2c:null 2d:["$","title",null,{"children":"Diving into the Waters of Shark Tank India | Akhila Sulgante | Observable"}] 32:Ta90,### ** What's the problem? **
Shark Tank India is a popular television show where entrepreneurs pitch their business ideas to a panel of investors in the hopes of securing funding. But there is no comprehensive platform where one can visualize and analyze the data from Shark Tank India can gain valuable insights into the success rate of pitches, investment patterns, and overall trends. ### ** Why is this relevant/important? **
Understanding Shark Tank India participation and investment patterns is important for several reasons: 1. Insights into the Indian startup ecosystem: Shark Tank India provides a platform for Indian entrepreneurs to showcase their businesses to potential investors. Understanding the participation and investment patterns can provide insights into the types of startups that are emerging in India, the challenges they face, and the opportunities that are available. 2. Investment trends: Shark Tank India can also provide insights into investment trends in the Indian market. By analyzing the types of businesses that investors are interested in, the amounts they are willing to invest, and the returns they are seeking, one can gain a better understanding of the investment landscape in India. 3. Learning opportunities: Shark Tank India can be a valuable learning opportunity for entrepreneurs, investors, and anyone interested in the startup ecosystem. By analyzing the pitches, business models, and investment decisions, one can learn about the factors that contribute to startup success and failure. 4. Economic impact: The success of startups can have a significant impact on the economy, creating jobs and driving innovation. Understanding the investment patterns in Shark Tank India can provide insights into the potential economic impact of startups in India and the types of businesses that are driving growth. ### **Introduction **
The primary goal of this project is to create a website that presents data from the TV show Shark Tank in an unbiased way. The website will summarize and display the participation data of each shark, while also exploring the investment portfolios of individual sharks. By doing this, the website aims to provide valuable information to people who are considering participating in the show. The website will allow users to gain insights into each company's offer, equity dilution, and valuation, which will help them make informed decisions about whether or not to participate in the show. Overall, the objective is to create a platform that provides an unbiased view of Shark Tank participation data and investment portfolios, enabling users to make informed decisions based on the information presented. 33:Tb5a,## Storyline Our website will be exploring two aspects of Shark Tank India's data In order to present this information effectively, we have adopted a top-down approach to our storytelling, beginning with an overview of the data before delving into more granular details at each stage. **Story 1: Understanding Participation in Shark Tank India** _By Kasi Viswanath_ - [Idea 1](https://observablehq.com/d/a860ca0bef55914e#state_season_wise_pitches): Summarizing the total number of participants and their distribution across different states - [Idea 2](https://observablehq.com/d/a860ca0bef55914e#statewise_pitches_diff): Comparing the percentage difference in participation between the two seasons for each state to identify trends and patterns - [Idea 3](https://observablehq.com/d/a860ca0bef55914e#statesDiffwithIndustryChart): Analyzing the participation of different industries in each state to understand the sector-wise distribution of participants - [Idea 4](https://observablehq.com/d/a860ca0bef55914e#Industry_gender_presenters_chart_iter2): Examine the gender balance of show participants to identify any gender-based differences in participation patterns - [Idea 5](https://observablehq.com/d/a860ca0bef55914e#industry_valuation): Examining the valuation difference trend on Shark Tank across industries involves analyzing the changes in the valuations of companies that appeared on the show over time and comparing them within different industries **Story 1: Understanding Investment Pattern in Shark Tank India** _By Akhila_ - [Idea 6](https://observablehq.com/d/a860ca0bef55914e#cell-1119): Summarizing the companies that received offers from Sharks, those that didn't, and those that accepted or rejected offers, categorized by industry - [Idea 7](https://observablehq.com/d/a860ca0bef55914e#cell-1155): Summarizing the Sharks' portfolios in each industry, including the amount invested in each company, to identify investment trends and patterns - [Idea 8](https://observablehq.com/d/a860ca0bef55914e#shark_industry_wise_equity): Analyzing the equity stakes held by the Sharks in each company to understand their investment strategies and priorities - [Idea 9](https://observablehq.com/d/a860ca0bef55914e#cell-419): Understanding the individual and collaborative investment patterns of the Sharks to identify any significant investment partnerships or trends - [Idea 10](https://observablehq.com/d/a860ca0bef55914e#sharks_total_investments): Summarizing the composition of different investment patterns on the show to provide users with an understanding of the types of investments that are typically made on the show By exploring these two aspects of Shark Tank India's data, we hope to provide our users with valuable insights into the show's dynamics and investment patterns, enabling them to make more informed decisions when considering participation or investment opportunities.34:T917,### Idea 1#: Summarizing the total number of participants and their distribution across different states. **Idiom**: Bar chart
