Working with Time Series

Why Dates are Tricky

CSV files load dates as strings: "2023-10-05". You cannot add or subtract strings mathematically.

Converting to Datetime

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

Once converted, you unlock powerful features:

• Extract parts: df['date'].dt.year, .dt.month, .dt.day_name()
• Time math: df['date'] + pd.Timedelta(days=7)
• Filter ranges: df[(df['date'] > '2023-01-01') & (df['date'] < '2023-12-31')]

Setting the Index

For time-series analysis (stock prices, sensor data), set the date as the DataFrame index:

df.set_index('date', inplace=True)

Pro Tip: Always use format= in to_datetime if your date string is weird (e.g., "05/10/2023" vs "10-05-2023"). It makes parsing 10x faster.