data binning

Data Preprocessing with Python Pandas — Part 5 Binning

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Data binning (or bucketing) groups data in bins (or buckets), in the sense that it replaces values contained into a small interval with a single representative value for that interval. Sometimes…

Spatial Binning with Google BigQuery

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Data binning is a useful common practice in Data Science and Data Analysis in several ways: discretization of a continuous variable in Machine Learning or simply making a histogram for ease of…

Data Binning with Pandas Cut or Qcut Method

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Binning the data can be a very useful strategy while dealing with numeric data to understand certain trends. Sometimes, we may need an age range, not the exact age, a profit margin not profit, a…

Binning Records on a Continuous Variable with Pandas Cut and QCut

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Today, I’ll be using the “City of Seattle Wages: Comparison by Gender –Wage Progression Job Titles” data set to explore binning — aka grouping records — along a single numeric variable. Find the data…...

Is Binning in Data Analysis a Good Idea?

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Data analysis is a very important part of the data scientist’s job. Because I am not actually employed by a company as a data scientist, I must acquire my skills by taking courses or entering…

All Pandas qcut() you should know for binning numerical data based on sample quantiles

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Numerical data is common in data analysis. Often you have numerical data that is continuous, very large scales, or highly skewed. Sometimes, it can be easier to bin those data into discrete…

A Beginner’s Guide to Converting Numerical Data to Categorical: Binning and Binarization

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That’s exactly what converting numerical data into categorical data can do for you! In today’s post, we’ll dive into two game-changing techniques: Binning and Binarization , perfect for scenarios like...

The Role of Data Blending and Data Munging in the Data Science Process

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Data science is a multidisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. The lifecycle of...

Data Scientists: STOP Randomly Binning Histograms

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Histograms are a crucial part of Exploratory Data Analysis. But we often abuse them by randomly choosing a number of bins. Let’s use math.

Databaiting

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Databaiting: to entice someone to submit their data by eliciting an emotional response. Is it a useful description?

Generating binary data by specifying the relative risk, with simulations

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The most traditional approach for analyzing binary outcome data is logistic regression, where the estimated parameters are interpreted as log odds ratios or, if exponentiated, as odds ratios (ORs). No...

Group data using bins and categories with pandas

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Today I’d like to show you how to bin discrete (integer) and continuous (float) data with custom intervals in pandas. Added to that, I will also show you how panda’s Categoricals can handle…