Graded Assignment-5 (May Term 2023):- Data Visualization Tools

[Jimmi-Kr]

With a plethora of both commercial & free visualization tools & libraries available, it can often be confusing to pick the right tool for your requirement. Also from the learning point of view, one doesn't know which tool or set of tools should invest time & effort in learning.

In her 2016 article "What I Learned Recreating One Chart Using 24 Tools", Lisa Charlotte Rost tried out 12 data vis applications and 12 data vis libraries and programming languages and reported a comparative evaluation.

In this assignment, you will recreate the exercise with at least 5 charting tools or libraries (total 5 not 5 each) for the given dataset (auto-mpg.csv). You may create any chart type, but using at least 2 variables from the dataset. Having decided on chart type & variables, repeat the same chart using the 5 chart tools or libraries. Paste your charts as a comment to this issue. Add text to each chart identifying the tool/library you used for the chart.

[faizanxmulla]

Name : Faizan Mulla Roll No. : 21f1003885

Variables used :

1. model year
2. Avg horsepower per year
3. Avg displacement per year

Chart type : Line plot

Colab file link : https://colab.research.google.com/drive/1mzz-N2YfBwaGj9RjWVf-ToGZqr9YaHUL?usp=drive_link

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Tools used:

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1. Google Sheets :

Link : https://docs.google.com/spreadsheets/d/e/2PACX-1vRsS2Rlaud8RW8hdzQRpiYWWdB26niD8CPhAB1tmIBttR2niW2lYUZhoWMvVkw6bBR_kX-Saw4zU9qK/pubchart?oid=2145235450&format=interactive

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2. Datawrapper :

Link : https://datawrapper.dwcdn.net/jHtTB/1/

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3. Looker Studio

Link : https://lookerstudio.google.com/s/lM3rLoG9xFI

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4. Power BI :

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5. Infogram

Link : https://infogram.com/dvd-ga5-1h7z2l83vykxg6o

[S-D-P]

Name: Siddhi Dhirajkumar Pandirkar RollNo: 21f1001177

Comparative Analysis of Car Brands: Average Weight v/s Average Performance Metrics

Chart: Combo Chart - Line & Bar Plot

Variables: X-Axis: Car Brands Primary Y-Axis (Bars): Weight (Average per brand) Secondary Y-Axis (Lines): Acceleration (Average per brand) Displacement (Average per brand) Horsepower (Average per brand)

Data Visualizations and corresponding tools:

1. Google Sheets

2. ChartJS

3. FusionCharts

4. Matplotlib

5. Flourish

[Prahlad19]

Data Visualization with different tools

Name: PRAHLAD SINGHANIA Roll no: 21f1006059

Variables:

 Miles per gallon (mpg)  Acceleration

Chart Type:

 Scatter plot

Motive:

 To find correlation between miles per gallon and acceleration

Tools:

1. Microsoft Excel:

2. Flourish:

3. RawGraphs 2.0:

4. DataWrapper:

5. Matplotlib:

6. Seaborn:

[VarnikaRB]

Name: Varnika Bagaria Roll no: 21f1007039

Variables:

Displacement Horsepower

Chart Type:

Line

Tools used:

1. Google Sheets:

   

2. Tableau:

   

3. Matplotlib:

   

4. Seaborn:

   

5. RawWrapper 2.0

   

[SrivinaySridhar]

Name : Srivinay Sridhar

Roll No. : 21f1006569

Average Mpg and Displacement by number of Cylinders

Chart type

Side by Side Bar chart

Variables used :

1. Cylinders
2. Average displacement grouped by cylinders (derived from displacement)
3. Average mpg grouped by cylinders (derived from mpg)

Purpose

To find the relation between efficiency (mpg) and a parameter of performance (displacement) with the number of cylinders.

Tools used

1. Google Sheets

2. Tableau

3. Plotly

4. Matplotlib

5. Data Wrapper

[anant7k]

Anant Kumar 21f1000683

Variables Used

1. MPG
2. Horsepower

Chart Type

• Scatterplot

Purpose

• To identify relationship between Miles per gallon and Horsepower

Handling Missing Horsepower Values

• Missing values were readily available on web.

