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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
Published
Clock icon Jun 04, 2021

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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.

A Cloud Guru - Labs - Cleanse Outlying Data Using the pandas Python Package
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A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

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