Logarithmic Functions on 2D NumPy Array

Python
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In mathematics, logarithmic functions are widely used to analyze and interpret data that vary exponentially over time or space. NumPy is a popular library that provides efficient tools for working with multidimensional arrays in Python. In this article, we will explore how to apply logarithmic functions to all elements of a 2D NumPy array using various methods which are mentioned below:

1. Using “np.power()” function:

  • imports the NumPy library and creates a 2D NumPy array.
  • then sets the base value to 2 and applies the logarithmic function with base 2 to the array using the np.power() function.
  • The resulting array is printed to the console.
  • This code calculates the exponentiation of base 2 to the elements of the input array.

2. Using a “for loop”:

  • imports the NumPy library and creates a 2D NumPy array.
  • then creates an empty NumPy array with the same shape as the input array.
  • code loops through every element in the input array and applies the natural logarithm using the np.log() function, and stores the result in the empty array.
  • Finally, the resulting array is printed to the console.
  • can also use np.log2() and np.log10() for logarithm with base 2 and 10 respectively.
  • This code calculates the natural logarithm of the elements of the input array using a for loop.

3. Using “np.vectorize()” function:

  • imports the NumPy library and creates a 2D NumPy array.
  • then creates a “log base 10 function using lambda functions” with the np.vectorize() function.
  • code applies the lambda function to the input array using the created function and stores the result in the same array.
  • Finally, the resulting array is printed to the console.
  • can also use np.log() (natural log) or np.log2() (log base 2) as well.
  • This code calculates the logarithm base 10 of the elements of the input array using a lambda function and the np.vectorize() function.