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Tools Used

1. Excel

2. Datawrapper

3. Python Matplotlib

4. Python Seaborn

5. Python Bokeh

[afnan-ahmad]

Comparing different chart tools / libraries

Created by: Afnan Ahmad | 21F1003730

Chart Type: Scatter plot Variables: miles per gallon (mpg) and weight (lb) Tools: Matplotlib, Plotly, Flourish, Google Sheets, Microsoft Excel

Matplotlib

Plotly

Flourish

Google Sheets

Microsoft Excel

[harshadpaikrao]

Name : Harshad Shahu Paikrao

Roll no. 21f1002085

Tools used:

• Tableau
• Floursh
• Seaborn
• Plotly
• RAWGraphs

Variables used:

• Miles per gallon (mpg)
• Horsepower
• Cylinders

Visualization type : Scatterplot

Purpose:

To find the relation between Horsepower and fuel efficiency (mpg) based on number of Cylinders.

Visualization:

1. Tableau Desktop

Tableau public link : https://public.tableau.com/app/profile/harshad.paikrao/viz/DVD_GA5/MileagevsHorsepower?publish=yes

2. Flourish

flourish link: https://public.flourish.studio/visualisation/14724231/

3. Seaborn

colab link : https://colab.research.google.com/drive/1z4QME0yJbr6HaqtqaXPtcIIIGkcEMw0g?usp=sharing

4. Plotly

colab link : https://colab.research.google.com/drive/1lUf26PHaPlKM47kDQxKIu_vRY-4n9BiJ?usp=sharing

5. RAWGraphs 2.0

[iSarthakGautam]

Name : Sarthak Gautam Roll No. : 21f1000864

Average MPG and Acceleration Over the Years

Variables used:

• Model Year
• Acceleration
• MPG (Miles per gallon)

Motive

To see the trend in mpg and acceleration over the years so as to understand how efficient yet powerful the cars are geeting with advent of new technology.

Vizualisations:

1. Plotly

Plotly is open source python graphing library that makes interactive, publication-quality graphs.

Link to colab file

2. Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualisations in Python. Matplotlib makes easy things easy and hard things possible.

Link to colab file

3. Tableau Public

Tableau Public is a free platform to explore, create and publicly share data visualizations online.

The great thing about tableau is it's ability to forecast without complex coding.

Link to tableau visualisation

4. Flourish

Flourish is an interactive data visualization tool that enables the creation of captivating data stories. It provides a wide range of user-friendly templates for animated charts, detailed maps, and rich data explorers.

Flourish Visualisation Link

5. Google Sheets/ Excel / Apple Numbers

These are popular spreadsheet software that offers hundreds of features to their users. The tools share many similarities with each other, as they have common goals.

Link to sheets

6. Seaborn

Seaborn is a Python data visualisation library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Link to colab file

[BarunSinha]

Name : Barun Kumar Sinha Roll No : 21f1002021

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Tools Used

• Matplotlib
• Seaborn
• Excel
• PowerBi
• Highcharts

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Variables Used

• Average Miles per gallon(Mpg)
• Average weight
• No of Cylinders

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Chart used : Line Chart

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Purpose

• Find relationship between Average miles per gallon and Average weights per cylinder

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Visualization

1. Matplotlib

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2. Seaborn

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3. Excel

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4. PowerBi

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5. Highcharts

[21f1004666]

Name: Andiboyina Mourya Chakradhar Nagesh Roll no.: 21f1004666

Tools Used:

• Plotly
• Matplotlib + Seaborn
• GGPlot2
• Google Sheets
• Flourish

Variables:

• Weights on X-axis
• mpg on the Y-axis
• displacement for continuous color scale (if possible)
• cylinders for shapes( if not possible use this column as discrete color)

Plotly

Matplotlib and Seaborn

GGPlot2

Google Sheets

Flourish

[Khushiin]

