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Graded Assignment-5 (May Term 2024):- Data Visualization Tools #32

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Jimmi-Kr opened this issue Jul 26, 2024 · 42 comments
Open

Graded Assignment-5 (May Term 2024):- Data Visualization Tools #32

Jimmi-Kr opened this issue Jul 26, 2024 · 42 comments

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@Jimmi-Kr
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Jimmi-Kr commented Jul 26, 2024

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.

Note: You can only use one from Matplotlib, seaborn, pandas, and Excel.

@Kaustav-Goswami9
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Kaustav-Goswami9 commented Aug 9, 2024

Name: Kaustav Goswami
Roll: 21f1001588

Variables used:

  • mpg (millions per gallon) on X-axis
  • displacement on Y-axis
  • cylinders as discrete colors

I have used the following 5 tools/library to represent a scatter plot using the above variables:

  • Matplotlib library
  • Flourish Tool
  • Bokeh library
  • ggplot2 library
  • RAWGraphs Tool
  1. Matplotlib Library
    image

  2. Flourish Tool

scatter visualization
  1. Bokeh Library
    bokeh_plot

  2. ggplot2 Library
    image

  3. RAWGraphs Tool
    viz

@sheikhuzairhussain
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sheikhuzairhussain commented Aug 9, 2024

Name: Sheikh Uzair Hussain
Roll Number: 21f1001254

Chart type: Scatterplot

Variables used:

  1. Horsepower (horsepower)
  2. Miles per gallon (mpg)
  3. Number of cylinders (cylinders)

Visualizations

Matplotlib (library)

image

Seaborn (library)

image

Plotly (library)

image

Power BI (app)

image

Tableau (app)

image

@SURAJARS
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SURAJARS commented Aug 9, 2024

Name : Suraj ARS
Roll No : 21f1005229

Variables used:

*mpg
*horsepower
*cylinders

1.Scatter plot using Matplotlib created mpg vs horsepower colored by cylinders
DVD GA5

2.Scatter plot using Plotly created mpg vs horsepower colored by cylinders
DVD GA5 1

3.Scatter plot using Altair created mpg vs horsepower colored by cylinders
DVD GA 5 2

4.Scatter plot using Tableau created mpg vs horsepower colored by cylinders
DVD GA 5 3

5.Scatter plot using Seaborn created mpg vs horsepower colored by cylinders
DVD GA 5 4

@neeraj-iit
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Name: Neeraj Yadav
Roll: 21f1005729

Variables used:

Horsepower on X-axis
mpg (millions per gallon) on Y-axis
Car Name

I have used the following 5 tools/library to represent a scatter plot using the above variables:

  • Tableau public
  • Excel
  • Qlik
  • Looker Studio
  • Plotly

1. Tableau public
Scatter plot of horsepower vs mpg
Tableau Public Chart

2. Excel
Scatter plot of horsepower vs mpg
Excel Chart

3. Qlik
Scatter plot of horsepower vs mpg
Qlik Chart

4. Looker Studio
Scatter plot of horsepower vs mpg
Looker Studio Chart

5. Plotly
Scatter plot of horsepower vs mpg
Plotly Chart

@Ashrey30
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Ashrey30 commented Aug 10, 2024

Name: Ashrey
Roll No.: 21f2000448

Variables used:

  • mpg (millions per gallon) on X-axis
  • displacement on Y-axis
  • cylinders as discrete colors

Tools/library used to represent the above variables:

  • Matplotlib
  • Pygal
  • Plotly
  • Altair
  • Bokeh

Matplotlib

Matplotlib

Pygal

image

Plotly

Plotly

Altair

Altair

Bokeh

Bokeh

@ShyamSundhar1411
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ShyamSundhar1411 commented Aug 11, 2024

