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Struggling to understand why this bokeh visual will not allow me to change plots and see the predicted data. The plot and select (dropdown-looking) menu appears, but I'm not able to change the plot for items in the menu.
Running Bokeh 1.2.0 via Anaconda. The code has been run both inside & outside of Jupyter. No errors display when the code is run. I've looked through the handful of SO posts relating to this same issue, but I've not been able to apply the same solutions successfully.
I wasn't sure how to create a toy problem out of this, so in addition to the code sample below, the full code (including the regression code and corresponding data) can be found at my github here (code: Regression&Plotting.ipynb, data: pred_data.csv, historical_data.csv, features_created.pkd.)
import pandas as pd
import datetime
from bokeh.io import curdoc, output_notebook, output_file
from bokeh.layouts import row, column
from bokeh.models import Select, DataRange1d, ColumnDataSource
from bokeh.plotting import figure
#Must be run from the command line
def get_historical_data(src_hist, drug_id):
historical_data = src_hist.loc[src_hist['ndc'] == drug_id]
historical_data.drop(['Unnamed: 0', 'date'], inplace = True, axis = 1)#.dropna()
historical_data['date'] = pd.to_datetime(historical_data[['year', 'month', 'day']], infer_datetime_format=True)
historical_data = historical_data.set_index(['date'])
historical_data.sort_index(inplace = True)
# csd_historical = ColumnDataSource(historical_data)
return historical_data
def get_prediction_data(src_test, drug_id):
#Assign the new date
#Write a new dataframe with values for the new dates
df_pred = src_test.loc[src_test['ndc'] == drug_id].copy()
df_pred.loc[:, 'year'] = input_date.year
df_pred.loc[:, 'month'] = input_date.month
df_pred.loc[:, 'day'] = input_date.day
df_pred.drop(['Unnamed: 0', 'date'], inplace = True, axis = 1)
prediction = lin_model.predict(df_pred)
prediction_data = pd.DataFrame({'drug_id': prediction[0][0], 'predictions': prediction[0][1], 'date': pd.to_datetime(df_pred[['year', 'month', 'day']], infer_datetime_format=True, errors = 'coerce')})
prediction_data = prediction_data.set_index(['date'])
prediction_data.sort_index(inplace = True)
# csd_prediction = ColumnDataSource(prediction_data)
return prediction_data
def make_plot(historical_data, prediction_data, title):
#Historical Data
plot = figure(plot_width=800, plot_height = 800, x_axis_type = 'datetime',
toolbar_location = 'below')
plot.xaxis.axis_label = 'Time'
plot.yaxis.axis_label = 'Price ($)'
plot.axis.axis_label_text_font_style = 'bold'
plot.x_range = DataRange1d(range_padding = 0.0)
plot.grid.grid_line_alpha = 0.3
plot.title.text = title
plot.line(x = 'date', y='nadac_per_unit', source = historical_data, line_color = 'blue', ) #plot historical data
plot.line(x = 'date', y='predictions', source = prediction_data, line_color = 'red') #plot prediction data (line from last date/price point to date, price point for input_date above)
return plot
def update_plot(attrname, old, new):
ver = vselect.value
new_hist_source = get_historical_data(src_hist, ver) #calls the function above to get the data instead of handling it here on its own
historical_data.data = ColumnDataSource.from_df(new_hist_source)
# new_pred_source = get_prediction_data(src_pred, ver)
# prediction_data.data = new_pred_source.data
#Import data source
src_hist = pd.read_csv('data/historical_data.csv')
src_pred = pd.read_csv('data/pred_data.csv')
#Prep for default view
#Initialize plot with ID number
ver = 781593600
#Set the prediction date
input_date = datetime.datetime(2020, 3, 31) #Make this selectable in future
#Select-menu options
menu_options = src_pred['ndc'].astype(str) #already contains unique values
#Create select (dropdown) menu
vselect = Select(value=str(ver), title='Drug ID', options=sorted((menu_options)))
#Prep datasets for plotting
historical_data = get_historical_data(src_hist, ver)
prediction_data = get_prediction_data(src_pred, ver)
#Create a new plot with the source data
plot = make_plot(historical_data, prediction_data, "Drug Prices")
#Update the plot every time 'vselect' is changed'
vselect.on_change('value', update_plot)
controls = row(vselect)
curdoc().add_root(row(plot, controls))
UPDATED: ERRORS:
1) No errors show up in Jupyter Notebook.
