I'm trying to plot a stock price using Plotly in Python 3.9 (Anaconda/Spyder), but the graph isn't displaying. The following code executes without error, but no plot.
import yfinance
tsla = yfinance.Ticker('TSLA')
hist = tsla.history(period='1y')
import plotly.graph_objects as go
fig = go.Figure(data=go.Scatter(x=hist.index,y=hist['Close'], mode='lines'))
fig.show()
I'm working on local version of Python, no web interaction.
The following code works for plotting in a browser window:
import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])
fig.write_html('first_figure.html', auto_open=True)
Just not sure why .show() isn't working. Seems to be the most basic function there is to Plotly.
Hoping there's something easy/obvious I'm missing.
Try adding this :
import plotly.io as pio
pio.renderers.default = 'browser'
It will open the plot in your default browser.
More info on renderers
I am trying to understand how to use callbacks for the new Bokeh EditTools (e.g. BoxEditTool or similar).
Specifically, I would like to see on the server side the coordinates of the newly added rectangles, but I am not sure how to do this.
I am running the following server app
def app( curdoc ):
TOOLS = "tap"
p = figure(title="Some Figure", tools=TOOLS)
source = ColumnDataSource( {'xs':[1], 'ys':[1], 'width':[.1],'height':[.1]})
r = p.rect('xs','ys','width','height', source=source)
p.add_tools(BoxEditTool( renderers = [r]))
def cb( attr, old, new ):
print(r.data_source.data)
r.data_source.on_change("selected", cb)
curdoc.add_root(column(p))
I do get printout from the cb when I select different rectangles, but the r.data_source.data does not change
Thanks for the help!
The behaviour you're describing is actually a bug in the current distribution of Bokeh (0.13.0). You can read more in the google groups discussion. To summarize, there was a problem with the synchronization of the data at the server, it has been resolved and merged.
Note that the on_change method for the Rect glyph ColumnDataSource should watch the 'data' attribute and not 'selected'.
Other than that your snippet looks good, but if you want a working example you can look here. This code is under development but at this stage it reads images and allows drawing ROIs, as well as a simple mechanism for serializing and loading them.
Hope this helps!
I'd like to draw a vertical line on my Bokeh plot which gets moved around by javascript in the browser at runtime. (It's a timebar that marks the current time on a time series plot.)
For drawing a static vertical line, I'm using:
from bokeh.models import Span
timebar = Span(location=where_I_want_the_timebar, dimension=height)
my_figure.add_layout(timebar)
In order to enable the interactivity, I think I need to get the location from a ColumnDataSource. However, I can't figure out how to do that, because Span does not accept a source argument.
Alternatively, is there another way for me to move the timebar at runtime?
I found a workaround. In python:
from bokeh.models import Span
timebar = Span(name='timebar' location=where_I_want_the_timebar, dimension=height)
my_figure.add_layout(timebar)
Then in javascript in the browser:
let timebar = Bokeh.documents[0].get_model_by_name('timebar')
timebar.attributes.location = my_new_desired_location
timebar.change.emit()
If someone posts a way to use a ColumnDataSource I will accept that answer.
I am trying to use the RangeSlider within my Bokeh-application, initializing the object as so
from bokeh.models.widgets import RangeSlider
#RangeSlider
slider = RangeSlider(title="OAS slider", start=0, end=1000, value=(0,2000),
step=0.1)
When trying to compile the app I get the following error message:
AttributeError: unexpected attribute 'value' to RangeSlider, possible attributes are
callback, callback_policy, callback_throttle, css_classes, disabled, end,
height, js_callbacks, name, orientation, range, sizing_mode, start, step,
tags, title or width
I could change the code to use range instead as so
#RangeSlider altered inputs
slider = RangeSlider(title="OAS slider", start=0, end=1000, range=(0,2000),
step=0.1)
and it works.
However, as the example on the Bokeh-homepage found here
https://github.com/bokeh/bokeh/blob/master/examples/app/export_csv/main.py
used the value and works, I would be more eager to understand why I get the error message, rather than just changing to range... The example in the link also used the key word format, which obviously also generates the error above.
