Python

The Zipf distribution, also sometimes referred to as the zeta distribution, is a discrete probability distribution used to model scenarios where the frequency of an item is inversely proportional to its rank. It’s implemented in NumPy using the np.random.zipf function. Here’s a breakdown of the key characteristics and its application in NumPy: Core Idea (Zipf’s…

The Pareto distribution, also known as the Pareto II or Lomax distribution, is a continuous probability distribution used to model situations where few events have large values, and many events have smaller values. or A distribution following Pareto’s law i.e. 80-20 distribution (20% factors cause 80% outcome). It’s implemented in NumPy using the np.random.pareto function.…

The Rayleigh distribution is a probability distribution used to model the magnitudes of random vectors whose components follow independent Gaussian (normal) distributions. It’s relevant in various fields like signal processing, physics, and oceanography. NumPy provides the np.random.rayleigh function to generate random samples from this distribution. Here’s a detailed breakdown of the Rayleigh distribution and its…

The chi-square distribution is a fundamental probability distribution used in hypothesis testing, particularly in chi-square tests. It’s implemented in Python’s NumPy library using the np.random.chisquare function. Properties: Applications: Generating Random Samples with np.random.chisquare: Parameters: Important Note: While np.random.chisquare generates samples from the chi-square distribution, it’s essential to use this within the context of chi-square tests…

The exponential distribution is a probability distribution used to model the time between events that happen randomly and independently at a constant rate. It’s implemented in Python’s NumPy library using the np.random.exponential function. Here’s a breakdown of the key points: Properties: Applications: Generating Random Samples with np.random.exponential: Output Parameters:

The multinomial distribution in Python’s NumPy library is represented by the np.random.multinomial function. It’s used to generate random samples that represent the number of times events from several categories occur in a fixed number of trials. Here’s a detailed explanation: Concept: Generalizes the binomial distribution (two possible outcomes) to scenarios with multiple (>2) possible categories.Models…

The logistic distribution, also known as the sigmoid distribution, is a continuous probability distribution in Python’s NumPy library. Logistic Distribution is used to describe growth. Used extensively in machine learning in logistic regression, neural networks etc. It’s modeled by the numpy.random.logistic function and is useful for representing S-shaped curves where the probability of an event…

The uniform distribution is represented by the numpy.random.uniform function. It’s used to generate random numbers that are uniformly distributed over a specified interval. Used to describe probability where every event has equal chances of occuring. Here’s a breakdown of what it does: Functionality: Generates random floating-point numbers.Numbers are distributed uniformly across a half-open interval [low,…

Difference Between Binomial and Poisson Distribution BINOMIAL POISSON Number of Trials and Outcomes Fixed number of trials (n) with exactly two possible outcomes (often denoted as success and failure).Examples: Flipping a coin 5 times (n=5) with heads (success) and tails (failure) as outcomes. Focuses on events occurring in a fixed interval (like time or space)…

Poisson Distribution The Poisson distribution is a discrete probability distribution that models the number of events occurring in a fixed interval of time or space, with a known average occurrence rate. It’s useful for situations where events happen independently and at a constant rate. In Python’s NumPy library, you can generate random samples from a…

Binomial Distribution describes the outcome of binary scenarios, e.g. toss of a coin, it will either be head or tails. Binomial Distribution is a Discrete Distribution. binomial() method in NumPy Python has three parameters: n – number of trials. p – probability of occurence of each trial (e.g. for toss of a coin 0.5 each).…

NORMAL OR GAUSSIAN DISTRIBUTION The random.normal() method is used to get a Normal Data Distribution. It has three parameters- loc – (Mean) where the peak of the bell exists. scale – (Standard Deviation) how flat the graph distribution should be. size – The shape of the returned array. Output- Explanation– random.normal(size=(2, 2)) – It will…

Seaborn is an open-source Python library built on top of matplotlib. It is used for data visualization and exploratory data analysis. Seaborn is commonly used for data science and machine learning task. How to install Seaborn? We can install Seaborn using following command, python and pip installation on the machine is pre required. Importing Matplotlib…

Random Permutations Random Permutations means arrangement of the elements of array in different ways. The shuffle() and permutation() methods from random module of python’s numpy library is used to generate permutations of arrays. Shuffling Arrays Shuffling means changing arrangement of elements in the array itself. The shuffle() method is used to shuffle the elements of…

What is Data Distribution? Data Distribution is a list of all possible values, and how often each value occurs or their relative frequency. The methods available in random module of numpy library is used to get the randomly generated data distributions. Random Distribution A random distribution is a set of random numbers that follow a…

Generating random no.- The random module of numpy library is used to generate random nos. in python. Output- 5 It will generate a random integer from 0 to 10. Generating random float- The rand() method returns a random float between 0 and 1. Output- Generating random array- The randint() method takes a size parameter where…

We can filter an array using boolean index list, which is a list of booleans corresponding to indexes in the array. If the value at an index is True that element is contained in the filtered array, if the value at that index is False that element is excluded from the filtered array. Output- The…

The sort() method is used to sort the array in an order, it returns a copy of the array, leaving the original array unchanged. Output-

The where() method is used to search the certain value in an array and return the indexes that get a match. Output- Search Sorted- The searchsorted() method performs a binary search in the sorted array, and returns the index where the specified value would be inserted to maintain the search order. Output- Explanation- np.searchsorted(arr, 3)…

The concatenate() method is used to join the numpy arrays by passing sequence of arrays along with the axis and in case if axis is not passed it will consider it as 0. Output- Joining Arrays Using Stack Functions- The stack() method is used for joining two arrays and stacking is same as concatenation, the…