Discipline: Data Science

Step into the world of data visualization, where numbers come alive and stories unfold. Data visualization transforms raw data into visually appealing representations that reveal hidden patterns and insights. Whether you’re an experienced data analyst or a beginner in data science, mastering data visualization is essential for effectively communicating your insights. 

Join us as we delve into why this skill is essential and how it can help you create compelling visualizations that engage and inform your audience.

Data Visualization: Bringing Numbers to Life

At its core, data visualization is about transforming raw data into visual representations that are easy to interpret and understand. It’s like painting a picture with numbers, allowing us to uncover patterns, trends, and relationships that might otherwise remain hidden in rows and columns of data. 

Types of Charts and Graphs

Different visualization methods serve distinct purposes and are suited to specific data and communication needs. Each type of visualization has its unique strengths and weaknesses, playing a unique role in visual storytelling. Therefore, choosing the right one makes a big difference in how effectively you communicate your insights. 

Let’s explore some of the most common types of charts and graphs, their strengths, and how they can be used effectively.

Bar charts

Bar charts are perfect for comparing categorical data and making comparisons between groups while highlighting patterns or trends over time. They use bars of different heights or lengths to represent data, making it easy to compare groups and identify patterns or trends.

Specifically, stacked bar charts are helpful for visualizing multiple categories, revealing both the total and the breakdown of each category’s share. Additionally, horizontal bar charts become relevant when handling lengthy category names or when emphasizing the numerical comparisons between groups.

Box plots

Box plots are good for visualizing the distribution of numerical data and identifying key statistics such as median, quartiles, outliers, and the variability of the data. Compared to bar charts, box plots provide a better understanding of the spread of the data, while allowing for easier identification of outliers and extreme values.

Histograms

Histograms are great for showing how numerical data is distributed. They use bars to represent frequency or count of values within predefined intervals, or bins. Histograms offer an intuitive way to grasp the distribution’s shape, central tendency, and variability. They make it easy to see patterns like peaks, clusters, or gaps.

Line graphs

Line graphs are ideal for illustrating patterns, trends, and correlations over time and comparing continuous data points. Unlike bar and box plots, line graphs provide a continuous view of the data, allowing for a more nuanced understanding of how different variables are related.

Scatter plots

Scatter plots are great for visualizing relationships and correlations between two continuous variables. They allow for the identification of potential outliers and clusters in the data and can provide insights into the strength and direction of correlations. 

Heatmaps

Heat maps are particularly effective for displaying relationships between two variables within a grid, using color gradients to represent different values or levels of intensity. Heatmaps make it easy to identify patterns and trends in large datasets that may not be immediately apparent.

Data Visualization Tools

To begin crafting engaging visualizations, you can start with Python libraries that are beginner-friendly, such as Matplotlib, Seaborn, and Plotly. These libraries are robust and offer an array of features designed to help you bring your data to life. For a more business-oriented approach, particularly when building dashboards, tools like Tableau and Power BI can be utilized. They offer a more professional edge and are particularly suited to business data visualization.

• Matplotlib (Python): A versatile library for creating a wide variety of plots and charts. Integrates well with other libraries like NumPy and Pandas. However, it may require more code for complex visualizations compared to other libraries and may need additional styling for visually appealing plots.

• Seaborn (Python): Built on top of Matplotlib, Seaborn specializes in statistical data visualization with elegant and attractive graphics. Creates complex plots such as violin plots, pair plots, and heatmaps easily. However, it may not offer as much flexibility for customization compared to Matplotlib.

• Plotly (Python): Plotly is known for its interactive and web-based visualizations. It is perfect for creating dynamic dashboards and presentations with zooming, panning, and hovering capabilities. However, the learning curve can be steep for beginners.

• Ggplot2 (R): An elegant R package for data visualization that implements the grammar of graphics. It offers high-quality and versatile charting options for creating customized and publication-quality plots.

• Tableau: A powerful data visualization tool that offers intuitive drag-and-drop functionality for creating interactive dashboards and reports. Tableau is widely used in the industry for its ease of use and robust features.

• Power BI: Power BI is Microsoft’s business analytics tool for visualizing and sharing data insights. It seamlessly integrates with Microsoft products and services, providing extensive capabilities for data analysis and visualization.

Top Charting Don’ts for Better Data Visualization

“The simple graph has brought more information to the data analyst’s mind than any other device.” — John Tukey

With that said, it is important to remember that not all data visuals are created equal. To help ensure that your visualizations are effective and easy to understand, here are some top charting don’ts to keep in mind.

