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# Compute the Natural Logarithm in Python

The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in base e. The method returns the natural logarithm of x, elementwise. This is a scalar if x is a scalar. The 1st parameter is the input value, array-like. The 2nd parameter is out, a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

Logarithm is a multivalued function − for each x there is an infinite number of z such that exp(z) = x. The convention is to return the z whose imaginary part lies in [-pi, pi]. For real-valued input data types, log always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the invalid floating point error flag. For complex-valued input, log is a complex analytical function that has a branch cut [-inf, 0] and is continuous from above on it. log handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

## Steps

At first, import the required library −

import numpy as np

Creating a numpy array using the array() method −

arr = np.array([1, 2, np.e, np.e**2])

Display the array −

print("Our Array...\n",arr)

Check the Dimensions −

print("\nDimensions of our Array...\n",arr.ndim)

Get the Datatype −

print("\nDatatype of our Array object...\n",arr.dtype)

Get the Shape −

print("\nShape of our Array...\n",arr.shape)

The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in base e −

print("\nResult (log)...\n",np.log(arr))

## Example

import numpy as np # Creating a numpy array using the array() method arr = np.array([1, 2, np.e, np.e**2]) # Display the array print("Our Array...\n",arr) # Check the Dimensions print("\nDimensions of our Array...\n",arr.ndim) # Get the Datatype print("\nDatatype of our Array object...\n",arr.dtype) # Get the Shape print("\nShape of our Array...\n",arr.shape) # The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in base e. print("\nResult (log)...\n",np.log(arr))

## Output

Our Array... [1. 2. 2.71828183 7.3890561 ] Dimensions of our Array... 1 Datatype of our Array object... float64 Shape of our Array... (4,) Result (log)... [0. 0.69314718 1. 2. ]