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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](

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Path Info

Clock icon Intermediate
Clock icon 30m
Clock icon Jun 04, 2021

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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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