• Don’t add 3D or blow-apart effects to your visuals. They just make things harder to understand. Stick to simple, flat designs for clarity.

• Don’t overwhelm your visualizations with excessive colors. Stick to universal color categories and use them only to distinguish different categories or to convey essential information within the dataset. Especially, avoid using rainbow palettes, which can make visuals messy and difficult to follow.

• Don’t overwhelm your visuals with excessive information. Packing it with too much information defeats the purpose of visual data processing. Consider changing chart types, simplifying colors, or adjusting axis positions to simplify the information presented to ensure a clearer picture. Keep it simple, keep it clear.

• Don’t switch up your style halfway through. Ensure that your colors, axes, and labels are uniform across all charts. This will allow for easy visual digestion and understanding.

• Don’t use pie charts. Our visual system struggles when estimating quantities from angles. Spare readers from “visual math” by doing extra calculations yourself. Go for visuals that clearly illustrate the relationships between variables.

Data Visualization Examples

Now, let’s explore how to create a variety of charts using Seaborn. With just a few lines of code, we can create a visually appealing chart that effectively conveys our data. We can customize the chart by changing the color palette, adjusting the plot size, font size, and style, and adding annotations or labels to the chart. Let’s delve into where each chart type would be most suitable, ensuring our presentations are clear, concise, and impactful.

Bar chart

This stacked bar plot shows the total number of Olympic medals won by the top five countries. It uses the `barplot()` function of the Seaborn library. Each country (x-axis) is represented by stacked bars for the total count of gold, silver, and bronze medals (y-axis). We opted for a stacked bar plot because this format helps us see each country’s medal count and the contribution of each medal type in a clear way. 

This visual tells the story that the United States leads with the most gold medals, followed by silver and bronze. Russia also stands out, especially in gold medals. Russia, Germany, the UK, and France have similar numbers of bronze and silver medals, but Russia excels in gold. We use color smartly to represent the medals accurately, keeping the focus on medal counts and country comparisons without distractions. 

This bar graph illustrates the top 20 movie genres (y-axis) ranked by their total gross earnings (x-axis) using the `barplot()` function of the Seaborn library. We opted for a horizontal graph to facilitate comparisons across genres, especially with numerous categories like the top 20 movie genres. Additionally, the horizontal layout provides ample space for longer genre names, enhancing readability and comprehension. 

This visual unveils the story that the Adventure genre leads the field with $8 billion in gross earnings, closely followed by Action with $7 billion. Drama and Comedy claim the next spots with $5 billion and $3.5 billion in gross earnings, respectively. Sport and Fantasy anchor the list at the bottom. By using only one color to represent the genre category, we ensure clarity without distracting color palettes. This allows the audience to focus on the data effortlessly.

Box plot

This graph presents a box plot illustrating the age distribution (y-axis) among male and female passengers (x-axis) across different passenger classes (hue). It uses the `boxplot()` function of the Seaborn library. This visual conveys the information that first-class passengers tend to be older with a wider range of age, ranging from 0 to 80 years. In contrast, third-class passengers tend to be younger, typically falling between 0 and 50 years. 

Notably, outliers are present in second- and third-class passengers. Especially among third-class females, older individuals are more prevalent. We maintain consistency between males and females by using three color categories to represent the three passenger classes. 

Histogram

This histogram displays the distribution of passenger counts (y-axis) across age groups (x-axis) for both survivors and non-survivors (hue) aboard the Titanic. It uses the `histplot()` function of the Seaborn library. This visual depicts a predominantly normal age distribution, slightly skewed to the right, suggesting that most passengers were younger rather than older. 

Notably, there is a second cluster in the survival group for younger ages, particularly among children aged 0-10. This suggests that children had a higher likelihood of survival compared to other age groups.

Line graph

This line graph offers a compelling insight into the temperature dynamics (y-axis) across the seasons (x-axis) in three bustling metropolises (hue): New York, London, and Sydney. Using the `lineplot()` function of the Seaborn library, we employed color to differentiate between cities in their temperature trends. This visual tells the story that New York and London exhibit similar temperature trends throughout the year, indicating a shared climate pattern. 

However, New York experiences a wider temperature range compared to London, with notably colder winters and hotter summers. In contrast, Sydney, positioned in the southern hemisphere, showcases an opposite climate behavior with hot winter months and cooler summers.

