How to declare array of zeros in python (or an array of a certain size)

The problem with multidimensional arrays with list multiplication

Creating multidimensional arrays with list multiplication may introduce unexpected behavior due to shared references:

# The botched way
botched_matrix = [[0] * 3] * 4 # Creates shared references!
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Modifying one element in a sublist inadvertently affects the other sublists:

botched_matrix[0][0] = 1 
# Alter ego matrix appears!
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The right way with list comprehension

For multidimensional arrays, use list comprehension to avoid the issue of shared references:

# The right way
right_matrix = [[0] * 3 for _ in range(4)]
right_matrix[0][0] = 1 # Changes only the needed spot. Phew!
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Using numpy for heavy-duty arrays

For larger, performance-critical arrays, NumPy's zeros comes to the rescue:

import numpy as np
large_zeros_np = np.zeros((1000, 1000)) # Easily creates a 1000x1000 array
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Deeper into list comprehension

List comprehension serves as a powerful tool for declaring zero arrays, handling dimensions flexibly and elegantly:

# A 3D array for the three-eyed ravens
three_d_zeros_list = [[[0] for _ in range(3)] for _ in range(4) for _ in range(5)]
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This allows for creating complex zero-filled data structures.

Custom function for versatile array creation

No numpy? No problem! Here's a custom function to create arrays of zeros of any shape:

def create_zeros(shape):
    if isinstance(shape, int):
        return [0] * shape
    return [create_zeros(sub_shape) for sub_shape in shape]

# Create a custom 3x4 array of zeros
custom_zeros = create_zeros([3, 4]) # Customizable and flexible, like a yoga instructor
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Efficient histograms with numpy

Combining numpy.zeros with numpy.histogram is an efficient approach for working with histograms:

# Datasaurus Rex: Rawr!
data = [ ... ] # Your data points here
histogram, bins = np.histogram(data, bins=range(10))
filled_slots = np.zeros_like(bins)
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This setup aids in performing complex data analysis and visualization.

Make your zero arrays work for you

Creating zero arrays with Python unpacking

You can mix zero arrays creation with Python's unpacking feature:

# Sharing is caring!
first, *rest = [0] * 4  # first == 0, rest == [0, 0, 0]
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Using array module for numeric arrays

For numeric data, Python’s array module provides a more memory-efficient alternative:

# Less is more!
from array import array
numeric_zeros = array('i', [0] * 10)  # 'i' for integers
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High precision arrays with numpy

When precision matters, consider specifying your data type, especially with floating-point numbers:

# Accuracy is the best policy!
import numpy as np
high_precision_zeros = np.zeros(10, dtype=np.float64)
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The right data type adds to the accuracy of computations.