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Cleanse Outlying Data Using the pandas Python Package

In this lab, we will load a CSV file into a pandas DataFrame. Once loaded, we will remove rows with an `age` more than 3 standard deviations from the mean and rows with `hours-per-week` below the 10% and above the 90% quantiles. We will then write the cleansed data to a file. Basic Python programming skills will be required for this lab. If you need a refresher, check out the following course: - [Certified Associate in Python Programming Certification](https://acloud.guru/overview/8169e8e7-91a7-4d92-b278-4dd08c787dc6)

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Path Info

Level
Clock icon Intermediate
Duration
Clock icon 30m
Last updated
Clock icon Sep 01, 2025

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Table of Contents

  1. Challenge

    Load the Data File

    Load the data.csv file into a pandas DataFrame.

  2. Challenge

    Resolve Outlying age Values

    Remove rows with an age more than 3 standard deviations from the mean.

  3. Challenge

    Resolve Outlying hours-per-week Values

    Remove rows with hours-per-week below the 10% and above the 90% quantiles.

  4. Challenge

    Write the Data to a New File

    Write the data to a new file named cleaned_data.csv.

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Pluralsight Skills gives leaders confidence they have the skills needed to execute technology strategy. Technology teams can benchmark expertise across roles, speed up release cycles and build reliable, secure products. By leveraging our expert content, skill assessments and one-of-a-kind analytics, keep up with the pace of change, put the right people on the right projects and boost productivity. It's the most effective path to developing tech skills at scale.

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