interquartile - Learn Data Science with Travis

Travis chat

Filters

interquartile

The interquartile range (IQR) is a statistical measure that represents the middle 50% of a dataset. It is calculated by finding the difference between the third quartile (Q3) and the first quartile (Q1). Quartiles divide a dataset into four equal parts, with Q1 being the median of the lower half and Q3 being the median of the upper half.

To find the IQR using Python, you can utilize libraries such as NumPy or Pandas. For example, you can use the numpy.percentile() function to calculate Q1 and Q3, and then subtract Q1 from Q3 to get the IQR. This measure is particularly useful for identifying outliers and understanding the spread of the data.

If you’re interested in a practical tutorial on how to implement this in Python, there are resources available that guide you through the process step-by-step ¹.

Use Python to Find the InterQuartile Range of a Dataset

Python in Plain English

A tutorial on finding the interquartile range of a dataset using Python.

Quintic Polynomial Roots-With ‘OSOS’ Quads and a Tricky Cubic

Towards AI

This post presents viable Quintic roots solutions, using a confected Y-Axis 180 deg rotation mirror image ‘OSOS’ twin function, with 2 opposing Quadratics to replicate 2 roots then a Cubic concoction…...

Don’t Get the Polywobbles Shifting Quartics? — It’s Simpler With a ‘Designer Ratio’!

Towards AI

Member-only story Don’t Get the Polywobbles Shifting Quartics? — It’s Simpler With a ‘Designer Ratio’! Simplifying Quartic Shifts using a ‘Designer Ratio’ Greg Oliver · Follow Published in Towards AI ...

Quartic Roots-Using Adapted Genetic Quadratics

Towards AI

Member-only story Quartic Roots-Using Adapted Genetic Quadratics Designing Quartics-Simpler With Genetic Quadratics Greg Oliver · Follow Published in Towards AI · 5 min read · Just now -- Share In pre...

An application of the resultant

R-bloggers

I will soon release an update of qspray on CRAN as well as a new package: resultant. This post shows an application of these two packages. Consider the two algebraic curves (f(x,y)=... Continue readin...

Quartic Polynomial Roots - With Quadratic Math and SOSO

Towards AI

This post presents a viable Quartic roots solution, using a confected Y-Axis 180 deg rotation mirror image ‘SOSO’ twin function, with 2 opposing Quadratics to replicate roots. An intuitive play when…

Part III - Intermediate Odds and Ends

Python 101

In Part III, you will learn about some Python internals that many would consider intermediate-level Python. You have graduated from the milk and you’re ready for some meat! In this section, we will t...

Q-Q plot

Analytics Vidhya

Q-Q plot is a graphical method which is used to check whether two different sets of data are related to same theoretical distributions are not . Here one of two sets of data is generated by us…

Polynomial Interpolation

Towards Data Science

Learn everything about Polynomial Interpolation: from Lagrange & Newton Polynomial Interpolation to Cubic Splines. Python examples. Joos Korstanje | Medium

Outlier identification using Interquartile Range

Towards Data Science

Identifying outliers is a very common task in data pre-processing. They can alter the perceived importance of a sample by a model and, if not handled properly, can alter the result of any analysis. A…...

Cubic Roots-Fit a Quadratic Between a Turning and Mid-Point!

Towards AI

Member-only story Cubic Roots-Fit a Quadratic Between a Turning Point And Midpoint! A Root Approximation Tool Kit Mixing and Matching Polynomial Architectures Greg Oliver · Follow Published in Towards...

Interpolation (

SciPy User Guide

Interpolation ( scipy.interpolate ) There are several general facilities available in SciPy for interpolation and smoothing for data in 1, 2, and higher dimensions. The choice of a specific interpolat...