Data Visualization with Seaborn

Seaborn is a Python library built on top of Matplotlib that makes creating attractive, detailed statistical charts easier with less code.

Creating a Scatter Plot

Seaborn scatter plots automatically add useful styling and can show extra information with colour.

import seaborn as sns

import pandas as pd



data = pd.DataFrame({

    "Age": [22, 25, 30, 35, 40],

    "Salary": [30000, 35000, 45000, 50000, 60000]

})



sns.scatterplot(x="Age", y="Salary", data=data)

Creating a Histogram with Distribution

Seaborn can show both the histogram and the distribution curve together, which helps you understand data spread more clearly.

sns.histplot(data["Salary"], kde=True)

Creating a Box Plot

Box plots help you spot outliers and understand how data spreads across quartiles.

sns.boxplot(x=data["Salary"])

Creating a Correlation Heatmap

Heatmaps show how strongly different features in a dataset relate to each other, which is useful before selecting features for a model.

correlation = data.corr()

sns.heatmap(correlation, annot=True, cmap="coolwarm")

Creating a Pair Plot

Pair plots show relationships between every pair of numerical columns in a dataset, all in one view.

sns.pairplot(data)

Seaborn makes it easier to spot patterns, outliers, and relationships in data visually, which helps you make better decisions during feature engineering and model building.