Month: March 2017

3 Most Useful Examples to Add Interactivity to Graph Data Using Bokeh Library

Bokeh is a Python library for building advanced and modern data visualization web applications. Bokeh allows to add interactive controls like slider, buttons, dropdown menu and so on to the data graphs. Bokeh provides a variety of ways to embed plots and data into HTML documents including generating standalone HTML documents. [6]

There is a sharp increase of popularity for Bokeh data visualization libraries (Fig 1), reflecting the increased interest in machine learning and data science over the last few years.

Fig 1. Trend for Bokeh and Other Data Visualizations Libraries. Source: Google Trends

In this post we put together 4 most common examples of data plots using Bokeh. The examples include such popular controls like slider, button. They use loading data from data files which is common situation in practice. The examples show how to add create plot and how to add interactivity using Bokeh and will help to make quick start in using Bokeh.

Our first example demonstrates how to add interactivity with slide control. This example is taken from Bokeh documentation [4]. When we change the slider value the line is changing its properties. See Fig 2 for example how the plot is changing. The example is using callbak function attached to slider control. 


# -*- coding: utf-8 -*-

from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource, Slider
from bokeh.plotting import Figure, output_file, show

# fetch and clear the document
from bokeh.io import curdoc
curdoc().clear()

output_file("callback.html")

x = [x*0.005 for x in range(0, 200)]
y = x

source = ColumnDataSource(data=dict(x=x, y=y))

plot = Figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)

def callback(source=source, window=None):
    data = source.data
    f = cb_obj.value
    x, y = data['x'], data['y']
    for i in range(len(x)):
        y[i] = window.Math.pow(x[i], f)
    source.trigger('change')

slider = Slider(start=0.1, end=4, value=1, step=.1, title="power",
                callback=CustomJS.from_py_func(callback))

layout = column(slider, plot)

show(layout)

Alternatively we can attach callback through the js_on_change method of Bokeh slider model: 


callback = CustomJS(args=dict(source=source), code="""
    var data = source.data;
    var f = cb_obj.value
    x = data['x']
    y = data['y']
    for (i = 0; i < x.length; i++) {
        y[i] = Math.pow(x[i], f)
    }
    source.trigger('change');
""")

slider = Slider(start=0.1, end=4, value=1, step=.1, title="power")
slider.js_on_change('value', callback)
Fig 2A. Initial Data plot
Fig 2B. Data plot after changed slider position

Our second example shows how to use button control. In this example we use Button on_click method to change graph. 


# -*- coding: utf-8 -*-

from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource
from bokeh.plotting import Figure, output_file, show

from bokeh.io import curdoc
curdoc().clear()

from bokeh.models.widgets import Button
output_file("button.html")


x = [x*0.05 for x in range(0, 200)]
y = x

source = ColumnDataSource(data=dict(x=x, y=y))

plot = Figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)

callback = CustomJS(args=dict(source=source), code="""
    var data = source.data;
    x = data['x']
    y = data['y']
    for (i = 0; i < x.length; i++) {
        y[i] = Math.pow(x[i], 4)
    }
    source.trigger('change');
""")


toggle1 = Button(label="Change Graph", callback=callback, name="1")
layout = column(toggle1 , plot)

show(layout)

Our 3rd example demonstrates how to load data from csv data file. This example is borrowed from stackoverflow [5] In this example we also use button, but here we load data from file to dataframe. We use two buttons, two files and 2 dataframes, the buttons allow to switch between data files and reload the graph.


# -*- coding: utf-8 -*-
from bokeh.io import vplot
import pandas as pd
from bokeh.models import CustomJS, ColumnDataSource
from bokeh.models.widgets import Button
from bokeh.plotting import figure, output_file, show

output_file("load_data_buttons.html")

df1 = pd.read_csv("data_file_1.csv")
df2 = pd.read_csv("data_file_2.csv")

df1.columns = df1.columns.str.strip()
df2.columns = df2.columns.str.strip()
plot = figure(plot_width=400, plot_height=400, title="xxx")
source = ColumnDataSource(data=dict(x=[0, 1], y=[0, 1]))
source2 = ColumnDataSource(data=dict(x1=df1.x.values, y1=df1.y.values, 
                                    x2=df2.x.values, y2=df2.y.values))

plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)

callback = CustomJS(args=dict(source=source, source2=source2), code="""
        var data = source.get('data');
        var data2 = source2.get('data');
        data['x'] = data2['x' + cb_obj.get("name")];
        data['y'] = data2['y' + cb_obj.get("name")];
        source.trigger('change');
    """)

toggle1 = Button(label="Load data file 1", callback=callback, name="1")
toggle2 = Button(label="Load data file 2", callback=callback, name="2")

layout = vplot(toggle1, toggle2, plot)

show(layout)

As mentioned on the web, Interactive data visualizations turn plots into powerful interfaces for data exploration. [7] The shown above examples demonstrate how to make the graph data interactive and hope will help to make quick start in this direction.

