data-visualization

I am trying to plot a histogram using the matplotlib.hist() function but I am not sure how to do it. I have a list probability = [0.3602150537634409, 0.42028985507246375, 0.373117033603708, 0.36813186813186816, 0.32517482517482516, 0.4175257731958763, 0.41025641025641024, 0.39408866995073893, 0.4143222506393862, 0.34, 0.391025641025641, 0.3130841121495327, 0.35398230088495575] and a list of names(strings). How do I make the probability as my y-value of each bar and names as x-values? If you want a histogram, you don't need to attach any 'names' to x-values, as on x-axis you would have bins:

to visualize a sequence of nodes connected by edges encoded in python. looking for a python library to visualize such graph data. either a library written in python or python bindings, is ok (i am aware of Visustin, but looking for alternatives) doug Graphviz is the best option in my opinion. Graphviz is the premiere graph rendering/layout library; it's mature, stable, open-source, and free of charge. It is not a dedicated flowchart or diagramming package, but its core use case--i.e., efficient and aesthetic rendering of objects comprised of nodes and edges, obviously subsumes flowchart

I am a new R user. I have a difficult time figuring out how to combine different barplot into one graph. For example, Suppose, the top five of professions in China, are, government employees, CEOs, Doctors, Athletes, artists, with the incomes (in dollars) respectively, 20,000,17,000,15,000,14,000,and 13,000, while the top five of professions in the US, are, doctors, athletes, artists, lawyers, teachers with the incomes (in dollars) respectively, 40,000,35,000,30,000,25,000 and 20,000. I want to show the differences in one graph. How am I supposed to do that? Beware that they have different

I am using networkX to draw a network plot from a distance matrix(emoji_sim, a DataFrame). Here is the code: G = nx.from_numpy_matrix(np.array(emoji_sim)) nx.draw(G, edge_color='silver', node_color='lightsalmon', with_labels=True) plt.show() I know there is a way to relabel the nodes as: G = nx.relabel_nodes(G, dict(zip(range(len(G.nodes())), range(1, len(G.nodes())+1)))) But I want to substitute the nodes label with images(possibly read from files or using Python Emoji package). Is there any way to do that? Thanks a lot! To clarify, I am trying to substitute the actual circle with images.

I am working on an assignment using R and the fitted density curve that is overlaid on the histogram is cut off at it's peak. Example: x <- rexp(1000, 0.2) hist(x, prob = TRUE) lines(density(x), col = "blue", lty = 3, lwd = 2) I have done a search on the internet for this but didn't find anything addressing this problem. I have tried playing with the margins, but that doesn't work. Am I missing something in my code? Thank you for your help! Here's the simple literal answer to the question. Make an object to hold the result of your density call and use that to set the ylim of the histogram. x <

Possible Duplicate: Draw bloxplots in R given 25,50,75 percentiles and min and max values I have a number of sets of summary statistics from various datasets: max, min, mean, median etc. I'd like to plot box-plots of these - or at least, similar plots to boxplots (I don't have UQ and LQ stats, although it may be possible to get those). I don't have the original data, so I can't just use the boxplot function in R. Is there an easy way to do this in R when you just have the summary statistics? If not, is there an easy way to do this using another free tool? The boxplot function in R uses a low

I am new to D3 and experimenting on few charts . While building a line chart using D3 V4 , I came across following errors . d3.line(...).x(...).y(...).interpolate is not a function I assume this error is due to the function interpolate not being available in D3 v4 . It would be great if someone could help me with the replacement function for the interpolate function . My code is in the below link https://ghostbin.com/paste/ztans In D3 v4.x, the line generator takes a curve to define the interpolation: While lines are defined as a sequence of two-dimensional [x, y] points, and areas are