# Logarithmic Functions on 2D NumPy Array

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.