-- This repository is dedicated to demonstrating effective ways of handling and analyzing date and time data using Pandas in Python.
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Date and Time Representation: Understanding different date and time representations in Pandas, including timestamps, periods, and timedeltas.
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Date and Time Operations: Comprehensive examples of performing operations like date arithmetic, time zone conversions, and date comparisons.
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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