Assignment-5

Name: Shyam Sundhar Ganesh
Roll Number: 21f3001249

Libraries/App Used

  1. Altair
  2. Plotly
  3. Seaborn
  4. Bokeh

Plots

1. Altair

Variables Used

  1. MPG
  2. Displacement
  3. Cylinders

Plot

image

2. Seaborn

Variables Used

All

Plot

image

3. Plotly

Variables Used

  1. Weight
  2. Displacement
  3. Cylinders

Plot

image

4. Flourish

Variables Used

  1. Displacement
  2. Weight

Plot

image

5. Bokeh

Variables Used

  1. MPG
  2. Displacement
  3. Cylinders

Plot

image

@21f1006304ds
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21f1006304ds commented Aug 11, 2024

Name : Rajesh Saha

Roll No. 21f1006304

Assignment : GA5

Variables Used :

  1. acceleration
  2. mpg
  3. cylinders
  4. origin

Tools / Libraries Used:

  1. matplotlib + seaborn + python
  2. Tableau
  3. Google Sheet
  4. Flourish
  5. PowerBI

matplotlib + seaborn + python

image

Tableau

image

Google Sheet

image

Flourish

image

PowerBI

image

@subhashree211002
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subhashree211002 commented Aug 11, 2024

SPG Gradesd Assignment 5

Name: Subhashree
Roll No.: 21f2001407


Chart 1: Bubble Chart Using Tableau Online

Tool Used: Tableau Online
Chart Type: Bubble Chart
Variables Used: Horsepower (X-axis), MPG (Y-axis), Weight (Bubble Size & Color)
Sheet 1 (1)


Chart 2: Bubble Chart Using Matplotlib

Tool Used: Matplotlib
Chart Type: Bubble Chart
Variables Used: Horsepower (X-axis), MPG (Y-axis), Weight (Bubble Color)
matplotlib


Chart 3: Bubble Chart Using Plotly

Tool Used: Plotly
Chart Type: Bubble Chart
Variables Used: Horsepower (X-axis), MPG (Y-axis), Weight (Bubble Color)
newplot (1)


Chart 4: Bubble Chart Using Polestar

Tool Used: Polestar
Chart Type: Bubble Chart
Variables Used: Horsepower (X-axis), MPG (Y-axis), Weight (Bubble Size & Color)
download (7)


Chart 5: Bubble Chart Using RAW Graphs

Tool Used: RAW Graphs
Chart Type: Bubble Chart
Variables Used: Horsepower (X-axis), MPG (Y-axis), Weight (Bubble Size & Color)
RG


Chart 6: Bubble Chart Using Google Sheets

Tool Used: Google Sheets
Chart Type: Bubble Chart
Variables Used: Horsepower (X-axis), MPG (Y-axis), Weight (Bubble Size)
MPG vs Horsepower (Bubble size represents weight)

@shelleyiitm
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shelleyiitm commented Aug 11, 2024

Name : Shelley R
Roll No: 21f1005512

Selected Variables :

  • Weight
  • mpg (miles per gallon)

Chart type : Scatter plot

In Excel:
Excel

In Plotly:
Plotly

In DataWrapper:
Data Wrapper

In HighCharts:
Highcharts

In Google sheets:
Google Sheet

@Indu16910
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Indu16910 commented Aug 11, 2024

Name: Kalla Tabu Venkata Indumathi
Roll no: ce22b062
Selected Variables :
Cylinders
Displacement
mpg (miles per gallon)

Chart type : Scatter plot

Matplotlib Python
Screenshot (1592)

Flourish
snapshot-1723343612008

Datawrapper
uTjYz-mpg-vs-displacement-nbsp-nbsp-

Seaborn
Screenshot (1588)

Raw graphs
Screenshot (1589)

@rukhsarrahman
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Name: Rukhsar Rahman
Roll No: 21f1003273

Variables Used :

  • Weight
  • MPG
  • Cyinders

Chart 1: Scatterplot Using Matplotlib
image

Chart 2: Scatterplot Using Seaborn
image

Chart 3: Scatterplot Using Pandas
image

Chart 4: Scatterplot Using Plotly
image

Chart 5: Scatterplot Using Altair
image

@RajRohitYadav19
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RajRohitYadav19 commented Aug 11, 2024

Name: Raj Rohit Yadav
Roll number: 21f1005377
email: 21f1005377@ds.study.iitm.ac.in

