Business Intelligence - BI | Data Science

Free Open Source Data Visualization Tools and Service

Bydatapandasadmin October 22, 2016December 28, 2016

There are a couple Free Open Source Data Visualization Tools and Services which you can make use of for your work data exploration and visualization purposes.

Below , I list a few ones in no order of importance.

Caravel: It is a Python based data exploration and visualization software by Airbnb. It is powerful and open source. Check this post on how to Freely install it with Heroku: Superset Caravel BI Tool Installation and Visualizations on Heroku

2. Metabase:

It is very easy to install and very user-friendly. The developers want everyone at your company to be ask and answer their data related question without needing to necessary know any coding.

I have not included Tableau and the likes , as the free versions only allows you to publish to the cloud and your company does not have much control over who sees your data and analysis.

Which 100% Free Open Source Data Visualization tools do you use? You can share in the comment box below:

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Data Science | Resources

DataSets Around the Web For Big Data, Machine Learning and DataScience Practices
ByBernard Adabankah September 8, 2018February 14, 2023

Here is a collection of several DataSet repositories, I have come across the web. ( I will adding more along the way) You can add to this by commenting on the post and I will update it in the this list Google Dataset Search : This is a great tool released by Google to help…

Read More DataSets Around the Web For Big Data, Machine Learning and DataScience Practices

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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Data Science | Uncategorized

Can the sample size be equal to the population size?
Bydatapandasadmin March 31, 2016October 23, 2016

What is population size: In simple terms, let’s us say population is the WHOLE SET and What is sample size: let us say it is the SUBSET (which is within the whole set) The norm Normally subsets are within the whole set and are smaller than the whole set Can they be equal ? Yes…

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Data Science | Resources | Uncategorized

Connect Push Commit Pull Your GitHub Repository To Your Local Computer’s Directory
Bydatapandasadmin April 22, 2017April 22, 2017

Introduction Github is a web version control system which is very useful when you are working on various projects or you want to keep track of changes you make to your project over time. It is a very useful tool as you venture into Data Science and Programming Objective After you have created a…

Read More Connect Push Commit Pull Your GitHub Repository To Your Local Computer’s Directory

Python

Simple Python Logging Callling Reload to Help Save Logging to File

Bydatapandasadmin September 3, 2016October 23, 2016

In this notes, we are looking at a simple but yet useful and powerful logging in Python In [77]:

import logging

#levels of logging are

#1 debug - detailed info

#2 info - confirmation that things went according to plan

#3 warning - something unexpected happened

#4 error - some function failed

#5 critical - something failed and application must close

from imp import reload

reload(logging) # this will help our logging to be saved to file

logging.basicConfig(filename='logFile3.log',format='%(asctime)s %(message)s', level=logging.DEBUG) # the logging will only log the level you set it to and any

#other levels ABOVE it but NOT below it

In [78]:

#lets define a simple function

def main():

try:

#mathFail = 1/0 # this will give an error which can be catched

if 1<2:

logging.debug('Entered into one of the if statements')

print('we are in the IF STATEMENT')

#this error will not break our entire script so we will catch the error and give some info

try:

urllib2.urlopen('http://www.google.com').read()

logging.info('Opened url fine')

except Exception as e:

logging.error('urllib2 url visit failed and the reason was %s' % str(e))

else:

logging.debug('Entered into the else statement')

print('We are in the ELSE STATEMENT')

except Exception as e:

print(str(e))

logging.critical(e)

In [79]:

main()

1 2	we are in the IF STATEMENT Could not open url

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Coursera | Data Science | Machine Learning | Python

K-Means Cluster Analysis – Data Analysis and Intrepretation
Bydatapandasadmin March 20, 2016August 12, 2017

Overview My research work deals with Ghana, a country from the Gapminder dataset as has already been discussed from the beginning and progression through this course I conducted a k-means cluster analysis to find out the underlying sets of the population of Ghana based on their similarity of responses on 22 variables that represent characteristics…

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One Comment

Sarang Manjrekar says:

December 13, 2016 at 3:32 am

I use Metabase, but facing the issue that it cant be used to get iFrames for embedding into web pages.

Reply

Free Open Source Data Visualization Tools and Service

DataSets Around the Web For Big Data, Machine Learning and DataScience Practices

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Can the sample size be equal to the population size?

Connect Push Commit Pull Your GitHub Repository To Your Local Computer’s Directory

Simple Python Logging Callling Reload to Help Save Logging to File

K-Means Cluster Analysis – Data Analysis and Intrepretation

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