Populating Superset Caravel MySQL database tables with Data via Heroku

Bydatapandasadmin December 28, 2016December 28, 2016

If you installed your Superset (Caravel) BI Tool with Heroku and if you are finding it difficult to initially populate your MySQL database tables with Data and you will be using MySQL the default database that come with your installation, then you can follow these steps to get data into Superset.

(By the way, you may visit this post on quick guide to installing Superset with Heroku: Superset Caravel BI Tool Installation and Visualizations on Heroku)

Log into your Heroku account and in the far right hand corner locate your account and click on Dashboard
You will see all your apps in the dashboard.
Click on Resources and click the “JawsDB MySQL” database.
You will get a window which will give you all the parameters you will need to connect to your Superset MySQL database
Follow the onscreen instruction and download and install MySQL Workbench
Once the MySQL Workbench is installed , you can now use the connections parameters provided to connect the workbench to your Superset MySQL database hosted on Heroku
Once you have filled all the sections and you have tested the connection and it works fine, connect to the database and check the schema inside the WorkBench
The schema you will see in the MySQL Workbench will be same schema you will find in your Superset database. Example is as in this
Now you can create your tables in the schema and insert data into it all inside the Workbench. (If you want to experiment with sample data, you can generate some from here with a complete insert statements. Visit this site) After that click on the refresh button next to the word SCHEMA.
You can preview your newly created table and check your data in the WorkBench
Once data is showing fine in your MySQL Workbench you can visit SuperSet SQL Lab and check your data showing there
You can then follow the Superset tutorial to start your visualizations
Below is a “playaround” dashboard, generated with Superset installation on Heroku

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Business Intelligence - BI | Data Warehousing | Tools

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There are a lot of ER Diagram Tools. However, by far, one of the favourite ones I use is the ER Assistant which is also free. You can download the ER Assistant free software from here: ER Assistant Download. Install the program after download. In this post, we will learn how to Create Entities Create…

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Business Intelligence - BI

Superset Caravel BI Tool Installation and Visualizations on Heroku
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Business Intelligence - BI | SQL

Rank a Place Visited By a Customer- Microsoft SQL Server – T-Transact

Bydatapandasadmin November 9, 2016

This is a short guide to getting ranking or rating from Microsoft Sql Server customers. Ranking is taken by checking how many times a customer has visited a particular country divided by the total number of travels the customer has made within the database

/*make a back up of the recommender rankings table*/

truncate table antrak.dbo.recommender_rankings_copy

insert into antrak.dbo.recommender_rankings_copy

select * from antrak.dbo.recommender_rankings

/*select total number of times a customer has travelled to a specific country*/

select

[First Name]

,Country

, count(*) as [# times visited]

into #travelpercountry

FROM

[Antrak].[dbo].[FlightBookingsMain]

group by

[First Name]

,Country

order by [First Name]

/*total number of times each customer has travelled to all destinations*/

select

[First Name]

, count(*) as [# times visited]

into #totaltravel

FROM

[Antrak].[dbo].[FlightBookingsMain]

group by

[First Name]

----,Country

order by [First Name]

-- lets view by the total bookings

select * from #totaltravel order by [First Name]

-- lets view bookings per country

select * from #travelpercountry order by [First Name]

/*get rating*/

drop table antrak.dbo.recommender_rankings

select

tt.[First Name]

,Country

,cast(round((cast(tpc.[# times visited] as decimal(3,0)) / cast(tt.[# times visited] as decimal(3,0)) * 100), 1) as float) as rating

into antrak.dbo.recommender_rankings

from

#totaltravel as tt

inner join

#travelpercountry as tpc

on tt.[First Name] = tpc.[First Name]

order by [First Name]

select * from antrak.dbo.recommender_rankings

/*lets refresh the temporary tables*/

drop table #totaltravel

drop table #travelpercountry

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Python

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Bydatapandasadmin August 26, 2016October 23, 2016

This is some quick notes about graphing or plotting graphs with Matplotlib in Python. Credits to sentdex. You can check him out on Youtube.

import matplotlib.pyplot as plt

%matplotlib inline

# simple syntax is matplotlib plots as plt.plot(x, y)

plt.plot([1,2,3], [5,6,7])

plt.show()

In [4]:

# plotting with variables and basic labelling of our plot

x = [1,2,3]

y = [5,6,7]

##adding a legend to the plot

#add another line vectors first

x2 = [4,6,8]

y2= [10,14,18]

plt.plot(x, y, label='First Line') # you have to give the plots a label before you can call the legends on them

plt.plot(x2, y2, label='Second Line')

plt.xlabel('Plot number')

plt.ylabel('Important var')

plt.title('Plotting\nNice Graph') #\nGives you a sub title

plt.legend() #call the legend

plt.show()

In [10]:

#plotting barcharts and histograms

x3 = [2,4,6,8,10]

y3 = [3,5,7,9,11]

#second set of bars

x4 = [1,3,5,7,9]

y4 = [2,6,6,12,4]

#plot barchart

plt.bar(x3, y3, label='Bar1', color='r')

plt.bar(x4,y4, label='Bar2', color ='c')