References
1. 5 Python Libraries for Creating Interactive Plots
2. 10 Useful Python Data Visualization Libraries for Any Discipline
3. The Most Popular Language For Machine Learning and Data Science
4. Welcome to Bokeh
5. Load graph data from files on button click with bokeh
6. Embedding Plots and Apps
7. How effective data visualizations let users have a conversation with data



How to Write to a Google Sheet with a Python Script

My post How to Write to a Google Spreadsheet with a Perl Script that was published some time ago is still getting a lot of visitors. This is not surprising as cloud computing is a fast-growing business. Below is the chart of number of searches for phrase “Google Sheet” from Google Trends site. The usage of Google Sheet looks increasing over the years.

“Google Sheet” trend, source Google Trends

So now I decided to look how to use python for Google Sheet.

My interest to this topic also come from the idea to have Adsense and Google Analytics data on the Google Drive and do analysis in python. Keeping files on Google Drive provides access to data from different computers or devices and allows sharing with other people.

Setup
I found good post [2] that helped me quickly setup everything that is needed on Google Drive.

Despite of good instructions I run into the following minor issues:
The file on Google Drive should be shared with the email in secret key file. But this email was not the same email that I use for usual login to Google site. Google created some new email, that is starting with project name. Only when I shared the Google Sheet with that new email it started to work.

The sheet name in the below statement for opening file, for some reason did not take upper case. The actual sheet name was “Sheet1″ but the statement below worked with “sheet1″ only ( s in low case):
sheet = client.open(“filename”).sheet1

Reading and Updating Google Sheet
Here is the small example of python code showing how to read or update Google Sheet.


sheet.update_cell(1, 2, "text 34")

for i in range (2):
    sheet.update_cell(1, i+3, i)

# Returns a list of Cell objects from a specified range.
ar=sheet.range('A1:D1') 
for obj in ar:
    print (obj.value)

Once you get understanding how to do basic operation you can write more complicated code as you need. There are many benefits in using files in the cloud.

Full python source code


# -*- coding: utf-8 -*-
import gspread
from oauth2client.service_account import ServiceAccountCredentials


scope = ['https://spreadsheets.google.com/feeds']
creds = ServiceAccountCredentials.from_json_keyfile_name('client_secret.json', scope)
client = gspread.authorize(creds)

sheet = client.open("File name").sheet1
sheet.update_cell(1, 2, "text 34")

for i in range (2):
    sheet.update_cell(1, i+3, i)

### Returns a list of Cell objects from a specified range.
ar=sheet.range('A1:D1') 
for obj in ar:nprint (obj.value)

Accessing through Web
We can use the same code to access Google Sheet through web. Here is the test example with flask. Python script is navigating through 4 cells, counting the sum and displaying it on the web. To access Google Sheet we used the same code as before.
Below you can find screenshot of web page and Google Sheet.
To run this example you need:
install flask
put this python file and client_secret.json file into the same flask folder
run this python script from command line
run web browser as on the screenshot.

Retrieving the content of Google Sheet through web page with python and flask

Below is the python computer code.


from flask import Flask
app = Flask(__name__)

@app.route('/')
def index():
  return 'Hello World!'


@app.route('/user/')
def user(name):

   import gspread
   from oauth2client.service_account import ServiceAccountCredentials
   scope = ['https://spreadsheets.google.com/feeds']
   creds = ServiceAccountCredentials.from_json_keyfile_name('client_secret.json', scope)
   client = gspread.authorize(creds)

   sheet = client.open("INSERT_REPORT_NAME_HERE").sheet1
   ar=sheet.range('A1:D1')
   sum=0 
   for obj in ar:
       sum = sum + int(obj.value)
   HTML_string='Hello, {}! Total = {}'
   return HTML_string.format(name , str(sum))  

if __name__ == '__main__':
   app.run(debug=True)

References
1. How to Write to a Google Spreadsheet with a Perl Script
2. Google Spreadsheets and Python