Graded Assignment-5 (May Term 2024):- 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.

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

[Kaustav-Goswami9]

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

2. Flourish Tool

3. Bokeh Library

4. ggplot2 Library

5. RAWGraphs Tool

[sheikhuzairhussain]

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)

Seaborn (library)

Plotly (library)

Power BI (app)

Tableau (app)

[SURAJARS]

Name : Suraj ARS Roll No : 21f1005229

Variables used:

mpg horsepower *cylinders

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

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

3.Scatter plot using Altair created mpg vs horsepower colored by cylinders

4.Scatter plot using Tableau created mpg vs horsepower colored by cylinders

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

[neeraj-iit]

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

2. Excel Scatter plot of horsepower vs mpg

3. Qlik Scatter plot of horsepower vs mpg

4. Looker Studio Scatter plot of horsepower vs mpg

5. Plotly Scatter plot of horsepower vs mpg

[Ashrey30]

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

Pygal

Plotly

Altair

Bokeh

[ShyamSundhar1411]

Assignment-5

Name: Shyam Sundhar Ganesh Roll Number: 21f3001249

Libraries/App Used

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

Plots

1. Altair

Variables Used

1. MPG
2. Displacement
3. Cylinders

Plot

2. Seaborn

Variables Used

All

Plot

3. Plotly

Variables Used

1. Weight
2. Displacement
3. Cylinders

Plot

4. Flourish

Variables Used

1. Displacement
2. Weight

Plot

5. Bokeh

Variables Used

1. MPG
2. Displacement
3. Cylinders

Plot

[21f1006304ds]

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

Tableau

Google Sheet

Flourish

PowerBI

[subhashree211002]

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)

---

Chart 2: Bubble Chart Using Matplotlib

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

---

Chart 3: Bubble Chart Using Plotly

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

---

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)

---

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)

---

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)

[shelleyiitm]

Name : Shelley R Roll No: 21f1005512

Selected Variables :

• Weight
• mpg (miles per gallon)

Chart type : Scatter plot

In Excel:

In Plotly:

In DataWrapper:

In HighCharts:

In Google sheets:

[Indu16910]

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

Chart type : Scatter plot

Matplotlib Python

Flourish

Datawrapper

Seaborn

Raw graphs

[rukhsarrahman]

Name: Rukhsar Rahman Roll No: 21f1003273

Variables Used :

• Weight
• MPG
• Cyinders

Chart 1: Scatterplot Using Matplotlib

Chart 2: Scatterplot Using Seaborn

Chart 3: Scatterplot Using Pandas

Chart 4: Scatterplot Using Plotly

Chart 5: Scatterplot Using Altair

[RajRohitYadav19]

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

2. Scatter plot using plotly

   

3. Scatter plot using altair

   

4. Scatter plot using tableau

   

5. Scatter plot using power bi

[abirChakrabortyIITM]

Name: Abir Subroto Chakraborty Roll no: 21f2000280

---

Columns used:

1. MPG

2. Horsepower

   ---

   Tools and Libraries used:

3. Matplotlib

4. D3.js

5. RAW Graph

6. Tableau

7. Flourish

   ---

   Charts:

8. Matplotlib:

9. D3.js:

10. RAWGraph:

11. Tableau:

12. Flourish:

[Ashutosh-tec]

Name: Ashutosh Kumar Barnwal Roll No: 21f1001709

Variables: Weight, Acceleration

1. Matplotlib

2. Tableau

3. RawGraphs

4. d3.js

   

5. Flourish

• Also the correlation matrix of mpg, cylinders, displacement, horsepower, weight, acceleration

[pranaydeep139]

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:

Using Plotly:

Using Bokeh:

Using Flourish:

Using R-ggplot2:

[Dheeraj-Sathianarayanan]

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

2) Plotly

3) Altair

4) Plotnine

5) HoloViews

[Afringowhar]

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

Altair library

Plotly library

PowerBI Tool

Google Data Studio

[harshymehta14]

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

2. Google Sheet

3. Matplotlib

4. Plotly

5. RAWGraphs 2

[Kirupa-Krishan]

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

2. Raw Graphs Tool

3. Flourish Tool

4. Google Sheet Tool

5. Plotly Library

[sahilrajpal121]

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

Flourish (chart link)

ggplot

Bokeh

Datawrapper (chart link)

[trxpti]

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

2. Using PowerBI

3. Using Tableau

4. Using Altair

5. Using 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]

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

Flourish

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

plotly

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

DataWrapper

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]

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

2. Power BI

3. Flourish

4. Tableau

5. Plotly

[Arshi81099]

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.

2. 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.

3. 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.

4. 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.

   

5. 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.

   

[Harsehraab]

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

Plotly

PowerBi

Flourish

Google sheets

[muskansindhu]

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
   

   

2. Chart using Seaborn
   

   

3. Chart using Plotly
   

   

4. Chart using Vega
   

   

5. Chart using Highcharts
   

   

[Nivoceros]

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

Matplotlib

Bokeh

Altair

[Sa-N98]

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

2.Matplotlib

3.Flourish

4.Datawrapper

5.Plotly

[Jigyasa2408]

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

2) Matplotlib

3) DataWrapper

4) Plotly

5) Bokeh

[praddyyyy]

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

2. Seaborn

3. Bokeh

4. Plotly

5. Altair

[sujashaaa]

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

2.Google Sheet

3.Looker Studio

4.Power BI

5.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.

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

• 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.

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

• 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.

• 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.

[21f1005544]

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

2.Power BI

3.Plotly

4.R

5.Google Sheets

[mnatasha1402]

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

2.Power Bi

3.Flourish

4.Data Wrapper

5.Bokeh

[Fashmina123]

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

Excel

Stats.blue

Canva

Datawrapper

[vpleaides8]

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

[trishulam]

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

2. Seaborn

3. Plotly

4. Tableau

5. Chart Wizard

[45sajal]

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

2) Bokeh

3) GGPlot

4) Seaborn

5) Flourish

[varunbalaji1303]

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:

PLOTLY:

GOOGLE SHEETS:

GGPLOT2:

DATAWRAPPER:

[DHIBIN-VIKASH]

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

2.Power BI

3. Plotly

4.Seaborn

5.Flourish

[bhumikaxyz]

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

2. Plotly

3. GGplot

4. Flourish

5. Bokeh

[prashantjnvu]

Name: Prashant Sharma Roll Number: 21f1004586

Chart type: Scatterplot

Variables used:

Horsepower (horsepower) Miles per gallon (mpg)

Chart type: Scatterplot

Variables used:

Horsepower (horsepower) Miles per gallon (mpg) Origin