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 Science | Python

Save Multiple Pandas DataFrames to One Single Excel Sheet Side by Side or Dowwards – XlsxWriter

Bydatapandasadmin October 5, 2016October 23, 2016

This tutorial is just to illustrate how to save Python Pandas dataframe into one excel work SHEET . You can save it column-wise, that is side by side or row-wise, that is downwards, one dataframe after the other. In [110]:

import sqlalchemy

import pyodbc

from pandas import DataFrame

from bokeh.plotting import figure, output_file, show

import pandas as pd

In [111]:

df = DataFrame

## connect to SqlServer Database and get information.

try:

import urllib

params = urllib.quote_plus("DRIVER={SQL Server Native Client 11.0};SERVER=.\MSSQLSERVER_ENT;DATABASE=Antrak;Trusted_Connection=yes;")

engine = sqlalchemy.create_engine('mssql+pyodbc:///?odbc_connect=%s"' % params)

resoverall = engine.execute("SELECT top 10 * FROM FlightBookingsMain")

df = df(resoverall.fetchall())

df.columns = resoverall.keys()

except (RuntimeError, TypeError, NameError):

print('Error in Conneccting')

print(RuntimeError, TypeError, NameError)

finally:

print("connected")

connected

In [112]:

1 2	#lets look at the head of the dataframe df.head()

Out[112]: First Name Last Name…

Business Intelligence - BI | R - Programming

R Shiny App for Mining Fields Evaluation
ByBernard Adabankah December 29, 2018December 29, 2018

Presenting your report in an interactive and user-friendly way is a very effective communicative tool which can help you convey message to your audience easily. R is a very powerful analytic tool which helps you to do that with the “shiny” package. Here, I simply look at an app about the Mining Fields Evaluation which was…

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Business Intelligence - BI | Data Warehousing | SQL

Entity Relationship Diagram – ERD and Cardinality Symbols Simple Explanations
Bydatapandasadmin December 30, 2016

Understading cardinality notation and being able to explain them will go a long way in your database design and implementation. Here we look at a simple explanation of the symbols. (Credit to Data Warehousing for Business Intelligence by University of Colorado System on Coursera) Symbols in the above diagram and their meanings: – Oval: means 0 –…

Read More Entity Relationship Diagram – ERD and Cardinality Symbols Simple Explanations
Business Intelligence - BI | Python

Superset Heroku Installation – Microsoft SQL Server Database Connection on Windows PC
Bydatapandasadmin January 6, 2018January 6, 2018

Clone this github repository into YOUR OWN repository and Edit the link in the Readme.md file from https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy?template=https://github.com/dugjason/superset-on-heroku to the link in YOUR OWN CLONED repository eg. https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy?template=https://github.com/benadaba/hrmr Now scroll down and COMMIT (save) the changes ALSO edit the requirements.txt file and include any database requirements you want installed . eg pymssql==2.1.3, pyodbc==4.0.21 (this version was generating an…

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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…

Read More Structured Pyramid Analysis Plan SMART GOAL Data Analysis

Populating Superset Caravel MySQL database tables with Data via Heroku

Save Multiple Pandas DataFrames to One Single Excel Sheet Side by Side or Dowwards – XlsxWriter

R Shiny App for Mining Fields Evaluation

Entity Relationship Diagram – ERD and Cardinality Symbols Simple Explanations

Superset Heroku Installation – Microsoft SQL Server Database Connection on Windows PC

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Structured Pyramid Analysis Plan SMART GOAL Data Analysis

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