Python

In this tutorial you will learn:

• What is a logarithm?
• Implementation of Log with different bases in Python
• Python Syntax

Logarithm

In order to simplify the calculations, Logarithms were introduced in 1614 by John Napier. Logarithm is inverse to an exponential function. Lets take a number x, its logarithm is the exponent to which another fixed number, the base b must be raised to produce x. The logarithm with base 10 is called as the common logarithm and it has many applications in the domain of engineering and science.

In this tutorial you will learn:

• What is rounding off the decimal?
• Python functions that could round off the decimals?
• Python Syntax

Rounding off the decimals

Rounding off is a simple technique for conversion of float numbers to integers, however, the resultant value will be less accurate (as we remove the part after decimal), but easier to use. The data in type of most of the arrays as a programmer we will be encountering while coding in python will be the integers and floats.

In this tutorial you will learn:

• What are Arithmetic functions?
• How to implement simple arithmetic function?
• Python Syntax

Arithmetic function

Arithmetic operations are the simple operations of addition, subtraction, multiplication and division. The simple arithmetic universal functions in python can take in an array as well as lists and tuples to perform the index wise operations of addition, subtraction, multiplication and division. The simple arithmetic functions can be performed unconditionally and conditionally as well.

In this tutorial you will learn:

• How to create own ufunc?
• Checking types of funcs?
• Python Syntax

Creating a ufunc

NumPy provides programmers a flexibility to create a ufunc by letting the programmers define a ufunc and adding that function in NumPy ufunc library using the inbuilt method “frompyfunc”.

In this tutorial you will learn:

• What is NumPy ufunc?
• Characteristics of ufunc?
• Why we use ufuncs?

NumPy ufunc

NumPy ufunc stands for NumPy universal function. In NumPy library mathematical functions are termed as universal functions and multiple universal functions are provided which could cover a variety of operations. NumPy unfunc is a vectorized wrapper which takes a fixed number of inputs and produce fixed number of output(s).

In this tutorial you will learn:

• What is Zipf Distribution?
• Zipf Distribution Implementation in python
• Visualization of Zipf Distribution

Zipf Distribution

Zipf Distribution is a discrete pareto distribution also known as Riemann zeta distribution. It is specified by probability mass function. Zipf distribution samples the data based on Zipf’s law which refer to the fact that many types of data studied in the physical and social sciences can be approximated with a Zipfian distribution.

In this tutorial you will learn:

• What is Pareto Distribution?
• Pareto Distribution Implementation in python
• Visualization of Pareto Distribution

Pareto Distribution

Pareto Distribution is a continuous power law distribution named after the famous 19th Century economist named Vilfredo Preto. Pareto Distribution is also known as Zipf distribution and as Riemann zeta distribution. It is commonly referred as Pareto Principle, “80-20” rule or Mathew Principle.

In this tutorial you will learn:

• What is Rayleigh Distribution?
• Rayleigh Distribution Implementation in python
• Visualization of Rayleigh Distribution

Rayleigh Distribution

Rayleigh Distribution is a continuous probability distribution named after English Lord Rayleigh. This distribution is for non-negative random variables and it is essentially a chi square distribution with two degrees of freedom. Rayleigh distribution is used to model the wind speeds, wave heights and sound radiations.

In this tutorial you will learn:

• What is Chi Square Distribution?
• Chi Square Distribution Implementation in python
• Visualization of Chi Square Distribution

Chi Square Distribution

Chi Square Distribution (also known as Chi-Squared Distribution) is a continuous distribution formed by taking the sum of squared standard normal deviates, while a standard normal deviates is the random sample from the normal distribution. As in Chi Square Distribution, the values are squared, so the result produced are always positive.