Assignment: GA5

Variables used:

  1. Weight
  2. Displacement
  3. Cylinders

Tools/libraries used:

  1. Matplotlib
  2. Plotly
  3. Altair
  4. Tableau
  5. PowerBI

Charts:

  1. Scatter plot using matplotlib
    matplotlib

  2. Scatter plot using plotly

plotly
  1. Scatter plot using altair
altair
  1. Scatter plot using tableau
tableau
  1. Scatter plot using power bi
    powerBI

@abirChakrabortyIITM
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Name: Abir Subroto Chakraborty
Roll no: 21f2000280


Columns used:

  1. MPG
  2. Horsepower

Tools and Libraries used:

  1. Matplotlib
  2. D3.js
  3. RAW Graph
  4. Tableau
  5. Flourish

Charts:

  1. Matplotlib:
    image

  2. D3.js:
    WhatsApp Image 2024-08-11 at 16 20 26_f4149358

  3. RAWGraph:
    WhatsApp Image 2024-08-11 at 16 04 07_67710a8b

  4. Tableau:
    abir DVD Tableau ss

  5. Flourish:
    abir-flour

@Ashutosh-tec
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Name: Ashutosh Kumar Barnwal
Roll No: 21f1001709

Variables: Weight, Acceleration

  1. Matplotlib
    Matplot_scatter_plot_weight_vs_acceleration

  2. Tableau
    Tablue_Ashutosh scatter plor

  3. RawGraphs
    rowGraph_acc_wt

  4. d3.js

d3 js_plot
  1. Flourish
    flourish_plot
  • Also the correlation matrix of mpg, cylinders, displacement, horsepower, weight, acceleration

correlation_heatmap

@pranaydeep139
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Name: Sakiley Pranay Deep
Roll number: 21f1005603

Variables used in plotting the scatterplot:

  • Displacement (X-axis)
  • Weight (Y-axis)
  • Horsepower (Hue)

(Selected based on correlation values obtained through the heatmap)

Libraries/tools used:

  1. Matplotlib
  2. Plotly
  3. Bokeh
  4. Flourish
  5. R-ggplot2

Charts:

Using Matplotlib:

download (6)

Using Plotly:

Screenshot 2024-08-11 103201

Using Bokeh:

Screenshot 2024-08-11 105253

Using Flourish:

aaaa

Using R-ggplot2:

download (7)

@Dheeraj-Sathianarayanan
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Dheeraj-Sathianarayanan commented Aug 11, 2024

Assignment 5


Name : Dheeraj S
Roll Number: 21F1002027

Variables used


horsepower
mpg
weight
cylinders

Libraries used


Seaborn
Plotly
Altair
Plotnine
HoloViews

Scatter Plots


1) Seaborn
Seaborn

2) Plotly
Plotly

3) Altair
Altair

4) Plotnine
plotnine

5) HoloViews
holoviews

@Afringowhar
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Afringowhar commented Aug 11, 2024

Name: Syed Afrin Gowhar
Roll: 21f2001140

Variables used:

  • weight on X-axis
  • mpg (millions per gallon) on Y-axis
  • cylinders as discrete colors

The tools that has been used to represent the scatter plot are:
Matplotlib Library
Atair library
Plotly Library
PowerBI Tool
Google Data Studio

Matplotlib Library

image

Altair library

WhatsApp Image 2024-08-11 at 23 23 54_120289b8

Plotly library

image

PowerBI Tool

image

Google Data Studio

image

@harshymehta14
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harshymehta14 commented Aug 11, 2024

Name - Harsh Y Mehta
Roll No - 21F1001295

Features used:

  1. Horse Power - refers to the power an engine produces
  2. Displacement - is a measurement of the total volume of all of an engine's cylinders, usually written in cubic centimetres (cc)

Understanding difference between Horsepower and displacement

Displacement: How Heavy a car can I push up this slope?
Horsepower: How Fast can I push a car up this slope?

