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:

Default

				1

						heroku git:clone --app YOURAPPNAME

Create an Admin user by using

Default

				1
2
3
4

						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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Python

ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook
Bydatapandasadmin August 21, 2016November 14, 2016

If you have the above problem, to fix this, follow these guidelines as advised by Flowerowl at this link (https://github.com/muricoca/crab/issues/92): The steps are as follows: Don’t install crab in this way: pip install crab / easy_install crab You can find crab in pypi.python.org, this is not our scikits.crab source Solution: git clone https://github.com/muricoca/crab.git python setup.py…

Read More ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook

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 | 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…

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

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…

Python

Getting Stock Prices from Yahoo and plotting Python 3 Matplolib Urllib

Bydatapandasadmin September 3, 2016October 23, 2016

This is some quick notes about getting stock data from Yahoo and plotting it using Matplotlib . The Python version used is Python 3.5 Credits to sentdex. You can check him out on Youtube. In [11]:

import matplotlib.pyplot as plt

import numpy as np

import urllib

import matplotlib.dates as mdate

import urllib.request

%matplotlib inline

#%matplotlib qt

#%matplotlib notebook

# interactive popout window plot

In [12]:

#getting data from the internet

#define our converter function

def bytespdate2num(fmt, encoding='utf-8'):

strconverter = mdate.strpdate2num(fmt)

def bytesconverter(b):

s = b.decode(encoding)

return strconverter(s)

return bytesconverter

In [13]:

#define a function which will take the stock data and plot the graph

def graph_data(stock):

stock_data_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'

source_code = urllib.request.urlopen(stock_data_url).read().decode()

stock_data =[]

split_source = source_code.split('\n') #split the code based on next line

#lets grab only the relevant stock data

for line in split_source:

split_line = line.split(',')

if(len(split_line))==6:

if 'values' not in line and 'labels' not in line:

stock_data.append(line)

date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data, delimiter=',',unpack=True,

converters={0: bytespdate2num('%Y%m%d')})

plt.plot_date(date,closep,'-', label='Price')

plt.xlabel('Date')

plt.ylabel('Price')

plt.title('Interesting stock graph from yahoo')

plt.legend()

In [14]:

1 2	#lets plot TSLA stock prices. graph_data('TSLA')

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Uncategorized

How to Install Whl Packages and Modules in Python
Bydatapandasadmin August 19, 2017August 19, 2017

Sometimes, you might need some packages which do not come with the standard Python libraries and you would want to install them separately. You will notice how frustrating it is when you get stuck because of a package you are missing. Sentdex on Youtube covers that accurately and you can watch him here: …

Read More How to Install Whl Packages and Modules in Python

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

ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

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

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

Getting Stock Prices from Yahoo and plotting Python 3 Matplolib Urllib

How to Install Whl Packages and Modules in Python

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” Things you should know

After deployment

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