Pandas Time Series

Introduction

Pandas provides powerful time series functionality for financial and temporal data.

DatetimeIndex

import pandas as pd

# Create datetime index
dates = pd.date_range("2024-01-01", periods=10, freq="D")
s = pd.Series(range(10), index=dates)

# Partial string indexing
s["2024-01"]
s["2024-01-05":"2024-01-08"]

Resampling

# Resample to different frequencies
s.resample("W").mean()     # Weekly
s.resample("M").sum()      # Monthly
s.resample("Q").last()     # Quarterly

# Upsampling with interpolation
s.resample("H").interpolate()

Time Zone Handling

# Set timezone
s = s.tz_localize("UTC")
s = s.tz_convert("US/Eastern")

Practice Problems

1. Create daily time series with missing dates
2. Resample to monthly/yearly frequency
3. Handle timezone conversions
4. Calculate rolling averages
5. Shift and lag time series