Could be a version issue. I am running Bokeh version 0.12.4
Thanks
This is a version issue. The RangeSlider was actually mostly broken for a long time, until we switched to a different underlying slider library to implement it. But some changes were necessary to make the switch. The correct property for 0.12.7 and newer is value and I would recommend to use that version or later if you want too use RangeSlider especially (0.12.4 is over a year old.)
Is there a way of creating animated graphs. For example showing the same graph, with different parameters.
For example is SAGE notebook, one can write:
a = animate([circle((i,i), 1-1/(i+1), hue=i/10) for i in srange(0,2,0.2)],
xmin=0,ymin=0,xmax=2,ymax=2,figsize=[2,2])
a.show()
This has horrible flickering, but at least this creates a plot that animates for me. It is based on Aron's, but Aron's does not work as-is.
import time, sys
from IPython.core.display import clear_output
f, ax = plt.subplots()
n = 30
x = array([i/10.0 for i in range(n)])
y = array([sin(i) for i in x])
for i in range(5,n):
ax.plot(x[:i],y[:i])
time.sleep(0.1)
clear_output()
display(f)
ax.cla() # turn this off if you'd like to "build up" plots
plt.close()
Update: January 2014
Jake Vanderplas has created a Javascript-based package for matplotlib animations available here. Using it is as simple as:
# https://github.com/jakevdp/JSAnimation
from JSAnimation import examples
examples.basic_animation()
See his blog post for a more complete description and examples.
Historical answer (see goger for a correction)
Yes, the Javascript update does not correctly hold the image frame yet, so there is flicker, but you can do something quite simple using this technique:
import time, sys
from IPython.display import clear_output
f, ax = plt.subplots()
for i in range(10):
y = i/10*sin(x)
ax.plot(x,y)
time.sleep(0.5)
clear_output()
display(f)
ax.cla() # turn this off if you'd like to "build up" plots
plt.close()
IPython widgets let you manipulate Python objects in the kernel with GUI objects in the Notebook. You might also like Sage hosted IPython Notebooks. One problem you might have with sharing widgets or interactivity in Notebooks is that if someone else doesn't have IPython, they can't run your work. To solve that, you can use Domino to share Notebooks with widgets that others can run.
Below are three examples of widgets you can build in a Notebook using pandas to filter data, fractals, and a slider for a 3D plot. Learn more and see the code and Notebooks here.
If you want to live-stream data or set up a simulation to run as a loop, you can also stream data into plots in a Notebook. Disclaimer: I work for Plotly.
If you use IPython notebook, v2.0 and above support interactive widgets. You can find a good example notebook here (n.b. you need to download and run from your own machine to see the sliders).
It essentially boils down to importing interact, and then passing it a function, along with ranges for the paramters. e.g., from the second link:
In [8]:
def pltsin(f, a):
plot(x,a*sin(2*pi*x*f))
ylim(-10,10)
In [9]:
interact(pltsin, f=(1,10,0.1), a=(1,10,1));
This will produce a plot with two sliders, for f and a.
If you want 3D scatter plot animations, the Ipyvolume Jupyter widget is very impressive.
http://ipyvolume.readthedocs.io/en/latest/animation.html#
bqplot is a really good option to do this now. its built specifically for animation through python in the notebook
https://github.com/bloomberg/bqplot
On #goger's comment of 'horrible flickering', I found that calling clear_output(wait=True) solved my problem. The flag tells clear_output to wait to render till it has something new to render.
matplotlib has an animation module to do just that. However, examples provided on the site will not run as is in a notebook; you need to make a few tweaks to make it work.
Here is the example of the page below modified to work in a notebook (modifications in bold).
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib import rc
from IPython.display import HTML
fig, ax = plt.subplots()
xdata, ydata = [], []
ln, = plt.plot([], [], 'ro', animated=True)
def init():
ax.set_xlim(0, 2*np.pi)
ax.set_ylim(-1, 1)
return ln,
def update(frame):
xdata.append(frame)
ydata.append(np.sin(frame))
ln.set_data(xdata, ydata)
return ln,
ani = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128),
init_func=init, blit=True)
rc('animation', html='html5')
ani
# plt.show() # not needed anymore
Note that the animation in the notebook is made via a movie and that you need to have ffmpeg installed and matplotlib configured to use it.