In [1]:

    

import numpy as np
import pandas as pd
import json
from ipywidgets import interact
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
output_notebook()


    

    






    

        
        Loading BokehJS ...
    






    

In [2]:

    

#your_local_path="F:/Study/Dashboard/Coffee/"


    

In [3]:

    

with open('Cafe.json') as f:
    i=1
    for line in f:
        coffee = pd.DataFrame(json.loads(line))


    

In [4]:

    

# coffee = pd.read_json('Cafe1.json')
#coffee = coffee.sample(n=1000)
#coffee.reset_index(drop=True)


    

In [5]:

    

stdf = coffee['Monthly Data'].apply(json.loads).apply(pd.Series)
stdf.reset_index(drop=True)
#stdf.head()


    

    
Out[5]:







  

    

      

      
2013:10
      
2013:11
      
2013:12
      
2013:9
      
2014:1
      
2014:10
      
2014:11
      
2014:12
      
2014:2
      
2014:3
      
...
      
2016:8
      
2016:9
      
2017:1
      
2017:2
      
2017:3
      
2017:4
      
2017:5
      
2017:6
      
2017:7
      
2017:8
    
  
  

    

      
0
      
{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 14.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 15.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 31.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 32.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 34.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 16.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 17.0, 'CostIncurredAtStore':...
      
...
      
{'NoOfEmployees': 114.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 122.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 146.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 157.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 170.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 176.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 190.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 206.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 215.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 222.0, 'CostIncurredAtStore'...
    
    

      
1
      
{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 14.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 15.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 30.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 31.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 34.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 17.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 18.0, 'CostIncurredAtStore':...
      
...
      
{'NoOfEmployees': 147.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 160.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 212.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 223.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 239.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 257.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 268.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 293.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 304.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 317.0, 'CostIncurredAtStore'...
    
    

      
2
      
{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 14.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 15.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 28.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 30.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 32.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 16.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 17.0, 'CostIncurredAtStore':...
      
...
      
{'NoOfEmployees': 94.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 103.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 135.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 148.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 157.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 166.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 179.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 193.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 204.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 223.0, 'CostIncurredAtStore'...
    
    

      
3
      
NaN
      
NaN
      
NaN
      
NaN
      
{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 24.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 26.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 27.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...
      
...
      
{'NoOfEmployees': 92.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 100.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 116.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 121.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 123.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 134.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 142.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 151.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 155.0, 'CostIncurredAtStore'...
      
NaN
    
    

      
4
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
{'NoOfEmployees': 18.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 19.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 21.0, 'CostIncurredAtStore':...
      
NaN
      
NaN
      
...
      
{'NoOfEmployees': 72.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 80.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 109.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 117.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 125.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 136.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 149.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 160.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 163.0, 'CostIncurredAtStore'...
      
{'NoOfEmployees': 174.0, 'CostIncurredAtStore'...
    
    

      
5
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...
      
NaN
      
NaN
      
...
      
{'NoOfEmployees': 49.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 53.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 62.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 67.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 74.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 77.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 83.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 90.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 97.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 99.0, 'CostIncurredAtStore':...
    
    

      
6
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
...
      
{'NoOfEmployees': 34.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 36.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 49.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 52.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 55.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 59.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 60.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 61.0, 'CostIncurredAtStore':...
      
{'NoOfEmployees': 66.0, 'CostIncurredAtStore':...
      
