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Cleanse Missing Data Using the pandas Python Package

In this lab, we will load a CSV file into a pandas DataFrame. Once loaded, we will count the number of missing values in the file. Next, we will drop any columns that are missing all values, and replace any remaining missing values. 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 missing-data.csv file into a pandas DataFrame, and count the number of missing values.

  2. Challenge

    Drop Any Columns That Are Missing All Values

    Make sure to drop only columns that are missing all values.

  3. Challenge

    Replace Remaining Missing Values with the Last Valid Observed Value of That Column

    This will leave some missing values at the beginning, as there was no last valid observed value.

  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

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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