Part 10: Intro to Visualization

Kerry Back, Rice University

Introduction

• Visualization is essential for data exploration and communication
• Python’s matplotlib is the foundational plotting library
• We’ll cover: line plots, bar charts, scatter plots, and customization
• Two plotting styles: functional and object-oriented

Setup

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

Basic Line Plot

# Create sample data
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [20000, 25000, 30000, 28000, 35000, 40000]

# Create a simple line plot
plt.figure(figsize=(10, 6))
plt.plot(months, sales)
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales ($)')
plt.show()

Practice: Line Plots

Exercise 1 (with Gemini): Ask Gemini to “create a line plot showing temperature over 7 days”

Exercise 2 (on your own): Type plt.plot([1, 2, 3], [10, 20, 15]) then plt.show() and run it.

Bar Charts

# Bar charts are great for comparing categories
departments = ['Sales', 'Engineering', 'Marketing', 'HR']
employees = [25, 40, 15, 8]

plt.figure(figsize=(10, 6))
plt.bar(departments, employees, color=['skyblue', 'lightgreen', 'coral', 'gold'])
plt.title('Employees by Department')
plt.xlabel('Department')
plt.ylabel('Number of Employees')
plt.show()

Practice: Bar Charts

Exercise 1 (with Gemini): Ask Gemini to “create a bar chart showing sales for 4 different products”

Exercise 2 (on your own): Type plt.bar(['A', 'B', 'C'], [10, 20, 15]) then plt.show() and run it.

Scatter Plots

# Scatter plots show relationships between two variables
experience = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
salary = [35000, 38000, 42000, 46000, 50000, 55000, 60000, 65000, 70000, 75000]

plt.figure(figsize=(10, 6))
plt.scatter(experience, salary, color='purple', alpha=0.7, s=100)
plt.title('Salary vs Years of Experience')
plt.xlabel('Years of Experience')
plt.ylabel('Salary ($)')
plt.show()

Practice: Scatter Plots

Exercise 1 (with Gemini): Ask Gemini to “create a scatter plot showing the relationship between study hours and test scores”

Exercise 2 (on your own): Type plt.scatter([1, 2, 3, 4], [5, 7, 6, 8]) then plt.show() and run it.

Histograms

# Histograms show the distribution of a single variable
np.random.seed(42)
test_scores = np.random.normal(78, 12, 100)

plt.figure(figsize=(10, 6))
plt.hist(test_scores, bins=15, color='lightblue', edgecolor='black', alpha=0.7)
plt.title('Distribution of Test Scores')
plt.xlabel('Test Score')
plt.ylabel('Number of Students')
plt.axvline(test_scores.mean(), color='red', linestyle='--', label=f'Mean: {test_scores.mean():.1f}')
plt.legend()
plt.show()

Two Plotting Styles

Functional Style (using plt functions):

• Simple and intuitive: plt.plot(), plt.scatter()
• Good for single plots

Object-Oriented Style (using axes):

• Uses figure and axes objects: fig, ax = plt.subplots()
• More control and flexibility
• Essential for multiple subplots

Object-Oriented Example

# Create sample data
data = {
    'Quarter': ['Q1', 'Q2', 'Q3', 'Q4'],
    'Revenue': [150000, 180000, 210000, 190000],
    'Profit': [30000, 40000, 50000, 40000]
}
df = pd.DataFrame(data)

# Create subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))

ax1.bar(df['Quarter'], df['Revenue'], color='steelblue')
ax1.set_title('Revenue by Quarter')
ax1.set_ylabel('Revenue ($)')

ax2.plot(df['Quarter'], df['Profit'], marker='o', color='green')
ax2.set_title('Profit Trend')
ax2.set_ylabel('Profit ($)')

plt.tight_layout()
plt.show()

Practice: Object-Oriented Plotting

Exercise 1 (with Gemini): Ask Gemini to “create a figure with 2 subplots side by side using the object-oriented style”

Exercise 2 (on your own): Type fig, ax = plt.subplots() then ax.plot([1, 2, 3]) then plt.show() and run it.

