But how can we get the median for a series with an even number of total items? For such a series, we identify the middle two values and take their mean. In the example I gave earlier, the median is 2. Getting the middle value in a series with an odd number of total items is easy. Median is the middle value of a series sorted in ascending order. This is a huge problem, and to fix this we use median and mode. That’s why the mean we got doesn’t represent the number of apples per child accurately.

This means that large numbers in a series can skew the mean towards them. This number increases the whole sum of apples by quite a lot, which in turn increases the mean. One of the children had 239 apples (like the guy in the math questions with 400 watermelons), which is an absurd amount of apples. In a Python list, this would look something like this.Īs you can see, both the methods yield the same result as the manual one 48.8. Suppose you have 5 children, and you give each one of them a few apples. So let’s add in LaTeX and Python to change the mood a bit. The definition above is correct and explains the concept in a very brief and concise manner…but it’s a bit too boring. Mean also called the average, is the total sum of a series divided by the total number of items in that series. These three are brought up all the time and for good reasons.Īnd don’t worry, this article won’t just teach you about Mean, Median, and Mode but also why they are used so much in Data Science and in the related fields, and their implementation in Python. If you’ve done any Data Science, Data Analysis, or Machine Learning project then you’ll know exactly what I am talking about. But even if you didn’t, don’t worry, that’s what I am here for!Įven though these topics are basic, they are very very very very handy in the real world. Everyone learns them in school…so I hope you paid attention back in your school days because it’ll be very relevant in this article. Mean, median, and mode are the most commonly used measures for central tendencies in descriptive statistics.

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