Scatter plot

This scatter plot depicts sepal length (x-axis) against petal length (y-axis) for three types of Iris flowers (hue) using the `scatterplot()` function of the Seaborn library. Looking at the graph we see that Setosa flowers are easily distinguishable by their shorter petal and sepal lengths. 

However, using sepal and petal length alone, it’s harder to differentiate between Versicolor and Virginica flowers. Nonetheless, there’s a consistent trend across both Versicolor and Virginica: as petal length increases, sepal length tends to increase as well. We utilize color to differentiate between the flower types, aiding in their visual distinction.

Heatmap

This correlation heatmap of the Iris dataset, generated using the `heatmap()` function, illustrates the relationships between each flower feature (x-axis) and all other flower features (y-axis). A correlation value close to 1 shows a strong positive correlation, indicating as one feature increases the other also increases. A correlation value close to -1 means a strong negative correlation, meaning as one feature increases the other decreases. 

The picture painted by this visual entails a strong positive correlation between similar measurements, like sepal length and petal length, and petal length and petal width. In contrast, weaker correlations are noted between unrelated features, such as sepal width and petal length.

Conclusion

In today’s data-driven world, mastering the art of data visualization is essential for effectively communicating your message and making informed decisions. However, creating impactful visualizations involves more than just crafting visually appealing charts or presenting large amounts of data. It requires thoughtful analysis of the data and the ability to deliver compelling narratives in a simple and elegant manner.

Achieving this balance between technical skills and aesthetic judgment is both a science and an art. Remember that the true strength of data visualization lies in its ability to simplify complex information and present it clearly and concisely. Start exploring today to reveal the full potential of data visualization.

Gain Data Visualization Skills at Flatiron

Unlocking the power of data goes beyond basic visualizations. Our Data Science Bootcamp dives deep into data visualization techniques, alongside machine learning, data analysis, and much more.  Equip yourself with the skills to transform data into insightful stories that drive results. Visit our website to learn more about our courses and how you can become a data science expert.

Coding a function, an app, or a website is an act of creation no different from writing a short story or painting a picture. From very simple tools we create something where there never was something. Painters have pigments. Writers have words. Coders have data types.

Data types govern just about every aspect of coding. Each type represents a specific kind of thing stored in a computer’s memory, and has different ways of being used in writing code. They also range in complexity from the humble integer to the sophisticated dictionary. This article will lay out the basic data types, using Python as the base language, and will also discuss some more advanced data types that are relevant to data analysts and data scientists.

Computers, even with the wondrous innovations of generative AI in the last few years, are still— just as their name suggests—calculators. The earliest computers were people who performed simple and complex calculations faster than the average person. (All the women who aided Alan Turing in cracking codes at Bletchley Park officially held the title of computers.)

Simple Numbers

It’s appropriate, then, that the first data type we discuss is the humble integer. These are the whole numbers 0 to 9, in their millions of combinations from 0 to 999,999,999,999 and beyond, including negative numbers. Different programming languages handle integers differently.

Python supports integers, usually denoted as int, as “arbitrary precision.” This means it can hold as many places as the computer has memory for. Java, on the other hand, recognizes int as the set of 32-bit integers ranging from -2,147,483,648 to 2,147,483,647. (A bit is the smallest unit of computer information, representing the logical state of on or off, 1 or 0. A 32-bit system can store 2^32 different values.)

The next step up the complexity ladder brings us to floating point numbers, or more commonly, floats. Floating point numbers approximate real numbers that include decimalized fractions from 0.0 to 99.999 and so on, again including negative numbers and repeating to the limits of a computer’s memory. The level of precision (number of decimal places) is constrained by a computer’s memory, but Python implements floats as 64-bit numbers.

With these two numeric data types, we can perform calculations. All the arithmetic operations are available with Python, along with exponentiation, rounding, and modulo operation. (Denoted %, modulo returns the remainder of division. For example, 3 % 2 returns 1.) It is also possible to convert a float to an int, and vice versa.

From Numbers to Letters and Words

Although “data” and “numbers” are practically synonymous in popular understanding, data types also exist for letters and words. These are called strings or string literals. 

“Abcdefg” is a string.

So is:

“When in the course of human events it becomes necessary for one people to dissolve the political bands which have connected them with another.”

For that matter so is:

“Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur vitae lobortis enim.”

Computer languages don’t care whether the string in question is a letter, a word, a sentence, or complete gobbledygook. They only care, really, whether the thing being stored is a number or not a number, and specifically whether it is a number the computer can perform arithmetic on. 

