import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import iplot
import plotly.offline as py_offline
import pandas_datareader as web
from datetime import datetime
py_offline.init_notebook_mode()
df = web.DataReader("aapl", 'morningstar').reset_index()
trace = go.Candlestick(x=df.Date,
open=df.Open,
high=df.High,
low=df.Low,
close=df.Close)
data = [trace]
iplot(data, filename='simple_candlestick')
That code worked pretty well inside a jupyter notebook. Now, I want to execute it inside a standard python script. Once it is executed, I wanted a window to pop-up to see the graph related to that code, but it failed. How could I modify this code so that it works?
Instead of iplot() in the last line, using py_offline.plot() should open the plot in a browser window.
py_offline.plot(data, filename='simple_candlestick')
or
py_offline.plot(data)
Related
import camelot
import pandas as pd
import matplotlib
file = 'foo.pdf'
tables = camelot.read_pdf(file, pages='all', flavor='stream')
camelot.plot(tables[0], kind='text').show()
The matplot window opens and suddenly closes in a flash without any user input whatsoever.
I want the window to remain open to examine the contents.
Edit: I am using Windows 11 and Python 3.9, running the code on Pycharm and it's the system interpreter rather than a virtual environment.
Not sure if you ever found your answer, but I will attach what I have found from the following answer by swenzel: https://stackoverflow.com/a/33062819
The plot is opening in a non-blocking window which disappears as soon as the script finishes. You can override this by importing matplotlib and using plot.show(block=True) at the end to show the window as a blocking window, which will keep the script from continuing until closed. See his code snippet below.
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("table.csv")
values = df["blah"]
values.plot()
print 1
df['blahblah'].plot()
print 2
plt.show(block=True)
Your code rewritten would look like the following:
import camelot
import pandas as pd
import matplotlib.pyplot as plt
file = 'foo.pdf'
tables = camelot.read_pdf(file, pages='all', flavor='stream')
camelot.plot(tables[0], kind='text')
plt.show(block=True)
I'm new to the plotly python package and I've faced with such problem:
There is a pandas dataframe that is updating in a loop and I have to plot data from it with plotly.
At the beginning all df.response values are None and then it starts to fill it. Here is an example:
at the beginning
after it starts to fill
I want plotly to react this changes, but I don't know how to do it in the most "canonical" and simple way. (It would be great if data updating loop and plotly updating would work simultaneously, but if plotly will react every several seconds it would be fine too). I found some functions but don't exactly understand how they work:
import plotly.graph_objects as go
import cufflinks as cf
from plotly.offline import init_notebook_mode
init_notebook_mode(connected=True)
cf.go_offline()
fig = go.Figure(data=go.Heatmap(z=df.response,
x=df.currents,
y=df.frequencies))
fig.update_layout(datarevision=???) # This one
...
given you want to update as data arrives you need an event / interrupt handling approach
this example uses time as the event / interupt, a dash Interval
simulates more data by concatenating additional data to dataframe then updates the figure in the callback
import plotly.graph_objects as go
import numpy as np
import pandas as pd
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
# initialise dataframe and figure
df = pd.DataFrame({"response":[], "currents":[], "frequencies":[]})
fig = go.Figure(data=go.Heatmap(z=df.response,
x=df.currents,
y=df.frequencies))
# Build App
app = JupyterDash(__name__)
app.layout = html.Div(
[
dcc.Graph(id="heatmap", figure=fig),
dcc.Interval(id="animateInterval", interval=400, n_intervals=0),
],
)
#app.callback(
Output("heatmap", "figure"),
Input("animateInterval", "n_intervals"),
State("heatmap", "figure")
)
def doUpdate(i, fig):
global df
df = pd.concat([df, pd.DataFrame({"response":np.random.uniform(1,5,100), "currents":np.random.randint(1,20,100), "frequencies":np.random.randint(1,50,100)})])
return go.Figure(fig).update_traces(go.Heatmap(z=df.response,
x=df.currents,
y=df.frequencies))
# Run app and display result inline in the notebook
app.run_server(mode="inline")
I believe this other answer has a less complicated solution than Rob Raymond's. I would personally go with this one. Essentially you just need to wrap the Figure in a FigureWidget—that's it. It responds to any data changes automatically.
Although I would be interested to know if there are benefits of using Dash over Plotly alone (in a Jupyter notebook).
I am using the waterfall_chart package in Python to create a waterfall figure. The package mainly uses matplotlib in the backend, so I was trying to use the tls.mpl_to_plotly(mpl_fig) function to covert the matplotlib figure into plotly. But when converting, an error pops up. Is there a way to convert waterfall_chart into plotly or is there an easy way to create the chart directly in plotly? I saw some previous discussion on similar chart in plotly, but it involved pretty manual coding of the chart number.
You could use the following code to recreate the chart.
import waterfall_chart
import matplotlib.pyplot as plt
import plotly.tools as tls
a = ['sales','returns','credit fees','rebates','late charges','shipping']
b = [10,-30,-7.5,-25,95,-7]
mpl_fig = plt.figure()
waterfall_chart.plot(a, b)
plt.show()
waterfall chart
But when I try to convert to plotly using mpl_to_plotly(), there is an error:
plotly_fig = tls.mpl_to_plotly(mpl_fig)
ValueError: min() arg is an empty sequence
The detail of the waterfall_chart package could be found here: https://github.com/chrispaulca/waterfall/blob/master/waterfall_chart.py
My answer addresses
[...] or is there an easy way to create the chart directly in plotly?
