Python Data Visualization

Data Visualization Fundamentals

Python offers powerful libraries for creating static, animated, and interactive visualizations. Effective data visualization helps uncover patterns, trends, and insights in your data.

                        # Core visualization libraries

                        import matplotlib.pyplot as plt  # Basic plotting

                        import seaborn as sns         # Statistical visualizations

                        import plotly.express as px   # Interactive plots

                        import pandas as pd            # Data manipulation

                        # Sample data

                        data = pd.DataFrame({

                            'x': [1, 2, 3, 4, 5],

                            'y': [10, 20, 15, 25, 30]

                        })

                        # Basic matplotlib plot

                        plt.plot(data['x'], data['y'])

                        plt.title('Simple Line Plot')

                        plt.show()

Matplotlib

Matplotlib is Python's foundational plotting library. It provides a MATLAB-like interface and is highly customizable for creating publication-quality figures.

                        # Common plot types

                        import numpy as np

                        # Create figure with subplots

                        fig, axes = plt.subplots(2, 2, figsize=(10, 8))

                        # Line plot

                        x = np.linspace(0, 10, 100)

                        axes[0,0].plot(x, np.sin(x), 'r-', label='sin(x)')

                        axes[0,0].set_title('Line Plot')

                        # Scatter plot

                        axes[0,1].scatter(np.random.rand(50), np.random.rand(50))

                        axes[0,1].set_title('Scatter Plot')

                        # Bar plot

                        axes[1,0].bar(['A', 'B', 'C'], [3, 7, 2])

                        axes[1,0].set_title('Bar Chart')

                        # Histogram

                        axes[1,1].hist(np.random.randn(1000), bins=30)

                        axes[1,1].set_title('Histogram')

                        plt.tight_layout()

                        plt.show()

Seaborn

Seaborn is built on Matplotlib and provides a high-level interface for drawing attractive statistical graphics. It works seamlessly with Pandas DataFrames.

                        # Load sample dataset

                        tips = sns.load_dataset('tips')

                        # Create a figure with multiple Seaborn plots

                        plt.figure(figsize=(12, 8))

                        # Scatter plot with regression line

                        plt.subplot(2, 2, 1)

                        sns.regplot(x='total_bill', y='tip', data=tips)

                        plt.title('Regression Plot')

                        # Box plot

                        plt.subplot(2, 2, 2)

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

                        plt.title('Box Plot')

                        # Violin plot

                        plt.subplot(2, 2, 3)

                        sns.violinplot(x='day', y='total_bill', hue='sex', data=tips, split=True)

                        plt.title('Violin Plot')

                        # Heatmap

                        plt.subplot(2, 2, 4)

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

                        plt.title('Correlation Heatmap')

                        plt.tight_layout()

                        plt.show()

Plotly & Interactive Visualization

Plotly creates interactive, publication-quality graphs. Plotly Express provides a simple syntax for complex charts, while the lower-level Graph Objects API offers more control.

                        # Interactive Plotly examples

                        import plotly.express as px

                        import plotly.graph_objects as go

                        # Sample data

                        gapminder = px.data.gapminder()

                        gapminder_2007 = gapminder[gapminder.year == 2007]

                        # Scatter plot with hover info

                        fig1 = px.scatter(gapminder_2007, x='gdpPercap', y='lifeExp',

                                        size='pop', color='continent',

                                        hover_name='country', log_x=True,

                                        title='Life Expectancy vs GDP per Capita (2007)')

                        fig1.show()

                        # Animated bubble chart

                        fig2 = px.scatter(gapminder, x='gdpPercap', y='lifeExp',

                                        size='pop', color='continent',

                                        hover_name='country', log_x=True,

                                        animation_frame='year',

                                        range_x=[100,100000], range_y=[25,90],

                                        title='Life Expectancy vs GDP per Capita Over Time')

                        fig2.show()

Data Visualization Best Practices

Effective visualizations communicate information clearly and accurately. Follow these guidelines:

Choose the right chart type - Match visualization to your data and message
Simplify - Remove unnecessary elements (chartjunk)
Label clearly - Include titles, axis labels, legends
Use color effectively - Be mindful of colorblind viewers
Highlight the important - Draw attention to key insights
Tell a story - Structure visualizations to guide the viewer
Consider your audience - Technical vs. non-technical viewers
Test your visualizations - Ensure they communicate effectively

                        # Example of good visualization practices

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

                        # Create clear, labeled plot

                        plt.plot([2015, 2016, 2017, 2018, 2019],

                                [100, 120, 90, 150, 200],

                                marker='o', label='Product A')

                        plt.plot([2015, 2016, 2017, 2018, 2019],

                                [80, 110, 130, 160, 180],

                                marker='s', label='Product B')

                        # Add clear labels and title

                        plt.title('Annual Sales Growth (2015-2019)', fontsize=14)

                        plt.xlabel('Year', fontsize=12)

                        plt.ylabel('Sales (in thousands)', fontsize=12)

                        plt.legend()

                        plt.grid(True, linestyle='--', alpha=0.7)

                        # Highlight important point

                        plt.annotate('Record Year', xy=(2019, 200),

                                    xytext=(2017, 210),

                                    arrowprops=dict(facecolor='black', shrink=0.05))

                        plt.tight_layout()

                        plt.show()