Name: Khushee A Namdeo Roll Number: 21f3001500

TOOLS USED

Microsoft Excel Infogram Seaborn Tableau Public DataWrapper

VARIABLES USED

Average Miles per gallon(Mpg) Average Horsepower

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Microsoft Excel:

Infogram:

Link: https://infogram.com/copy-column-chart-1hdw2jpox5xwj2l?live

Seaborn:

Link: https://colab.research.google.com/drive/1yG_2VX1tvaphzOgdSjFApO5jne8ahuTo

Tableau Public:

Link: https://public.tableau.com/newWorkbook/17493862-715a-4374-b03d-33099448dcf4#1

DataWrapper:

Link: https://datawrapper.dwcdn.net/Z8CIo/1

[sejalanandIITM]

Name: Sejal Anand Roll No: 21f1002620

Variables Used

• Acceleration
• MPG (Fuel Efficiency)
• Country Code (Origin Encoded)

Objective

The scatter plot aims to illustrate how the acceleration of various car models relates to their fuel efficiency (measured in mpg), while also showing how this relationship varies across different countries or manufacturers.

Tools Used

• Plotly Express
• Tableau
• ggplot
• Flourish
• PowerBI

Visualizations

Plotly Express

• interactive visualizations
• capable of integration with Python code

Tableau

• user-friendly interface
• drag-and-drop functionality

ggplot

• allows a high degree of customization

Folium

• user-friendly nature
• interactive visualizations without requiring coding expertise.

PowerBI

• drag and drop
• robust data visualization capabilities

[dipak-patil-iitm]

Name: Dipak Patil Roll No: 21f1004451

Variables Used

• Weight
• MPG

Tools Used

1. Matplotlib
2. Google Sheet
3. Tableau
4. PowerBI
5. ggplot

Visualizations

1. Matplotlib https://colab.research.google.com/drive/1Y4YAZywYTNvliMYLDeSZgl09EOK4Xsds?usp=sharing

2. Google Sheet https://docs.google.com/spreadsheets/d/1oAz3s98oFHXdlBn2OMpi7_zDhqqAmbXldXFnIfl_dgY/edit?usp=sharing

3. Tableau https://public.tableau.com/app/profile/dipak.patil2471/viz/DataVizWeek5_16920927103500/Sheet1#1

4. PowerBI

5. ggplot https://colab.research.google.com/drive/1Y4YAZywYTNvliMYLDeSZgl09EOK4Xsds#scrollTo=C-vuFiPswqfn&line=5&uniqifier=1

[deep87we]

Data Visualisation with different python Libraries:

Name:Deepanshu Mahajan Roll no-21f1006962

Variables Used

Acceleration MPG (Fuel Efficiency)

Objective:

The intention behind the scatter plot is to visually represent the connection between the acceleration of cars and their fuel efficiency (measured in miles per gallon - mpg).

Visualisations:

1.Matplotlib Library:

2.Seaborn

3.Plotly

4.Pandas Plotting

5.Bokeh

[mb1AtGithub]

Manisha Bapat 21f1000449

Tried following tools for plotting scatter plot between mpg and weight. Also used #cylinders as category where ever possible. 1) Raw graphs 2) Lyra 3) Tableau 4) Infogram 5) D3

1) Raw Graphs

2) Lyra

3) Tableau

4) Infogram

5) D3

[Vishvam10]

Name : Vishvam Sundararajan S Roll No : 21f1005939

Data Visualization Tools

Variables Used

• Acceleration
• Weight
• Horsepower
• MPG
• Model Year

Tools Used

• Matplotlib
• RAW Graphs
• Seaborn
• Google Sheets
• Microsoft Excel

Visualizations

1. Matplotlib : (Bar Graph + Line Graph) Number of Cars Manufactured and Average Displacement vs Model Year

2. RAW Graphs : (Bubble Chart) Mpg, Acceleration and Cylinder

3. Seaborn : (Bar Graph + Line Graph) Number of Cars Manufactured and Average Displacement vs Model Year

4. Google Sheet : (Scatter Plot) Acceleration vs Horsepower

5. Microsoft Excel : (Scatter Plot ) Weight vs Acceleration

[dhruvsanan]