2) CLI shows a UserWarning: Pandas doesn't allow columns to be careated via a new attribute name, referencing `historical_data.data = ColumnDatasource.from_df(new_hist_source).
Ultimately, the plot should have a line for historical data, and another line or dot for predicted data derived from sklearn. It also has a dropdown menu to select each item to plot (one at a time).
Your update_plot is a no-op that does not actually make any changes to Bokeh model state, which is what is necessary to change a Bokeh plot. Changing Bokeh model state means assigning a new value to a property on a Bokeh object. Typically, to update a plot, you would compute a new data dict and then set an existing CDS from it:
source.data = new_data # plain python dict
Or, if you want to update from a DataFame:
source.data = ColumnDataSource.from_df(new_df)
As an aside, don't assign the .data from one CDS to another:
source.data = other_source.data # BAD
By contrast, your update_plot computes some new data and then throws it away. Note there is never any purpose to returning anything at all from any Bokeh callback. The callbacks are called by Bokeh library code, which does not expect or use any return values.
Lastly, I don't think any of those last JS console errors were generated by BokehJS.
Here the code I wrote.
I took the data from pandas DF (not pasted here).
The x values are from DF index columns that is a DateTime column.
The issue that I want to resolve is in line:
TOOLTIPS = [("index", "$index"),("(Time,Temperature)", "($x, $y)"),]
when I have to change the $x format to a correct format in order to see the the time format in the hover window on the bokeh plot.
see the python code
import datetime as dt
from bokeh.plotting import figure, output_file, show
from bokeh.layouts import gridplot
from bokeh.models import ColumnDataSource, CDSView, BooleanFilter
from bokeh.models import DatetimeTickFormatter
x=df_bases.index
y0=df_bases["base_1"]
y1=df_bases["base_5"]
y2=df_bases["base_12"]
# output to static HTML file
output_file("temperatures from thermocouples.html")
# add some renderers
output_file("Thermocouples temperature.html", title="Thermocouples temperature")
TOOLTIPS = [("index", "$index"),("(Time,Temperature)", "($x, $y)"),]
# create a new plot with a datetime axis type
p = figure( tooltips=TOOLTIPS , plot_width=1250, plot_height=580, x_axis_type="datetime", x_axis_label='Time',
y_axis_label='Temperature [°C]', title="Thermocouples temperature")
p.line(x, y0, legend="thermocouple 1", line_width=1 , color='navy', alpha=1)
p.line(x, y1, legend="thermocouple 5", color="green")
p.line(x, y2, legend="thermocouple 12", line_width=1 , color='orange', alpha=1)#, line_dash="4 4")
p.border_fill_color = "whitesmoke"
p.xaxis.formatter=DatetimeTickFormatter(
microseconds = ['%Y-%m-%d %H:%M:%S.%f'],
milliseconds = ['%Y-%m-%d %H:%M:%S.%3N'],
seconds = ["%Y-%m-%d %H:%M:%S"],
minsec = ["%Y-%m-%d %H:%M:%S"],
minutes = ["%Y-%m-%d %H:%M:%S"],
hourmin = ["%Y-%m-%d %H:%M:%S"],
hours=["%Y-%m-%d %H:%M:%S"],
days=["%Y-%m-%d %H:%M:%S"],
months=["%Y-%m-%d %H:%M:%S"],
years=["%Y-%m-%d %H:%M:%S"],
)
p.title.align = 'center'
# create a column data source for the plots to share
source = ColumnDataSource(data=dict(x=x, y0=y0, y1=y1, y2=y2))
# create a view of the source for one plot to use
view = CDSView(source=source)
# show the results
show(p)
Currently (as of Bokeh 1.2) the hover tool does not have any "always on" mode It only hovers in response to hit-testing glyphs that are added to the plot. Additionally there is no way to apply formatting to "special vars" like $x (that will be possible starting in Bokeh 2.0). Custom formatters can only be applied to hover tooltips for data columns. Given that, my best suggestion is to switch to using #xinstead (which interrogates the "x" data column, not the x mouse position". If you do that, you can use all the techniques in the Formatting Tooltip Fields section of the docs.
