Data Science Jumpstart with 10 Projects Transcripts
Chapter: Project 5: Cleaning Heart Disease Data in Pandas
Lecture: Converting the Numeric Sex Column into a String
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0:00
In this section, I'm going to show you how to clean up the sex column. First of all, let's just look at the sex column.
0:06
You'll note that I am keeping that previous conversion from age in there. I'm going to build up a chain.
0:12
I'm going to pull off the sex column and then look at the value counts. Again, if you want to walk through this chain or recipe,
0:19
here's the original data frame. I'm converting age to an int8. That doesn't change anything visibly here. I'm going to plot the sex column.
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This is ones and zeros. Let's just validate that and we see that it is ones and zeros.
0:32
What I want to do is I want to replace one with male and zero with female. Let's try and run that. When I do that, Arrow isn't very happy.
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Arrow doesn't like to change the type under the covers for us. This would be going from an integer column to a string column.
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Arrow's not very happy about that. Instead, what I'm going to do is I'm first going to change it to a string type
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and then I'm going to replace those values with male and female. Let's run that and see if that works. It looks like it did work.
1:02
Let's just validate that by doing value counts and that looks like that worked. We showed how to convert the sex type.
1:09
We had to convert the numbers to strings and then we can convert the string versions of the numbers into the strings that we want them to be.