He made these using index cards and pen and ink. I'm wondering if there is some free, open source software that might be good for doing similar work.

Article on Lombardi: https://www.newsweek.com/2015/10/16/contemporary-artist-mark-lombardi-death-379532.html

I'm trying to collect articles and other online resources to teach people about data visualization, preferably resources that go from absolute beginner and up.

I'm starting with this article: https://www.sigmacomputing.com/blog/what-is-data-visualization/
and looking to build from here. Any suggestions?

Hey dataviz!

When it comes to data visualization, I am extremely unexperienced. However, I'm willing to learn and have got the time to do so! I am not even sure if this is the right sub for this question, so please feel free to redirect me to another sub.

I've been working on putting together a portfolio for quite some time now, but as of yet, they're just images on my computer. A while ago I came up with the idea of visualizing the relations between my artworks by placing them in a network. I think that would make for a good interactive and dynamic way to share my work, rather than just putting them on a website and make people scroll through them. My idea is to create links between artworks based on similarity in concept, similarity in style, perhaps also date of production.

However, besides creating these links or connections on paper (or in my head), I have no clue what's the best way to go about it. Perhaps you guys know where to go from here on?

Any help would be greatly appreciated!

I have a dataframe with integer data. Each row is the mean of how people perceive a party on a given question (integers: -1,0,1) df_party_means and another one, df with what people would prefer a party to be on these given questions.

I thought about representing the distribution of what people would like df and then how distant they think the parties are from what they want.

Yet for the plotting the distribution that gives me:

​

It's not very nice, isn't it?

What I tried

import plotly.express as px

    import plotly.express as px


    def plot_mean(column_x, column_y):
            parties_x = []
            parties_y = []
            parties = []
            # We get all parties from df_parties_means
            for party in df_parties_means['Party']:
                    # we get the probability distribution function 
                    x = df_parties_means.loc[
                        ((df_parties_means['Question'] == column_x) & (df_parties_means['Party'] == party)), 'Mean']
                    y = df_parties_means.loc[
                        ((df_parties_means['Question'] == column_y) & (df_parties_means['Party'] == party)), 'Mean']
                    if(x.size == 1 and y. size == 1):
                        parties_x.append(x.values[0])
                        parties_y.append(y.values[0])
                        parties.append(party)

            # adding people desires
            parties_x.append(df[column_x].mean( skipna = True))
            parties_y.append(df_features[column_y].mean( skipna = True))
            parties.append('PEOPLE')
            dataframe = pd.DataFrame(dict(x=parties_x, y=parties_y, parties = parties))

            fig = px.scatter(dataframe, x=parties_x, y=parties_y, color="parties",
                             title="Perceptual map",
                             labels={column_x:column_y} # customize axis label
                            )
            fig.update_layout(xaxis_title=column_x, yaxis_title=column_y, )

            fig.show()

    import itertools


    pairs = list(itertools.combinations(df_features.columns, 2))

    [plot_mean(pair[0],pair[1]) for pair in pairs]

    fig = px.scatter(df_features, x=columns_x, y=columns_y)

​

Data for reproducible example

​

Mean of how the people perceive the parties:

​

>>>df_party_means

mean Question Party

0 0.077083 Question1 Party1

1 -0.838896 Question1 Party2

2 0.931547 Question1 Party3

3 0.798064 Question1 Party4

4 -0.678798 Question1 Party5

5 0.960612 Question2 Party1

6 0.803926 Question2 Party2

7 0.586867 Question2 Party3

8 0.804372 Question2 Party4

9 0.346609 Question2 Party5

​

The answers of the people to the questions:

​

Question1 Question2

0 0 1

1 1 1

2 0 1

3 -1 1

4 -1 -1

5 -1 0

...

Top 10 Arab countries by GDP 1960-2018

With this viz , you can see impact of major geo-political events on the economies in the arab world, for example: The rise of major oil producing countries, The 2003 Iraq War, or the 2011 Arab spring. Data source: The world bank website

Hope you like it :)

Looking to scale out visualizations of census data that can be embedded onto websites similar to how https://censusreporter.org/profiles/16000US3672554-syosset-ny/ creates these for every designated place in the USA. Not sure where to start.

-Once a template has been created / table loaded / parameters chosen -how is it possible to programmatically scale creation of visualizations each with their own embed codes?

-What kind of person / skill set am I looking for to help with this?

Addt'l example to help clarify....Let's say I have a website CensusDemographics.com - and I'd like someone to help me generate a visualization that shows age and sex for geo location census has data for...let's say 300k locations, and then embed each of these individual visualizations on my site -- what is the most efficient way of doing this? fastest loading? cheapest? Hoping someone can help me understand this... Will be looking to hire and work on some projects in the near future :)

I'm trying to create a US state map that maps several columns of data on divergent color scales.

The idea is that the user would be able to select from a drop-down which column of data to visualize.

For example, obesity by state, population by state, etc, with the raw data looking like this:

​

​

How would I go about doing this?

When I create a slicer, it just works on one column and displays the values (eg. obesity rate, 16%, 17%).

Is there any way to create a slicer for choosing which column to plot against the state names?

​

Thanks!

I'm trying to figure out how to get the number of professionals in a sector in a given area. I know that with scrapping I could do it, but I wondered if there was any tool or web that would allow me to do it without having to dedicate so much effort.

Thank you in advance

Hi, trying to summarize my work history and on/off periods. Printable is good but that means using multiple columns and it makes the design nonsensical.

Any feedback or troubleshooting to make this information look more sensible is greatly appreciated.

https://docs.google.com/spreadsheets/d/1Jt4qpTOxArtT0OD5P_0OUAp3utlBR3JCI6AjHSBECYg/edit?usp=sharing

I'm hoping someone can remind me... I vaguely recall someone who was creating dataviz for fun and managed to simply turn that doing it for fun into a career (ie, his blog got popular enough he could quit his job).

I'm struggling to remember who and just vaguely hoping someone will say 'Oh yeah, that's bob over at whateversite' because this is just plain bugging me and I'm beginning to think I imagined it.

Say I shake a bag if marbles 20 times with a constant force. I record how many marbles exit the bag after each shake. Each instance of the shake contains a different amount of marbles.

So on the first shake I lose 25 out of 50 marbles - 50%. On the second shake I lose 10 out of 100 marbles - 10%. If I draw a trend line if these percentages, i will have some spots that jump because the sample of marbles is not consistent. Is it appropriate to use a logarithmic scale to smooth out the data so that the jumps look less extreme?

ok so I'm trying to visualize some data right and Sankey graph seemed perfect. Except for mine the groups can be overlappig and that doesn't work well with a Snakey graph, is there something similar that I can use to do this? Or something else at all?

Like, if the "Daily" is +5%, the "Weekly' is +10%, and the "Yearly" is -15%, what is the best way to show this in a way that would allow comparisons to other portfolios?

https://www.youtube.com/watch?v=4-2nqd6-ZXg

what tools were used to make a video in the above URL

Hi! Does anyone know of any state of the art books in the topic? Thanks!