Styling with Seaborn Themes

import seaborn as sns

# Create sample data
np.random.seed(42)
performance_data = pd.DataFrame({
    'marketing_spend': np.random.exponential(20000, 150),
    'revenue': np.random.exponential(50000, 150),
})
performance_data['revenue'] = performance_data['revenue'] + performance_data['marketing_spend'] * 1.5

Whitegrid Theme

sns.set_theme(style="whitegrid")

plt.figure(figsize=(10, 6))
plt.scatter(performance_data['marketing_spend'], performance_data['revenue'],
           alpha=0.7, s=60, color='steelblue')
plt.title("Whitegrid Theme", fontsize=16, fontweight='bold')
plt.xlabel('Marketing Spend ($)')
plt.ylabel('Revenue ($)')
plt.tight_layout()
plt.show()

Darkgrid Theme

sns.set_theme(style="darkgrid")

plt.figure(figsize=(10, 6))
plt.scatter(performance_data['marketing_spend'], performance_data['revenue'],
           alpha=0.7, s=60, color='lightcoral')
plt.title("Darkgrid Theme", fontsize=16, fontweight='bold')
plt.xlabel('Marketing Spend ($)')
plt.ylabel('Revenue ($)')
plt.tight_layout()
plt.show()

Custom Matplotlib Styling

plt.rcdefaults()
fig, ax = plt.subplots(figsize=(10, 6))

ax.set_facecolor('#f8f9fa')
fig.patch.set_facecolor('white')

ax.scatter(performance_data['marketing_spend'], performance_data['revenue'],
          alpha=0.7, s=60, color='#2E86C1', edgecolors='white', linewidth=0.5)

ax.set_title('Professional Custom Styling', fontsize=16, fontweight='bold', pad=20)
ax.set_xlabel('Marketing Spend ($)', fontsize=12)
ax.set_ylabel('Revenue ($)', fontsize=12)
ax.grid(True, color='white', linewidth=1.5, alpha=0.8)

plt.tight_layout()
plt.show()

Setting Global Font Sizes

plt.rc('font', size=14)
plt.rc('axes', titlesize=18)
plt.rc('axes', labelsize=16)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
plt.rc('legend', fontsize=14)

# Example with larger fonts
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [120, 135, 148, 162, 155, 170]

plt.figure(figsize=(10, 6))
plt.plot(months, sales, marker='o', linewidth=3, color='navy')
plt.title('Monthly Sales Growth')
plt.xlabel('Month')
plt.ylabel('Sales (thousands)')
plt.grid(True, alpha=0.3)
plt.show()

Time Series Data

from datetime import datetime
import matplotlib.dates as mdates

# Create realistic stock data
np.random.seed(42)
dates = pd.date_range('2022-01-01', periods=730, freq='D')
trend = np.linspace(100, 150, 730)
seasonality = 10 * np.sin(2 * np.pi * np.arange(730) / 365.25)
noise = np.random.normal(0, 5, 730)
stock_price = trend + seasonality + noise

stock_data = pd.DataFrame({
    'date': dates,
    'price': stock_price
})
stock_data['ma_30'] = stock_data['price'].rolling(window=30).mean()
stock_data['ma_90'] = stock_data['price'].rolling(window=90).mean()

Time Series Visualization

fig, ax = plt.subplots(figsize=(15, 6))

ax.plot(stock_data['date'], stock_data['price'], alpha=0.7, linewidth=1,
        label='Daily Price', color='black')
ax.plot(stock_data['date'], stock_data['ma_30'], linewidth=2,
        label='30-Day MA', color='blue')
ax.plot(stock_data['date'], stock_data['ma_90'], linewidth=2,
        label='90-Day MA', color='red')

ax.set_title('Stock Price with Moving Averages', fontsize=14, fontweight='bold')
ax.set_ylabel('Price ($)', fontsize=12)
ax.legend()
ax.grid(True, alpha=0.3)

ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))
plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)

plt.tight_layout()
plt.show()

Summary

• Matplotlib provides powerful visualization tools
• Two styles: functional (plt.plot()) and object-oriented (ax.plot())
• Seaborn themes simplify professional styling
• Custom styling gives fine-grained control
• Time series require special date formatting
• Always consider: clarity, readability, and purpose