Name: Dhruv Sanan RollNo: 21f1004102

Comparative Analysis of Car Brands: Average Weight v/s Average Acceleration Metrics

Chart: Combo Chart - Line & Bar Plot

Variables used :

model year Avg Weight per year Avg Acceleration per year

Data Visualizations and corresponding tools:

Tableau

https://public.tableau.com/app/profile/dhruv1897/viz/cardata_16922881079500/Sheet2?publish=yes

Google Sheets

https://docs.google.com/spreadsheets/d/1SZ78RpItqo_O0Fto9QZ9aeHP7RUmJV3AVIAvvbCjm7E/edit?usp=sharing

Data Wrapper

https://datawrapper.dwcdn.net/ROskW/1/

Matplotlib

https://colab.research.google.com/drive/1OfK_d5fRXvZmPQQ69s2Rtr9fY7mbvEOO?usp=sharing

Seaborn

https://colab.research.google.com/drive/1OfK_d5fRXvZmPQQ69s2Rtr9fY7mbvEOO?usp=sharing

[viboognesh]

Name: G Vibu Vignesh Roll No. PH17B006

Chart: ScatterPlot

Variables used: MPG vs Displacemetn

Seaborn

Excel

Matplotlib

Pandas

Plotly

[21f1005173]

Name : M.S.Srinivass Roll No : 21f1005173

Variables Used

• Brands ( extracted from car names )
• Average Horsepower
• Average Displacement

Tools Used

• Excel
• Google Sheets
• Seaborn
• Matplotlib
• Infogram

Excel

Google Sheets

Seaborn

Matplotlib

Infogram

[priyanka-maz]

Name - Priyanka Mazumdar Roll No. - 21f1000367

One Chart Using 5 Tools

Dataset Used - Automobile Dataset

Given dataset had 9 columns:

• mpg - Miles per gallon
• cylinders - No. of cylinders in car
• displacement - Displacement of car
• horsepower - Horsepower of car
• weight - Weight of car
• acceleration - Acceleration of car
• model year - Year when Car model was released
• origin - Place of origin
• car name - Name of car

We haven't been provided units for each parameter.

Parameters used in Chart

• weight
• horsepower
• mpg

Idea of my Chart

My main aim was to find the correlation between weight of the car and its horsepower. To segregate the scatter plot, I have used colour encoding of miles per gallon to see if there exists any relationship

1. Seaborn Scatter Plot

Link to Colab

2. Flourish Scatter Plot

Link to Flourish

3. Tableau Scatter Plot

Link to Tableau Viz

4. RawGraphs 2.0 Scatter Plot

5. Google Sheets Scatter Plot

Link to Google Sheet

Conclusion

1. Customising using code based platforms like Seaborn, Vega is very flexible and let's one get all the customization required. Cleaning of data can also be done before visualizing.
2. Visualizing using platforms such as Tableau and Flourish which have inbuilt templates makes the task easier and short. It is not extremely flexible but a wide range of choices are available.
3. Visualizing using platforms such as Google Sheets or Excel isn't very flexible or aesthetic as the options are limited. Cleaning of data in these platforms is also more time-consuming than platforms like Colab.

[faridkhan5]

Name: Farid Khan Roll No: 21f1002045

Chart: Line Chart Variables: Weight, Horsepower, and Acceleration

1. Excel

   

2. Seaborn

   

3. Flourish

   

4. Plotly

   

5. Matplotlib

   

Analysis:

• I binned the vehicles into their respective weight range and plotted the average acceleration and average horsepower in each weight range.

• From the chart, it is evident that horsepower is directly proportional to weight whereas acceleration decreases slightly with an increase in weight.

• So to conclude, the heavier the car, the higher will be its horsepower and the lower will be its acceleration.