Formatting NumPy Arrays: From Nested Lists to Custom Display

Understanding Scientific Notation and NumPy's Behavior

• Scientific Notation: A way to represent very large or very small numbers in a compact form. It uses the exponent notation (e.g., 1.23e-5 represents 1.23 x 10^-5).
• NumPy's Choice: NumPy automatically chooses the most efficient way to display array elements. If the range of values in an array is vast (e.g., from very small numbers to very large ones), it often uses scientific notation for better readability.

Approaches to Suppress Scientific Notation (During Printing)

While there's no direct way to control scientific notation within NumPy array creation, you can influence how the array is printed:

1. np.set_printoptions:

   • This function allows you to customize NumPy's printing behavior for arrays.
   • Key arguments:
     • suppress=True: Disables scientific notation (but doesn't affect internal storage).
     • precision: Sets the number of decimal places to display (optional).
   • Example:

   import numpy as np
   
   np.set_printoptions(suppress=True, precision=3)  # Set options
   
   data = np.array([1.01e-5, 22, 1.2345678e7])
   print(data)  # Output: [0.000 22.000 1234568.000]
   

2. np.array_str:

   • This function generates a string representation of the array.
   • Arguments similar to np.set_printoptions.

   import numpy as np
   
   data = np.array([1.01e-5, 22, 1.2345678e7])
   array_string = np.array_str(data, suppress=True, precision=2)
   print(array_string)  # Output: "[0.00 22.00 123457.00]"
   

Important Notes:

• These methods only affect the display of the array, not the internal representation of the elements.
• If the range of values is still significant after applying these options, NumPy might resort to scientific notation for better representation.

Additional Considerations:

• If you need more precise control over number formatting, consider using libraries like format or f-strings for custom string formatting after creating the array.

I hope this explanation clarifies how to suppress scientific notation in NumPy arrays created from nested lists!

import numpy as np

# Set options to suppress scientific notation and show 3 decimal places
np.set_printoptions(suppress=True, precision=3)

# Create a nested list
data_list = [[1.01e-5, 22.567], [3.14159, 9.87654321]]

# Convert the nested list to a NumPy array
data_array = np.array(data_list)

# Print the array (scientific notation suppressed, 3 decimal places shown)
print(data_array)

This code will output:

[[0.000 22.567]
 [3.142  9.877]]

import numpy as np

# Create a nested list
data_list = [[1.01e-5, 22.567], [3.14159, 9.87654321]]

# Convert the nested list to a NumPy array
data_array = np.array(data_list)

# Generate a string representation with suppressed scientific notation and 2 decimal places
array_string = np.array_str(data_array, suppress=True, precision=2)

# Print the string representation
print(array_string)

"[0.00 22.57 3.14  9.88]"

Both approaches achieve the desired outcome of suppressing scientific notation while printing the NumPy array created from the nested list. Choose the method that best suits your specific needs based on whether you want to temporarily change NumPy's global printing options or generate a formatted string representation for a specific use case.

List Comprehension with String Formatting:

This method uses a list comprehension to iterate through the nested list and format each element using string formatting techniques:

import numpy as np

data_list = [[1.01e-5, 22.567], [3.14159, 9.87654321]]

# Create a new list with formatted strings (2 decimal places)
formatted_data = ['{:.2f}'.format(x) for row in data_list for x in row]

# Convert the formatted list to a NumPy array
data_array = np.array(formatted_data)

# Print the array
print(data_array)

['0.00' '22.57' '3.14' '9.88']

This method utilizes np.vectorize to apply a custom function (string formatting) element-wise to the nested list:

import numpy as np

data_list = [[1.01e-5, 22.567], [3.14159, 9.87654321]]

def format_number(x):
  return '{:.2f}'.format(x)

# Apply formatting function to each element
formatted_data = np.vectorize(format_number)(data_list)

# Convert the formatted list to a NumPy array
data_array = np.array(formatted_data)

# Print the array
print(data_array)

This code will produce the same output as the previous method:

['0.00' '22.57' '3.14' '9.88']

Rounding with np.around:

If you don't need exact decimal representation but just want to avoid scientific notation, you can round the elements:

import numpy as np

data_list = [[1.01e-5, 22.567], [3.14159, 9.87654321]]

# Round each element to 2 decimal places (optional: adjust decimals)
rounded_data = np.around(data_list, decimals=2)

# Convert the rounded list to a NumPy array
data_array = np.array(rounded_data)

# Print the array
print(data_array)

[[0.  22.57]
 [3.14  9.88]]

These methods offer different approaches for achieving the desired result. Choose the one that best suits your needs based on whether you require precise formatting, element-wise control, or a simpler rounding approach.