Visualizing Data with Matplotlib and Seaborn

 📊 Visualizing Data with Matplotlib and Seaborn

🔧 Getting Started

📦 Installation (if not already installed):

pip install matplotlib seaborn

📚 Import the libraries:

import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd

🗂️ Sample Dataset

Let’s use Seaborn’s built-in dataset for simplicity:

df = sns.load_dataset("tips")  # Dataset of restaurant bills

df.head()

🎨 1. Matplotlib Basics

Matplotlib is low-level but highly customizable.

✅ Line Plot

plt.plot(df['total_bill'])

plt.title('Total Bill Over Time')

plt.xlabel('Index')

plt.ylabel('Total Bill ($)')

plt.show()

✅ Bar Chart

df['day'].value_counts().plot(kind='bar')

plt.title('Count of Records per Day')

plt.xlabel('Day')

plt.ylabel('Count')

plt.show()

✅ Histogram

plt.hist(df['total_bill'], bins=10, color='skyblue')

plt.title('Distribution of Total Bill')

plt.xlabel('Total Bill ($)')

plt.ylabel('Frequency')

plt.show()

🌈 2. Seaborn Basics

Seaborn is built on top of Matplotlib and makes beautiful statistical plots with much less code.

✅ Histogram / KDE Plot

sns.histplot(df['total_bill'], kde=True, color='purple')

plt.title('Total Bill Distribution with KDE')

plt.show()

✅ Box Plot

sns.boxplot(x='day', y='total_bill', data=df)

plt.title('Total Bill by Day')

plt.show()

✅ Violin Plot

sns.violinplot(x='day', y='tip', data=df)

plt.title('Tip Distribution by Day')

plt.show()

✅ Scatter Plot

sns.scatterplot(x='total_bill', y='tip', hue='sex', data=df)

plt.title('Tip vs Total Bill')

plt.show()

🔗 3. Relationships Between Variables

✅ Pair Plot

sns.pairplot(df, hue='sex')

plt.show()

✅ Heatmap (Correlation Matrix)

corr = df.corr(numeric_only=True)

sns.heatmap(corr, annot=True, cmap='coolwarm')

plt.title('Correlation Matrix')

plt.show()

🧠 4. Customizing Your Plots

plt.figure(figsize=(10, 6))

sns.barplot(x='day', y='total_bill', data=df, palette='Blues')

plt.title('Average Total Bill by Day')

plt.xlabel('Day of Week')

plt.ylabel('Total Bill ($)')

plt.tight_layout()

plt.show()

📌 Best Practices

✅ Use clear titles and labels

✅ Choose the right chart for your data

✅ Use colors to add meaning, not just decoration

✅ Keep it simple and readable

✅ Use figsize to adjust plot size for reports or presentations

🔄 Combining Seaborn with Matplotlib

You can always use plt functions after Seaborn to fine-tune:

sns.boxplot(x='day', y='tip', data=df)

plt.title('Tips by Day')

plt.ylabel('Tip Amount ($)')

plt.grid(True)

plt.show()

🧪 Suggested Mini Project

Dataset: iris (available via sns.load_dataset('iris'))

Goal: Explore the relationships between flower measurements.

Tasks:

Histogram of sepal_length

Box plot of petal_length by species

Pairplot of all numeric columns

Correlation heatmap

✅ Summary Table

Task Matplotlib Function Seaborn Function

Line plot plt.plot() —

Bar chart plt.bar() sns.barplot()

Histogram plt.hist() sns.histplot()

Box plot — sns.boxplot()

Scatter plot plt.scatter() sns.scatterplot()

Correlation heatmap imshow() (complex) sns.heatmap()

Multi-variate overview — sns.pairplot()

Learn Data Science Course in Hyderabad

Read More

Data Manipulation with dplyr in R

10 Pandas Functions Every Data Scientist Should Know

Focus on the practical, code-based aspects of data science.

Python & R for Data Science

Visit Our Quality Thought Training Institute in Hyderabad

Get Directions