**Title**: State-wise Participation
**Description**: This graph shows the state-wise distribution of companies that appeared on Shark Tank across both seasons. This will help us identify the budding innovation hubs of India except for metropolitan cities
**What can be learned from them**: The graph provides valuable insights into the emerging innovation hubs in India. By analyzing this data, we can identify areas where there is significant entrepreneurial activity and potential for growth. This can be helpful for the state government to identify and encourage entrepreneurs in that region. This information can be useful for state governments looking to encourage entrepreneurship and promote economic development in their region. By identifying the states where participation was low, they can develop targeted policies and initiatives to encourage and support entrepreneurs in those areas, leading to increased innovation and economic growth. #### Insight / Made-up insight: The companies that appeared on Shark Tank India were not exclusively from metropolitan cities, but instead came from diverse regions and states, some of which may have limited resources. #### Data (what) Attribute used: **Pitcher's state(Categorical):** This attribute projects states that the pitchers belong to **Count of companies(Quantitative, sequential):** This is a _derived_ attribute that displays the number of companies that participated #### Tasks (why) - T1: Summarize the count of companies by state - Sub Task1: Locate one of the states with major metropolitan city(Maharastra, Karnataka, Delhi)[Locate, Exterme] - Sub Task2: Locate one of the states that are not well-equipped with resources and not so developed(Odisha, Bihar, Madhya Pradesh, Chhattisgarh, Jharkhand, Arunachal Pradesh) [Source: TimesOfIndia](http://timesofindia.indiatimes.com/articleshow/23094131.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst) - Sub Task 3: Compare the number of participants in both of the states #### How Marks: Line Position: To indicate the count of companies in each state Color hue: To differentiate the Seasons of the show #### Prototypes 1. Tried a line chart35:T6b7,### Idea 2#: Comparing the percentage difference in participation between the two seasons for each state to identify increase and decrease in the state's participation Idiom: Diverging bar chart Title: Difference in Participation of each state in 2 Seasons Description: This chart is helpful in understanding which states are participating better and also identifying which state had a major drop in season 2 compared to season 1. what can be learned from it: It will help state governments and VCs to understand the companies that are in the state and encourage and hopefully fund more companies #### Insight / Made-up insight Participation of non-Hindi speaking states increased in season 2 Attribute used:
**Pitcher's state(Categorical):** This attribute projects states that the pitchers belong to
**Count of companies(Quantitative, sequential):** This is a _derived_ attribute that displays the number of companies that participated
**Relative difference(Quantitative, diverging):** This is a _derived_ attribute that displays the difference of participation of states in season 2 compared to Season 1
#### Tasks (why) T1: Identify the positive percentage change in companies
Sub Task1: Locate non-Hindi speaking states[Locate, distribution]
Sub Task2: Locate states where Hindi is spoken prevalently[Locate, distribution]
Sub Task2: Identify the trends between these states[Identify, Trend]
#### How Marks: Line
Position: To indicate the count of companies in each state, To identify the relative difference in participation
Color hue: To differentiate the increase and decrease in participation
Expressivity Principle: Separate and aligned
36:T855,### Idea 3#: Analyzing the participation of different industries in each state to understand the sector-wise distribution of participants **Idiom**: Diverging bar chart
**Title**: Difference in Industry Participation for each state
**Description**: This chart will help us see which industries are budding and declining in each state
**what can be learned from it**: having a clear understanding of the types of businesses that are operating within a state can help state governments in several ways. For instance, they can identify the industries that are thriving and those that need support or investment. They can also assess the economic impact of these companies, such as the number of jobs they create, the amount of revenue they generate, and their contribution to the state's overall GDP. This information can be valuable in developing policies and initiatives that support the growth of these companies and attract more investment to the state. (Few are out of our scope) #### Insight / Made-up insight Participation of Agriculture related companies increased in most states Attribute used:
**Pitcher's state(Categorical):** This attribute projects states that the pitchers belong to
**Industry(Categorical):** This attribute indicates industries that participated in Shark Tank
**Count of companies(Quantitative, sequential):** This is a _derived_ attribute that displays the number of companies that participated
**Relative difference(Quantitative, diverging):** This is a _derived_ attribute that displays the difference of participation of industry in each state #### Tasks (why) T1: Identify the change in Agriculture company participation in each state Sub Task1: Compare the change in the agriculture industry in each state Sub Task2: Identify if the change is more on the positive or negative side #### How Marks: Line Position: To indicate the difference in participation of each state Color hue: To differentiate the Industry Expressivity Principle: Separate and align #### Prototypes Iteration 1: Summarizing the industry using bar chart and building on it 1. Heat Map37:T647,### Idea 4#: Examine the gender balance of show participants to identify any gender-based differences in participation patterns **Idiom**: Grouped bar chart
**Title**: Participation pattern across industry
**Description**: This chart will help us understand the participation pattern across different industries
**What can be learned from it**: Understanding the gender balance in different industries is essential for promoting gender equality in the entrepreneurial world and creating a more inclusive and diverse India. #### Made-up insight
The beauty industry has more female presenters than male presenters
Attribute used:
**Gender(Categorical):** This attribute indicates the presenters gender
**Industry(Categorical):** This attribute indicates industries that participated in Shark Tank
**Count of companies(Quantitative, sequential):** This is a _derived_ attribute that displays the number of companies that participated #### Tasks (why)
T1: Locate beauty industry on the bar chart[Locate, feature]
Sub Task1: Identify the number of male presenters[Idenitfy, distribution]
Sub Task2: Identify the number of female presenters[Identify, distribution]
Sub Task 3: Compare the number of male and female presenters[Compare, exterme]
#### How Marks: Line
Position: Count of companies presented by different genders
Color hue: To differentiate the genders
Expressivity Principle: Separate, order, and align
#### Prototypes
1. Bar chart summarizing gender participation 2. Grouped bar chart with alignment 38:T7d6,### Idea 5#: Examining the valuation difference trend on Shark Tank across industries involves analyzing the changes in the valuations of companies that appeared on the show over time and comparing them within different industries **Idiom**: Lollipop chart
