Develop Reference

Unable to use Bokeh and Panda to read a csv and plot it

I'm trying to plot a line graph from a simple CSV file with two columns using Bokeh for data visualisation and Panda to read the CSV and handle the data. However, i can't seem to pass the data I've imported using pandas to Bokeh to plot my line graph.
This is running locally on my computer. I've tried and debugged each section of the code and the sole problem seems to occur when I pass the data from pandas to bokeh.
I've tried printing the columns I've selected from my csv to check that the entire column has been selected too.
#Requirements for App
from bokeh.plotting import figure, output_file, show
import pandas as pd
from bokeh.models import ColumnDataSource
#Import data-->Weight measurements over a period of time [ STUB ]
weight = pd.read_csv("weight.csv")
#Define parameters
x=weight["Date"]
y=weight["Weight"]
#Take data and present in a graph
output_file("test.html")
p = figure(plot_width=400, plot_height=400)
p.line(x,y,line_width=2)
show(p)
I expect to get a line graph that plots each weight entry each day but I get a blank plot.

This should work. Pandas doesn't know that it is working with dates so you have to specify this with pd.to_datetime().
#!/usr/bin/python3
from bokeh.plotting import figure, output_file, show
import pandas as pd
from bokeh.models import DatetimeTickFormatter, ColumnDataSource
#Import data-->Weight measurements over a period of time [ STUB ]
weight = pd.read_csv("weight.csv")
#Define parameters
weight["Date"] = pd.to_datetime(weight['Date'])
weight["Weight"] = pd.to_numeric(weight['Weight'])
source = ColumnDataSource(weight)
#Take data and present in a graph
output_file("test.html")
p = figure(plot_width=400, plot_height=400, x_axis_type='datetime')
p.line(x='Date',y='Weight',line_width=2, source=source)
p.xaxis.formatter=DatetimeTickFormatter(
minutes=["%M"],
hours=["%H:%M"],
days=["%d/%m/%Y"],
months=["%m/%Y"],
years=["%Y"]
)
show(p)

Verbatim labels in legend in bokeh plots

I'm trying to use bokeh in python for interactive analysis of my plots.
My data are stored in pandas.Dataframe. I'd like to have a legend with column names as labels. However, bokeh extracts values from respective column instead.
import pandas as pd
from bokeh.plotting import figure
from bokeh.io import output_notebook, show
from bokeh.models import ColumnDataSource
output_notebook()
BokehJS 0.12.13 successfully loaded.
df = pd.DataFrame({'accuracy': np.random.random(10)}, index=pd.Index(np.arange(10), name='iteration'))
df
output:
accuracy
iteration
0 0.977427
1 0.057319
2 0.307741
3 0.127390
4 0.662976
5 0.313618
6 0.214040
7 0.214274
8 0.864432
9 0.800101
Now plot:
p = figure(width=900, y_axis_type="log")
source = ColumnDataSource(df)
p.line(x='iteration', y='accuracy', source=source, legend='accuracy')
show(p)
Result:
Desired output, obtained with adding space: legend='accuracy'+' ':
Although I've reached my goal, the method does not satisfy me. I think, there should be more elegant and official way to tell between column name and legend label.

There is. Bokeh tries to "do the right thing" in most situations, but doing that makes for a few corner cases where the behavior is less desirable, and this is one of them. However, specifically in this instance, you can always be explicit about whether the string is to be interpreted as a value or as field:
from bokeh.core.properties import value
p.line(x='iteration', y='accuracy', source=source, legend=value('accuracy'))

Bokeh Slider using Pandas datetime index

I'm trying to add a slider via Bokeh to my plot which is connected to a pandas dataframe.
The plot is using the datetime index to show how Air Quality Index over one year.
I would like to add a slider for each month, January - December 2016.
I'm not able to find a clear example with code that connects the slider to a plot which is connected to a pandas dataframe. Someone help please!
I was able to find the following code, but the plot was generated with random data. The output of this code is exactly what I'm looking to do but with time series data.
from bokeh.io import output_notebook, show, vform
from bokeh.plotting import figure, Figure
from bokeh.models import ColumnDataSource, Slider, CustomJS
import numpy as np
output_notebook()
x = np.sort(np.random.uniform(0, 100, 2000))
y = np.sin(x*10) + np.random.normal(scale=0.1, size=2000)
fig = Figure(plot_height=400, x_range=(0, 2))
source = ColumnDataSource(data={"x":x, "y":y})
line = fig.line(x="x", y="y", source=source)
callback = CustomJS(args=dict(x_range=fig.x_range), code="""
var start = cb_obj.get("value");
x_range.set("start", start);
x_range.set("end", start+2);
""")
slider = Slider(start=0, end=100, step=2, callback=callback)
show(vform(slider, fig))
I also found the source code of making this type of slider (below/linked here) but I am unsure how to implement it. As you can probably tell, I'm fairly new to Bokeh. Please help!
class DateRangeSlider(AbstractSlider):
""" Slider-based date range selection widget. """
#property
def value_as_datetime(self):
''' Convenience property to retrieve the value tuple as a tuple of
datetime objects.
'''
if self.value is None:
return None
v1, v2 = self.value
if isinstance(v1, numbers.Number):
d1 = datetime.utcfromtimestamp(v1 / 1000)
else:
d1 = v1
if isinstance(v2, numbers.Number):
d2 = datetime.utcfromtimestamp(v2 / 1000)
else:
d2 = v2
return d1, d2
value = Tuple(Date, Date, help="""
Initial or selected range.
""")
start = Date(help="""
The minimum allowable value.
""")
end = Date(help="""
The maximum allowable value.
""")
step = Int(default=1, help="""
The step between consecutive values.
""")
format = Override(default="%d %b %G")