An aside: to humans, “1234” and 1234 are roughly the same. We humans can infer from context whether something is a calculable number, like on a receipt, or a non-calculable number, like a ZIP code. “1234” is a string. 1234 is an integer. We call those quotation marks “delimiters” and they serve the same role for the computer that context serves for us humans.  

Strings come with their own methods that allow us to do any number of manipulations. We can capitalize words, reverse them, and slice them up in a multitude of ways. Just as with floats and ints, it’s also possible to tell the computer to treat a number as if it were a string and do those same manipulations on it. This is handy when, for example, you encounter a street address in data analysis, or if you need to clean up financial numbers that someone left a currency mark on.

Encoding numbers and letters is a valuable tool, but we also need ways to check that things are what they claim to be. This is the role of the boolean data type, usually shortened to bool. This data type takes on one of two values: True or False, although it is also often represented as 1 or 0, respectively. The bool also gives rise to the boolean operators (<,>, !=, ==) which evaluate the truth value of some expression, like 2 < 3 (True) or “Thomas” == “Tom” (False).

Ints, floats, and strings are the most basic data types that you will find across computing languages. And they are very useful for storing singular things: a number, a word. (Strictly speaking, an entire library could be encoded as a single string.)

These individual data types are great if we only want to encode simple, individual things, or make one set of calculations. But the real value of (digital) computers is their ability to do lots of calculations very very quickly. So we need some additional data types to collect the simpler ones into groups.

From Individuals to Collections

The first of these collection data types is the list. This is simply an unordered collection of objects of any data type. In Python, lists are set off by brackets ([]). (“Ordered” means that the elements are arranged any old way, and not sorted high-to-low or low-to-high.)

For example, the below is a list:

[1, 3.7, 2, 3.4, 4, 6.74, 5.0]

 This is also a list:

[“John”, “Mary”, “Sue”, “Alphonse”]

Even this a list:

[1, “John”, 2.2, “Mary”, 3] 

It’s important to note that within a list, each element (or item) of the list still operates according to the rules of its data type. But know that the list as a whole also has its own methods. So it’s possible to remove elements from a list, add things to a list, sort the list, as well as do any of the manipulations the individual data types support on the appropriate elements (e.g., calculations on a float or an integer, capitalize a string).

The last and probably most complicated of the basic data types is the dictionary, usually abbreviated dict. Some languages refer to this as a map. Dictionaries are unordered collections of key-value pairs, set off by braces ({}). Individual key-value pairs inside a dictionary are set off with a comma. They enable a program to use one value to get another value. So, for example, a dictionary for a financial application might contain a stock ticker and that ticker’s last closing price,like so:

{“AAPL”: 167.83,

“GOOG”: 152.62,

“META”: 485.58}

In this example, “AAPL” is the key, 167.83 is the value. A program that needed the price of Apple’s stock could then get that value by calling the dictionary key “AAPL.” And as with lists, the individual items of the dictionary, both keys and values, retain the attributes of their own data types.

These pieces form the basics of data types in just about every major scripting language in use today. With these (relatively) simple tools you can code up anything from the simplest function to a full spreadsheet program or a Generative Pre-trained Transformer (GPT). 

Extending Data Types into Data Analysis

If we want to extend the data types that we have into even more complicated forms, we can get out of basic Python and into libraries like Numpy and Pandas. These bring additional data types that expand Python’s basic capabilities into more robust data analysis and linear algebra, two essential functions for machine learning and AI.

First we can look at the Numpy array. This handy data type allows us to set up matrices, though they really just look like lists, or lists of lists. However, they are less memory intensive than lists, and allow more advanced calculation. They are therefore much better when working with large datasets.

If we combine a bunch of arrays, we wind up with a Pandas DataFrame. For a data analyst or machine learning engineer working in Python this is probably your most common tool. It can hold and handle all the other data types we have discussed. The Pandas DataFrame is, in effect, a more powerful and efficient version of the Excel spreadsheet. It can handle all the calculations that you need for exploratory data analysis and data visualization.

Data types in Python, or any programming language, form the basic manipulable unit. Everything a language uses to store information for future use is a data type of some kind, and each type has specific things it can store, and rules governing what it can do.

