Can someone please let me know what kind of graph this is called?
I am running an asset allocation with different risk/return combinations and would like to show the change in asset allocation through a graph.
The graph shown has time on the x axis and I would like to change that to increase in risk (volatility) Thank you so much!
I am not sure what in Matplot this is called but have no luck finding the right graph to do so.
there is powerful package plotly. It has lots of plot types and python api, you can check examples here https://plotly.com/python/. There are some which might suit for you
Take a look to contour plot https://plotly.com/python/contour-plots/ and https://plotly.com/python/knn-classification/#probability-estimates-with-gocontour
Related
I have a datafram with following structure
,mphA,gyrA,parC,tet59,qnrVC
sample1,TRUE,FALSE,FALSE,FALSE,FALSE
sample2,TRUE,FALSE,FALSE,FALSE,TRUE
sample3,FALSE,FALSE,FALSE,TRUE,FALSE
sample4,FALSE,FALSE,FALSE,TRUE,TRUE
sample5,TRUE,FALSE,TRUE,FALSE,TRUE
sample6,TRUE,TRUE,FALSE,FALSE,FALSE
sample7,TRUE,TRUE,TRUE,FALSE,TRUE
sample8,TRUE,TRUE,TRUE,TRUE,TRUE
sample9,FALSE,TRUE,TRUE,FALSE,TRUE
sample10,TRUE,TRUE,FALSE,FALSE,TRUE
And I need to generate a frequency vs total count bar plot similar to the following figure in python. Its a combination of 3 plots so I guess you need to plot them independently and put them in a single canvas. I frequently see this plot in journals so I guess it should be implemented already. However, I did not have any success with online search. Does anybody know how it can be done? Thanks.
It can be done easily using UpSetPlot
https://pypi.org/project/UpSetPlot/
Recently I started using SHAP. I really liked the way they explain the contribution of each feature in the model.
I somehow face difficulty to understand the decision plot.
I could not find explanation about this plot
I would be thankful if someone explain the plot below because I am a bit confusing about the base value and the model output.
Usually I see in decision plot all the lines starting at same point from bottom and then they separate based on feature contribution.
Is it always like that? Because my plot is different in this case.
And also, does the blue color means positive influence and the red is negative?
Many thanks in advance for any sharing ideas with me to understand the plot.
Regards
If you haven't already you should check out the documentation notebook at: https://github.com/slundberg/shap/blob/master/notebooks/plots/decision_plot.ipynb is talks about the coloring.
As for why the lines don't start at the bottom in your plot, I think that is because there are more features that are cut off. I could imagine summing all those features and showing them combined like in the SHAP waterfall plot, but that would need to be coded up (feel free to open that as an issue or PR for that if you like).
I'm trying to analyze a set of costs using python.
The columns in the data frame are,
'TotalCharges', 'TotalPayments', 'TotalDirectVariableCost', 'TotalDirectFixedCost', 'TotalIndirectVariableCost', 'TotalIndirectFixedCost.
When I tried to plot them using the whisker plots, this is how they could display
I need to properly analyze these data and understand their behavior.
The following are my questions.
Is there any way that I can use wisker plots more clearly?
I believe since these are costs, we cannot ignore them as outliars. So keeping the data as it is what else I can use to represent data more clearly?
Thanks
There are a couple of things you could do:
larger print area
rotate the axis
plot one axis log scale
That said, I think you should examine once again your understanding of what a box and whisker plot is for.
Additionally, you might consider posting this on the Math or Cross Validated site as this doesn't have much to do with code.
The graph below shows a ranking of countries at 10 different points. The cool thing with this graph is that it allows you to track changes in the ranking over time. I want to create create something similar, but I have no idea how it was created...
My guess is that it was created using some design tool like adobe indesign, but my hope is that there might be some other tools for obtaining such a graphic (maybe even a way to do it within Mathematica or Python?)?
Any ideas and/or suggestions on where to look would be much appreciated.
PS: In case I did not post this in the most appropriate of stackExchange's many forums--my apology.
it is called a bump chart, you can learn how to make it with python in this article
How to make a bump chart
I am trying to create a 2D Contour Map in Python that looks like this:
In this case, it is a map of chemical concentration for a number of points on the map. But for the sake of simplicity, we could just say it's elevation.
I am given the map, in this case 562 by 404px. I am given a number of X & Y coordinates with the given value at that point. I am not given enough points to smoothly connect the line, and sometimes very few data points to draw from. It's my understanding that Spline plots should be used to smoothly connect the points.
I see that there are a number of libraries out there for Python which assist in creation of the contour maps similar to this.
Matplotlib's Pyplot Contour looks promising.
Numpy also looks to have some potential
But to me, I don't see a clear winner. I'm not really sure where to start, being new to this programming graphical data such as this.
So my question really is, what's the best library to use? Simpler would be preferred. Any insight you could provide that would help get me started the proper way would be fantastic.
Thank you.
In the numpy example that you show, the author is actually using Matplotlib. While there are several plotting libraries, Matplotlib is the most popular for simple 2D plots like this. I'd probably use that unless there is a compelling reason not to.
A general strategy would be to try to find something that looks like what you want in the Matplotlib example gallery and then modify the source code. Another good source of high quality Matplotlib examples that I like is:
http://astroml.github.com/book_figures/
Numpy is actually a N-dimensional array object, not a plotting package.
You don't need every pixel with data. Simply mask your data array. Matplotlib will automatically plot the area that it can and leave other area blank.
I was having this same question. I found that matplotlib has interpolation which can be used to smoothly connect discrete X-Y points.
See the following docs for what helped me through:
Matplotlib's matplotlib.tri.LinearTriInterpolator docs.
Matplotlib's Contour Plot of Irregularly Spaced Data example
How I used the above resources loading x, y, z points in from a CSV to make a topomap end-to-end