Python NumPy Log + Examples

In this Python NumPy tutorial, we will discuss the Python NumPy log and also cover the below examples:

Python NumPy log10
Python NumPy logspace
Python NumPy logical and
Python NumPy log base 2
Python NumPy logical operators
Python NumPy logical or
Python NumPy logical not
Python NumPy log base
Python NumPy log 1p

Table of Contents

Python NumPy log

In this section, we will learn about the Python NumPy log.
The logarithmic function is used to calculate the user to find real logarithm of x, where x belongs to all the input array values.
It is the inverse of the exponential method as well as an element-wise natural log.
In logarithmic, we can easily use the function np.logspace().
In logspace, the iterable sequence starts at the base to the power of start and lasts with the base stop.
The natural logarithm is the reverse of the exponential method so that the log value of the exponential of X will give you the X so the logarithm in base E is called the natural logarithm.
In the log function, we will provide an array of values NumPy log function will generate the logarithm for these elements.
array_like input value.

Syntax:

Here is the Syntax of the python numpy log

numpy.log 
         (
          x,
          out=None,
          where=True,
          casting='same_kind',
          order='K',
          dtype=None
         )

It consists of few parameters.
- X: array_like(input value)
- OUT: ndarray(A location into which the result is stored. If provided, it should have a shape that the inputs provided. If not provided or None, a new-allocated array is returned).
- Where: array_like, optional(This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the function result. Elsewhere, the output array will always be precise in its original value).
- Returns: The natural logarithm of x, element-wise

Example:

import numpy as np

a = np.array([1,2,5,6,7])
log_value = np.log(a)
print(log_value)

Here is the Screenshot of the following given code

Read: Python NumPy where with examples

Python NumPy log10

In this section, we will learn about the Python NumPy log10.
It is a statistical function that helps the user to calculate the Base-10 logarithm of x where x is an array input value.
It is used to get the natural logarithm of any object or items with the base 10.
For real value input dtypes, log 10 always return real output. For each value that cannot be represented as a real number.

Syntax:

Here is the Syntax of Python numpy log10

numpy.log10
         (
          x,
          out=None,
          where=True,
          casting='same_kind',
          order='K',
          dtype=None
         )

Example:

import numpy as np

a = np.array([2,4,8,9,7])
log_value = np.log10(a)
print(log_value)

Here is the Screenshot of the following given code

Read: Python NumPy linspace

Python NumPy logspace

In this section, we will learn about the Python NumPy log space.
It returns number spaces evenly interval on a log scale.
The sequence starts at the base to the power of start and ends with a base stop.
Log space always returns even numbers on a log scale. Logspace has the same arguments

Syntax:

Here is the Syntax of NumPy logspace

numpy.logspace
              (
               start,
               stop,
               num,
               endpoint=True,
               base,
               dtype=None
              )

Example:

import numpy as np

a = (np.logspace(4,8,num=3,base=2)) #start and stop
print(a)

Here is the Screenshot of the following given code

Read Python NumPy empty array

Python NumPy logical and

In this section, we will learn about Python NumPy logical_and() and.
In this method, we can easily use the logical operators to use np.logical_and() method with multiple conditions
The logical AND has been used to define the condition. The first logical_and() function has been applied in a one-dimensional array that will return the array of indices of the input array where the condition will return true.
It helps users to find out the true value of arr1 and arr2 element-wise. Both the numpy arrays should be of the same shape.

Syntax:

Here is the syntax of NumPy logical_and

numpy.logical_and
                 (
                  x1,
                  x2,
                  out=None,
                  Where=True,
                  casting='same-kind'
                  dtype=None
                 )

It consists of few parameters
- X1: array_like(input_arrays)
- OUT: ndarray(A location into which the result is stored. If provided, it should have a shape that the inputs provided. If not provided or None, a new-allocated array is returned).
- Returns: The boolean result of the logical AND operation applied to the elements of x1 and x2; the shape is determined.

Example:

import numpy as np

x = np.arange(5)
y = np.logical_and(x>1, x<4)
print(y)

Here is the Screenshot of the following given code

Read: Python NumPy concatenate + 9 Examples

Python NumPy log base 2

In this section, we will learn about Python NumPy log base 2.
It helps the user to calculate the Base-2 logarithm of x where x is an array input value.
It is used to get the natural logarithm of any object with base 2.
It returns the base-2 logarithm of x. This is a scalar if x is a scaler.

