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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)
Lab Info
Table of Contents
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Challenge
Load the Data File
Load the
missing-data.csvfile into a pandas DataFrame, and count the number of missing values. -
Challenge
Drop Any Columns That Are Missing All Values
Make sure to drop only columns that are missing all values.
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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.
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Challenge
Write the Data to a New File
Write the data to a new file named
cleaned_data.csv.
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