Superset Caravel BI Tool Installation and Visualizations on Heroku

Bydatapandasadmin December 28, 2016March 13, 2017

Superset (formerly called Caravel ) BI Tool by http://airbnb.io/superset is a free Data Visualization tool which you can harness its powers for your visualization analytic needs.

If you are finding problems installing on your windows PC or you simply want your own online version you can simply sign up FREE with Heroku (at least choosing their Free Plan initially) . The Free version can host your Superset BI Tool and if your needs increase later on along the line you can upgrade to their flexible advanced plans.

2. Once you are signed up, with Heroku, it will be ideal to go through their Getting Started Documentation to familiarise yourself with the Heroku platform, if you are new to it.

3. After you familiarize yourself with the platform (or if you didn’t) simple visit this link to click on the Deploy to Heroku button to install Superset in less than 5 minutes

4. Give your app a name (which is your Superset url), choose your region and click on the Deploy button

5. Once it has finished installing, click on View / Open App to view your Superset / Caravel

6. You will be welcomed to your Fresh new Superset Screen

You might want to login into your BI Tool in the far right hand corner , but alas, you do not know the login details yet. You will have to create your own login now and become administrator of the tool.

This is where the initial familiarisation with the Heroku platform comes into play.

7. You will need the following steps to create your login details (this can be found at the previous link here). This the part you need from the link

” Things you should know

After deployment

Superset will be accessible at YOURAPPNAME.herokuapp.com.
To make changes to your app like creating admin user, clone your app locally using the Heroku Toolbelt:

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						heroku git:clone --app YOURAPPNAME

Create an Admin user by using

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						heroku run bash --app YOURAPPNAME
fabmanager create-admin --app superset
superset db upgrade
superset init

Check Papertrail logs for debugging any errors.”

8. Once you are logged in, here is a good guide to get started with creating your visualisations: Click this link Superset tutorials

9. If you have challenges populating your Superset database table with data, check out this post: Populating Superset Caravel MySQL database tables with Data via Heroku

10. Below is a screenshot of a “playaround” dashboard created with Superset -Heroku installation

11. All the best

If you need help implementing this in your business please Conact Us

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Aggregation – Pandas Numpy Python Series DataFrame
Bydatapandasadmin August 17, 2016October 23, 2016

In this quick notes, we will go through aggregation in Python. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy.

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import numpy as np
import pandas as pd
from pandas import Series, DataFrame

url = “http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/” In [2]:

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dframe_wine = pd.read_csv('winequality-red.csv', sep=';')

In [4]:

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dframe_wine.head()

Out[4]: fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density…

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Array Processing – Python Numpy – How to work with Arrays in Python

Bydatapandasadmin August 19, 2016October 23, 2016

Hi Guys, Thanks for all your emails. In this note, we will be looking at Array Processing in Python. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy. In [3]:

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

In [4]:

1	points = np.arange(-5,5,0.01)

In [5]:

1	dx, dy = np.meshgrid(points,points)

In [6]:

Out[6]:

array([[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99],

[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99],

...,

[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99],

[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99]])

In [7]:

Out[7]:

array([[-5. , -5. , -5. , ..., -5. , -5. , -5. ],

[-4.99, -4.99, -4.99, ..., -4.99, -4.99, -4.99],

[-4.98, -4.98, -4.98, ..., -4.98, -4.98, -4.98],

...,

[ 4.97, 4.97, 4.97, ..., 4.97, 4.97, 4.97],

[ 4.98, 4.98, 4.98, ..., 4.98, 4.98, 4.98],

[ 4.99, 4.99, 4.99, ..., 4.99, 4.99, 4.99]])

In [8]:…

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())

Want more information like this?

Superset Caravel BI Tool Installation and Visualizations on Heroku

” Things you should know

After deployment

If you need help implementing this in your business please Conact Us

Populating Superset Caravel MySQL database tables with Data via Heroku

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

Structured Pyramid Analysis Plan SMART GOAL Data Analysis

Aggregation – Pandas Numpy Python Series DataFrame

Array Processing – Python Numpy – How to work with Arrays in Python

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

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” Things you should know

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