Syntax:

numpy.log2
         (
          x,
          out=None,
          where=True,
          casting='same_kind',
          order='K',
          dtype=None
         )

Example:

import numpy as np

a = np.array([1,4,8,9,7])
log_value = np.log2(a)
print(log_value)

Here is the Screenshot of the following given code

Python numpy log base2 — Python numpy log base 2

Read Python NumPy repeat

Python NumPy logical operators

In this section, we will learn about Python NumPy logical operators.
Python NumPy logical operators are to compute truth value using the truth table i.e represents boolean value
Numpy logical operators are logical_and logical_not.
In this example first, we declare a NumPy array, and each element against the given condition to compute the truth value using python NumPy logical operators.

Syntax:

numpy.log 
         (
          x,
          out=None,
          where=True,
          casting='same_kind',
          order='K',
          dtype=None
         )

Example:

import numpy as np

x = np.arange(5)
y = np.logical_and(x>1, x<4) #
print(y)
z = np.logical_or(x<2,x<3)
print(z)
a = np.logical_not(x<2,x<3)
print(a)

Here is the Screenshot of the following given code

Read: Python sort NumPy array

Python NumPy logical or

In this section, we will learn about Python NumPy logical OR.
It is a logical function and it helps the user to find out the true value of arr1 or arr2 element-wise.
But the most important point is array must be of the same shape.
It returns a boolean result with the same shape as arr1 and arr2 of the logical or operation on elements of arr1 and arr2.

Syntax:

Here is the Syntax of logical or

numpy.logical_or
                (
                  x1,
                  x2,
                  out=None,
                  Where=True,
                  casting='same-kind'
                  dtype=None
                 )

Example:

import numpy as np

arr1 = [2, 4, False, 6]
arr2 = [2,3,True,7]
log_arr = np.logical_or(arr1, arr2)
print(log_arr)

Here is the Screenshot of the following given code

Read Python NumPy absolute value

Python NumPy logical not

In this section, we will learn about Python NumPy logical not.
It is a logical function and it helps the user that computes the truth value of NOT arr element-wise.
It returns with boolean results of Not array element-wise.

Syntax:

Here is the Syntax of logic not

numpy.logical_and
                 (
                  arr1,
                  arr2,
                  out=None,
                  Where=True,
                  casting='same-kind'
                  dtype=None
                 )

Example:

import numpy as np

arr1 = [2, 4, False, 6]
arr2 = [2,3.2,True,False]
log_arr = np.logical_not(arr1)
log_arr2= np.logical_not(arr2)
print(log_arr)
print(log_arr2)

Here is the Screenshot of the following given code

Read: Python NumPy matrix + Examples

Python NumPy log base

In this section, we will learn about the Python NumPy log base.
The Numpy log() function offers a possibility of finding logarithmic values concerning user-defined bases.
The natural logarithm is the reverse of the exponential function so that log of the exponential of X will give you the X so the logarithm in base E is called the natural logarithm.

Syntax:

numpy.log 
         (
          x,
          out=None,
          where=True,
          casting='same_kind',
          order='K',
          dtype=None
         )

Example:

import numpy as np
data = 10
base = 4
log_val = np.log(data)/np.log(base)
print(log_val)

In the above example, we have calculated the logarithmic value of 10 with base 4.

Here is the Screenshot of the following given code.

Python NumPy log 1p

In this section, we will learn about the Python NumPy log 1p.
It is a statistical function that is used to get the natural logarithm value x+1, where x is a value of a numpy array.
The log1p is the reverse of exp(x)-1.
The numpy.log1p() method accepts two parameters which are arr and out parameters and returns a numpy array of natural logarithms of value x+1 of the elements of the given array values.

Syntax:

numpy.log1p
          (
          arr,
          out=None,
          where=True
         )

It consists of two parameters:
- ARR: It is the object whose log is to be calculated.
- OUT: It is the location the result is stored in.

Example:

import numpy as np

arr= ([2,3,4,5,6])
out = np.log1p(arr)
print(out)

Here is the Screenshot of the following given code

You may like the following Python tutorials:

In this Python NumPy tutorial, we discussed Python NumPy log and also cover the below examples:

Python NumPy log10
Python NumPy logspace
Python NumPy logical and
Python NumPy log base 2
Python NumPy logical operators
Python NumPy logical or
Python NumPy logical not
Python NumPy log base
Python NumPy log 1p

Bijay Kumar

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.

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