# Compute the logarithm base 10 with scimath in Python

To compute the logarithm base 10 with scimath, use the scimath.log10() method in Python Numpy. The method returns the log base 10 of the x value(s). If x was a scalar, so is out, otherwise an array object is returned.

For a log10() that returns NAN when real x < 0, use numpy.log10 (note, however, that otherwise numpy.log10 and this log10 are identical, i.e., both return -inf for x = 0, inf for x = inf, and, notably, the complex principle value if x.imag != 0). The 1st parameter, x is the value(s) whose log base 10 is (are) required.

## Steps

At first, import the required libraries −

import numpy as np

Creating a numpy array using the array() method −

arr = np.array([10**1, -10**1, -10**2, 10**2, -10**3, 10**3])


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 object...\n",arr.shape)


To compute the logarithm base 10 with scimath, use the scimath.log10() method −

print("\nResult (log10)...\n",np.emath.log10(arr))

## Example

import numpy as np

# Creating a numpy array using the array() method
arr = np.array([10**1, -10**1, -10**2, 10**2, -10**3, 10**3])

# 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 object...\n",arr.shape)

# To compute the logarithm base 10 with scimath, use the scimath.log10() method in Python Numpy
print("\nResult (log10)...\n",np.emath.log10(arr))

## Output

Our Array...
[ 10 -10 -100 100 -1000 1000]

Dimensions of our Array...
1

Datatype of our Array object...
int64

Shape of our Array object...
(6,)

Result (log10)...
[1.+0.j 1.+1.36437635j 2.+1.36437635j 2.+0.j
3.+1.36437635j 3.+0.j ]