When i run my Jupyter-notebook with python2.7 and try to print items (of a list) using a for-loop it just won't output the print statement after importing the following packages:
import sys
import os
from hachoir_core.cmd_line import unicodeFilename
from hachoir_metadata import extractMetadata
from hachoir_parser import createParser
from hachoir_core.i18n import getTerminalCharset
from hachoir_core.tools import makePrintable
import pandas as pd
example code:
items = [1, 3, 0, 4, 1]
for item in items:
print (item)
output is blank.
When I use the exact same code before importing, it does show.
Looks like hachoir imports are the problem, whenever I import anything containing it, the output stops showing.
Reposting as an answer: The hachoir_metadata module appears to do something odd with stdout which breaks IPython: Bug report.
As described in that link, you need to add the following code before importing hachoir_metadata:
from hachoir_core import config
config.unicode_stdout = False
Related
The Seaborn code does not work.
I use jupyterlite to execute seaborn python code. first, i import seaborn in the following way --
import piplite
await piplite.install('seaborn')
import matplotlib.pyplot as plt
import seaborn as sn
%matplotlib inline
But when I insert seaborn code like the following one then it shows many errors that i do not understand yet --
link of the code
the problem that I face
But I insert this code in the google colab it works nicely
google colab
The issue is getting the example dataset as I point out in my comments.
The problem step is associated with:
# Load the example dataset for Anscombe's quartet
df = sns.load_dataset("anscombe")
You need to replace the line df = sns.load_dataset("anscombe") with the following:
url = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/anscombe.csv' # based on [Data repository for seaborn examples](https://github.com/mwaskom/seaborn-data)
from pyodide.http import open_url
import pandas
df = pandas.read_csv(open_url(url))
That's based on use of open_url() from pyodide.http, see here for more examples.
Alternative with pyfetch and assigning the string obtained
If you've seen pyfetch around, this also works as a replacement of the sns.load_dataset() line based on John Hanley's post, that uses pyfetch to get the CSV data. The code is commented further:
# GET text at URL via pyfetch based on John Hanley's https://www.jhanley.com/blog/pyscript-loading-python-code-in-the-browser/
url = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/anscombe.csv' # based on [Data repository for seaborn examples](https://github.com/mwaskom/seaborn-data)
from pyodide.http import pyfetch
response = await pyfetch(url)
content = (await response.bytes()).decode('utf-8')
# READ in string to dataframe based on [farmOS + JupyterLite: Import a CSV of Animals](https://gist.github.com/symbioquine/7641a2ab258726347ec937e8ea02a167)
import io
import pandas
df = pandas.read_csv(io.StringIO(content))
Is there a way to create custom hotkeys/commands in jupiter?
For example, lets say I press Ctrl+1 and jupiter automatically pastes this code:
import pandas as pd
import util as x
asdasd
adsasd
asasdasd
I came across the problem when I tried to use xlrd to import an .xls file and create dataframe using python.
Here is my file format:
xls file format
When I run:
import os
import pandas as pd
import xlrd
for filename in os.listdir("."):
if filename.startswith("report_1"):
df = pd.read_excel(filename)
It's showing "XLRDError: Unsupported format, or corrupt file: Expected BOF record; found b'Report g'"
I am pretty sure nothing wrong with xlrd (version 1.0.0) because when I remove the first row, dataframe can be created.
Wonder if there is any way that i can load the original file format?
Try following that accounts for a header line:
df = pd.read_excel(filename, header=0)
I want to develop bokeh apps on a jupyter notebook instance that runs behind jupyterhub (AKA an authenticating proxy). I would like to have interactive bokeh apps calling back to the notebook kernel. I don't want to use the notebook widgets etc because I want to be able to export the notebook as a python file and have something I can serve with bokeh server.
The following code in my notebook gives an empty output with no errors:
from bokeh.layouts import row
from bokeh.models.widgets import Button
from bokeh.io import show, output_notebook
from bokeh.application.handlers import FunctionHandler
from bokeh.application import Application
output_notebook()
# Create the Document Application
def modify_doc(doc):
layout = row(Button(label="Hello,"),Button(label="world!"))
doc.add_root(layout)
handler = FunctionHandler(modify_doc)
app = Application(handler)
# Output = BokehJS 0.12.10 successfully loaded.
# New cell
show(app, notebook_url="my-jupyterhub-url.com:80")
# Output = "empty" cell
Inspecting the cell a script tag has been added:
<script src="http://my-jupyterhub-url.com:46249/autoload.js?bokeh-autoload-element=f8fa3bd0-9caf-473d-87a5-6c7b9680648b&bokeh-absolute-url=http://my-jupyterhub-url.com:46249" id="f8fa3bd0-9caf-473d-87a5-6c7b9680648b" data-bokeh-model-id="" data-bokeh-doc-id=""></script>
This will not work because port 46249 isn't open on the jupyterhub proxy. Also the path that routes to my jupyter instance is my-jupyterhub-url.com/user/my-username/ so my-jupyterhub-url.com/autoload.js wouldn't route anywhere.
