Data cleaning in Pandas
In the real world, data is messy and incomplete. That’s why it’s so important to know how to clean your data. In this class, I introduce a number of techniques you can use to turn bad data into better, cleaner data.
What you should know
Course Content
Course length
2 hours
Number of lessons
1
Training materials
1 Jupyter notebook
Coding exercises
3
About the Course
The world is a messy place, and a lot of the data we have to deal with is messy, too. That’s why cleaning our data is so crucial; data scientists report they spend 80 percent (!) of their time on this task. In this webinar, we’ll review techniques for cleaning your data, making it easier to query, manipulate, and work with.
What you’ll learn
- Why clean data?
- Setting dtypes
- Selecting columns
- Removing NaN values
- Replacing NaN values
- Interpolation
- Replacing values
Instructor
This course, like all others on LernerPython.com, is taught by Reuven Lerner.
Pricing
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LernerPython
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- Unlimited access to all Python, Git, Regexp courses
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LernerPython + Data
$500
per user / year
- Everything in Lerner Python Level
- Unlimited access to NumPy, Pandas, and SQL courses
- Pandas office hours and private lectures
- Bamboo Weekly Subscription ($100 Annual Value)