1. Flourish

image

2. Google Sheet

image

3. Matplotlib

image

4. Plotly

image

5. RAWGraphs 2

image

@Kirupa-Krishan
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Kirupa-Krishan commented Aug 11, 2024

Name: Kirupa Krishan G
Roll: 21f1006352

Variables used:

  • Number of Cylinders X-axis
  • HorsePower on Y-axis
  • Weights as discrete colors(binned into 5 parts)/Continuous scale

I have used the following 5 tools/library to represent a scatter plot using the above variables:

  • Matplotlib library
  • Raw Graphs Tool
  • Flourish Tool
  • Google Sheet Tool
  • Plotly Library
  1. Matplotlib library
    matplotlib

  2. Raw Graphs Tool

raw graphs

  1. Flourish Tool
    flourish

  2. Google Sheet Tool
    excel

  3. Plotly Library
    plotly

@sahilrajpal121
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Name: Sahil Rajpal
Roll: 21f1006804

Variables used:

  • Model Year X-axis
  • MPG on Y-axis
  • Origin year to group the columns on

Average MPG over the Years by Origin (Line Chart)

Tools/Libraries used:

  • Seaborn Library
  • Flourish Tool
  • ggplot Library
  • Datawrapper Tool
  • Bokeh Library

Seaborn

seaborn_plot

Flourish (chart link)

DVD GA-5 flourish

ggplot

ggplot_plot

Bokeh

bokeh_plot

Datawrapper (chart link)

datawrapper_plot

@trxpti
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trxpti commented Aug 11, 2024

Name - Tripti Arya
Roll Number - 21f1005935

Graded Assignment 5

For the given assignment, I first focused on understanding the data attributes and their underlying purposes. Then, I selected two specific variables to plot using different libraries to compare how each library handles the same type of visualization for the same variable data.

Variables i have used for the Creating Visualization:

  1. Vehicle Weight. - Attributed as "weight" in data.
  2. Miles per Gallon (MPG) - Attributed as "mpg" in n data.

I selected these two variables because they exhibit a clear correlation, where the weight of a car influences its fuel efficiency. Heavier cars typically have lower MPG (miles per gallon), making this relationship important to explore. By visualizing this correlation, one can better understand how vehicle weight impacts fuel efficiency, providing valuable insights into car design and performance.

Application and Libraries i have used to Create the visualization:

  1. Matplotlib
  2. PowerBI
  3. Tableau
  4. Altair
  5. Flourish

Scatter plot for those two variable using those 5 libraries i have choosed to explore

  1. Using Matplotlib
    weight_vs_mpg_matplotlib

  2. Using PowerBI
    image

  3. Using Tableau
    Screenshot 2024-08-11 230501

  4. Using Altair
    visualization

  5. Using Flourish
    mpg_vs_weight_flourish

Conclusion
By trying out five different applications or libraries, I have found that all are able to give a clear visual of the given data and specify the correlation between variables. However, I believe there are still significant differences in some aspects, such as the missing X and Y axes in Power BI visuals and the different styles of showing data points. Some use color contrast, while others have uniform color encoding for all data points. Among them, Tableau uses a very different way of labeling the X and Y axes, and its data spreading technique is very different from the other tools I used.

@srinivesh
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srinivesh commented Aug 11, 2024

Graded Assignment 5

Name: S R Srinivasan
Roll Number: 21f1002566

Variables used:

Horsepower (horsepower)
Miles per gallon (mpg)
Number of cylinders (cylinders)

Chart type:

Scatter Plot

Approach

Use the most used performance measures of a vehicle - power vs fuel economy, and visualize this across the engine type - as given by the number of cylinders.