Since you did not provide a complete example (no data to run), I can only provide partial untested suggestions:
# use #x{%F} to specify the %F datetime format (or choose another) for the x column
TOOLTIPS = [("index", "$index"),("(Time,Temperature)", "(#x{%F}, $y)")]
# tell bokeh to use the "datetime" formatter for the x column
p.hover.formatters = {'x': 'datetime'}
# just a suggestion, often useful for timeseries plots
p.hover.mode = 'vline'
I'm attempting to create a BoxPlot using Bokeh. When I get to the section where I need to identify outliers, it fails if a given category has no outliers.
If I remove the "problem" category, the BoxPlot executes properly. it's only when I attempt to create this BoxPlot with a category that has no outliers it fails.
Any instruction on how to remedy this?
The failure occurs at the commented section "Prepare outlier data for plotting [...]"
import numpy as np
import pandas as pd
import datetime
import math
from bokeh.plotting import figure, show, output_file
from bokeh.models import NumeralTickFormatter
# Create time stamps to allow for figure to display span in title
today = datetime.date.today()
delta1 = datetime.timedelta(days=7)
delta2 = datetime.timedelta(days=1)
start = str(today - delta1)
end = str(today - delta2)
#Identify location of prices
itemloc = 'Everywhere'
df = pd.read_excel(r'C:\Users\me\prices.xlsx')
# Create a list from the dataframe that identifies distinct categories for the separate box plots
cats = df['subcategory_desc'].unique().tolist()
# Find the quartiles and IQR for each category
groups = df.groupby('subcategory_desc', sort=False)
q1 = groups.quantile(q=0.25)
q2 = groups.quantile(q=0.5)
q3 = groups.quantile(q=0.75)
iqr = q3 - q1
upper = q3 + 1.5*iqr
lower = q1 - 1.5*iqr
# Find the outliers for each category
def outliers(group):
cat = group.name
return group[(group.price > upper.loc[cat][0]) | (group.price < lower.loc[cat][0])]['price']
out = groups.apply(outliers).dropna()
# Prepare outlier data for plotting, we need coordinates for every outlier.
outx = []
outy = []
for cat in cats:
# only add outliers if they exist
if not out.loc[cat].empty:
for value in out[cat]:
outx.append(cat)
outy.append(value)
I expect that the Box-and-whisker portion of categories with no outliers merely show up without the outlier dots.
Have you tried the code from official documentation, https://docs.bokeh.org/en/latest/docs/gallery/boxplot.html?
# prepare outlier data for plotting, we need coordinates for every outlier.
if not out.empty:
outx = []
outy = []
for keys in out.index:
outx.append(keys[0])
outy.append(out.loc[keys[0]].loc[keys[1]])
I have been trying to plot a graph which has a dataframe having 3 columns . One is the "Hour", Second is the "amount" in Rupees and the third consist of "machine codes". I need to analyze the amount of transaction a machine does on an hourly basis. There are total 67 unique machine codes.