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Predictive modeling is a process in data science that forecasts future outcomes based on historical data and statistical algorithms. It involves building mathematical models that learn patterns from past data to make predictions about unknown or future events. These models analyze various variables or features to identify relationships and correlations, which are then used to generate predictions. Well over half of the Flatiron School’s Data Science Bootcamp program involves learning about various predictive models.

Applications of Predictive Modeling

One common application of predictive modeling is in finance, where it helps forecast stock prices, predict market trends, and assess credit risk. In marketing, predictive modeling helps companies target potential customers more effectively by predicting consumer behavior and preferences. For example, companies can use customer data to predict which products a customer is likely to purchase next or which marketing campaigns will yield the highest return on investment.
Healthcare is another field that uses predictive modeling. Predictive modeling plays a vital role in identifying patients at risk of developing certain diseases. It also helps improve treatment outcomes and optimize resource allocation. By analyzing patient data, such as demographics, medical history, and lifestyle factors, healthcare providers can predict potential health issues and intervene early to prevent or manage them effectively. 

Manufacturing and logistics widely use predictive modeling to optimize production processes, predict equipment failures, and minimize downtime. By analyzing data from sensors and machinery, manufacturers can anticipate maintenance needs and schedule repairs before breakdowns occur, reducing costs and improving efficiency.

Overall, predictive modeling has diverse applications across various industries, helping businesses and organizations make more informed decisions, anticipate future events, and gain a competitive advantage in the marketplace. Its ability to harness the power of data to forecast outcomes makes it a valuable tool for driving innovation and driving success in today’s data-driven world.

The Steps for Building a Predictive Model

Below is a step-by-step guide to building a simple predictive machine learning model using Python pseudocode. Python is a versatile, high-level programming language known for its simplicity and readability, making it an excellent choice for beginners and experts alike. Its extensive range of libraries and frameworks, particularly in fields such as data science, machine learning, artificial intelligence, and scientific computing, has solidified its place as a cornerstone in the data science community. While Flatiron School Data Science students learn other technical skills and languages, such as dashboards and SQL, the primary language that students learn and use is Python.

Step 1

In Step 1 below (in the gray box), Python libraries are imported. A Python library is a collection of functions and methods that allows you to perform many actions without writing your code. It is a reusable chunk of code that you can use by importing it into your program, saving time and effort in coding from scratch. Libraries in Python cover a vast range of programming needs, including data manipulation, visualization, machine learning, network automation, web development, and much more. 

The two most widely used Python libraries are NumPy and pandas. The former adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level functions to operate on these arrays. The latter is a high-level data manipulation tool built on top of the Python programming language. It is most well-suited for structured data operations and manipulations, akin to SQL but in Python. 

The third imported Python library is scikit-learn, which is an open-source machine learning library that proves a wide range of supervised and unsupervised learning algorithms. It is built on NumPy, SciPy, and Matplotlib, offering tools for statistical modeling, including classification, regression, clustering, and dimensionality reduction. In data science, scikit-learn is extensively used for developing predictive models and conducting data analysis.  Its simplicity, efficiency, and easy for integration with other Python libraries makes it an essential tool for machine learning practitioners and researchers.

Step 2

Now that the libraries have been imported in Step 1, the data needs to be brought in—as can be seen in Step 2. Since we’re considering predictive modeling, we’ll use the feature variables to predict the target variable. 

In a dataset for a predictive model, feature variables (also known as predictors or independent variables) are the input variables that are used to predict the outcome. They represent the attributes or characteristics that help the model learn patterns to make predictions. For example: In a dataset for predicting house prices, feature variables might include:

• Square_Feet: The size of the house in square feet
• Number_of_Bedrooms: The number of bedrooms in the house
• Age_of_House: The age of the house in years
• Location_Rating: A rating representing the desirability of the house’s location

The target variable (also known as the dependent variable) is the output variable that the model is trying to predict. Continuing with our housing example, the target variable would be:

• House_Price: The price of the house

Thus, In this scenario, the model learns from the feature variables (Square_Feet, Number_of_Bedrooms, Age_of_House, Location_Rating) to accurately predict the target variable (House_Price).

Step 3

Note, We split the dataset into training and test sets in Step 2. We did this to evaluate the predictive model’s performance on unseen data, ensuring it can generalize well beyond the data it was trained on. This split helps identify and mitigate overfitting, where a model performs well on its training data but poorly on new, unseen data, by providing a realistic assessment of how the model is likely to perform in real-world scenarios.

