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Cleaning Data in Python

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

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4 Hours13 Videos44 Exercises60,042 Learners3500 XPData Analyst TrackData Scientist TrackImporting & Cleaning Data Track

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Course Description

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. The time spent cleaning is vital since analyzing dirty data can lead you to draw inaccurate conclusions. Data cleaning is an essential task in data science. Without properly cleaned data, the results of any data analysis or machine learning model could be inaccurate. In this course, you will learn how to identify, diagnose, and treat a variety of data cleaning problems in Python, ranging from simple to advanced. You will deal with improper data types, check that your data is in the correct range, handle missing data, perform record linkage, and more!

  1. 1

    Common data problems


    In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

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    Data type constraints
    50 xp
    Common data types
    100 xp
    Numeric data or ... ?
    100 xp
    Summing strings and concatenating numbers
    100 xp
    Data range constraints
    50 xp
    Tire size constraints
    100 xp
    Back to the future
    100 xp
    Uniqueness constraints
    50 xp
    How big is your subset?
    50 xp
    Finding duplicates
    100 xp
    Treating duplicates
    100 xp
  2. 2

    Text and categorical data problems

    Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

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  3. 3

    Advanced data problems

    In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.

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  4. 4

    Record linkage

    Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.

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In the following tracks

Data Analyst Data Scientist Importing & Cleaning Data


richieRichie CottonmaggiematsuiMaggie Matsuiamy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson
Adel Nehme Headshot

Adel Nehme

Content Developer @ DataCamp

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.
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I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA