The most significant component of any artificial intelligence or Data Science related task is data. However, how does one understand how to effectively utilize this data in its raw state?
Looking at the data and glancing over some minor details might not always be sufficient to accurately compute a solution. Hence, we require visualization techniques.
Visualizations play a critical role in deciphering the patterns of data and help us to analyze the most efficient machine learning or deep learning methods that a data science enthusiast can use to attain high-quality results. These are one of the most important steps to be followed for exploratory data analysis (EDA) to compute desirable solutions.
Before we get started with this article on seaborn, I would recommend checking out one of my previous works on matplotlib visualization techniques from the link provided below. It should be a strong starting point to get more familiar with different types of visualizations.
8 Best Visualizations To Consider For Your Data Science Projects!
9 Best Seaborn Visualizations For Data Science:
In this article, we will focus on the seaborn library. We will learn the numerous visualization techniques that are available in this library that we can utilize in almost every project. Seaborn is a Python Data Visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Seaborn helps to simplify complex visualizations with its simplicity and helps to add an additional aesthetic appeal. Apart from all the amazing features that seaborn has, it is also built on top of the matplotlib library. Hence, we can produce more powerful and productive visualizations utilizing the combination of both these libraries. However, in this article, we will focus solely on the seaborn library.
Getting Started:
Let us quickly get started with the Seaborn library by importing it. The following code snippet shows how to import the library as required. Once the importing is completed, we can proceed to compute further computations and visualizations.
# Import the seaborn library for visualization
import seaborn as sns
The best part about the seaborn library is that it offers a bunch of default datasets through which you can begin training and testing your Visualization techniques. While there are several dataset options like planets, tips, titanic, etc., amongst many others, we will utilize the iris dataset for this project. Below is the code snippet to load the iris dataset in the seaborn library.
# Load the Iris dataset
iris_data = sns.load_dataset("iris")
In the Iris data, we have three species of flowers, namely setosa, versicolor, and virginica. Our task is to visualize the numerous parameters, such as sepal width, sepal height, petal length, and petal width, that are associated with each of these species. Using these features associated with each of the mentioned species, we will use some of the best options in the seaborn library to distinguish them accordingly. Below is a brief look at our dataset.
iris_data[:5]

1. Scatter Plot:
sns.scatterplot(x = "sepal_length",
y = "sepal_width",
data = iris_data,
hue = "species")

One of the best techniques to start visualization is by applying a scatter plot to the available data. The scattered data plot provides the user with a brilliant option to see how distinguishable the data is from each other. From the above scatter plot image, we can notice that it is fairly easy to distinguish setosa from versicolor and virginica. However, versicolor and virginica seem to share some close similarities.
For defining a scatter plot in the seaborn library, we can directly mention the x-axis and y-axis parameters that we need to compute for the visualization. Once we choose the x-axis and y-axis attributes, we can mention the dataset and specify the hue to specifies for enabling color coding to the visualized plot.
2. Histogram Plot:
sns.histplot(x = "species", y = "sepal_width", data = iris_data)

From the previous scatter plot, we were already able to generate a lot of information about the iris dataset. We can also use other plots, such as the histogram graphs, thereby allowing the user to visualize how distinguishable some of the features are. The above image shows a histogram of the species based on their sepal width.
In the above code snippet, we have used the histplot function in the seaborn library with the iris dataset, mentioning the species and the sepal width accordingly. It is highly recommended that the users measure the varieties of the species with each of the other feature parameters as well.
3. Bar Plot:
sns.barplot(x = "species", y = "sepal_width", data = iris_data)

Similar to the histogram plot, we can also plot the bar plot using the barplot function in the seaborn library with the iris dataset, mentioning the species and the sepal width accordingly. The visualization above represents a bar plot and shows a more colorful and aesthetically pleasing look at the sepal width of each of the mentioned species.
4. Box Plot:
sns.boxplot(x = "species", y = "sepal_width", data = iris_data)

Unlike the previous two plots, we will focus on two other plots that will give us a more specific and appropriate range in which the parameters of the different varieties of the flowers fall in. Firstly, we will look at the box plot in the seaborn library that will provide the user with a specific range of each of the species.
The concept of median, percentile, and quantile is used in these methods for plotting the graphs. The ends of the box plots represent the whiskers which are built in an inter-quartile range. The box plot can be plotted in the seaborn library with by mentioning the iris dataset, the species, and the particular parameter.
5. Violin Plot:
sns.violinplot(x = "species", y = "sepal_width", data = iris_data)

To simplify the concept of box plots and median ranges, we can use the violin plots through which we can gain a more intuitive understanding of the operating range of the specific features. Similar to the box plot, the violin plot can be plotted in the seaborn library by mentioning the iris dataset, the species, and the particular parameter.
6. Facet Grid with Distplot:
from warnings import filterwarnings
filterwarnings("ignore")
sns.FacetGrid(iris_data, hue="species", height = 5).map(sns.distplot, "petal_width").add_legend()

In the next visualization, we can make use of the distribution plots (dist. plot) for gaining an understanding of the data distribution in the iris dataset. The distribution plot helps us to gain an intuitive understanding of the probability density of the species, i.e., the probability per unit on the x-axis. We can plot the graph for the following, as shown in the above code snippet.
7. Pair Plot:
sns.pairplot(iris_data, hue="species", height=3)

One of the most significant techniques of visualizations in seaborn, especially for a task like an iris dataset, is the utility of pair plots. The above image shows a detailed representation of the pair plots of numerous features and perhaps the most detailed view of understanding our iris dataset. Pair plots help to characterize and distinguish the best features among two particular variables.
The above code snippet can be utilized to plot the pair plots for the various species of the iris dataset. Pair plots are some of the best options for the analysis of two-dimensional data. However, their utility falters with higher dimensionalities of data, and they are not extremely useful in cases where the dataset is humungous.
8. Cluster Map:
sns.clustermap(iris_data.drop("species", axis = 1))

The clustered map in seaborn allows the user to plot a matrix dataset as a hierarchically-clustered heatmap. Cluster maps are a great tool for determining how many data points are located in a specific region. The cluster map function in seaborn might slightly complex, but it allows the user with a detailed understanding of most of the features that are provided in the dataset. The cluster maps can be a significant visualization technique for specific projects and tasks.
9. Heatmaps:
sns.heatmap(iris_data.corr())

Finally ,we will look at the heatmaps function in the seaborn library, which is one of the most useful visualization techniques. The heatmap visualization helps us in the computation of the correlation between the different variables and parameters. Using the heatmap function, we can gain a brief understanding of how several variables are related to each other.
For performing the heatmap operation on the iris dataset, it is best to take the correlation of the iris data by using the corr() function. Once we have a correlation table, we can plot it with the command shown in the above code snippet to produce the result shown in the above figure. Heatmaps are a significant visualization technique for hyperparameter tuning of machine learning models.
Conclusion:

An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem. — John Tukey
Visualization and exploratory data analysis (EDA) will always remain some of the essential components of Data Science projects. These are the only methods through which we can gain a somewhat decent understanding of the type of data that we are dealing with in the specific project. Hence, every data scientist must learn and become more familiar with these visualization techniques.
In this article, we learned about the seaborn library, which is one of the best tools for visualization in Python for data science tasks and projects. Seaborn is more comfortable in handling Pandas data frames. It uses basic sets of methods to provide beautiful graphics in Python. We understood a variety of different visualization techniques in the seaborn library, through which we gained a better understanding of the data or datasets utilized in a particular project.
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Check out some of my other articles in relation to the topic covered in this piece that you might also enjoy reading!
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Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!