Skip to main content
HomePythonCleaning Data in Python

Cleaning Data in Python

4.3+
50 reviews
Intermediate

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

Start Course for Free
4 Hours13 Videos44 Exercises
105,352 LearnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
GroupTraining 2 or more people?Try DataCamp For Business

Loved by learners at thousands of companies


Course Description

Discover How to Clean Data in Python

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. Data cleaning is an essential step for every data scientist, as analyzing dirty data can lead to inaccurate conclusions.

In this course, you will learn how to identify, diagnose, and treat various 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!

Learn How to Clean Different Data Types

The first chapter of the course explores common data problems and how you can fix them. You will first understand basic data types and how to deal with them individually. After, you'll apply range constraints and remove duplicated data points.

The last chapter explores record linkage, a powerful tool to merge multiple datasets. You'll learn how to link records by calculating the similarity between strings. Finally, you'll use your new skills to join two restaurant review datasets into one clean master dataset.

Gain Confidence in Cleaning Data

By the end of the course, you will gain the confidence to clean data from various types and use record linkage to merge multiple datasets. Cleaning data is an essential skill for data scientists. If you want to learn more about cleaning data in Python and its applications, check out the following tracks: Data Scientist with Python and Importing & Cleaning Data with Python.
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.
  1. 1

    Common data problems

    Free

    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.

    Play Chapter Now
    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.

    Play Chapter Now
  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.

    Play Chapter Now
  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.

    Play Chapter Now

In the following tracks

Associate Data Scientist in PythonData Engineer in PythonImporting & Cleaning Data with Python

Collaborators

Collaborator's avatar
Maggie Matsui
Collaborator's avatar
Richie Cotton
Collaborator's avatar
Amy Peterson
Adel Nehme HeadshotAdel Nehme

VP of Media, 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.
See More

Don’t just take our word for it

*4.3
from 50 reviews
64%
22%
4%
8%
2%
Sort by
  • Jose H.
    6 months

    Los videos son muy amenos de ver, con ejemplos claros y ejercicios bien elegidos

  • Josue U.
    6 months

    Interesting course, it brings new knowledge to me.

  • Ankush B.
    6 months

    Well organised course explaining the concepts used for cleaning data with Python.

  • Edwin A.
    7 months

    I thought this is a recommended course for those who want to learn about cleaning data in Python

  • jean-jacques s.
    9 months

    It is more that I wanted to know!

"Los videos son muy amenos de ver, con ejemplos claros y ejercicios bien elegidos"

Jose H.

"Interesting course, it brings new knowledge to me."

Josue U.

"Well organised course explaining the concepts used for cleaning data with Python."

Ankush B.

FAQs

Join over 13 million learners and start Cleaning Data in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.