**Title**: Reduction in valuation across different industries on Shark Tank India
**Description**: This chart will help us understand how sharks evaluate company valuation in different industries
**What can be learned from it**: It will help future participants prepare for the bargain they might get for their company in terms of valuation
#### Insight / Made-up insight Companies in the Entertainment industries see the maximum difference in valuation between participants and sharks
Attribute used:
**Industry(Categorical):** This attribute indicates industries that participated in Shark Tank
**Count of companies(Quantitative, sequential):** This is a _derived_ attribute that displays the number of companies that participated
**Average Valuation Requested:** This is a _dervied_ attribute that indicates the average valuation asked by companies in a particular industry
**Average Valuation offered:** This is a _dervied_ attribute that indicates the average valuation offered by sharks in a particular industry
#### Tasks (why)
T1: Derive the average valuation asked by companies in Entertainment industry[Derive, distribution]
Sub Task1: Derive the average offered offered by sharks in Entertainment industry[Derive, distribution]
Sub Task2: Compare the difference with other industries[Identify, extreme] #### How
Marks: Line, point
Channel
Position: indicating average valuation asked and offered
Color hue: To differentiate valuation asked and offered
length: Difference in average valuation asked and offered
Expressivity Principle: Express
#### Prototypes 1. Plot with points indicating the Average valuation asked and offered39:T7aa,### Idea 6#: Summarizing the companies that received offers from Sharks, those that didn't, and those that accepted or rejected offers, categorized by industry **Idiom**: Zoomable sunburst
**Title**: Overview of Investments in Shark Tank India
**Description**: This chart shows us the percentage of companies that received an offer from sharks and see if they have accepted it or not, and break it down into industries to understand which industry received the most offers and also to know which industry did not receive a lot of offers
**what can be learned from it**: Industries that are preferable to invest by Sharks on Shark Tank India
#### Insight / Made-up insight Sharks are offering good deals for companies in the agriculture industry

Attribute used:
**`Industry`** (_Categorical_): This attribute indicates the industry the startup name belongs to
**` Received Offer`** (_Categorical_): This attribute indicates if the company received an offer
**`Accepted Offer`** (_Categorical_): This attribute indicates if the company accepted the offer
#### Tasks (why) Task 1: Identify the industries that received an offer from Sharks by summing the companies in it[Identify, distribution]
Sub Task 1: Identify the industries that accepted the offer[Idetify, distribution]
Sub Task 2: Identify the industries that did not accept the offer[Identify, distribution]
Sub Task 3: Identify the industry that did not reject any offer[Identify, feature]
#### How Marks: Area
Channels:
Position: To identify the levels in the sunburst
Size: To indicate the companies in each category
Color hue: To differentiate companies that received offers and the ones that did not
Principle: Separate, order, and align
#### Prototypes 1. Tree with levels of offers received or not and then a level to show if the offers were accepted and a last level to show the industries3a:T69e,### Idea 7#: Summarizing the Sharks' portfolios in each industry, including the amount invested in each company, to identify investment trends and patterns **Idiom**: Cascading Treemap
**Title**: Investment Pattern of each Shark and their Interest
**Description**: This chart shows us shark's investment in each industry and also tells us which shark is more interested in what industry
**What can be learned from it**: We will know which shark is more likely to invest in which industry by knowing the industries of their choice
#### Insight / Made-up insight Almost all sharks are interested in investing in the food industry even though it is a very cluttered market Attribute used:
**`Industry`** (_Categorical_): This attribute indicates the industry the startup name belongs to
**`{Shark} Investment Amount`** (_Quantitaive, sequential_): This attribute determines the amount each shark has invested
**` Sharks`** (_Categorical_): This attribute was not directly available and was extracted from the column "{Shark} Investment Equity", by splicing the names of the shark from the column #### Tasks (why) Task 1: Summarize the investment of all sharks, modularizing by industry[Summarize, distribution] Sub-task 1: Identify the industry that got the most investment amount[Identity, exterme] sub-task 2: Compare the most invested industry of all sharks[Compare, trend] #### How Mark: Area Channel Position: Sharks that invested the most to least, industries that got most investment amount to least Color hue: To differentiate levels Principle: Separate, order and align #### Prototypes 1. Summarized the sharks investment amount 2. Heat map3b:T7bc,### Idea 8#: Analyzing the equity stakes held by the Sharks in each company to understand their investment strategies and priorities Idiom: Bar chart
Title: Stakes of Sharks in each company they have invested
Description: By studying this chart, entrepreneurs and other investors can gain a better understanding of the investment strategies and preferences of the various "sharks," which can help inform their own decision-making when seeking investment opportunities or considering potential partners. Additionally, this chart may be used by analysts and researchers to track trends in investment activity across different industries and over time
what can be learned from it: We learn details about each shark's investment pattern and mostly the equity they prefer to have in a company
#### Insight / Made-up insight
Peyush is interested in investing in companies where he can own at least 10% of the equity stake Attribute used:
**`Industry`** (_Categorical_): This attribute indicates the industry the startup name belongs to
**`Startup Name`** (_Categorical_): These attribute indicates the startup name that got an offer
**`{Shark} Investment Equity`** (_Quantitative, Sequential_): This attribute is used to link startups with sharks if they have made an investment
**Derived Attribute**
**` Sharks`** (_Categorical_): This attribute was not directly available and was extracted from the column "{Shark} Investment Equity", by splicing the names of the shark from the column
#### Tasks (why) Task 1: Summarize the companies that Peyush is an investor[Summarize, distribution]
Sub Task 1: Identify the least equity Peyush has received for his investment[Identify, extreme]
