Navigate Data Wrangling

As data analysts, we might wish for clean, consistent, and ready-to-explore datasets. But more often than not, we face a very different reality. Columns may be misnamed or missing, values duplicated or out of range, formats inconsistent across files, and data scattered across various files and sources.

Before we can explore trends, create visualizations, or run any analysis, we need to make sense of the chaos in our data. This is where data wrangling comes in. It’s the essential process of turning raw, messy data into clean, structured, and usable information.

Data wrangling

Data wrangling is the process of cleaning, structuring, and enriching raw data into a desired format for better decision-making. It involves taking messy, unorganized, or incomplete datasets and transforming them into something usable. Whether we are removing duplicate entries, fixing incorrect values, merging datasets, or creating new calculated fields, all of it falls under the broad umbrella of data wrangling.

The goal of data wrangling isn’t just about cleaning up data for the sake of neatness. It’s about preparing data so that we can extract reliable insights, build meaningful charts, and support sound decision-making.

Data wrangling workflow

Think of the data wrangling workflow as a reliable guide for preparing any dataset, from disorganized data to analysis-ready. Let’s break it down into a clear, step-by-step process:

Each step builds on the last, and skipping any one can cause problems later on. Let’s explore what happens at each stage and why it matters.

1. Ingest: Collecting the raw materials

Before we can clean or analyze anything, we need to gather raw data, and that’s what ingestion is all about. This step brings data together from different sources into one place, where we can begin working with it.

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