Amongst the 24 tools explored by the Lisa Charlotte Rost, and others not listed by her, I have decided to try these 5:

  1. Excel (what I knew before this course!)
  2. Flourish
  3. plotly
  4. DataWrapper
  5. Canva (need a twist at the end)

Encoding

Miles-per-gallon (MPG) and HorsePower as the corelated variables
Number of cylinders to visually bin the scatter

Visualizations

Microsoft Excel

image

Flourish

GA5_Fuel_Efficiency_flourish
https://public.flourish.studio/visualisation/19027955/

plotly

image
https://plotly.com/~kernelguy/1/

DataWrapper

image

Canva

GA5_Fuel_Efficiency_canva

https://www.canva.com/design/DAGNjs_7QU8/0mKhQWl5eJTvyUquZwHsJw/edit?utm_content=DAGNjs_7QU8&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton

@pranam-pagi
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pranam-pagi commented Aug 11, 2024

Name: Pranam Premanand Pagi
Roll No: 21f3002964

Chart Type

Scatter Plot

Variable Used

  • Miles Per Gallon (mpg) on X axis
  • displacement (cubic inches) on Y axis
  • Number of cylindersas discrete colors

The three variables—mpg (miles per gallon), displacement, and cylinders—were chosen because they represent key aspects of a vehicle's engine and fuel efficiency, making them ideal for exploring relationships in the data:

1. MPG (Miles Per Gallon):

  • Reason: MPG is a direct measure of fuel efficiency, indicating how far a vehicle can travel on a gallon of fuel. It's a critical metric for understanding a car's environmental impact and operating cost.
  • Interest: Analyzing how other engine characteristics, like displacement and the number of cylinders, affect MPG can provide insights into trade-offs between power and fuel economy.

2. Displacement:

  • Reason: Displacement measures the total volume of all the cylinders in an engine. It's closely related to engine size and power output, with larger displacements typically associated with more powerful engines.
  • Interest: By plotting displacement against MPG, we can observe how engine size influences fuel efficiency, helping to understand the balance between engine power and fuel consumption.

3. Cylinders:

  • Reason: The number of cylinders in an engine is another important factor in engine design, affecting both performance and fuel consumption. Engines with more cylinders generally produce more power but may be less fuel-efficient.
  • Interest: Using cylinders as a categorical variable (discrete colors) allows us to visually differentiate vehicles by engine type, making it easier to see patterns and clusters in the data.

The following libraries/ tools were used to represent the scatter plot using the above variables

  1. Matplotlib
  2. Power BI
  3. Flourish
  4. Tableau
  5. Plotly

Visualisations

1. Matplotlib

image

2. Power BI

Screenshot 2024-08-14 070852

3. Flourish

GA5@2x

4. Tableau

Sheet 1

5. Plotly

newplot

@Arshi81099
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Arshi81099 commented Aug 11, 2024

Graded Assignment 5

Name: Arshi Khan
Roll Number: 21f3002806

Variables Used:

  1. Horsepower (horsepower)
  2. Miles per gallon (mpg)

Chart Type:

Scatter Plot (and Bubble Charts)

Approach:
Use the core performance measures of a vehicle—power versus fuel economy—and visualize this across engine types.

Here’s the list:

  • Tableau
  • D3.js
  • Raw Graphs
  • Python - Matplotlib
  • Flourish
  1. Tableau

Overview: Tableau provides a powerful and interactive interface to create scatter plots. You can easily visualize the relationship between Horsepower and MPG with interactive features like filters and tooltips. The scatter plot allows for dynamic exploration of the data with options to enhance visuals and integrate various analytical insights.

Screenshot 2024-08-11 at 8 40 15 PM
  1. D3.js

Overview: In D3.js, the scatter plot is highly customizable and can be integrated into web pages. It provides a detailed and interactive visualization with customizable axes, scales, and tooltips. This approach allows for a high degree of control over the appearance and behavior of the plot, making it suitable for web-based data visualization.

Screenshot 2024-08-11 at 8 41 13 PM
  1. Raw Graphs
    Overview: Raw Graphs offers a straightforward way to create scatter plots with minimal configuration. It provides a clean and intuitive interface for visualizing the relationship between Horsepower and MPG, with options for basic customization. The resulting plot is easy to understand and can be quickly generated from your data.
Screenshot 2024-08-11 at 8 42 15 PM
  1. Python - Matplotlib
    Overview: Matplotlib delivers a static but highly customizable scatter plot. It’s ideal for creating publication-quality charts with precise control over the plot's appearance. This approach is best for generating visualizations within Python scripts or Jupyter notebooks, offering flexibility in data manipulation and plot design.
Screenshot 2024-08-11 at 8 43 32 PM
  1. Flourish
    Overview: Flourish provides an interactive and visually appealing scatter plot. With Flourish, you can create engaging and customizable charts with interactive elements like hover effects and filtering. The platform is user-friendly and designed for creating visually appealing and interactive data visualizations without extensive coding.
Screenshot 2024-08-11 at 8 44 20 PM

