Bar plot has a similar aim to the histogram. It lets us discover and show the underlying frequency distribution of a set

Consider what happens if an object A overrides the hierarchy by interacting with another object B. It doesn’t matter what relationship A has with B, except that B is not the direct parent class. Then A must contain a private reference to B, because otherwise, it couldn’t interact.

Basically, there are a lot of data visualization types, such as bar plot, histogram, time series plot, pie chart, etc. You can easily find out the catalogue of data visualization HERE. In this tutorial, we will create 8 types of data viz using plotnine package.

After exploring the raw dataset, it’s time to merge those into one cleaned data to visualize. We conduct the left join with the athlete event as the left table and NOC column as the index level names to join on. It produces the cleaned data with 271,116 rows and 17 columns.

An area chart is an extension of a line graph, where the area under the line is filled in. While a line graph measures change between points, an area chart emphasizes the data volume.

I feel appreciative of my family and parents for helping me dream and also pushing me to see that I needed to make changes to be happy. And finally, I am thankful for a wife who encouraged me to follow my dream in 2011 and supported me throughout. In the best of times and the worst of times, she has only ever used CircleUp as a positive example for our (now) three kids — to chase their dreams. And most of all I feel proud of my daughter for speaking her mind.

Thank you to everyone who contributed to this post including Ed Batista, Emma Stubbs, Grace Gellman, Henry Davis, Jordan Brenner, Kate Doerksen, Kieran Snyder, Maddy Allen, Sam Hinkie and Will Quist.

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es, hobbies… All this has been flushed down the drain for the past… at least 5 years. So I do intend on reconnecting with friends, because I believe one thing: true friends will always stay, even if you get back to them after years of being off the radar. And for all the other ones that can’t care enough, well, screw them. I can’t care enough either.”

Plotnine is the implementation of the R package ggplot2 in Python. It replicates the syntax of R package ggplot2 and visualizes the data with the concept of the grammar of graphics. It creates a visualization based on the abstraction of layers. When we are making a bar plot, we will build the background layer, then the main layer of the bar plot, the layer that contains title and subtitle, and etc. It is like when we are working with Adobe Photoshop. The plotnine package is built on top of Matplotlib and interacts well with Pandas. If you are familiar with the ggplot2, it can be your choice to hand-on with plotnine. The template of ggplot2 is as follows.

To discuss and practise with the plotnine package, we are using the Olympics data 1896–2016. It’s a cleaned dataset but has several columns with different scale measurement, so theoretically we can create a lot of data visualization and insights from the data.

You can directly download the data from the dropbox HERE. The file contains two datasets, athlete event and noc region. The athlete event is a record of all athlete, their characteristics, health information and citizenship, and medal acquisition from 1896–2016. Whereas, the noc region records the National Olympic Committee (NOC).

ggplot(data = ) ( mapping = aes(), stat = , position = )

A histogram is the most commonly used graph to show frequency distributions. It lets us discover and show the underlying frequency distribution of a set of numerical data. To construct a histogram from numerical data, we first need to split the data into intervals, called bins.

There’s just one problem. The whole point of encapsulation is to keep pieces of data safe from one another and thus make computing more efficient. This doesn’t work without strict hierarchies.

Our first task, of course, to import the data into our Jupyter Notebook and explore the values from columns. The athlete event dataset has 271,116 records or rows and 15 columns or attributes.

Thank you to everyone who contributed to this post including Ed Batista, Emma Stubbs, Grace Gellman, Henry Davis, Jordan Brenner, Kate Doerksen, Kieran Snyder, Maddy Allen, Sam Hinkie and Will Quist.