Exploratory Data Analysis with Data Visualization

Introduction to data visualization

Data visualization is an important component of Exploratory Data Analysis (EDA) because it allows a data analyst to “look at” their data and get to know the variables and relationships between them. In order to choose and design a data visualization, it is important to consider two things:

• The question you want to answer (and how many variables that question involves).
• The data that is available. (is it quantitative or categorical?)

In this article, we’ll use data visualization to explore a dataset from StreetEasy, which contains information about housing rentals in New York City. The first few rows of the dataset are printed below (note that we have subsetted the original data for the purposes of this article):

What is univariate analysis?

Univariate analysis focuses on a single variable at a time. Univariate data visualizations can help us answer questions like:

• What is the typical price of a rental in New York City?
• What proportion of NYC rentals have a gym?

Depending on the type of variable (quantitative or categorical) we want to visualize, we need to use slightly different visualizations.

How to visualize quantitative variables?

Box plots (or violin plots) and histograms are common choices for visually summarizing a quantitative variable. These plots are useful because they simultaneously communicate information about minimum and maximum values, central location, and spread. Histograms can additionally illuminate patterns that can impact an analysis (eg., skew or multimodality).

For example, suppose we are interested in learning more about the price of apartments in NYC. A good starting place is to plot a box plot of the rent variable. We could plot a boxplot of rent as follows:

# Load libraries
import seaborn as sns
import matplotlib.pyplot as plt 

# Create the plot
sns.boxplot(x='rent', data=rentals)
plt.show()

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![boxplot of rental prices showing that the middle 50% of prices are between 2500 and 5000 dollars per month, with many large outliers]

We can see that most rental prices fall within a range of $2500-$5000; however, there are many outliers, particularly on the high end. For more detail, we can also plot a histogram of the rent variable.

# Create a histogram of the rent variable
sns.displot(rentals.rent, bins=10, kde=False)
plt.show()

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![A seaborn histogram of the rent variable that shows the distribution spread between ten bins.]

The histogram highlights the long right-handed tail for rental prices. We can get a more detailed look at this distribution by increasing the number of bins:

# Create a histogram of the rent variable
sns.displot(rentals.rent, bins=50, kde=False)
plt.show()

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![A seaborn histogram of the rent variable that shows the distribution spread between fifty bins.]

How to visualize categorical variables?

For categorical variables, we can use a bar plot (instead of a histogram) to quickly visualize the frequency (or proportion) of values in each category. For example, suppose we want to know how many apartments are available in each borough. We can visually represent that information as follows:

# Create a barplot of the counts in the borough variable
# The palette parameter will set the color scheme for the plot
sns.countplot(x='borough', data=rentals, palette='winter')
plt.show()

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![A bar plot displaying the distribution of the borough variable. The Manhattan category contains ~3500, Queens ~ 500, and Brooklyn ~ 1000.]

Alternatively, we could use a pie chart to communicate the same information:

# Define the labels in pie chart
borough_labels = ["Manhattan", "Brooklyn", "Queens"]
 
# Generate pie chart of boroughs
plt.pie(rentals.borough.value_counts(), labels = borough_labels)
plt.show() 

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![A pie chart displaying the borough variable’s distribution.]

In general, many data analysts avoid pie charts because people are better at visually comparing areas of rectangles than wedges of a pie. For a variable with a small number of categories (i.e., fewer than three), a pie chart is a reasonable choice; however, for more complex data, a bar chart is usually preferable.

What is bivariate analysis?

In many cases, a data analyst is interested in the relationship between two variables in a dataset. For example:

• Do apartments in different boroughs tend to cost different amounts?
• What is the relationship between the area of an apartment and how much it costs?

Depending on the types of variables we are interested in, we need to rely on different kinds of visualizations.

Visualizing one quantitative variable and one categorical variable

Two good options for investigating the relationship between a quantitative variable and a categorical variable are side-by-side box plots and overlapping histograms.

For example, suppose we want to understand whether apartments in different boroughs cost different amounts. We could address this question by plotting side by side box plots of rent by borough:

# Create a box plot of the borough variable relative to rent
sns.boxplot(x='borough', y='rent', data=rentals, palette='Accent')
plt.show()

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![A seaborn box plot showing the rent prices for each category in the borough variable.]

This plot indicates that rental prices in Manhattan tend to be higher and have more variation than rental prices in other boroughs. We could also investigate the same question in more detail by looking at overlapping histograms of rental prices by borough:

plt.hist(rentals.rent[rentals.borough=='Manhattan'], label='Manhattan', density=True, alpha=.5)
plt.hist(rentals.rent[rentals.borough=='Queens'], label='Queens', density=True, alpha=.5)
plt.hist(rentals.rent[rentals.borough=='Brooklyn'], label='Brooklyn', density=True, alpha=.5)
plt.legend()
plt.show()

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![Overlapping histograms showing rental prices for each borough]

Using this visualization, we can see the long right-handed tail in rental prices, especially for Manhattan, and comparatively low prices in Queens.

Visualizing two quantitative variables

A scatter plot is a great option for investigating the relationship between two quantitative variables. For example, if we want to explore the relationship between rent and size_sqft, we could create a scatter plot of these two variables:

# Create a scatterplot of the size_sqft variable relative to rent
sns.scatterplot(rentals.size_sqft, rentals.rent)
plt.show()

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![A seaborn scatterplot with rent values along the y-axis, and square footage values along the x-axis.]

The plot indicates that there is a strong positive linear relationship between the cost to rent a property and its square footage. Larger properties tend to cost more money.

Visualizing two categorical variables

Side by side (or stacked) bar plots are useful for visualizing the relationship between two categorical variables. For example, suppose we want to know whether rentals that have an elevator are more likely to have a gym. We could plot a side by side bar plot as follows:

sns.countplot(x='has_elevator', hue='has_gym', data=rentals)
plt.show()

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![barplot with has_elevator on the x-axis, and the number of apartments on the y-axis. For buildings with an without elevators, counts are split and colored by whether or not the building has a gym.]

This plot tells us that buildings with elevators are approximately equally likely to have a gym or not have a gym; meanwhile, apartments without elevators are very unlikely to have a gym.

What is multivariate analysis?

Sometimes, a data analyst is interested in simultaneously exploring the relationship between three or more variables in a single visualization. Many of the visualization methods presented up to this point can include additional variables by using visual cues such as colors, shapes, and patterns. For example, we can investigate the relationship between rental price, square footage, and borough by using color to introduce our third variable:

sns.scatterplot(rentals.size_sqft, rentals.rent, hue = rentals.borough, palette='bright')
plt.show()

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![scatter plot of rental prices versus square footage, with each point colored to represent what borough the listing is in]

This plot shows many things at once: the positive linear relationship between price and area, the fact that Manhattan apartments tend to be larger than the other boroughs (because there are more blue points on the right-hand side of the plot compared to the other colors), and that Manhattan apartments tend to cost more money (because the blue points are mostly on top of the green and orange points).

Even though it’s possible to add even more information to this plot (for example, we could use triangles and squares to indicate whether or not an apartment has a gym), it’s not always a good idea to overload a single visualization.

Another common data visualization for multivariate analysis is a heat map of a correlation matrix for all quantitative variables:

# Define the colormap which maps the data values to the color space defined with the diverging_palette method  
colors = sns.diverging_palette(150, 275, s=80, l=55, n=9, as_cmap=True)

# Create heatmap using the .corr method on df, set colormap to cmap
sns.heatmap(rentals.corr(), center=0, cmap=colors, robust=True)
plt.show()

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![heatmap showing correlations between each pair of quantitative variables in the dataset (visualized as a grid of squares, with each variable name labeling the x and y axes). Dark purple squares indicate strong correlations, whereas dark green colors indicate negative correlations.]

This heat map shows is that the rental price, number of bedrooms, number of bathrooms, and size of an apartment are all highly positively correlated (higher values of one are associated with higher values of the others). Meanwhile, building age is weakly negatively correlated with rent, bathrooms, minutes to the subway, and floor (higher values of building age are associated with lower values of these other variables).

Conclusion

In this article, we’ve summarized some of the important considerations for choosing a data visualization based on the question a data analyst wants to answer and the type of data that is available. When it comes to designing a visualization, there’s no one right way, but “looking” at the data is an important component of both choosing a summary statistic and getting to know a dataset.