Sub Task 2: Identify the most equity Peyush has in a company[Identify, extreme]
#### How Marks: Line Channel: Position: The equity in company shark got for their investment #### Prototypes 1. Bar chart without equity detail3c:Tc58,### Idea 9#: Understanding the individual and collaborative investment patterns of the Sharks to identify any significant investment partnerships or trends **Idiom**: Force-directed network **Title**: Investment Pattern of each shark and the Companies that Participated in Shark Tank India **Description**: This chart shows which shark invested in what companies and companies that did not get any investments and the ones that got combined or individual investment **what can be learned from it**: We can see which companies got investments and what sharks are involved in each company and industry #### Insight / Made-up insight There are more joint investments in Shark Tank India than Individual investments, there are companies where all sharks have invested Attribute used: **`Industry`** (_Categorical_): This attribute indicates the industry the startup name belongs to **`Startup Name`** (_Categorical_): These attribute indicates the startup name **`{Shark} Investment Equity`** (_Quantitative, Sequential_): This attribute is used to link startups with sharks if they have made an investment **Derived Attribute** **` Sharks`** (_Categorical_): This attribute was not directly available and was extracted from the column "{Shark} Investment Equity", by splicing the names of the shark from the column #### Tasks (why) (_Insight_): There are more joint investments in Shark Tank India than Individual investments, there are companies where all sharks have invested
Compare the number of companies that got individual investment and mutual from Sharks
Sub-task 1: (_Explore_) the companies that got individual investment from sharks(_paths_)
Sub-task 2: (_Explore_) the companies that got mutual investment from sharks(_paths_)
Sub-task 3: (_Compare_) the number of companies that got joint and individual investment #### How * **Marks:** Point, Line * **Channel:** * Color Hue: To identify different industries that the nodes are associated to * Attribute: Startup Name * Task: T1, T2 * Using **seperate** * **Expressiveness principle** - Since Startups are associated with a particular industry making easier to differentiate when identified by colors * **Effectiveness principle** - The different colors of the nodes indicate different industries * Size: To differentiate shark nodes from start-up nodes * Attribute: Sharks(dervied) * Task: T1, T2 * Using **seperate** * **Expressiveness principle** - Since the sharks are displayed with the bigger circle and the startup with the smaller one, it shows all the data very efficiently * **Effectiveness principle** - Since the size is reasonably different, it is easier to differentiate between Sharks and Startup nodes * Position: To draw the nodes on the screen * Attribute: Sharks(dervied) and start up name * Task: T1, T2 * Using **Express** * Although the position is not going to directly express an attribute, it is directed by the forces on the graph #### Prototypes 1. Pie chart showing each sharks investment in a different industry to identify the involvement of sharks in each industry3d:T6bc,### Idea 10#: Summarizing the composition of different investment patterns on the show to provide users with an understanding of the types of investments that are typically made on the show **Idiom**: Upset chart
**Title**: Investment Composition on Shark Tank India
**Description**: This chart shows how many percent of the companies that made deals with sharks had individual contributors and how many made joint deals involving multiple sharks
**What can be learned from it**: We can learn which sharks in shark tank India prefer making collaborative investments and sharks that like to make individual investments
#### Insight / Made-up insight
Aman and Namita like to make more deals together
Attribute used:
**Sharks(Categorical):** This attribute indicates the sharks in Shark Tank India
**Degree(Quantitative, sequential):** This is a _derived_ attribute that indicates number of sharks involvement in deals
**Prevalence(Quantitative, sequential):** This is a _derived_ attribute that percentage of the companies in different combinations of sharks involvement
**Seasons(Categorical):** This indicates the seasons of Shark Tank India
#### Tasks (why) T1: Identify the percentage of companies where two or more sharks are involved[Identify, distribution]
Sub Task1: Locate the pair that has the most investment[Locate, extreme]
Sub Task 2: Identify the pair that has the most investment[Identify, extreme]
#### How
Marks: Line, point
Position: To indicate the prevalence and degree
Color hue: To differentiate the sharks
Expressivity Principle: Separate, order, and align
#### Prototypes 1. Network graph in Idea 93e:Te53,{ const highlight = vl.selectSingle("highlight") .fields("Conditions") .on("mouseover") .init({"Conditions":[]}) .empty("none") // .nearest(true); const dealsBySharks = Array.from(dealsbyShark.entries()) .map(row => ({ "Conditions": row[0], "Count": row[1].length, "Prevalence": row[1].length / rawData.length })); const dealsByEachSharks = dealsBySharks .map(row => row["Conditions"] .split(", ") .map((s, _, all) => ({ "Condition": s, "Conditions": row["Conditions"], "Count": row.Count, "Prevalence": row.Prevalence, "Degree": all.length }) ) ).flat(); const sortAttr = sortBy === "Degree" ? vl.max().field("Degree").order("descending") : vl.fieldQ("Prevalence").order("descending"); const barChart = vl .data(dealsBySharks) .encode( vl.x().fieldN("Conditions") .sort(sortAttr) .axis(null), vl.y().fieldQ("Prevalence") .title("Prevalence of Intersection %" ) .axis({format : ".2%", orient: "right"}), vl.tooltip(["Conditions", "Prevalence", "Count", "Degree"]) ) .layer( vl.markBar({tooltip: true}) .select(highlight) .encode( vl.color().value(defaultColor) .if(highlight, vl.value("firebrick")) ), vl.markText({dy: -5, fontSize: 8}) .encode( vl.text() .fieldQ("Prevalence") .title("Prevalence of Intersection %") .format(".1%") ) ) // .width({step: 20}) .height(200); const circles = vl.markCircle({tooltip: true, size:100, opacity: 1}) .encode( vl.x().fieldN("Conditions") .sort(sortAttr) .axis(null), vl.y() .fieldN("Condition") .sort(vl.max("Prevalence").order("descending")) .title(null), vl.tooltip(["Conditions", "Condition", "Degree", "Count"]) ); const