@Harsehraab
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Harsehraab commented Aug 11, 2024

**Name: ** Harsehraab Singh Sarao
Email: 21f1000507@ds.study.iitm.ac.in

Variables used:

  • weight
  • acceleration

I used weight and acceleration to highlight to focus on the performance aspect of the the vehicles. High performance cars tend to handle better and accelerate quicker due to their light weight.

Tools used:

  • Matplotlib
  • Plotly
  • PowerBi
  • Flourish
  • Google sheets

Visualizations

Matplotlib

matplot

Plotly

Screenshot 2024-08-11 135751

PowerBi

powerbi

Flourish

flourish

Google sheets

Acceleration vs Weight

@muskansindhu
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Name: Muskan Sindhu
Roll: 21f1003710

Variables Used:

  • X-axis: mpg (millions per gallon)
  • Y-axis: Weight of car

Tools/Libraries Used:

  1. Matplotlib library
  2. Seaborn
  3. Plotly
  4. Vega
  5. Highcharts

  1. Chart using Matplotlib
matplotlib
  1. Chart using Seaborn
sns-plot
  1. Chart using Plotly
plotly
  1. Chart using Vega
visualization
  1. Chart using Highcharts
highcharts

@Nivoceros
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Name: Nivedita Jayaswal
Roll number: 21f1004471

Variables used:

  • Horsepower
  • Weight
  • MPG
  • Acceleration

Graphing tools used:
Plotly
Matplotlib
Bokeh
Altair

Insights:

  1. Horsepower vs. MPG: Higher horsepower generally leads to lower MPG, indicating that more powerful cars are less fuel-efficient.
  2. Weight's Impact: Heavier cars typically have lower MPG, reinforcing the idea that weight negatively affects fuel efficiency.
  3. Acceleration: Acceleration varies but doesn’t show a strong direct relationship with horsepower or MPG.
  4. Clusters: Cars tend to cluster by horsepower and weight, with distinct groups showing similar performance and efficiency characteristics.

Plotly

image

Matplotlib

image

Bokeh

image

Altair

image

@Sa-N98
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Sa-N98 commented Aug 11, 2024

Name: Saranya Nayak
Roll No. : 21f1005767

Variables used:

  1. weight (on Y-axis)
  2. mpg (on X-axis)
  3. cylinders (category)

Tools used:

PowerBi
Matplotlib
Flourish
Datawrapper
Plotly

Visualizations:

1. PowerBI

image

2.Matplotlib

image

3.Flourish

snapshot-1723390980115

4.Datawrapper

image

5.Plotly

image

@Jigyasa2408
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Jigyasa2408 commented Aug 11, 2024

Name -Jigyasa
Roll No. - 21f1001644

Chart Type - Scatter Plot

Variables - Horsepower, Displacement and Cylinders

Tools/ Libraries Used :

  1. Flourish
  2. Matplotlib
  3. DataWrapper
  4. Plotly
  5. Bokeh

Visualizations :

  1. Flourish
    image

  2. Matplotlib
    image

  3. DataWrapper
    image

  4. Plotly
    image

  5. Bokeh
    image

@praddyyyy
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Name: Pradeeshwar A
Roll No.: 21F1007071

Variables used:

  • horsepower on X-axis
  • MPG (millions per gallon) on Y-axis

I have used the following 5 tools/library to represent a scatter plot using the above variables:

  • Matplotlib
  • Seaborn
  • Bokeh
  • Plotly
  • Altair

Data Cleaning:

  • Converted the horsepower column from object to float, handling any non-numeric values.