This feels like it could be a common requirement but a search hasn't revealed a solution to be yet.
Any ideas?
So I've found a solution that I'm not happy about but works.. just about.
First install nbserverproxy on your Jupyter instance. This allows you to proxy through JupyterHub (where you are authenticated) onto arbitrary ports on your Jupyter machine/container. I installed by opening a terminal from the Jupyter web front end and typing:
pip install git+https://github.com/jupyterhub/nbserverproxy --user
jupyter serverextension enable --py nbserverproxy --user
Then restart your server. For my install of JupyterHub this was control panel -> stop my server wait then start my server.
Finally I monkey patched the Ipython.display.publish_display_data (since the source code revealed that bokeh used this when calling show) in the notebook like so.
from unittest.mock import patch
from IPython.display import publish_display_data
orig = publish_display_data
import re
def proxy_replacer(display_data):
for key, item in display_data.items():
if isinstance(item, str):
item= re.sub(r'(/user/tam203)/?:([0-9]+)', r'\1/proxy/\2', item)
item = re.sub(r'http:' , 'https:', item)
display_data[key] = item
return display_data
def mock(data, metadata=None, source=None):
data = proxy_replacer(data) if data else data
metadata = proxy_replacer(metadata) if metadata else metadata
return orig(data, metadata=metadata, source=source)
patcher = patch('IPython.display.publish_display_data', new=mock)
patcher.start()
With that all done I was then able to run the following an see a nice dynamically updating plot.
import random
from bokeh.io import output_notebook
output_notebook()
from bokeh.io import show
from bokeh.server.server import Server
from bokeh.application import Application
from bokeh.application.handlers.function import FunctionHandler
from bokeh.plotting import figure, ColumnDataSource
def make_document(doc):
source = ColumnDataSource({'x': [], 'y': [], 'color': []})
def update():
new = {'x': [random.random()],
'y': [random.random()],
'color': [random.choice(['red', 'blue', 'green'])]}
source.stream(new)
doc.add_periodic_callback(update, 100)
fig = figure(title='Streaming Circle Plot!', sizing_mode='scale_width',
x_range=[0, 1], y_range=[0, 1])
fig.circle(source=source, x='x', y='y', color='color', size=10)
doc.title = "Now with live updating!"
doc.add_root(fig)
app = Application(FunctionHandler(make_document))
show(app, notebook_url="<my-domain>.co.uk/user/tam203/")
So while I'm happy to have found a work around it doesn't really feel like a solution. I think a smallish change in bokeh could solve this (something like a url template string where you can specify the path and the port).
According to the official bokeh documentation show(obj, notebook_url=remote_jupyter_proxy_url) accepts a notebook_url argument value. Apparently this can be a function that accepts a port argument value.
The documentation goes further by providing a reference implementation for the function remote_jupyter_proxy_url in the context of jupyterhub/jupyterlab and proxy extension.
Is there any documentation specifying how to pass Bokeh parameters via holoview?
I am reading the tutorials but I think there is something small I have missed.
There is an example online which describes this in Ipython but I am trying to do it via a python WITHOUT Ipython notebook.
http://holoviews.org/Tutorials/Bokeh_Backend.html?highlight=bokeh
When I run this program I get the curves but the color does not change and I also get this error: WARNING:root:Curve01537: Setting non-parameter attribute style={'line_color': 'green'} using a mechanism intended only for parameters
How can we set the parameter?
Code Example here
from pprint import pprint, pformat
import holoviews as hv
import numpy as np
import pathlib, os
import webbrowser
import lasio, las
from holoviews import Store
from holoviews.plotting.bokeh.element import (line_properties, fill_properties, text_properties)
def plot_bokeh(plot):
#Create renderer instance
myrenderer = hv.Store.renderers['bokeh'].instance(fig='html')
out_file_name = "".join(["./OUTPUT/","gyro", "_graph.html"])
with open (out_file_name, 'w') as f:
#Plot static html
f.write (myrenderer.static_html(plot))
f.close()
webbrowser.open_new_tab(pathlib.Path(os.path.abspath(out_file_name)).as_uri())
def holoview_sandbox():
curve_opts = dict(line_color='green')
xs = np.linspace(0, np.pi*4, 100)
data = (xs, np.sin(xs))
holo_plot = hv.Curve(data, label='MY LABEL' , style=curve_opts)
plot_bokeh(holo_plot)
if __name__ == '__main__':
holoview_sandbox()
In HoloViews the options aren't bound to the objects themselves, which has various benefits including being able to plot with different backends. The pure-Python way of setting style options is this:
curve_opts = dict(line_color='green')
xs = np.linspace(0, np.pi*4, 100)
data = (xs, np.sin(xs))
holo_plot = hv.Curve(data, label='MY LABEL')(style=curve_opts)
The Options Tutorial describes how to set options like this, but please let us know if you found some of that unclear.
This syntax works as well
holo_plot.opts(style={'color': 'green'})
When you change the entry 'line_color' to 'color' in the dict() of Philipp's answer, then this works for the matplotlib backend as well.
Details about setting options can also be found here in addition to Philipp's link.