Python Tutorial

Python HOME Python Intro Python Get Started Python Syntax Python Comments Python Variables Python Data Types Python Numbers Python Casting Python Strings Python Booleans Python Operators Python Lists Python Tuples Python Sets Python Dictionaries Python If...Else Python While Loops Python For Loops Python Functions Python Lambda Python Arrays Python Classes/Objects Python Inheritance Python Iterators Python Scope Python Modules Python Dates Python Math Python JSON Python RegEx Python PIP Python Try...Except Python User Input Python String Formatting

File Handling

Python File Handling Python Read Files Python Write/Create Files Python Delete Files

Python NumPy

NumPy Intro NumPy Getting Started NumPy Creating Arrays NumPy Array Indexing NumPy Array Slicing NumPy Data Types NumPy Copy vs View NumPy Array Shape NumPy Array Reshape NumPy Array Iterating NumPy Array Join NumPy Array Split NumPy Array Search NumPy Array Sort NumPy Array Filter NumPy Random NumPy ufunc

Python Pandas

Pandas Tutorial Pandas Getting Started Pandas Series Pandas DataFrames Pandas Read CSV Pandas Read JSON Pandas Analyzing Data Pandas Cleaning Data Pandas Correlations Pandas Plotting

Python Matplotlib

Matplotlib Intro Matplotlib Get Started Matplotlib Pyplot Matplotlib Plotting Matplotlib Markers Matplotlib Line Matplotlib Subplots Matplotlib Scatter Matplotlib Bars Matplotlib Histograms Matplotlib Pie Charts

Python SciPy

SciPy Intro SciPy Getting Started SciPy Constants SciPy Optimizers SciPy Sparse Data SciPy Graphs SciPy Spatial Data SciPy Matlab Arrays SciPy Interpolation SciPy Significance Tests

Machine Learning

Getting Started Mean Median Mode Standard Deviation Percentile Data Distribution Normal Data Distribution Scatter Plot Linear Regression Polynomial Regression Multiple Regression Scale Train/Test Decision Tree

Python MySQL

MySQL Get Started MySQL Create Database MySQL Create Table MySQL Insert MySQL Select MySQL Where MySQL Order By MySQL Delete MySQL Drop Table MySQL Update MySQL Limit MySQL Join

Python MongoDB

MongoDB Get Started MongoDB Create Database MongoDB Create Collection MongoDB Insert MongoDB Find MongoDB Query MongoDB Sort MongoDB Delete MongoDB Drop Collection MongoDB Update MongoDB Limit

Python Reference

Python Overview Python Built-in Functions Python String Methods Python List Methods Python Dictionary Methods Python Tuple Methods Python Set Methods Python File Methods Python Keywords Python Exceptions Python Glossary

Module Reference

Random Module Requests Module Statistics Module Math Module cMath Module

Python How To

Remove List Duplicates Reverse a String Add Two Numbers

Python Examples

Python Examples Python Compiler Python Exercises Python Quiz Python Certificate

NumPy Logs


NumPy provides functions to perform log at the base 2, e and 10.

We will also explore how we can take log for any base by creating a custom ufunc.

All of the log functions will place -inf or inf in the elements if the log can not be computed.

Log at Base 2

Use the log2() function to perform log at the base 2.


Find log at base 2 of all elements of following array:

import numpy as np

arr = np.arange(1, 10)

Try it Yourself »

Note: The arange(1, 10) function returns an array with integers starting from 1 (included) to 10 (not included).

Log at Base 10

Use the log10() function to perform log at the base 10.


Find log at base 10 of all elements of following array:

import numpy as np

arr = np.arange(1, 10)

Try it Yourself »

Natural Log, or Log at Base e

Use the log() function to perform log at the base e.


Find log at base e of all elements of following array:

import numpy as np

arr = np.arange(1, 10)

Try it Yourself »

Log at Any Base

NumPy does not provide any function to take log at any base, so we can use the frompyfunc() function along with inbuilt function math.log() with two input parameters and one output parameter:


from math import log
import numpy as np

nplog = np.frompyfunc(log, 2, 1)

print(nplog(100, 15))
Try it Yourself »