dotsIntersectionChart = vl .data(dealsByEachSharks) .layer( circles .markLine({ size: 5}) // Intersecting lines .encode( vl.y() .fieldN("Condition") .sort(vl.max("Prevalence").order("descending")), vl.y2().fieldN("Condition"), vl.color().value(defaultColor) .if(highlight, vl.value("firebrick")), vl.detail().fieldN("Conditions") ), circles .select(highlight) .encode( vl.color().fieldN("Condition") .scale({scheme: "category20"}) .legend(null) // vl.color().value(defaultColor) .if(highlight, vl.value("firebrick")) ), ); const countBySymBase = vl .markBar( { tooltip: true}) .data(dealsBySharks) .encode( vl.x().max("Prevalence").title("Prevalence of Condition %").axis({format : ".2%"}), vl.y().fieldN("Condition") .sort(vl.max("Prevalence").order("descending")) .title(null), vl.color().fieldN("Condition").scale({scheme: "category20"}).legend(null), vl.tooltip(["Condition", vl.max("Count"), vl.max("Prevalence")]) ) .width(150); return vl.vconcat( barChart, dotsIntersectionChart ) .resolve({scale: {x: "shared"}}) .spacing(0) .config( { stroke: null }) .title({ text: `Investment pattern`, subtitle: `%s out of ${rawData.length} filtered people` }) .render() }3f:Tf23,sharks_total_investments = { const highlight = vl.selectSingle("highlight") .fields("Conditions") .on("mouseover") .init({"Conditions":[]}) .empty("none") // .nearest(true); const dealsBySharks = Array.from(dealsbyShark.entries()) .map(row => ({ "Conditions": row[0], "Count": row[1].length, "Prevalence": row[1].length / rawData.length })); const dealsByEachSharks = dealsBySharks .map(row => row["Conditions"] .split(", ") .map((s, _, all) => ({ "Condition": s, "Conditions": row["Conditions"], "Count": row.Count, "Prevalence": row.Prevalence, "Degree": all.length, "url" : images[s] }) ) ).flat(); const sortAttr = sortBy === "Degree" ? vl.max().field("Degree").order("descending") : vl.fieldQ("Prevalence").order("descending"); const barChart = vl .data(dealsBySharks) .encode( vl.x().fieldN("Conditions") .sort(sortAttr) .axis(null), vl.y().fieldQ("Prevalence") .title("Prevalence of Intersection %" ) .axis({format : ".2%", orient: "right"}), vl.tooltip(["Conditions", "Prevalence", "Count", "Degree"]) ) .layer( vl.markBar({tooltip: true}) .select(highlight) .encode( vl.color().value(defaultColor) .if(highlight, vl.value("firebrick")) ), vl.markText({dy: -5, fontSize: 8}) .encode( vl.text() .fieldQ("Prevalence") .title("Prevalence of Intersection %") .format(".1%") ) ) // .width({step: 20}) .height(200); const circles = vl.markImage({tooltip: true, size:100, opacity: 1}) .encode( vl.x().fieldN("Conditions") .sort(sortAttr) .axis(null), vl.y() .fieldN("Condition") .sort(vl.max("Prevalence").order("descending")) .title(null), vl.url().field("url"), vl.tooltip(["Conditions", "Condition", "Degree", "Count"]) ); const dotsIntersectionChart = vl .data(dealsByEachSharks) .layer( circles .markLine({ size: 5}) // Intersecting lines .encode( vl.y() .fieldN("Condition") .sort(vl.max("Prevalence").order("descending")) .title(null), vl.y2().fieldN("Condition") .title(null), vl.color().value(defaultColor) .if(highlight, vl.value("firebrick")), vl.detail().fieldN("Conditions") ), circles .select(highlight) .encode( vl.color().fieldN("Condition") .scale({scheme: "category20"}) .legend(null) .title(null) // vl.color().value(defaultColor) .if(highlight, vl.value("firebrick")) ), ).width(width - 200) const countBySymBase = vl .markBar( { tooltip: true}) .data(dealsBySharks) .encode( vl.x().max("Prevalence").title("Prevalence of Condition %").axis({format : ".2%"}), vl.y().fieldN("Condition") .sort(vl.max("Prevalence").order("descending")) .title(null), vl.color().fieldN("Condition").scale({scheme: "category20"}).legend(null), vl.tooltip(["Condition", vl.max("Count"), vl.max("Prevalence")]) ) .width(width - 200); return vl.vconcat( barChart.width(width - 200), dotsIntersectionChart.height(300) ) .resolve({scale: {x: "shared"}}) .spacing(0) .config( { stroke: null }) .title({ text: `Investment Patterns`, subtitle: `%s out of ${rawData.length} filtered people` }) .render() }40:T522,### Evaluation Plan - Akhila I will be running a usability study for Idiom 10 to evaluate the following task T1: Identify the percentage of companies where two or more sharks are involved[Identify, distribution]
Sub Task1: Locate the pair that has the most investment[Locate, extreme]
Sub Task 2: Identify the pair that has the most investment[Identify, extreme]
__Script__ Welcome to the study, thank you for agreeing to participate in this study. I appreciate your time as there is no incentive for participation. This is not a test and there are no wrong answers. Please think out loud and I would appreciate your honest opinion. __Tasks to test__ * Q1. Please identify what percentage of the companies got joint investments from more than 3 sharks * Q2. Please identify the shark that invested the most * Q3. Please identify the sharks that prefers to invest together __Expected answers__ * Q1. 0.6, 0.3, 0.3 * Q2. Peyush * Q3. Namita and Aman __Post test questionnaire__ on a scale of 1 to 5, being 1 very ineffective/useless and 5 being very effective/useful: * PT1.1. How effective was the visualization for helping you find the Shark with most investment * PT1.2 How obvious was the visualization to find percentage of companies that got joint investment from more than 3 sharks41:T553,### Evaluation Plan - Kasi I will be running an usability study for Idiom 1, I'll be evaluating tasks... T1: Identify the change in Agriculture company participation in each state Sub Task1: Compare the change in the agriculture industry in each state Sub Task2: Identify if the change is more on the positive or negative side __Script__ Welcome to the study, thank you for agreeing to participate in this study. I appreciate your time as there is no incentive for participation. This is not a test and there are no wrong answers. Please think out loud and I would appreciate your honest opinion. __Tasks to test__ * Q1. Please identify the industry that had significant increase of pitches in Karnataka * Q2. Please identify the state which saw decline of pitches in multiple industry * Q3. What states had no change in pitches across seasons __Expected answers__ * Q1. Beauty Industry * Q2. Maharastra * Q3. Uttarakhand, Bihar and Kerala __Post test questionnaire__ on a scale of 1 to 5, being 1 very ineffective/useless and 5 very effective/useful: * PT1.1. How effective was the visualization in finding the state with the maximum increase in pitches * PT1.2 How effective was the visualization in finding the state with the maximum decrease in pitches across multiple industry * PT1.3 How easy was it to identify the states that had no difference 