I've done some searching around, and cannot easily find a solution this problem. Effectively, I want to have multiple tick locators on a single axis such that I can do something like in the plot below.
Note how the x-axis starts off logarithmic, but becomes linear once 500 is reached. I figured one possible solution was to simply divide the data into two portions, plot it on two graphs, each with their own locators, and then put the graphs right next to each other so they're seamless, but that seems very unpythonic. Anyone have a better solution?
I suspect the following URL might be of use:
http://matplotlib.org/examples/axes_grid/parasite_simple2.html (click on the plot to have the python code)
If you need some specialized graphs, it's always a good idea to have a look at the Matplotlib gallery:
http://matplotlib.org/gallery.html
EDIT: It is possible to make custom ticks on the X-axis:
http://matplotlib.org/examples/ticks_and_spines/ticklabels_demo_rotation.html
You may find an implementation of this scale by Jesús Torrado here.
Related
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.
I'm looking to augment my scatterplot (Python code, using Matplotlib and/or Seaborn) with marginal distributions (here plotted as histograms, but could also be kernel density estimates):
with a visualization of the differences (histogram/density estimate), like so:
I could probably roll my own, but this seems like such a common use case that I'm suspecting this might be implemented somewhere already in Matplotlib or Seaborn. A good fifteen minutes of Googling did not yield anything, and it also has not been asked before here on StackOverflow. Does anyone know of an off-the-shelf solution for this? (If no one does, I'll write my own and post it of course.) Thanks!
I have a time-series plot of data in which I want to examine a section in more detail. Kind of like this, but with the second plot being below the first, and instead of the box bounding the section of data, bounding the x-axis labels instead.
Is there a simple way to go about this or am I going to have to write this from scratch?
EDIT: This past question seems to be after the same thing, but was never solved.
Is there a way to let matplotlib know to recompute the optimal bounds of a plot?
My problem is that, I am manually computing a bunch of boxplots, putting them at various locations in a plot. By the end, some boxplots extend beyond the plot frame. I could hard-code some xlim and ylim's for now, but I want a more general solution.
What I was thinking was a feature where you say "ok plt I am done plotting, now please adjust the bounds so that all my data is nicely within the bounds".
Is this possible?
EDIT:
The answer is yes.
Follow-up question: Can this be done for the ticks as well?
You want to use matplotlib's automatic axis scaling. You can do this with either axes.axis with the "auto" input or axes.set_autoscale_on
ax.axis('auto')
ax.set_autoscale_on()
If you want to auto-scale only the x or y axis, you can use set_autoscaley_on or set_autoscalex_on.
I am using python to plot points. The plot shows relationship between area and the # of points of interest (POIs) in this area. I have 3000 area values and 3000 # of POI values.
Now the plot looks like this:
The problem is that, at lower left side, points are severely overlapping each other so it is hard to get enough information. Most areas are not that big and they don't have many POIs.
I want to make a plot with little overlapping. I am wondering whether I can use unevenly distributed axis or use histogram to make a beautiful plot. Can anyone help me?
I would suggest using a logarithmic scale for the y axis. You can either use pyplot.semilogy(...) or pyplot.yscale('log') (http://matplotlib.org/api/pyplot_api.html).
Note that points where area <= 0 will not be rendered.
I think we have two major choices here. First adjusting this plot, and second choosing to display your data in another type of plot.
In the first option, I would suggest clipping the boundries. You have plenty of space around the borders. If you limit the plot to the boundries, your data would scale better. On top of it, you may choose to plot the points with smaller dots, so that they would seem less overlapping.
Second option would be to choose displaying data in a different view, such as histograms. This might give a better insight in terms of distribution of your data among different bins. But this would be completely different type of view, in regards to the former plot.
I would suggest trying to adjust the plot by limiting the boundries of the plot to the data points, so that the plot area would have enough space to scale the data and try using histograms later. But as I mentioned, these are two different things and would give different insights about your data.
For adjusting you might try this:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,y1,y2))
You would probably need to make minor adjustments to the axis variables. Note that there should definetly be better options instead of this, but this was the first thing that came to my mind.