Now numpy provides easy functions for finding central tendency – you can find them in my Numpy Part II lesson: Python: Numpy Part II.
But we have learned enough about Python so far, that maybe it would be more fun to build our own functions. In this lesson we are going to build our own statistics library with mean, median, mode, and quantile
or even easier:
Remember with median – if your data contains an odd number of elements (n%2==1), you just take the middle value. However, if your data contains and even number of elements (n%2==0) then you add the two middle numbers and divide by 2.
We handle that through the use of an if statement in our function.
For mode, we want to find the most common element in the list. For this task, I will import Counter from collections.
d.most_common() returns each unique element and the number of times it appears
d.most_common(1) returns the
or in function form:
In my numpy part II lesson I use a more elegant solution, but I want you to see that there is always more than one way to do something in Python.
Quantiles are cut points in set of data. They can represent the bottom ten percent of the data or the top 75% or any % from 0 to 100.
In my solution, I am adding a slicing argument to the sorted() function. Below shows all elements up to (but not including) index 4
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