This project demonstrates the use of Pandas, a powerful Python library for data manipulation and analysis. Pandas provides data structures like DataFrame and Series to handle structured data efficiently.

• Reading and writing data from various formats like CSV, Excel, SQL, JSON, etc.
• Handling missing data with ease.
• Data filtering, selection, and slicing.
• Grouping and aggregation.
• Merging and joining datasets.
• Time series functionality.
• Data visualization integration.

pip install pandas

import pandas as pd

# Creating a DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)

# Display DataFrame
print(df)

# Read data from CSV
df_csv = pd.read_csv('data.csv')

# Basic operations
print(df['Age'].mean())  # Calculate average age
print(df[df['Age'] > 28])  # Filter rows where age > 28

• Efficient handling of large datasets.
• Intuitive syntax for data analysis.
• Integrates well with other Python libraries like NumPy, Matplotlib, and Scikit-learn.
• Supports both numerical and categorical data.

Contributions, issues, and feature requests are welcome. Feel free to check the issues page.

This project is licensed under the MIT License.