1. 1

    A common way to communicate a high-level overview of a dataset is to find the values that split the data into four groups of equal size. By doing this, we can then say whether a new datapoint fall…

  2. 2

    We’ll come back to the music dataset in a bit, but let’s first practice on a small dataset. Let’s begin by finding the second quartile (Q2). Q2 happens to be exactly the median . Half of the dat…

  3. 3

    Now that we’ve found Q2, we can use that value to help us find Q1 and Q3. Recall our demo dataset: […] In this example, Q2 is […] . To find Q1, we take all of the data points smaller than Q…

  4. 4

    You just learned a commonly used method to calculate the quartiles of a dataset. However, there is another method that is equally accepted that results in different values! Note that there is no …

  5. 5

    We were able to find quartiles manually by looking at the dataset and finding the correct division points. But that gets much harder when the dataset starts to get bigger. Luckily, there is a funct…

  6. 6

    Great work! You now know how to calculate the quartiles of any dataset by hand and with NumPy. Quartiles are some of the most commonly used descriptive statistics. For example, You might see scho…

  1. 1

    Quantiles are points that split a dataset into groups of equal size. For example, let’s say you just took a test and wanted to know whether you’re in the top 10% of the class. One way to determine …

  2. 2

    The NumPy library has a function named […] that will quickly calculate the quantiles of a dataset for you. […] takes two parameters. The first is the dataset that you are using. The second…

  3. 3

    In the last exercise, we found a single “quantile” — we split the first 23% of the data away from the remaining 77%. However, quantiles are usually a set of values that split the data into g…

  4. 4

    One of the most common quantiles is the 2-quantile. This value splits the data into two groups of equal size. Half the data will be above this value, and half the data will be below it. This is als…

  5. 5

    Nice work! Here are some of the major takeaways about quantiles: Quantiles are values that split a dataset into groups of equal size. If you have […] quantiles, the dataset will be split i…

  1. 1

    One of the most common statistics to describe a dataset is the range. The range of a dataset is the difference between the maximum and minimum values. While this descriptive statistic is a good s…

  2. 2

    The interquartile range is the difference between the third quartile (Q3) and the first quartile (Q1). If you need a refresher on quartiles, you can take a look at our lesson . For now, all you …

  3. 3

    In the last exercise, we calculated the IQR by finding the quartiles using NumPy and finding the difference ourselves. The SciPy library has a function that can calculate the IQR all in one step. …

  4. 4

    Nice work! You can now calculate the Interquartile Range of a dataset by using the SciPy library. The main takeaway of the IQR is that it is a statistic, like the range, that helps describe the spr…

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