Calculating Mean, Median and Mode in Python

Introduction

When we’re trying to describe and summarize a sample of data, we probably start by finding the mean (or average), the median, and the mode of the data. These are central tendency measures and are often our first look at a dataset.

In this tutorial, we’ll learn how to find or compute the mean, the median, and the mode in Python. We’ll first code a Python function for each measure followed by using Python’s statistics module to accomplish the same task.

With this knowledge, we’ll be able to take a quick look at our datasets and get an idea of the general tendency of data.

Calculating the Mean of a Sample

If we have a sample of numeric values, then its mean or the average is the total sum of the values (or observations) divided by the number of values.

Say we have the sample [4, 8, 6, 5, 3, 2, 8, 9, 2, 5]. We can calculate its mean by performing the operation:

(4 + 8 + 6 + 5 + 3 + 2 + 8 + 9 + 2 + 5) / 10 = 5.2

The mean (arithmetic mean) is a general description of our data. Suppose you buy 10 pounds of tomatoes. When you count the tomatoes at home, you get 25 tomatoes. In this case, you can say that the average weight of a tomato is 0.4 pounds. That would be a good description of your tomatoes.

The mean can also be a poor description of a sample of data. Say you’re analyzing a group of dogs. If you take the cumulated weight of all dogs and divide it by the number of dogs, then that would probably be a poor description of the weight of an individual dog as different breeds of dogs can have vastly different sizes and weights.

How good or how bad the mean describes a sample depends on how spread the data is. In the case of tomatoes, they’re almost the same weight each and the mean is a good description of them. In the case of dogs, there is no topical dog. They can range from a tiny Chihuahua to a giant German Mastiff. So, the mean by itself isn’t a good description in this case.

Now it’s time to get into action and learn how we can calculate the mean using Python.

Calculating the Mean With Python

To calculate the mean of a sample of numeric data, we’ll use two of Python’s built-in functions. One to calculate the total sum of the values and another to calculate the length of the sample.

The first function is sum(). This built-in function takes an iterable of numeric values and returns their total sum.

The second function is len(). This built-in function returns the length of an object. len() can take sequences (string, bytes, tuple, list, or range) or collections (dictionary, set, or frozen set) as an argument.

Here’s how we can calculate the mean:

>>> def my_mean(sample):
...     return sum(sample) / len(sample)
...

>>> my_mean([4, 8, 6, 5, 3, 2, 8, 9, 2, 5])
5.2

We first sum the values in sample using sum(). Then, we divide that sum by the length of sample, which is the resulting value of len(sample).

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Calculating Mean, Median and Mode in Python
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2) Reversing a string in Python.

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Mean, median, and mode are fundamental topics of statistics. You can easily calculate them in Python, with and without the use of external libraries.

These three are the main measures of central tendency. The central tendency lets us know the “normal” or “average” values of a dataset. If you’re just starting with data science, this is the right tutorial for you.

Mean, median, mode the three measurements of central tendency

By the end of this tutorial you’ll:

  • Understand the concept of mean, median, and mode
  • Be able to create your own mean, median, and mode functions in Python
  • Make use of Python’s statistics module to quickstart the use of these measurements

If you want a downloadable version of the following exercises, feel free to check out the GitHub repository.

Let’s get into the different ways to calculate mean, median, and mode.

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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

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Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

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Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
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>>> print(FirstName, LastName)
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