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Bar Chart')

plt.legend()

plt.show()

In [14]:

#plotting histogram.

population_dist = [24,45,5,6,42,65,34,75,75,34,75,7,24,26,87,98,98,67,86, 65,63, 87,98,76,53,35]

#ids = [x for x in range(len(population_dist))] for bar chart plotting

#plot barchart

#plt.bar(ids, population_dist, label='hist')

#histogram works with bins so we will have to create bin 'containers' for our distribution

bins = [10,20,30,40,50,60,70,80,90,100]

plt.hist(population_dist, bins, histtype='bar', rwidth=0.5)

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Histogram')

plt.legend()

plt.show()

In [20]:

## scatter plot.

#basic skeletion

x = [1,2,3,4,5,6,7,8,9]

y = [4,5,8,3,6,9,5,4,8]

plt.scatter(x,y, label='ScatterShow', color='c', marker='^' , s=60) # s = size of the marker

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Scatter')

plt.legend()

plt.show()

In [23]:

#stacked plot

days = [1, 2, 3, 4, 5]

sleeping = [6,8,7,3,8]

eating = [6,9,7,3,6]

working = [9,8,5,7,8]

#working around legends for stackplots

plt.plot([],[], color='red', label='eating', linewidth=2)

plt.plot([],[], color='blue', label='sleeping', linewidth=2)

plt.plot([],[], color='yellow', label='working', linewidth=2)

plt.stackplot(days, eating, sleeping, working, color=['red','blue','yellow'])

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Stacked Plot')

plt.legend()

plt.show()

In [30]:

#pie charts

days = [1, 2, 3, 4, 5]

sleeping = [6,8,7,3,8]

eating = [6,9,7,3,6]

working = [9,8,5,7,8]

slices = [2,4,6,8,1]

colors = ['red','blue','green','brown','pink']

activities = ['sleeping','eating','working','dancing','travelling']

plt.pie(slices, labels=activities, colors=colors, startangle=90, shadow=True, explode=(0,0.1,0,0,0), autopct='%1.1f%%')

Out[30]:

([<matplotlib.patches.Wedge at 0x814e588>,

<matplotlib.patches.Wedge at 0x7fb7080>,

<matplotlib.patches.Wedge at 0x96f9ba8>,

<matplotlib.patches.Wedge at 0x82853c8>,

<matplotlib.patches.Wedge at 0x785a080>],

[<matplotlib.text.Text at 0x82ac4a8>,

<matplotlib.text.Text at 0x96f9438>,

<matplotlib.text.Text at 0x8026390>,

<matplotlib.text.Text at 0x94ac400>,

<matplotlib.text.Text at 0x785ada0>],

[<matplotlib.text.Text at 0x7fb7fd0>,

<matplotlib.text.Text at 0x96f97b8>,

<matplotlib.text.Text at 0x8285f98>,

<matplotlib.text.Text at 0x94aca58>,

<matplotlib.text.Text at 0x65bf860>])

In [48]:

#modifying the matplotlib

#plotting barchart.

x3 = [2,4,6,8,10]

y3 = [3,5,7,9,11]

#second set of bars

x4 = [1,3,5,7,9]

y4 = [2,6,6,12,4]

fig = plt.figure(facecolor='#f0f0f0') #set facecolour or background colour which will be maintained once we save our graph

ax1 = plt.subplot2grid((1,1),(0,0))

#tilt the xlabels

for labels in ax1.xaxis.get_ticklabels():

labels.set_rotation(45)

#plot barchart

ax1.bar(x3, y3, label='Bar1', color='r')

ax1.bar(x4,y4, label='Bar2', color ='c')

ax1.grid(True) #add a grid

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Bar Chart')

plt.legend()

#customise the legend

leg = ax1.legend(loc=2,ncol=6, prop={'size':10})

leg.get_frame().set_alpha(0.4)

##define spacing around the ticks and labels

plt.subplots_adjust(left = 0.09, bottom=0.20, right=0.94, top= 0.93, wspace = 0.2, hspace = 0)

plt.show()

#lets save the graph

fig.savefig('matplotlib.png', facecolor=fig.get_facecolor())

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Business Intelligence - BI

Structured Pyramid Analysis Plan SMART GOAL Data Analysis
Bydatapandasadmin September 20, 2016September 26, 2016

The key advice for any Data Analysis project is to never start any analysis without first having a structured plan. You should set your goals and they should be S.M.A.R.T Below are the two charts to help with Analysis. This is part of Data Visualization and Communication with Tableau by Duke University on Coursera. Set your…

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Populating Superset Caravel MySQL database tables with Data via Heroku

How To Use ER Assistant – FREE Entity Relationship Diagram (ERD) Creation Tool

Superset Caravel BI Tool Installation and Visualizations on Heroku

Data Analytics Framework – Simple Short Notes For Aspiring Data Analysts

Rank a Place Visited By a Customer- Microsoft SQL Server – T-Transact

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Structured Pyramid Analysis Plan SMART GOAL Data Analysis

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