- A Cloud Guru
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)
Table of Contents
Load the Data File
data.csvfile into a pandas DataFrame.
Resolve Outlying age Values
Remove rows with an
agemore than 3 standard deviations from the mean.
Resolve Outlying hours-per-week Values
Remove rows with
hours-per-weekbelow the 10% and above the 90% quantiles.
Write the Data to a New File
Write the data to a new file named
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