I'm new to plotly/plotly express and i'm having a really hard time finding any working example other than the documentation examples for the library (which are really basic and standard).
I have, let's say a scatter plot, in plotly express:
fig = px.scatter(any_random_data)
And i want to add to that plot an image in a fixed (x,y) position, but i don't know (and can't find!) if there is any kind of method for that.
I've seen there is an add_trace() method to add traces to the plot (i guess), is there any similar function for adding images? (Like add_image() or something)
Related
I am unsure whether this functionality is possible through Plotly or if it is achievable with other plotting packages so I am open to different solutions.
I am trying to plot two violin plots in a single figure where one violin is oriented horizontally while the other is vertical. I would like to specify the point at which they intersect (i.e the primary axis of each). Ideally each would be transparent and interactive.
Somewhat poor illustration of what I need
Thank you for any proposed solutions!
There are multiple Python libraries doing Violin Plots. Plotly is among them, check its manual.
Besides, Seaborn is really good at this:
It is a Python visualization library based on Matplotlib. The code snipped for the above example can be found in Seaborn's gallery.
I want the similar visualization as shown below using Bokeh. As I am new to Bokeh , I might be wondering is there any code that is as concise as the below one using Seaborn ?
My main focus is how to write code for same visualization in Bokeh
dataset
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv',index_col = 0)
Plotting the dataset
import seaborn as sns
sns.pairplot(data, x_vars = ['TV', 'Radio','Newspaper'], y_vars = ['Sales'],
size =7, aspect =.7, kind = 'reg')
Also the code in Seaborn doesn't require to enter the best fitted line . It automatically generates the best fitted line in the graph with a shadow of confidence interval . Is this kind of plotting possible in Bokeh ?
The above charts are certainly possible with Bokeh, in the sense that Bokeh could draw them, without any question. But it would take some work, and require more code, than with Seaborn. Basically you'd have to compute all the coordinates and set up all the glyphs "by hand". As of Bokeh 0.12.2, there is not currently any comparable "single line" high level function or chart in bokeh.charts.
Adding more high level chart types lie this to bokeh.charts is definitely something that we'd like, but it will probably require motivated new contributors to make that happen. Fortunately, this area of Bokeh is pure-python and probably the most approachable for new contributors. If you are possibly interested in contributing to Bokeh, I encourage you to reach out on the public mailing list or gitter chat channel. We are always happy to answer questions and help people get started.
I am trying to configure my Bokeh plots in Python such that they look a bit nicer. For example, is there a way to fix the maximum zoom out? Such that Bokeh cannot zoom out more than what is specified by the x-axis? E.g. look at bokeh example, and especially "Datetime axes". I would like to fix the axis size so that you cannot zoom out more than the initial x axis is wide.
Another question; is there a way to fill an area under a curve in a specified color? Like in the figure USDSEK. I can provide code, but I don't think it's necessary for the problem at hand.
UPDATED for 2019:
Bokeh now supports "directed areas" (which can also be stacked) see e.g.
https://docs.bokeh.org/en/latest/docs/gallery/stacked_area.html
I'm preparing a set of reports using open source ReportLab. The reports contain a number of charts. Everything works well so far.
I've been asked to take a (working) bar chart that shows two series of data and overlay a fitted curve for each series.
I can see how I could overlay a segmented line on the bar graph by creating both a line chart and bar chart in the same ReportLab drawing. I can't find any reference for fitted curves in ReportLab, however.
Does anyone have any insight into plotting a fitted curve to a series of data in ReportLab or, failing that, a suggestion about how to accomplish this task (I'm thinking that chart would need to be produced in matplotlib instead)?
I would recommend using MatPlotLib. This is exactly the sort of thing it's designed to handle and it will be much easier than trying to piece together something in ReportLab alone, especially since you'll have to do all the calculation of the line on your own and figure out the details of how to draw it in just the right place. MatPlotLib integrates easily with ReportLab; I've used the combination several times with great results.
I while ago, I was comparing the output of two functions using python and matplotlib. The result was as good as simple, since plotting with matplotlib is quite easy: I just plotted two arrays with different markers. Piece of cake.
Now I find myself with the same problem, but now I have a lot of pair of curves to compare. I initially tried plotting everything with different colors and markers. This did not satisfy me since the ranges of each curve are not quite the same. In addition to this, I quickly ran out of colors and markers that were sufficiently different to identify (RGBCMYK, after that, custom colors resemble any of the previous ones).
I also tried subplotting each pair of curves, obtaining a window with many plots. Too crowded.
I tried one window per plot, too many windows.
So I was just wondering if there is any existing widget or if you have any suggestion (or a different idea) to accomplish this:
I want to see a pair of curves and then select easily the next one, with a slidebar, button, mouse scroll, or any other widget or event. By changing curves, the previous one should disappear, the legend should change and its axis as well.
Well I managed to do it with an event handler for mouse clicks. I will change it for something more useful, but I post my solution anyway.
import matplotlib.pyplot as plt
figure = plt.figure()
# plotting
plt.plot([1,2,3],[10,20,30],'bo-')
plt.grid()
plt.legend()
def on_press(event):
print 'you pressed', event.button, event.xdata, event.ydata
event.canvas.figure.clear()
# select new curves to plot, in this example [1,2,3] [0,0,0]
event.canvas.figure.gca().plot([1,2,3],[0,0,0], 'ro-')
event.canvas.figure.gca().grid()
event.canvas.figure.gca().legend()
event.canvas.draw()
figure.canvas.mpl_connect('button_press_event', on_press)
Sounds like you want to embed matplotlib in an application. There are some resources available for that:
user interface examples
Embedding in WX
I really like using traits. If you follow the tutorial Writing a graphical application for scientific programming , you should be able to do what you want. The tutorial shows how to interact with a matplotlib graph using graphical user interface.