Kindy check here the data sample Here
These are the Libraries i have been using
import numpy as np
from bokeh.io import output_notebook, show
from bokeh.layouts import row
from bokeh.palettes import Viridis3
from bokeh.plotting import figure
from bokeh.models import CheckboxGroup, CustomJS
output_notebook()
p = figure()
props = dict(line_width=4, line_alpha=0.7)
x = sl['Hour']
y = sl['amount']
Now I have appended a list labels[] with all the machine codes
labels = []
active1 = []
for s in sl['machinecode'].unique():
labels.append(s)
active1.append(0)
I basically want to create checkboxes for all those machine codes , a user when check any machine code , a graph gets plotted . if a user again checks another machine code , the line of that machine code gets appended into a graph so that I could compare between machines.
j =0
for i in sl['machinecode'].unique():
l = p.line(x, y, color=Viridis3[0], legend="Line:" , **props)
j=j+1
checkbox = CheckboxGroup(labels=labels,
active=active1, width=100)
checkbox.callback = CustomJS(args=dict(l=l, checkbox=checkbox),
code="""
l0.visible = 0 in checkbox.active;
l1.visible = 1 in checkbox.active;
l2.visible = 2 in checkbox.active;
""""")
layout = row(checkbox, p)
show(layout)
The above code is showing something really different kindly check here what the graph is actually showing , it is plotting for every machine with a single color , checkboxes does not command the graph actually
Is it possible to add Tooltips to a Timeseries chart?
In the simplified code example below, I want to see a single column name ('a','b' or 'c') when the mouse hovers over the relevant line.
Instead, a "???" is displayed and ALL three lines get a tool tip (rather than just the one im hovering over)
Per the documentation (
http://docs.bokeh.org/en/latest/docs/user_guide/tools.html#hovertool), field names starting with “#” are interpreted as columns on the data source.
How can I display the 'columns' from a pandas DataFrame in the tooltip?
Or, if the high level TimeSeries interface doesn't support this, any clues for using the lower level interfaces to do the same thing? (line? multi_line?) or convert the DataFrame into a different format (ColumnDataSource?)
For bonus credit, how should the "$x" be formatted to display the date as a date?
thanks in advance
import pandas as pd
import numpy as np
from bokeh.charts import TimeSeries
from bokeh.models import HoverTool
from bokeh.plotting import show
toy_df = pd.DataFrame(data=np.random.rand(5,3), columns = ('a', 'b' ,'c'), index = pd.DatetimeIndex(start='01-01-2015',periods=5, freq='d'))
p = TimeSeries(toy_df, tools='hover')
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
("Series", "#columns"),
("Date", "$x"),
("Value", "$y"),
]
show(p)
Below is what I came up with.
Its not pretty but it works.
Im still new to Bokeh (& Python for that matter) so if anyone wants to suggest a better way to do this, please feel free.
import pandas as pd
import numpy as np
from bokeh.charts import TimeSeries
from bokeh.models import HoverTool
from bokeh.plotting import show
toy_df = pd.DataFrame(data=np.random.rand(5,3), columns = ('a', 'b' ,'c'), index = pd.DatetimeIndex(start='01-01-2015',periods=5, freq='d'))
_tools_to_show = 'box_zoom,pan,save,hover,resize,reset,tap,wheel_zoom'
p = figure(width=1200, height=900, x_axis_type="datetime", tools=_tools_to_show)
# FIRST plot ALL lines (This is a hack to get it working, why can't i pass in a dataframe to multi_line?)
# It's not pretty but it works.
# what I want to do!: p.multi_line(df)
ts_list_of_list = []
for i in range(0,len(toy_df.columns)):
ts_list_of_list.append(toy_df.index.T)
vals_list_of_list = toy_df.values.T.tolist()
# Define colors because otherwise multi_line will use blue for all lines...
cols_to_use = ['Black', 'Red', 'Lime']
p.multi_line(ts_list_of_list, vals_list_of_list, line_color=cols_to_use)
# THEN put scatter one at a time on top of each one to get tool tips (HACK! lines with tooltips not yet supported by Bokeh?)
for (name, series) in toy_df.iteritems():
# need to repmat the name to be same dimension as index
name_for_display = np.tile(name, [len(toy_df.index),1])
source = ColumnDataSource({'x': toy_df.index, 'y': series.values, 'series_name': name_for_display, 'Date': toy_df.index.format()})
# trouble formating x as datestring, so pre-formating and using an extra column. It's not pretty but it works.
p.scatter('x', 'y', source = source, fill_alpha=0, line_alpha=0.3, line_color="grey")
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Series", "#series_name"), ("Date", "#Date"), ("Value", "#y{0.00%}"),]
hover.mode = 'mouse'
show(p)
I’m not familiar with Pandas，I just use python list to show the very example of how to add tooltips to muti_lines, show series names ,and properly display date/time。Below is the result.
