-- This repository is dedicated to demonstrating effective ways of handling and analyzing date and time data using Pandas in Python.

• Date and Time Representation: Understanding different date and time representations in Pandas, including timestamps, periods, and timedeltas.

• Date and Time Operations: Comprehensive examples of performing operations like date arithmetic, time zone conversions, and date comparisons.

• Data Analysis with Dates and Times: Techniques for analyzing time series data, including resampling, shifting, and rolling calculations.

• import pandas as pd

df = pd.DataFrame({'date': ['2023-01-01', '2023-01-02', '2023-01-03'], 'value': [10, 20, 15]})

df['date'] = pd.to_datetime(df['date'])

df['year'] = df['date'].dt.year df['month'] = df['date'].dt.month

• Example 1: Handling Date and Time Data
• Example 2: Date and Time Operations
• Example 3: Data Analysis with Dates and Times