Finding the average of a list

Boosting performance with fmean

Performance is key when dealing with large datasets. From Python 3.8 onwards, use statistics.fmean:

import statistics

# As fast as a cheetah on a sugar rush!
numbers = [1, 2, 3, 4, 5]
average = statistics.fmean(numbers)
print(average)  # Output: 3.0
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The fmean function minimizes error in floating-point arithmetic. Perfect for lists of floating-point numbers!

Keeping up with Python's evolution

Different Python versions offer varying methods for average calculations:

• Python 3.4–3.7 favors statistics.mean—Call it a "crossfit workout" for numerical stability!

  import statistics
  
  # You're mean, I mean, mean
  numbers = [1, 2, 3, 4, 5]
  average = statistics.mean(numbers)
  print(average)  # Output: 3
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• Python 2.x needs this to ensure floating-point division:

  numbers = [1, 2, 3, 4, 5]
  average = sum(numbers) / float(len(numbers))
  print(average)  # Does this Python make me look 2.0?
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Remember, just like fashion, you've gotta keep up with Python's changes!

Making array calculations efficient with numpy

Working with large arrays? numpy.mean is your beast of burden:

import numpy as np

# Now you're thinking with arrays
numbers = np.array([1, 2, 3, 4, 5])
average = np.mean(numbers)
print(average)  # Output: 3.0
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Remember, numpy's prowess lies in handling arrays. Bigger the better!

Precision in your pocket with the statistics module

The statistics module is a lifesaver when precise calculation is paramount:

import statistics

# My prices are always mean... I mean average!
prices = [19.99, 23.45, 35.89]
average_price = statistics.mean(prices)
print(average_price)  # Always gets the mean right, even for cents!
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Apart from decimal numbers, it's also great with monstrous datasets!

Handling miscreants: negative numbers and zero

In case of negative numbers or zeros, fear not! Our formula remains unfazed:

# Like water off a duck's back
numbers = [5, -5, 0]
average = sum(numbers) / len(numbers)
print(average)  # Output:0
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Just remember, zeroes and negative numbers will bring your average down!

Dancing with data types

sum() gets along with integers and floats. Note that the output will be a float if there's a float in the list:

# Float like a butterfly, calculate like a bee
numbers = [1.0, 2, 3]
average = sum(numbers) / len(numbers)
print(average)  # Hey, where's the point in 2.0?
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So, keep an eye on your data types!

Wrestling with nested lists

Nested list - Flatten it first!

import itertools

# Flatten it like a pancake!
numbers = [[1, 2], [3, 4]]
flat_numbers = list(itertools.chain(*numbers))
average = sum(flat_numbers) / len(flat_numbers)
print(average)  # Output: 2.5
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It ensures only numerical values enter the ring.