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30:{"id":"a860ca0bef55914e","slug":"projectprogressreport","title":"Diving into the Waters of Shark Tank India","thumbnail":"4fe99c1e367788078957dad020b1eecb2c3578b1d3bf44c31cc3ee51f6f0b681","access_level":"public","create_time":"$D2023-04-04T17:37:28.761Z","update_time":"$D2023-04-08T05:45:19.994Z","view_time":null,"first_public_time":"$D2023-04-04T17:37:28.761Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"e76678b312b62ab3","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":"starter_2024","bio":""},"version":2083,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"667daa7e86be0e8e","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":null,"bio":""},{"type":"individual","id":"fcd94e1c1ca12ae9","login":"kasivisu4","name":"Kasi Viswanath","avatar_url":"https://avatars-next.observableusercontent.com/48e36723913e7e5b18b906e1d7cde12fad4146715e78bbf74d3db7969a248b56","tier":null,"bio":"I love building things that make people's life easier"}],"members":[{"type":"individual","id":"667daa7e86be0e8e","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":null,"role":"editor"},{"type":"individual","id":"fcd94e1c1ca12ae9","login":"kasivisu4","name":"Kasi Viswanath","avatar_url":"https://avatars-next.observableusercontent.com/48e36723913e7e5b18b906e1d7cde12fad4146715e78bbf74d3db7969a248b56","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Diving into the Waters of Shark Tank India","cells":[{"id":0,"value":"# Diving into the Waters of Shark Tank India\n_Project Progress Report_\n- By [Akhila Sulgante](https://akhilasulgante.github.io/portfolio) and [Kasi Viswanath Vandanapu](https://kasivisu4.github.io/)\n- [Slides](https://docs.google.com/presentation/d/1oD8Y9sGY3MA2f5auiIW9Wg8trag-7jdXDDg3vzeZyGw/edit#slide=id.g22b8047b3b0_6_75)\n- Class: [CS 7295 class](https://johnguerra.co/classes/data_visualization_spring_2023/)\n- Professor: [prof. John Alexis Guerra Gómez](https://johnguerra.co/)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1153,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"## Data sources and abstraction\n\n** Data Sources **\n1. [Kaggle dataset on Shark Tank India](https://www.kaggle.com/datasets/smitshah1701/sharktankindia)\nThe dataset on Shark Tank India available on Kaggle comprises comprehensive information on the pitches made during Season 1 and 2, as well as the corresponding details of shark investments\n\n2. [Shark Tank India Official Website](https://sharktank.co.in/)\nWe were using this website just to cross verify the Kaggle Dataset\n\n** All the data is publicly available and free to use ** \n\n** Abstraction**
\n**Dataset Availability:** Static
\n**Data Type:** Table
\nAbstraction on indiviual attribute is performed below with why and how for a particular idiom\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":904,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":932,"value":"### State-wise participation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"statewise_pitches = {\n let lines = vl.markLine({tooltip:true})\n .encode(vl.x().fieldN(\"Pitchers State\").title(\"State\"),\n vl.y().count().title(\"Number of Companies\"),\n vl.color().fieldN(\"Season Number\"))\n\n let circles = vl.markCircle({tooltip:true}).encode(vl.x().fieldN(\"Pitchers State\").title(\"State\"),\n vl.y().count().title(\"Number of Companies\"),vl.color().fieldN(\"Season Number\"))\n\n return vl.layer(lines,circles)\n .data(rawData.filter(d=>d[\"Pitchers State\"]))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1269,"value":"Final Graph","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":910,"value":"state_season_wise_pitches = vl.markBar()\n.encode(vl.y().fieldN(\"Pitchers State\").title(\"State\").sort(\"-x\"),\n \n vl.x().count().title(\"Number of Companies\"),\n vl.color().fieldN(\"Season Number\"))\n.data(rawData.filter(d=>d[\"Pitchers State\"]))\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":935,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":374,"value":"statewise_pitches_diff = {\n let bars = vl.markBar()\n .encode(vl.y().fieldN(\"state\"),\n vl.x().fieldQ(\"diff\").title(\"Difference in Pitches across seasons\"),\n vl.text().fieldQ(\"diff\"), vl.color().if(\"datum.diff>0\", vl.color().value('#339933')).value('#ff6666')) \n\n let zeroLine = vl.markLine().encode(vl.y().fieldN(\"state\"),\n vl.x().value(110),vl.color().value(\"#0099cc\"))\n\n return vl.layer(zeroLine,bars).data(statesDiff)\n .width(600).render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":940,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"vl.markBar()\n.encode(vl.y().fieldN(\"Industry\").sort(\"-x\"), vl.x().count())\n .width(400)\n .height(400)\n.data(rawData)\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"vl.markBar({tooltip:true}).encode(vl.y().fieldN(\"Pitchers State\"),vl.x().fieldN(\"Industry\"),vl.color().count(),).data(rawData.filter(d=>d[\"Pitchers State\"])).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":670,"value":"key = Swatches(statesDiffwithIndustryChart.scales.color)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":631,"value":"statesDiffwithIndustryChart = StackedBarChart(statesDiffwithIndustry, {\n x: d => d.diff,\n y: d => d.state,\n z: d => d.industry,\n xFormat: \"+%\",\n xLabel: \"← Reduced Pitches · Normal · Increased Pitches →\",\n yDomain: d3.groupSort(statesDiffwithIndustry, D => d3.sum(D, d => -Math.min(0, d.diff)), d => d.state),\n marginLeft: 200,\n colors: d3.schemeSet1.concat(d3.schemeSet2),\n zDomain: industries,\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":950,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1111,"value":"### Iteration -1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":70,"value":"vl.markBar({tooltip:true})\n.encode(vl.y().fieldN(\"Presenter\").sort(\"-x\"), vl.x().sum(\"count\").title(\"Number of Presenters\"))\n .width(400)\n .height(400)\n.data(presentersData)\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1992,"value":"rawData","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1356,"value":"Industry_gender_presenters_chart_iter1 = {\n let malePresenters = vl.markBar({\"xOffset\": -8,\"size\": 16,tooltip:true})\n.encode(vl.x().fieldN(\"Industry\"),vl.y().sum(\"Male Presenters\").title(\"Presenters\"),vl.color({\"datum\":\"Male\"})).width(900)\n\n let femalePresenters = vl.markBar({\"xOffset\": 8,\"size\": 16,tooltip:true})\n.encode(vl.x().fieldN(\"Industry\"),vl.y().sum(\"Female Presenters\").title(\"Presenters\"),vl.color({\"datum\":\"Female\"})).width(900)\n\n let transGenderPresenters = vl.markBar({\"xOffset\": 24,\"size\": 16,tooltip:true})\n.encode(vl.x().fieldN(\"Industry\"),vl.y().sum(\"Transgender