How Can I Customize NumPy Array Printing to Improve Readability?

Customizing NumPy Array Printing: Suppressing Scientific Notation and Adjusting Precision

NumPy arrays often display floating-point values in scientific notation or with excessive decimal places, making them difficult to read for debugging or analysis. This article explores solutions to customize the printing of NumPy arrays, allowing for more readable output.

NumPy Printoptions

One approach involves using the numpy.set_printoptions function to adjust various options that affect how arrays are printed. By setting precision to a desired value, the number of decimal places displayed can be controlled.

import numpy as np

# Set precision to 3 decimal places
np.set_printoptions(precision=3)

# Example array
x = np.random.random(10)

# Improved printing
print(x)

Suppressing Scientific Notation

Scientific notation can be suppressed using the suppress option. Setting suppress to True forces the output to use fixed-point representation, even for very small or large numbers.

# Suppress scientific notation
np.set_printoptions(suppress=True)

# Example array
y = np.array([1.5e-10, 1.5, 1500])

# Enhanced printing
print(y)

Local Print Options

To apply custom print options locally, use the numpy.printoptions context manager. Options set within the context manager will be applied to all array printing within that block.

with np.printoptions(precision=3, suppress=True):
    print(x)

Preventing Zero Stripping

By default, NumPy removes trailing zeros from floats. To preserve these zeros, specify a formatter function using the formatter parameter of np.set_printoptions.

# Preserve trailing zeros
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})

# Improved printing
print(x)

By utilizing these techniques, developers can customize the printing of NumPy arrays to suit their specific needs, resulting in more readable output for easier analysis and debugging.

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