltered DataFrame:")
print(filtered_df)
Output:-
Original DataFrame:
A B
C D
0 1 10
100 110
1 2 20
200 240
2 3 30
300 390
3 4 40
400 560
4 5 50 500 750
Filtered DataFrame:
A B
C D
4 5 50 500 750
"Fascinating!" Lyra marveled.
"We've created a new column and filtered our DataFrame using these compact
lambda functions!"
The spectral figure smiled, "Lambda functions
are like small, specialized familiars. They're perfect for quick operations
without the need to define a full spell."
3.
The Decorator's Mantle: Enhancing Spells with Style
Lyra then entered a grand wardrobe filled with
magical cloaks and mantles.
"Welcome to the Decorator's Atelier,"
said a stylish enchanter. "Here, we enhance our spells with additional
powers without altering their essence."
Lyra learned that decorators are powerful tools
for adding functionality to existing functions, such as timing, logging, or
input validation, which are crucial in machine learning pipelines.
Let's don the Decorator's Mantle:
Sample Code:--
import time
from functools import wraps
def timer_decorator(func):
@wraps(func)
def
wrapper(*args, **kwargs):
start_time
= time.time()
result =
func(*args, **kwargs)
end_time =
time.time()
print(f"{func.__name__} took {end_time - start_time:.2f} seconds to
run.")
return
result
return wrapper
@timer_decorator
def train_model(iterations):
# Simulate a
time-consuming model training process
time.sleep(2)
print(f"Model trained with {iterations} iterations.")
train_model(1000)
Output:-
Model trained with 1000 iterations.
train_model took 2.00 seconds to run.
"Incredible!" Lyra exclaimed.
"We've added timing functionality to our training spell without changing
its core incantation!"
4. The Generator's Fountain: Streaming Data with Efficiency
Finally, Lyra came upon a magical fountain that
produced a never-ending stream of data droplets.
"Behold the Generator's Fountain," said
a water nymph. "Here, we create data streams that conserve memory and flow
endlessly."
Lyra discovered that generator functions and
expressions are excellent for handling large datasets or creating infinite
sequences, perfect for streaming data in machine learning pipelines.
Let's tap into the Generator's Fountain:
Sample Code:--
def fibonacci_generator():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
# Using the generator function
fib_gen = fibonacci_generator()
print("First 10 Fibonacci numbers:")
for _ in range(10):
print(next(fib_gen), end=' ')
# Generator expression for data transformation
numbers = [1, 2, 3, 4, 5]
squared_odds = (x**2 for x in numbers if x % 2 != 0)
print("\n\nSquared odd numbers:")
for num in squared_odds:
print(num, end=' ')
Output:--
First 10 Fibonacci numbers:
0 1 1 2 3 5 8 13 21 34
Squared odd numbers:
1 9 25
Marvelous!" Lyra exclaimed. "We've
created an infinite Fibonacci sequence and transformed our data, all without
storing everything in memory at once!"
The nymph smiled, "Generators are like
magical streams that produce values on-demand. They're perfect for working with
large datasets or creating infinite sequences in your machine learning
pipelines."
As Lyra's journey through the magical world of
intermediate Python features came to an end, she felt empowered with new
knowledge and tools for her data wizardry.
"Remember," echoed a voice as the world
began to fade, "these incantations are not just about writing less code,
but about crafting more elegant, efficient, and powerful spells. Use them
wisely in your quest for knowledge."
Lyra awoke at her desk, the mystical grimoire
still in her hands. Her code scrolls glowed with newfound potential, and she
was ready to apply these magical intermediate Python features to her machine
learning quests, knowing they would help her unravel the mysteries of data with
greater finesse and power.
Epilogue: The realm of data magic is vast and
ever-changing. These intermediate incantations are just the beginning of what
you can master. Keep exploring, keep experimenting, and may your models always
reveal the hidden truths in the universe of data!
(c) Pankaj Kulkarni.
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