The task is to automate the Visualization. The CSV file contains large nos of features (column names e:g. 32 nos it may increase in future). The task is to plot Interactive Visualization. All the examples I found are hardcoded for the dynamic features selection.
But the requirement is to make the stuff dynamic. How to make it dynamic? Please guide.
I have successfully plotted the graph dynamically, but could not connect the interactive part. The code is as follows:
import pandas as pd
from bokeh.plotting import figure
from bokeh.io import show
from bokeh.models import CustomJS,HoverTool,ColumnDataSource,Select
from bokeh.models.widgets import CheckboxGroup
from bokeh.models.annotations import Title, Legend
import itertools
from bokeh.palettes import inferno
from bokeh.layouts import row
def creat_plot(dataframe):
data=dataframe
#Converting the timestamp Column to Timestamp datatype so that it can be used for Plotting on X-axis
data['timestamp'] = pd.to_datetime(data['timestamp'])
#Segregating Date and Time from timestamp column. It will be used in Hover Tool
date = lambda x: str(x)[:10]
data['date'] = data[['timestamp']].applymap(date)
time= lambda x: str(x)[11:]
data['time'] = data[['timestamp']].applymap(time)
#Converting whole dataframe ColumnDatasource for easy usage in hover tool
source = ColumnDataSource(data)
# List all the tools that you want in your plot separated by comas, all in one string.
TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover"
# New figure
t = figure(x_axis_type = "datetime", width=1500, height=600,tools=TOOLS,title="Plot for Interactive Features")
#X-axis Legend Formatter
t.xaxis.formatter.days = '%d/%m/%Y'
#Axis Labels
t.yaxis.axis_label = 'Count'
t.xaxis.axis_label = 'Date and Time Span'
#Grid Line Formatter
t.ygrid.minor_grid_line_color = 'navy'
t.ygrid.minor_grid_line_alpha = 0.1
t.xgrid.visible = True
t.ygrid.visible= True
#Hover Tool Usage
t.select_one(HoverTool).tooltips = [('Date', '@date'),('Time', '@time')]
#A color iterator creation
colors = itertools.cycle(inferno(len(data.columns)))
#A Line type iterator creation
line_types= ['solid','dashed','dotted','dotdash','dashdot']
lines= itertools.cycle(line_types)
column_name=[]
#Looping over the columns to plot the Data
for m in data.columns[2:len(data.columns)-2]:
column_name.append(m)
a=t.line(data.columns[0], m ,color=next(colors),source=source,line_dash=next(lines), alpha= 1)
#Adding Label Selection Check Box List
column_name= list(column_name)
checkboxes = CheckboxGroup(labels = column_name, active= [0,1,2])
show(row(t,checkboxes))
The above function can be used as follows:
dataframe= pd.read_csv('data.csv')
creat_plot(dataframe)
**The above code is executed on following requirements:
- Bokeh version: 2.2.3
- Panda Version: 1.1.3
The plot should be linked with the checkbox values. The features selected through the checkboxes shall be plotted only.
The solution to the above requirement is as follows:
import pandas as pd
from bokeh.plotting import figure
from bokeh.io import show,output_file
from bokeh.models import CustomJS,HoverTool,ColumnDataSource,Select
from bokeh.models.widgets import CheckboxGroup
from bokeh.models.annotations import Title, Legend
import itertools
from bokeh.palettes import inferno
from bokeh.layouts import row
def creat_plot(dataframe):
data=dataframe
#Converting the timestamp Column to Timestamp datatype so that it can be used for Plotting on X-axis
data['timestamp'] = pd.to_datetime(data['timestamp'])
#Segregating Date and Time from timestamp column. It will be used in Hover Tool
date = lambda x: str(x)[:10]
data['date'] = data[['timestamp']].applymap(date)
time= lambda x: str(x)[11:]
data['time'] = data[['timestamp']].applymap(time)
#Converting whole dataframe ColumnDatasource for easy usage in hover tool
source = ColumnDataSource(data)
# List all the tools that you want in your plot separated by comas, all in one string.
TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover"
# New figure
t = figure(x_axis_type = "datetime", width=1500, height=600,tools=TOOLS,title="Plot for Interactive Visualization")
#X-axis Legend Formatter
t.xaxis.formatter.days = '%d/%m/%Y'
#Axis Labels
t.yaxis.axis_label = 'Count'
t.xaxis.axis_label = 'Date and Time Span'
#Grid Line Formatter
t.ygrid.minor_grid_line_color = 'navy'
t.ygrid.minor_grid_line_alpha = 0.1
t.xgrid.visible = True
t.ygrid.visible= True
#Hover Tool Usage
t.select_one(HoverTool).tooltips = [('Date', '@date'),('Time', '@time')]
#A color iterator creation
colors = itertools.cycle(inferno(len(data.columns)))
#A Line type iterator creation
line_types= ['solid','dashed','dotted','dotdash','dashdot']
lines= itertools.cycle(line_types)
feature_lines = []
column_name=[]
#Looping over the columns to plot the Data
for m in data.columns[2:len(data.columns)-2]:
column_name.append(m)
#Solution to my question is here
feature_lines.append(t.line(data.columns[0], m ,color=next(colors),source=source,line_dash=next(lines), alpha= 1, visible=False))
#Adding Label Selection Check Box List
column_name= list(column_name)
#Solution to my question,
checkbox = CheckboxGroup(labels=column_name, active=[])
#Solution to my question
callback = CustomJS(args=dict(feature_lines=feature_lines, checkbox=checkbox), code="""
for (let i=0; i<feature_lines.length; ++i) {
feature_lines[i].visible = i in checkbox.active
}
""")
checkbox.js_on_change('active', callback)
output_file('Interactive_data_visualization.html')
show(row(t, checkbox))