  • There were 6 missing values in the horsepower column after conversion. I handled these missing values, typically by filling them with the mean or median, and then proceeded with the visualizations.

  • Plot chosen for visualization: scatter plot

  • It visualizes the relationship between horsepower and mpg (miles per gallon). This will allow us to see how the power of a car's engine impacts its fuel efficiency.

  1. Matplotlib
    image

  2. Seaborn
    image

  3. Bokeh
    image

  4. Plotly
    image

  5. Altair
    image

@sujashaaa
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sujashaaa commented Aug 11, 2024

Name: Sujasha
Roll No.: 21F3001115

Variables used:
horsepower,MPG, Number of cylinders

I have used the following 5 tools/library to represent a scatter plot using the above variables:

  • Tableu
  • Google Sheet
  • Looker Studio
  • Power BI
  • Qlik
  1. Tableu
Tableau

2.Google Sheet
gsheet

3.Looker Studio
Looker Studio

4.Power BI
Power BI

5.Qlik
Qlik

@SriNandhiniThiyagarajan

Graded Assignment 5

Name : SriNandhini T
Roll Number : 21f2001390
Email : 21f2001390@ds.study.iitm.ac..in

Variables Used:

  1. Horsepower (horsepower)
  2. Miles per gallon (mpg)
  3. No of Cylinders

Chart Type:

Scatter Plot (and Bubble Charts)

Approach:

The approach is to visualize the relationship between horsepower and fuel efficiency (MPG) across different engine types, using a scatter plot where data points are color-coded by the number of cylinders. This highlights the trade-off between power and fuel economy in vehicles.

Tools Used :

  • Python - Matplotlib
  • D3.js
  • Altair
  • plotly
  • Tableau
  • Google Sheets

Visualizations

  • Matplotlib
    While Matplotlib is a straightforward tool, its lack of interactivity makes it less versatile compared to more user-friendly options like Plotly, Altair, and Tableau.

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  • D3.js
    Although D3.js offers high customization, it is more complex to use than beginner-friendly tools such as Plotly, Altair, and Tableau.

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  • Altair
    I found Altair to be both easy and intuitive, offering a great balance of simplicity and interactivity compared to the more complex D3.js and the less interactive Matplotlib.

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  • plotly
    Plotly stands out for its interactivity and ease of use, making it a preferred choice alongside Altair and Tableau for beginners.

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  • Tableau
    Tableau's user-friendly interface and easy learning curve make it one of the best tools for beginners, much like Plotly and Altair, while being more accessible than D3.js or Matplotlib.

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  • Google Sheets
    Despite its utility, Google Sheets lacks the flexibility to handle three variables in a single graph as effectively as tools like Plotly, Tableau, or Altair, requiring additional pre-processing steps.

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@21f1005544
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Graded Assignment 5

Name: John Joshi Alapatt
Roll.No: 21f1005544

Variables used:

MPG, horsepower, cylinders

Tools/ Libraries used:

Matplotlib
Power BI
Plotly
R
Google sheets

1. Matplotlib

image

2.Power BI

image

3.Plotly

image

4.R

image

5.Google Sheets

image

@mnatasha1402
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mnatasha1402 commented Aug 11, 2024

Name: Natasha Mittal
Roll No.: 21f1005823

Variables used:
MPG (millions per gallon) on y-axis
Weight on x-axis
Cylinders as discrete colors

Chart Type:
Scatter Plot

Approach:
The approach is to visualize the relationship between Weight and fuel efficiency (MPG) across different engine types, using a scatter plot where data points are color-coded by the number of cylinders. This highlights how the number of cylinders affects the relationship between weight and MPG.

