Gain clarity on percentiles and their practical significance in data analysis. Learn how NumPy simplifies percentile calculations to efficiently interpret data distributions.
Key Insights
- Percentile indicates the percentage of values in a dataset that fall below a certain value, making it a useful measure for understanding data distribution.
- NumPy provides a straightforward method for calculating percentiles: the
percentilemethod, which easily computes percentile values from a set of numerical data. - The example demonstrates the calculation of the 75th and 25th percentiles, which indicate that 75% of the sample degrees are below 90.5 and 25% are below 71.
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Let's talk about percentile. Percentile is a fairly simple measurement. It's the percent of values in a collection that are lower than a given number.
If we want to calculate, give me the number that most degrees are less than, 75%. We could say NumPy, NumPy has our back on this. NumPy has a percentile method.
And when you pass the list that we want it to analyze and give it a percentile number. Let's set that to a variable. Percentile 75.
Makes sense to me. And print it. I run that 90.5. So that means in our degrees, let's just take a look at our degrees again.
We can remember what these values are. Okay, 75% of them are below 90.5. So 91 or lower, or sorry, 89 or lower. Nope, 90 or lower.
There we go. So 25% of them are above there. So how about the 25th percentile? Ones that are below that value.
We can take a look at the same measurement degrees, 25. Percentiles are very useful when you're trying to understand a dataset. What is the general distribution of it? So 71, 75% of them are under 71.