Finding the Correlation
Finally, let's move to the last method of this section called .corr(). It helps out a lot to find the relationship between numerical data. Imagine that you have a dataset on houses:
Let's examine the output of the data.corr() in our case:
So, let's do it step by step: You have vertical and horizontal values; each pair overlaps. In each overlap, we can receive a value from -1 to 1.
- 1 means that two values depend on each other in a directly proportional way (if one value increases, the other increases too);
- -1 means that two values depend on each other in an inversely proportional way (if one value increases, the other decreases);
- 0 means that the two dependent values aren't proportional.
If the dataset contains non-numeric columns, such as in the cars.csv dataset used in the task, you should set the argument numeric_only=True to compute the correlation using only the numeric columns.
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You'll end this section with an effortless task: apply the .corr() function to the dataset. Then, try to analyze the numbers you get.
Solution
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Completion rate improved to 3.03Finding the Correlation
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Finally, let's move to the last method of this section called .corr(). It helps out a lot to find the relationship between numerical data. Imagine that you have a dataset on houses:
Let's examine the output of the data.corr() in our case:
So, let's do it step by step: You have vertical and horizontal values; each pair overlaps. In each overlap, we can receive a value from -1 to 1.
- 1 means that two values depend on each other in a directly proportional way (if one value increases, the other increases too);
- -1 means that two values depend on each other in an inversely proportional way (if one value increases, the other decreases);
- 0 means that the two dependent values aren't proportional.
If the dataset contains non-numeric columns, such as in the cars.csv dataset used in the task, you should set the argument numeric_only=True to compute the correlation using only the numeric columns.
Swipe to start coding
You'll end this section with an effortless task: apply the .corr() function to the dataset. Then, try to analyze the numbers you get.
Solution
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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