Ben: “I’ve been in this business for 19 years. So I’ve had waves of competitors taking in money and I’ve gone through th

Author : jdav
Publish Date : 2021-01-06 06:50:23


Ben: “I’ve been in this business for 19 years. So I’ve had waves of competitors taking in money and I’ve gone through th

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We need to have information on the dispersion of the data. A box and Whisker plot is a graph that gives us a good indication of how the values in the data are spread out. Although box plots may seem primitive in comparison to a histogram or density plot, they have the advantage of taking up less space, which is useful when comparing distributions between many groups or data.

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.

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.

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.

Bar plot has a similar aim to the histogram. It lets us discover and show the underlying frequency distribution of a set of categorical data. As we know that categorical data can not be measured by the mathematics equation, such as multiplication, subtraction, etc but can be counted.

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.

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.

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).

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.

For Ben, a blend of cigarette smoke and hair spray is the smell of business. It’s because his mom used to run a hair salon in their kitchen, and that’s how Ben was exposed to entrepreneurship as a kid.

Could MailChimp have grown even faster if it had taken VC money? Perhaps. But it’s also likely that the corporate-obsessed investors would have gradually chipped away MailChimp’s culture of creativity and innovation, which made the company special in the first place.

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diversifying our results is Maximal Marginal Relevance (MMR). MMR tries to minimize redundancy and maximize the diversity of results in text summarization tasks. Fortunately, a keyword extraction algorithm called EmbedRank has implemented a version of MMR that allows us to use it for diversifying our keywords/keyphrases.

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.

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.



Category : general

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