Presenters\").title(\"Presenters\"),vl.color({\"datum\":\"Trans\"})).width(900)\n \n\n return vl.layer(malePresenters,femalePresenters,transGenderPresenters)\n .data(rawData).render()\n\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1339,"value":"Industry_gender_presenters_chart_iter2 = GroupedBarChart(Industry_gender_presenters, {\n x: d => d.Industry.split(\"/\")[0],\n y: d => d.count,\n z: d => d.presenter,\n colors: d3.schemeSpectral[2],\n width,\n height: 500,\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1944,"value":"{\n let oa = \n vl.markPoint()\n .encode(\n vl.y().fieldN('Industry').title(\"Industry\"),\n vl.x().mean('Valuation Requested').title(\"valuation\"),\n vl.color().value(\"#669999\"),\n // vl.text().value(\"●\")\n )\n \n\n let da = \n vl.markSquare()\n .encode(\n vl.y().fieldN('Industry').title(\"Industry\"),\n vl.x().mean('Deal Valuation'),\n vl.color().value(\"firebrick\"),\n // vl.text().value(\"●\")\n )\n\n\n let final = vl.layer(oa, da)\n .data(rawData.filter(d=>d[\"Received Offer\"]))\n .width(1000)\n .height(500)\n .render({\"renderer\":\"svg\"})\n\n return final\n}\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1301,"value":"Legend(industry_valuation.color, {title: \"Age (years)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1289,"value":"industry_valuation = DotPlot(industry_avg , {\n x: d => d.value,\n y: d => d.Industry,\n z: d => d.valuation,\n xLabel: \" Avg valuation (INR in Lakhs) →\",\n width\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":990,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1140,"value":"### Iteration - 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1136,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1983,"value":"Final Graph","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1119,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":953,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1059,"value":"#### Iteration-1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":115,"value":"shark_Investments_chart = {\n let bar = vl.markBar()\n.encode(vl.y().fieldN(\"Shark\").sort(\"-x\").axis(null), vl.x().sum(\"Shark Investment Amount\"),vl.color().fieldN(\"Season Number\")).height(300).width(600)\n \n\n\nlet images = vl.markImage().encode(vl.y().fieldN(\"Shark\").axis(null),vl.url().field(\"url\"),vl.tooltip(vl.field(\"Shark\"))).height(300).width(50)\n \nreturn vl.hconcat(images,bar).data(shark_Investments).render({renderer:\"svg\"})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1040,"value":"sharkheatmap = vl.markBar({tooltip:true})\n .encode(vl.y().fieldN(\"Shark\"),\n vl.x().fieldN(\"Industry\"),\n vl.color().mean(\"Shark Investment Amount\").title(\"Investement Amount\"))\n .data(shark_Investments)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2013,"value":"Final graph","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1155,"value":"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":948,"value":"$3b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2024,"value":"viewof sset = {\n return Inputs.select(sharkSet,{label:\"Sharks\"})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":125,"value":"vl.markBar()\n .data(rawData.filter((d)=> d[`${sset} Investment Equity`] > 1))\n // .transform(vl.calculate(`datum[${sset} Investment Equity]+ '% Equity'`).as(\"percent\"))\n .encode(\n vl.y().fieldN('Startup Name').title(\"Startup Name\").sort(\"-x\"),\n vl.color().fieldN(\"Industry\").scale({scheme:\"Category20\"}).title(\"Deal amount in Lakhs(INR)\"),\n vl.tooltip().fieldN(\"Total Deal Amount\"),\n vl.x().fieldQ('Total Deal Amount').title(\"Deal amount in Lakhs(INR)\"),\n vl.href().field(\"Company Website\")\n )\n .width(600)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1157,"value":"viewof shark = Inputs.select(sharkSet,{label:\"shark\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1172,"value":"sharkSelectedData = shark_Investments.filter(d=>d.Shark.split(\" \")[0]===shark)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1183,"value":"shark_industry_wise_equity = {\n let layer;\n for(let industry of industries){\n let chart = vl.markBar().encode(vl.x().fieldN(\"Startup Name\"),vl.y().fieldQ(\"Total Deal Equity\")).data(sharkSelectedData.filter(d=>d.Industry===industry)).width(600)\n // let avg_line = vl.markLine().encode(vl.x().fieldN(\"Startup Name\"), vl.y().sum(\"Total Deal Equity\").aggregate(\"mean\"),vl.color().value(\"#000\"))\n\n // chart = vl.layer(chart,avg_line).data(sharkSelectedData.filter(d=>d.Industry===industry)).width(600)\n layer = layer ? vl.vconcat(layer,chart) : chart\n }\nreturn layer.render()}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1199,"value":"industries","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1178,"value":"shark_Investments","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":429,"value":"vl.markArc({tooltip : true})\n .encode(\n vl.theta().sum(\"Shark Investment Amount\"),\n vl.color().fieldN(\"Shark\"),\n vl.facet().fieldN(\"Industry\").columns(4)\n )\n .data(shark_Investments)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2036,"value":"Final Graph","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":419,"value":"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":943,"value":"$3d","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":689,"value":"viewof sortBy = Inputs.select([ \"Prevalence\", \"Degree\"],{label:\"sort by\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":860,"value":"viewof Season = Inputs.select([ \"1\", \"2\"],{label:\"Season\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":753,"value":"images","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":700,"value":"dealsbyShark = {let res = rawData.filter(d=>d[\"Season Number\"]===+Season).map(d=>({...d}))\n let keys = Object.keys(res[0])\n .filter((d) => d.indexOf(\"Investment Amount\") != -1 && d.indexOf(\"Guest\") === -1)\n let dealsbyShark = []\n for(let row of res){\n let investers = []\n for(let key of keys){\n if(+row[key]>0){\n investers.push(key.split(\" \")[0])\n }\n }\n if(investers.length>0){\n\n dealsbyShark.push({Investors:investers.join(\", \"),row})\n }\n }\n return d3.group(dealsbyShark,d=>d.Investors)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":695,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2082,"value":"$40","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":629,"value":"$41","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":712,"value":"import {vl} from \"@john-guerra/vega-lite-api\" ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":709,"value":"defaultColor = \"#999\" ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":894,"value":"Inputs.table(rawData)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":896,"value":"rawData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2073,"value":"## Data Exploration","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":211,"value":"### Finding the correlation b/w Yearly Revenue and deals not received","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":474,"value":"vl.markBar({tooltip:true})\n .data(deal_diff)\n .encode(\n vl.y().fieldN(\"Industry\").sort(\"x\"),\n vl.x().mean(\"diff\")\n) .width(800)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"deal_diff = {\n let deals = []\n for(let data of (rawData.filter(d=>d[\"Deal Valuation\"]))){\n let res = {}\n res [\"Industry\"] =data[\"Industry\"]\n res[\"diff\"] = (data[\"Deal Valuation\"] - data[\"Valuation Requested\"] + 1) / ( data[\"Valuation Requested\"] + 1)\n deals.push(res)\n }\n\nreturn deals \n }\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":259,"value":"Finding the average difference between equity asked and equity diluted across the industry","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":588,"value":"vl.markBar({tooltip:true})\n .data(equity_diff)\n .encode(\n vl.y().fieldN(\"Industry\").sort(\"x\"),\n vl.x().mean(\"diff\")\n) .width(800)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":572,"value":"equity_diff = {\n let deals = []\n for(let data of (rawData.filter(d=>d[\"Deal Valuation\"]))){\n let res = {}\n res [\"Industry\"] =data[\"Industry\"]\n res[\"diff\"] = (data[\"Total Deal Equity\"] - data[\"Original Offered Equity\"] + 1) / ( data[\"Original Offered Equity\"] + 1)\n deals.push(res)\n }\n\nreturn deals \n }\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"### Finding the co-relation b/w Startup Start date nd deal received","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":295,"value":"vl.markBar({tooltip:true})\n .data(rawData.filter(d=>d[\"Started in\"]))\n .encode(\n vl.y().count(),\n vl.x().fieldO('Started in'),\n vl.color().fieldN(\"Received Offer\")\n )\n .width(800)\n .render()\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":302,"value":"vl.markLine({tooltip:true})\n .data(rawData.filter(d=>d[\"Started in\"] && d[\"Received Offer\"]===1))\n .encode(\n vl.y().count(),\n vl.x().fieldQ('Started in'),\n vl.color().fieldN(\"Pitchers Average Age\")\n )\n .width(800)\n .render({renderer:\"svg\"})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"sharkSet = new Set([\"Ashneer\", \"Aman\", \"Namita\", \"Anupam\", \"Vineeta\", \"Peyush\", \"Ghazal\", \"Amit\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"rawData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":118,"value":" shark_Investments = \n{\n let data = aq.from(rawData)\n.fold(Object.keys(rawData[0])\n .filter((d) => d.indexOf(\"Investment Amount\") != -1 && d.indexOf(\"Guest\") === -1), { as :[\"Shark\", \"Shark Investment Amount\"]}).objects()\n\n for(let d of data){\n d[\"url\"] = images[d[\"Shark\"].split(\" \")[0]]\n }\n\n return data\n\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"rawData = archive.file(\"Shark Tank India.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"archive = FileAttachment(\"archive.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"import {aq, op} from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":672,"value":"import {Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":633,"value":"import {StackedBarChart} from \"@d3/diverging-stacked-bar-chart\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":743,"value":"images = ({\n\"Aman\":\"https://i.postimg.cc/XYknVVnw/Aman-converted.png\",\n\"Amit\":\"https://i.postimg.cc/g2nKp0V7/Amit-converted.png\",\n\"Anupam\":\"https://i.postimg.cc/RVPT2tHf/Anupam-converted.png\",\n\"Ashneer\":\"https://i.postimg.cc/Wpw86Cp5/Ashneer-converted.png\",\n\"Ghazal\":\"https://i.postimg.cc/PJT4nzyb/Ghazal-converted.png\",\n\"Namita\":\"https://i.postimg.cc/GmVxm1zm/Namita-converted.png\",\n\"Peyush\":\"https://i.postimg.cc/sDfcnmvs/Peyush-converted.png\",\n\"Vineeta\":\"https://i.postimg.cc/vThvYg2J/vineeta-converted.png\",\n\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1329,"value":"Industry_gender_presenters = aq.from(rawData).groupby(\"Industry\").rollup({ 'Male' : op.sum('Male Presenters'), 'Female' : op.sum('Female Presenters'),'Trans' : op.sum('Transgender Presenters')}).fold([\"Male\",\"Female\",'Trans'],{as:[\"presenter\",\"count\"]}).objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1325,"value":"import {GroupedBarChart} from \"@d3/grouped-bar-chart\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1303,"value":"import {Legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1284,"value":"import {DotPlot} from \"@d3/dot-plot\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1118,"value":"industry_avg = aq.from(rawData).groupby(\"Industry\").rollup({ 'Deal Valuation' : op.mean('Deal Valuation'), 'Valuation Requested' : op.mean('Valuation Requested')}).fold([\"Deal Valuation\", \"Valuation Requested\"],{as:[\"valuation\",\"value\"]}) .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":655,"value":"industries = Array.from(d3.group(rawData,d=>d[\"Industry\"]).keys())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":638,"value":"statesDiffwithIndustry = {\n let data = d3.groups(rawData.filter(d=>d[\"Pitchers State\"]),d=>d[\"Pitchers State\"],d=>d[\"Industry\"],d=>d[\"Season Number\"])\n let finalData = []\n for(let [state,v1] of data){\n for(let [Industry,v2] of v1){\n let res = {}\n\n let record = {1:0,2:0}\n for(let [season,v3] of v2){\n record[season] = v3.length\n }\n let diff = (record[2]-record[1] + 1) /(record[1]+1)\n res[\"state\"] = state\n res[\"industry\"] = Industry\n res[\"diff\"] = diff\n finalData.push(res)\n }\n }\n return finalData\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":342,"value":"statesDiff = {\n let states = {}\n for(let data of (rawData.filter(d=>d[\"Pitchers State\"]))){\n states[data[\"Pitchers State\"]] = states[data[\"Pitchers State\"]] || {}\n states[data[\"Pitchers State\"]][data[\"Season Number\"]] = states[data[\"Pitchers State\"]][data[\"Season Number\"]] || 0\n states[data[\"Pitchers State\"]][data[\"Season Number\"]] +=1\n }\n\n for(let data of Object.values(states)){\n data[\"diff\"] = ((data[2] || 0) - (data[1]|| 0)) *100 /((data[1] || 1)) \n }\n\nreturn Object.entries(states).map(d=>{return {\n state:d[0],\n diff:d[1].diff\n}\n}) \n }\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1986,"value":"presentersData = aq.from(rawData).fold([\"Male Presenters\",\"Female Presenters\",\"Transgender Presenters\"],{as:[\"Presenter\",\"count\"]}).objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W42","stdlib":"1"}} 31:"$30:body"