this is a more general question asking about Python libraries that are able to showcase networks with following criterias: Different size of the nodes depending on a metric (e.g. sum of some variable X received by all other nodes) Direction of the connection between the nodes (e.g. Node A sends X units to Node B, while Node B sends Y units to Node A and Q units to Node C) - So a node can receive and send at the …

I really like how this visualization represents the survey participants. Is any tool for that? (Or R/python library?)

If I had Data that looked like this: Path 1 - Green, Yellow, Orange Path 2 - Green, Purple, Blue, Maroon Path 3 - Green, Blue, Maroon, Red Path 4 - Green, Cyan, Grey, Magenta It could be visualized to look like this: Each path could be a particular learning journey (degree, certification, etc) and each color could be a topic that must be learned in persuit of that learning journey. Multiple learning journies can touch on the same concepts, …

I've collected bus arrival times at my local bus stop from the past month - so I have every time my bus (a specific bus number) shows up at my bus stop for each day of the week (Monday, Tuesday, etc.). I am struggling at determining the best and clearest way to display this data. Eventually I want to use a clustering algorithm to help understand the most likely time the bus will show up. So on a Monday I …

I need to create this type of chart, where you can see a quantity is split into a hierarchy of classes or taxonomy. In the example below the quantity is a household monthly income, and the classes are different monthly expenditures, classified broadly to the left, and progressively split into more detailed sub-classes. However, I don't know what it is called and thus cannot find which tool to use.

Suppose you have a big number of polynomials such as xyz+xy+z where xyz, xy and z are monomials. You wanna visualize the polynomials together something such as atlas below and show how they are related graphically. The example is from here. The edges could correspond to sum while the vertices could be monomials. It is also possible to do it so that each variable in monomial has their own vertex while two different kinds of edges are drawn: edges inside …

I'm plotting fluctuations in a trinomial distribution across two dimensions. colorspace_plot <- function(zz) { zz$z2 <- zz$x * zz$y zz$z3 <- zz$x * (1-zz$y) zz <- cbind(zz, t(apply(zz[,c("z1","z2","z3")], 1, function(x) x/sum(x)) )) setnames(zz, c("x", "y", "z1", "z2", "z3", "p1", "p2", "p3")) zz$pcolor <- with(zz, rgb(p1, p2, p3, 1)) pcolors <- zz$pcolor names(pcolors) <- pcolors return( ggplot(zz, aes(x,y)) + geom_bin2d(aes(fill=pcolor), binwidth=1/11) + scale_fill_manual(values = pcolors ) + guides(fill="none") ) } clear <- data.frame(x=rep(seq(from=0, to=1,length.out=11),times=11), y=rep(seq(from=0, to=1,length.out=11),each=11), z1=rep(1/3,11*11)) colorspace_plot(clear) I don't know …

I'm struggling to find a solution to produce a line chart which shows a trend from a Time Series metric. I try to make my line chart look similar to the following: Basically, I want to make use of relative change rates, but I struggle to find a calculation which makes a visualization as seen above possible. The graph should always be relative to a specific time window, meaning that I can query my data dynamically with a start and …

I have a visualization problem. Creating a comparison report of PR event efficiency. Say, show or exhibition. There are two dimensions of comparison: compare vs the same event performance in the past years compare vs another type of analogical/competitive events There is also a number of comparison aspects: Audience Media Coverage Social Buzz ROI .... etc Each aspect is a set of some final KPI-s (just numbers, which can be compared vs another "dimensions"), plus maybe some descriptive text and …