NaN
    
  

7 rows × 48 columns

In [6]:

    

coffee = coffee[['Store ID', 'Name', 'Brand', 'Store Number', 'Phone Number',
       'Ownership Type', 'Street Combined', 'Street 1', 'Street 2', 'Street 3',
       'City', 'State', 'Country', 'Coordinates', 'Latitude', 'Longitude',
       'Start Date', 'Monthly Data']]


    

In [7]:

    

#len(coffee.Country.unique())


    

In [8]:

    

stdf.columns = range(0,len(stdf.columns))


    

In [9]:

    

stdf['City'] = coffee['City']
stdf['Name'] = coffee['Name']
stdf_city = stdf[stdf['City'] == 'Pune']
#len(stdf_city['Name'].unique())


    

In [10]:

    

#stdf_city[0].apply(pd.Series)


    

In [11]:

    

from bokeh.palettes import Spectral11
from bokeh.models import HoverTool
from bokeh.models import LinearInterpolator, CategoricalColorMapper
from bokeh.models import ColumnDataSource

sss = stdf_city[0].apply(pd.Series)
Profit = sss['Profit']
source = ColumnDataSource(dict(
    x= sss['CostIncurredAtStore'],
    y= sss['RevenueOfStore'],
    profit = sss['Profit'],
    profit_scaled = Profit.apply(lambda x: abs(x/5)),
    stores = stdf_city['Name'],    
    emp = sss['NoOfEmployees'],
        
    ))


def update_m (month):
    sss = stdf_city[month].apply(pd.Series)
    Profit = sss['Profit']
    new_data = dict(
        x= sss['CostIncurredAtStore'],
        y= sss['RevenueOfStore'],
        stores = stdf_city['Name'],    
        emp = sss['NoOfEmployees'],
        profit = Profit,
        profit_scaled = Profit.apply(lambda x: abs(x/5))
        
    )
    source.data = new_data #updating the source data with the newdata i.e data of each year
    p1.title.text = str(month)  #updating the title 
    push_notebook() #push this into chart

size_mapper = LinearInterpolator(     #this is to give size for each data point according to their population
    x=[5, 15],
    y = [3,5]
)

#to give color to each type of data point
color_mapper = CategoricalColorMapper(
   factors = list(stdf_city['Name'].unique()),  #this tells the compiler to color the continents
  palette = Spectral11,)

hover = HoverTool(tooltips = [("Employee Count","@emp"),("Profit","@profit"), ("Revenue","@y"), ("Cost Incurred","@x")], #when u hover mouse on data points
                  show_arrow=False)



PLOT_OPTS = dict(     #the dimensions of figure is given
    height =450,
    width = 750,
    x_axis_type='log',
    x_range=[1300, 30000],
    y_range=(1500,30000)
)


p1 = figure( #how do u want the overall dimensions of fig
    title = str('2013 October Cost Incurred vs Revenue'),toolbar_location='above',  #title should always be in string format
    title_location = 'above',
    tools=[hover],
    **PLOT_OPTS)


p1.circle(
    x='x',y='y', #these have been wriiten before and is being called for the sake of hovering to work and is defined in update function
    size={'field':'profit_scaled'},  #we cant use the size of data point as population as the china population is one billion and all the pixels gets filled and hence we use a mapper and give the rangee of X and y axis
    color = {'field':'stores','transform':color_mapper}, #this will color all the regions defined by color_mapper
    legend='stores', #a legend of which color is what continent
    source=source,  #what is the data source
    alpha=0.9) #how much of transparency of data pojint

p1.legend.border_line_color = None  #to remove the border
p1.legend.location = (10,0)  #this is going to take legend out of the chart box
p1.right.append(p1.legend[0]) #this is going to place the legend to the right
show(p1, notebook_handle=True) #notebook_handle will take the consideration of viewing each year that we defined in update


    

    




/home/shwetakamal/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/api.py:107: RuntimeWarning: '<' not supported between instances of 'str' and 'int', sort order is undefined for incomparable objects
  result = result.union(other)
/home/shwetakamal/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/api.py:107: RuntimeWarning: '<' not supported between instances of 'int' and 'str', sort order is undefined for incomparable objects
  result = result.union(other)






    











  








    















    
Out[11]:




<Bokeh Notebook handle for In[11]>

In [12]:

    

interact(update_m, month=(0,47,1))


    

    






 
 










    
Out[12]:





<function __main__.update_m(month)>