• Data Visualization with Pandas is the presentation of data in a graphical format.
• Pandas itself does not have built-in advanced charting features, but it integrates well with Matplotlib and Seaborn for visualizing data. You can use the .plot() method in Pandas to create various types of charts.
• It helps people understand the significance of data by summarizing and presenting a huge amount of data in a simple and easy-to-understand format and helps communicate information clearly and effectively.

📌 Basic Chart Visualization with Pandas

        import pandas as pd
        import matplotlib.pyplot as plt

        # Sample DataFrame
        data = {'Category': ['A', 'B', 'C', 'D'],
        'Values': [10, 25, 7, 30]}

        df = pd.DataFrame(data)

        # Line Plot
        df.plot(x='Category', y='Values', kind='line', marker='o', title='Line Chart')

        # Show Plot
        plt.show()
    

📌 Types of Charts with Pandas .plot() You can specify the kind parameter to create different charts:

        df.plot(kind='line')      # Line chart
        df.plot(kind='bar')       # Bar chart
        df.plot(kind='barh')      # Horizontal bar chart
        df.plot(kind='hist')      # Histogram
        df.plot(kind='box')       # Boxplot
        df.plot(kind='area')      # Area chart
        df.plot(kind='scatter', x='Category', y='Values')  # Scatter plot
    

📌 Using Seaborn for Better Styling

        import seaborn as sns

        sns.barplot(x='Category', y='Values', data=df)
        plt.show()