Using Visualizations for Your Exploratory Data Analysis

No data science project should skip the exploratory data analysis stage. Enhance it with the five data visualization types we’ll show you in the article.

Today, we’re exploring an often neglected topic in data science: using visualizations for exploratory data analysis (EDA). It’s essential for data cleaning and preparation.

We’ll first talk about the importance of EDA and why visualizations are crucial in EDA. After that, we’ll move to discussing the five most common types of visualizations for EDA and the purpose they serve.

We’ll sign off by suggesting several cool tools for creating visualizations and giving you some visualization tips.

What is EDA?

EDA is a part of a data science workflow that is all about getting to know your data.

It’s the step where you dig deep to uncover patterns, spot anomalies, test hypotheses, and uncover patterns, like in the image below.

This is all done before you make any assumptions or build your models.

Why are Visualizations Crucial in EDA?

As the saying goes, “A picture is worth a thousand words.” Cliche or not, visuals really do help us see the story our data is telling at a glance. They make it easier to identify trends, outliers, and the relationships between variables. Trust me, staring at rows of numbers just isn't the same.

Types of Visualizations for EDA

Let's explore some of the key types of visualizations you should have in your EDA toolkit.

1. Scatter Plots

Scatter plots are fantastic for examining relationships between two continuous variables. For example, if you're analyzing the relationship between study hours and test scores, a scatter plot can help you see if more study time correlates with higher scores.

It's a great tool for easily determining if there are outliers in data; just draw a trend line. In this example, you see one outlier marked as a red dot.

2. Histograms

Histograms show the distribution of a single variable. They're perfect for understanding the spread and central tendency of your data. For instance, if you're looking at the ages of your survey respondents, a histogram can show you the age distribution.

Histograms can be helpful when wanting to see the tails of the distribution, which makes cutting off your data or resegmenting it much easier.

In the example, the upper bound is at 58.50.

3. Box Plots

Box plots, or box-and-whisker plots, are great for summarizing the distribution of a data set and identifying outliers. They display the median, quartiles, and potential outliers in your data.

They are especially useful when comparing the distributions of multiple groups side by side, unlike histograms.

We can see from the example that each group has an outlier; they are at the age of 60, 70, and 80.

4. Bar Charts

For categorial data comparison, bar charts are your go-to when cleaning and preparing data. Want to compare the sales of different product categories? A bar chart will do the trick.

It is also the best chart for identifying missing data. One look at this chart and you will see there's no data in the ‘Home & Kitchen Product Categories’ and ‘Toys’ categories, so you can investigate that.

5. Heatmaps

Heatmaps are excellent for visualizing data in matrix form. They're especially useful for displaying correlations between variables in a dataset. The color intensity helps you quickly spot strong relationships.

In this example, visibility and humidity are strongly correlated. On the other hand, there's no correlation between temperature-visibility, wind-speed precipitation, and wind speed-visibility variables. In addition, you can see there's also some negative correlation, for example, between humidity and temperature.

Tools for Creating Visualizations

There are plenty of tools out there to create these visualizations, each with its own strengths.

Some popular ones include:

• Python data visualization libraries (Matplotlib, seaborn, Plotly) – great for detailed and customized plots
• ggplot2 for R – excellent for creating complex multi-layered graphics
• BI tools (Tableau and Power BI) –  ideal for interactive and sharable dashboards
• Excel – simple but effective for basic visualizations

Practical Tips for Effective Visualizations

1. Keep it Simple: Avoid clutter. The cleaner your plot, the easier it is to understand.
2. Label Clearly: Ensure your axes, titles, and legends are clearly labeled.
3. Use Appropriate Colors: Colors should enhance, not distract. Use a color palette that makes sense for your data.
4. Be Consistent: Use the same style and color scheme across your visuals to maintain a professional look.

Conclusion

Visualizations are a powerful tool in your EDA arsenal.

The most common are:

• scatter plots
• histograms
• box plots
• bar charts
• heatmaps

They not only help you understand your data better but also communicate your findings more effectively. So, next time you dive into a dataset, remember to let your visuals do the talking.

Data visualization tools, such as Python or R libraries, BI tools, or Excel can help you with that.