With newer versions of plotly you can use plotly.graph_objs.Waterfall.
Below is a basic example with your data sample with a setup that uses iplot in an off-line Jupyter Notebook:
Plot:
Code:
# imports
import plotly
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from IPython.core.display import display, HTML
import plotly.figure_factory as ff
import plotly.graph_objs as go
import pandas as pd
import numpy as np
# setup
display(HTML("<style>.container { width:35% !important; } .widget-select > select {background-color: gainsboro;}</style>"))
init_notebook_mode(connected=True)
np.random.seed(1)
import plotly.offline as py
import plotly.graph_objs as go
py.init_notebook_mode(connected = False)
# your values
a = ['sales','returns','credit fees','rebates','late charges','shipping']
b = [10,-30,-7.5,-25,95,-7]
# waterfall trace
trace = go.Waterfall(
x = a,
textposition = "outside",
text = [str(elem) for elem in b],
y = b,
connector = {"line":{"color":"rgb(63, 63, 63)"}},
)
layout = go.Layout(
title = "Waterfall chart, plotly version 3.9.0",
showlegend = True
)
iplot(go.Figure([trace], layout))
Check your version with:
import plotly
plotly.__version__
Update your version in a cmd console using:
pip install plotly --upgrade
List a has a length of 6, list b has a length of 5.
Matplotlib refuses to display an empty array, list or whatever.
Solve it to add a number or 0 to b or add an if to your code, to avoid matplotlib gets an empty sequence.
I get an 'PlotlyRequestError: No message' when I execute the code.
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
Filedata = pd.read_csv('C:\\Documents\\Book4.csv')
data = [go.Scatter(x=Filedata.ix[:,0],y=Filedata.ix[:,1])]
layout = go.Layout(
title='Analysis 2016',
xaxis=dict(title='Startdate'),
yaxis=dict(title='Conductivity'))
fig = go.Figure(data=data, layout=layout)
py.iplot(fig)
This is because you are trying to plot online which requires credentials based authentication. To plot offline, use plotly.offline's plot class to accomplish this without authentication.
from plotly.offline import plot
and then use this plot to plot your figure.
I had the same issue and I solved the problem by importing plotly like this:
import plotly.plotly as py
import plotly.graph_objs as go
# these two lines allow your code to show up in a notebook
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode()
And then calling iplot like this:
plotly.offline.iplot(...)
I had the same issue and I solved the problem by importing plotly and cufflinks like this:
from plotly.offline import iplot
import cufflinks as cf
and then apply
cf.go_offline()
I am using Plotly offline on Jupyter.
I am plotting curves:
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.graph_objs import *
import datetime as dt
list_date = [dt.datetime(2016,1,1).date(), dt.datetime(2016,1,2).date(), dt.datetime(2016,1,3).date(), dt.datetime(2016,1,4).date()]
data = []
for i in range(3) :
list = [i/2+1, i/2+2, i/2+3, i/2+4]
data.append(Scatter(x=list_date, y=list, name='y'+str(i)))
figure = Figure(data=data)
iplot(figure)
And I get a very nice graph!
In the latter case, the user wants to add a bar graph on it (in addition to the two lines already there).
list_bar = [0.5, 1.5, 2.5, 3.5]
data = [Bar(x=list_date, y=list_bar, name='bar')]
figure.update(data=data)
iplot(figure)
But I have only the bar chart, not the previous 2 lines. How to have offline the equivalent of the online function fileopt='append'?
py.plot(data, filename='append plot', fileopt='append')
In the latest plotly version 3, a FigureWidget has been added specifically to handle your problem of wanting to update an existing offline figure.
For me, running pip install plotly --upgrade got me the latest version within my Anaconda environment.
I've modified your example code below to use the new FigureWidget, and left in your old code that needed changing with comments. The new FigureWidget is meant to be compatible with the ordinary Figure that you were using.
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.graph_objs import *
import datetime as dt
# list_date = [dt.datetime(2016,1,1).date(), dt.datetime(2016,1,2).date(), dt.datetime(2016,1,3).date(), dt.datetime(2016,1,4).date()]
list_date = [dt.datetime(2016,1,1), dt.datetime(2016,1,2), dt.datetime(2016,1,3), dt.datetime(2016,1,4)]
data = []
for i in range(3) :
list = [i/2+1, i/2+2, i/2+3, i/2+4]
data.append(Scatter(x=list_date, y=list, name='y'+str(i)))
# figure = Figure(data=data)
# iplot(figure)
figure = FigureWidget(data=data)
figure
I've commented out the portions that were changed so you can see them for reference.
One other thing to note, due to a problem within ipykernel.json_util, the json_clean function that serializes Plotly JSON objects to show on your Jupyter screen doesn't know what to do with a datetime.date object -- only datetime objects. If you don't remove the .date you will get an exception and no graph. I'm guessing this would happen for datetime.time objects as well because it seems it is also unhandled in the current ipykernel code.
When you're ready to run your updated code, you simple create your data and use the add_trace function:
list_bar = [0.5, 1.5, 2.5, 3.5]
figure.add_trace(Bar(x=list_date, y=list_bar, name='bar'))
And your plot automatically updates in the previous cell with the added trace.
Lastly, there's a good guide about the new FigureWidget for those interested.