Mastering Pandas in Python: Data Analysis and Manipulation Made Easy

Chapter 6: Data Visualization and Integration in Pandas

🔹 1. Introduction

Data visualization is an essential step in the data analysis process. It allows you to explore the relationships between different variables and communicate insights effectively. While Pandas provides some basic plotting functionality, it is highly compatible with other visualization libraries like Matplotlib and Seaborn.

In this chapter, we will cover:

• Basic plotting using Pandas' plot() function
• Customization of plots with labels, titles, and legends
• Advanced visualizations using Matplotlib and Seaborn
• Integrating Pandas with other visualization libraries like Plotly

By the end of this chapter, you’ll be able to visualize your data easily and make your analyses more insightful and communicative.

🔹 2. Basic Plotting with Pandas

Pandas has a simple plotting interface that integrates seamlessly with Matplotlib, making it easy to create a variety of plots with just a few lines of code.

✅ Plotting a Line Chart

The simplest plot you can create is a line chart. Here's an example using Pandas' built-in plot() function:

import pandas as pd

import matplotlib.pyplot as plt

# Sample data

data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],

        'Sales': [100, 120, 150, 170, 200, 220]}

df = pd.DataFrame(data)

# Plotting the 'Sales' column

df.plot(x='Month', y='Sales', kind='line', marker='o', title='Monthly Sales')

plt.show()

Output:
A simple line plot showing sales over the months.

✅ Plotting a Bar Chart

A bar chart is useful for comparing categorical data:

df.plot(x='Month', y='Sales', kind='bar', color='skyblue', title='Monthly Sales')

plt.show()

Output:
A bar chart representing sales in each month.

🔹 3. Customizing Plots

Pandas' plot() function provides various parameters for customizing the plot, such as colors, styles, and labels.

✅ Adding Titles and Labels

df.plot(x='Month', y='Sales', kind='line', marker='o', title='Monthly Sales')

plt.xlabel('Month')

plt.ylabel('Sales')

plt.show()

✅ Changing Line Style and Color

df.plot(x='Month', y='Sales', kind='line', linestyle='--', color='green', marker='x', title='Monthly Sales')

plt.show()

✅ Multiple Series in One Plot

You can plot multiple columns in a DataFrame on the same plot:

df['Profit'] = [40, 60, 80, 100, 120, 140]

df.plot(x='Month', y=['Sales', 'Profit'], kind='line', marker='o', title='Sales and Profit')

plt.show()

🔹 4. Advanced Visualizations with Matplotlib

While Pandas provides basic plotting, Matplotlib offers much more flexibility and advanced options.

✅ Creating a Scatter Plot

df.plot(kind='scatter', x='Sales', y='Profit', color='red', title='Sales vs Profit')

plt.show()

✅ Creating a Histogram

Histograms are useful for understanding the distribution of data:

df['Sales'].plot(kind='hist', bins=10, color='lightblue', title='Sales Distribution')

plt.show()

✅ Customizing Plots with Matplotlib

fig, ax = plt.subplots(figsize=(8, 6))

ax.plot(df['Month'], df['Sales'], label='Sales', color='blue', linestyle='-', marker='o')

ax.set_xlabel('Month')

ax.set_ylabel('Sales')

ax.set_title('Sales Trend')

ax.legend()

plt.show()

🔹 5. Visualizing with Seaborn

Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive, informative statistical graphics. It integrates well with Pandas.

✅ Scatter Plot with Seaborn

import seaborn as sns

sns.scatterplot(x='Sales', y='Profit', data=df, color='blue', title='Sales vs Profit')

plt.show()

✅ Box Plot with Seaborn

A box plot shows the distribution of data based on a five-number summary:

sns.boxplot(x='Month', y='Sales', data=df, palette='Set2')

plt.title('Sales Distribution by Month')

plt.show()

🔹 6. Plotly Integration for Interactive Visualizations

While Matplotlib and Seaborn are great for static plots, Plotly provides interactive charts that are useful for exploratory analysis or sharing data insights.

✅ Scatter Plot with Plotly

import plotly.express as px

fig = px.scatter(df, x='Sales', y='Profit', title='Sales vs Profit')

fig.show()

Plotly’s interactive charts allow for zooming, panning, and hovering over data points to display additional information.

✅ Line Chart with Plotly

fig = px.line(df, x='Month', y='Sales', title='Monthly Sales')

fig.show()

🔹 7. Summary Table