I just worked through a similar situation with my project. I did not use the pandas datetime functionality as my dates were mixed format, but it was easy to update once I cleaned my data. The important part is to have your callback function adjust the .data attribute of your ColumnDataSource.
In the example you have, the callback function is written in Javascript. I used the code from the example Iain references, but I had to do a small workaround for panda dataframes. In the example below, data is a list of panda dataframes.
def callback(attrname, old, new):
month = slider.value
source.data = ColumnDataSource(data[month]).data
This replaces the current data for the graph with data from a different pandas dataframe. If, like me, all your data is in one data frame, you could also do some pandas filtering to return the data that you want to display.
data_to_use = data[data['Month'] == month[slider.value]]
Again, when I did that, I had to convert data_to_use to a ColumnDataSource and then replace the .data attribute of the source for my graph.

The Gapminder example from the Bokeh gallery does this using years, a similar approach should work for Months for your dataset. As you are only worried about months, you don't need to work with a datetime index, just get it as a list.
gapminder

Bokeh: chart from pandas dataframe won't update on trigger

I have got a pandas dataframe whose columns I want to show as lines in a plot using a Bokeh server. Additionally, I would like to have a slider for shifting one of the lines against the other.
My problem is the update functionality when the slider value changes. I have tried the code from the sliders-example of bokeh, but it does not work.
Here is an example
import pandas as pd
from bokeh.io import vform
from bokeh.plotting import Figure, output_file, show
from bokeh.models import CustomJS, ColumnDataSource, Slider
df = pd.DataFrame([[1,2,3],[3,4,5]])
df = df.transpose()
myindex = list(df.index.values)
mysource = ColumnDataSource(df)
plot = Figure(plot_width=400, plot_height=400)
for i in range(len(mysource.column_names) - 1):
name = mysource.column_names[i]
plot.line(x = myindex, y = str(name), source = mysource)
offset = Slider(title="offset", value=0.0, start=-1.0, end=1.0, step=1)
def update_data(attrname, old, new):
# Get the current slider values
a = offset.value
temp = df[1].shift(a)
#to finish#
offset.on_change('value', update_data)
layout = vform(offset, plot)
show(layout)
Inside the update_data-function I have to update mysource, but I cannot figure out how to do that. Can anybody point me in the right direction?

Give this a try... change a=offset.value to a=cb_obj.get('value')
Then put source.trigger('change') after you do whatever it is you are trying to do in that update_data function instead of offset.on_change('value', update_data).
Also change offset = Slider(title="offset", value=0.0, start=-1.0, end=1.0, step=1, callback=CustomJS.from_py_func(offset))
Note this format I'm using works with flexx installed. https://github.com/zoofio/flexx if you have Python 3.5 you'll have to download the zip file, extract, and type python setup.py install as it isn't posted yet compiled for this version...

Display only part of Y-axis on Bokeh

Using Bokeh 0.8.1, how can i display a long timeserie, but start 'zoomed-in' on one part, while keeping the rest of data available for scrolling ?
For instance, considering the following time serie (IBM stock price since 1980), how could i get my chart to initially display only price since 01/01/2014 ?
Example code :
import pandas as pd
import bokeh.plotting as bk
from bokeh.models import ColumnDataSource
bk.output_notebook()
TOOLS="pan,wheel_zoom,box_zoom,reset,save"
# Quandl data, too lazy to generate some random data
df = pd.read_csv('https://www.quandl.com/api/v1/datasets/GOOG/NYSE_IBM.csv')
df['Date'] = pd.to_datetime(df['Date'])
df = df[['Date', 'Close']]
#Generating a bokeh source
source = ColumnDataSource()
dtest = {}
for col in df:
dtest[col] = df[col]
source = ColumnDataSource(data=dtest)
# plotting stuff !
p = bk.figure(title='title', tools=TOOLS,x_axis_type="datetime", plot_width=600, plot_height=300)
p.line(y='Close', x='Date', source=source)
bk.show(p)
outputs :
but i want to get this (which you can achieve with the box-zoom tool - but I'd like to immediately start like this)

So, it looks (as of 0.8.1) that we need to add some more convenient ways to set ranges with datetime values. That said, although this is a bit ugly, it does currently work for me:
import time, datetime
x_range = (
time.mktime(datetime.datetime(2014, 1, 1).timetuple())*1000,
time.mktime(datetime.datetime(2016, 1, 1).timetuple())*1000
)
p = bk.figure(
title='title', tools=TOOLS,x_axis_type="datetime",
plot_width=600, plot_height=300, x_range=x_range
)