Tools/library used to represent the above variables:

  1. Plotly
  2. PowerBi
  3. Flourish
  4. Data Wrapper
  5. Bokeh

Visualizations:
1.Plotly
image

2.Power Bi
image

3.Flourish
image

4.Data Wrapper
image

5.Bokeh
image

@Fashmina123
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Fashmina123 commented Aug 11, 2024

Name: Fashmina Mohamed Aboobucker
Roll No: 21f3003099

Variables:

  1. MPG
  2. Horsepower

Tools/Library:

  1. Flourish
  2. Excel
  3. Stats.blue
  4. Canva
  5. Datawrapper

Flourish
Flourish

Excel
Excel

Stats.blue
Stats blue

Canva
Canva

Datawrapper
Datawrapper

@vpleaides8
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Name: Kruttika Milind Soni
Roll no.: 21f1001029

Visualisation using different tools

I have made a scatterplot of Miles/gallon and horsepower, with number of cylinders represented by colour

These are the tools I used

  1. matplotlib
  2. Lyra
  3. Tableau
  4. plotly
  5. ggplot2

matplotlib
lyra
Sheet 1
newplot
ggplot2

@trishulam
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Name: N K Vamsi Krishna
Roll: 21f1003596

Variables used:

No of Cylinders on X-axis
Horsepower on Y-axis

I have used the following 5 tools/library to represent a scatter plot using the above variables:

Google Sheets
Seaborn
Plotly
Tableau
Chart Wizard

  1. Google Sheets
    No  of cylinders vs Horsepower (Google Sheets)

  2. Seaborn
    Scatter Plot of Number of Cylinders vs Horsepower (Seaborn)

  3. Plotly
    newplot

  4. Tableau
    Sheet 1

  5. Chart Wizard
    Untitled-project

@45sajal
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45sajal commented Aug 11, 2024

Name: Sajal Dhingra
Roll No.: 21f2001213

Variables used:

Weight on X-axis
Acceleration on Y-axis

Chart Type:

Scatter Plot

Tools/library used to represent the above variables:

Plotly
Flourish
Seaborn
Bokeh
GGPlot

  1. Plotly

WhatsApp Image 2024-08-11 at 23 21 30_b9fd0bf2

  1. Bokeh

WhatsApp Image 2024-08-11 at 23 20 59_4da254a1

  1. GGPlot

WhatsApp Image 2024-08-11 at 23 23 29_60ea2658

  1. Seaborn

WhatsApp Image 2024-08-11 at 23 22 04_82c24b9f

  1. Flourish

WhatsApp Image 2024-08-11 at 23 20 40_2d19a8ec

@varunbalaji1303
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Name: Varun Balaji
Roll No: 21f1005027

VARIABLES USED:

  1. MPG
  2. Horsepower

TOOLS USED:

  1. Matplotlib
  2. Plotly
  3. Google Sheets
  4. ggplot2
  5. Datawrapper

Plots:

MATPLOTLIB:
Screenshot 2024-08-11 at 11 33 23 PM

PLOTLY:
Screenshot 2024-08-11 at 11 34 10 PM

GOOGLE SHEETS:
Screenshot 2024-08-11 at 11 35 05 PM

GGPLOT2:
Screenshot 2024-08-11 at 11 35 35 PM

DATAWRAPPER:
Screenshot 2024-08-11 at 11 36 09 PM

@DHIBIN-VIKASH
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Name: Dhibin Vikash
Roll No. : 21f3001664

Variables used:
Y-axis -- MPG (millions per gallon)-
X-axis --Weight
Color -- cylinders

Tools/ Libraries used:
Matplotlib
Power BI
Plotly
Seaborn
Flourish

1.Matplotlib
download

2.Power BI
image

  1. Plotly
    image

4.Seaborn
download (1)

5.Flourish
image

@bhumikaxyz
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About Me

Name: Bhumika Taneja
Roll Number: 21f1006329

Description

I have plotted a scatter plot between the weight and acceleration of the vehicles.

5 Charts Using 5 Tools

1. Matplotlib

matplotlib

2. Plotly

plotly

3. GGplot

GGplot

4. Flourish

Flourish

5. Bokeh

Bokeh

@prashantjnvu
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Name: Prashant Sharma
Roll Number: 21f1004586

Chart type: Scatterplot

Variables used:

Horsepower (horsepower)
Miles per gallon (mpg)

image

image

image image

Chart type: Scatterplot

Variables used:

Horsepower (horsepower)
Miles per gallon (mpg)
Origin

image

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