We can more easily identify outliers by using the NumPy sort function np.sort(heights_array). We can then begin to identify where possible errors or anomalies lie. You can get people who are between 120cm and 190cm, but it is unlikely that the smallest measurement of 10cm, or the tallest measurement of 1200cm are accurate.
NumPy: Outliers and Sorting
Sometimes, from the range of a given dataset, we will see elements that are unusually larger or smaller than the other elements. In an array of heights, for example, we may see numbers that are very short, or very tall. These elements are known as outliers.
We can more easily identify outliers by using the NumPy sort function np.sort(heights_array). We can then begin to identify where possible errors or anomalies lie. You can get people who are between 120cm and 190cm, but it is unlikely that the smallest measurement of 10cm, or the tallest measurement of 1200cm are accurate.
We can more easily identify outliers by using the NumPy sort function np.sort(heights_array). We can then begin to identify where possible errors or anomalies lie. You can get people who are between 120cm and 190cm, but it is unlikely that the smallest measurement of 10cm, or the tallest measurement of 1200cm are accurate.
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