Now comes the key moment in Step 3, where we use our statistical learning model. In this case, we’re using multiple linear regression, which is an extension of simple linear regression. It is designed to predict an outcome based on multiple independent variables, and fits a linear equation to the observed data where the target variable is modeled as a linear combination of two or more feature variables, incorporating a separate coefficient (slope) for each independent variable plus an intercept. This approach allows for the examination of how various feature variables simultaneously affect the outcome. It provides a more comprehensive analysis of the factors influencing the dependent variable.

Step 4

In Step 4, we evaluate the model to find out how well it fits the data. There are a myriad of metrics that one can use to evaluate predictive learning models. In the pseudocode below, we use the MSE, or the mean squared error.

The MSE is a commonly used metric to evaluate the performance of a regression model. It measures the average squared difference between the actual values (observed values) and the predicted values generated by the model. Mathematically, it is calculated by taking the average of the squared differences between each predicted value and its corresponding actual value. The formula for MSE is:

In this formula, 

• n is the number of observations
• y_i represents the actual value of the dependent variable for the i^th observation
• ŷ_i represents the predicted value of the dependent variable for the i^th observation

A lower MSE value indicates that the model’s predictions are closer to the actual values, suggesting a better fit of the model to the data. Conversely, a higher MSE value indicates that the model’s predictions are further away from the actual values, indicating poorer performance.

Step 5

At this point, one usually would want to tune (i.e., improve on the model). But for this introductory explanation, Step 5 will be to use our model to make predictions.

Summary of Predictive Modeling

The pseudocode in Steps 1 through 5 shows the basic steps involved in building a simple predictive machine learning model using Python. You can replace placeholders like `’your_dataset.csv’`, `’feature1’`, `’feature2’`, etc., with actual data and feature names in your dataset. Similarly, you can replace `’target_variable’` with the name of the target variable you are trying to predict. Additionally, you can experiment with different models, preprocessing techniques, and evaluation metrics to improve the model’s performance.

 Predictive modeling in data science involves using statistical algorithms and machine learning techniques to build models that predict future outcomes or behaviors based on historical data. It encompasses various steps, including data preprocessing, feature selection, model training, evaluation, and deployment. Predictive modeling is widely applied across industries for tasks such as forecasting, classification, regression, anomaly detection, and recommendation systems. Its goal is to extract valuable insights from data to make informed decisions, optimize processes, and drive business outcomes. 

Effective predictive modeling requires a combination of domain knowledge, data understanding, feature engineering, model selection, and continuous iteration to refine and improve model performance over time that lead to actionable insights.

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The data visualizations in this blog post—which are tied to best camping locales for viewing the 2024 total solar eclipse—are not optimized for a mobile screen. For the best viewing experience, please read on a desktop or tablet.

I once read about a married couple who annually plan vacations to travel the world in pursuit of solar eclipses. They spoke about how, regardless of their location, food preferences, or language abilities, they always managed to share a moment of awe with whoever stood near them as they gazed up at the hidden sun.

While I can’t speak to the experience of viewing an eclipse abroad, I did travel to the path of totality for a solar eclipse in 2017, and I can confirm the feeling of awe and the sense of shared experience with strangers. Chasing eclipses around the world isn’t something I can easily squeeze into my life, but when an eclipse is nearby, I make an effort to go see it. 

On April 8, 2024, a solar eclipse will pass over the United States. It will cast the moon’s shadow over the states of Texas, Oklahoma, Arkansas, a sliver of Tennessee, Missouri, Kentucky, Illinois, Indiana, Ohio, Michigan, Pennsylvania, New York, Vermont, New Hampshire, and Maine.

As I’ve been making plans for this eclipse, I’ve been using NASA’s data tool for exploring where in the path of totality I might view the eclipse from. This tool is exceptional and includes the time the eclipse will begin by location, a simulation of the eclipse’s journey, and a weather forecast. I have several friends who have traveled to see an eclipse only to be met with a gray cloudy sky, so keeping an eye on the weather is very important. 

But, as I was using this tool, I found myself wanting to know what camping options fall within the path of totality for the 2024 total solar eclipse. I, like many, prefer to make a small camping vacation out of the experience, and the location of parks is information not provided by NASA’s data tool. So I found a dataset detailing the location of 57,000 parks in the United States, isolated the parks that fall within the path of totality, and plotted them.

Below is the final visualization, with an added tooltip for viewing the details of the park. For those interested in the code for this project, check out the links to the data and my data manipulation and visualization code.

Happy eclipsing, everyone!

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