Thanks to #bs123's answer and #tterry's answer in Bokeh Plotting: Enable tooltips for only some glyphs
my result
# -*- coding: utf-8 -*-
from bokeh.plotting import figure, output_file, show, ColumnDataSource
from bokeh.models import HoverTool
from datetime import datetime
dateX_str = ['2016-11-14','2016-11-15','2016-11-16']
#conver the string of datetime to python datetime object
dateX = [datetime.strptime(i, "%Y-%m-%d") for i in dateX_str]
v1= [10,13,5]
v2 = [8,4,14]
v3= [14,9,6]
v = [v1,v2,v3]
names = ['v1','v2','v3']
colors = ['red','blue','yellow']
output_file('example.html',title = 'example of add tooltips to multi_timeseries')
tools_to_show = 'hover,box_zoom,pan,save,resize,reset,wheel_zoom'
p = figure(x_axis_type="datetime", tools=tools_to_show)
#to show the tooltip for multi_lines,you need use the ColumnDataSource which define the data source of glyph
#the key is to use the same column name for each data source of the glyph
#so you don't have to add tooltip for each glyph,the tooltip is added to the figure
#plot each timeseries line glyph
for i in xrange(3):
# bokeh can't show datetime object in tooltip properly,so we use string instead
source = ColumnDataSource(data={
'dateX': dateX, # python datetime object as X axis
'v': v[i],
'dateX_str': dateX_str, #string of datetime for display in tooltip
'name': [names[i] for n in xrange(3)]
})
p.line('dateX', 'v',source=source,legend=names[i],color = colors[i])
circle = p.circle('dateX', 'v',source=source, fill_color="white", size=8, legend=names[i],color = colors[i])
#to avoid some strange behavior(as shown in the picture at the end), only add the circle glyph to the renders of hover tool
#so tooltip only takes effect on circle glyph
p.tools[0].renderers.append(circle)
# show the tooltip
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("value", "#v"), ("name", "#name"), ("date", "#dateX_str")]
hover.mode = 'mouse'
show(p)
tooltips with some strange behavior,two tips displayed at the same time
Here is my solution. I inspected the glyph render data source to see what are the names on it. Then I use those names on the hoover tooltips. You can see the resulting plot here.
import numpy as np
from bokeh.charts import TimeSeries
from bokeh.models import HoverTool
from bokeh.plotting import show
toy_df = pd.DataFrame(data=np.random.rand(5,3), columns = ('a', 'b' ,'c'), index = pd.DatetimeIndex(start='01-01-2015',periods=5, freq='d'))
#Bockeh display dates as numbers so convert to string tu show correctly
toy_df.index = toy_df.index.astype(str)
p = TimeSeries(toy_df, tools='hover')
#Next 3 lines are to inspect how are names on gliph to call them with #name on hover
#glyph_renderers = p.select(dict(type=GlyphRenderer))
#bar_source = glyph_renderers[0].data_source
#print(bar_source.data) #Here we can inspect names to call on hover
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
("Series", "#series"),
("Date", "#x_values"),
("Value", "#y_values"),
]
show(p)
The original poster's code doesn't work with the latest pandas (DatetimeIndex constructor has changed), but Hovertool now supports a formatters attribute that lets you specify a format as a strftime string. Something like
fig.add_tool(HoverTool(
tooltip=[
('time', '#index{%Y-%m-%d}')
],
formatters={
'#index': 'datetime'
}
))