Time Indexing¶

Time-based indexing allows powerful selection and filtering of time series data using date strings.

Date-based Selection¶

Select data using date strings.

1. Exact Date¶

import pandas as pd
import numpy as np

dates = pd.date_range('2025-01-01', periods=365)
df = pd.DataFrame({'value': np.random.randn(365)}, index=dates)

df.loc['2025-01-15']  # Single date

2. Date Range¶

df.loc['2025-01-15':'2025-01-20']  # Inclusive range

3. Partial String¶

df.loc['2025-01']     # Entire January
df.loc['2025-03']     # Entire March

Slicing Time Series¶

Slice by date ranges.

1. Start to Date¶

df.loc[:'2025-03-31']  # From start to March 31

2. Date to End¶

df.loc['2025-10-01':]  # From October 1 to end

3. Year Selection¶

df.loc['2025']  # All of 2025

Multiple Levels¶

Work with datetime components.

1. Extract Components¶

df['year'] = df.index.year
df['month'] = df.index.month
df['day'] = df.index.day
df['weekday'] = df.index.dayofweek

2. Filter by Component¶

# Select Mondays only (weekday 0)
mondays = df[df.index.dayofweek == 0]

3. First of Month¶

first_of_month = df[df.index.day == 1]

between_time and at_time¶

Select by time of day (for intraday data).

1. at_time¶

# DataFrame with datetime index including time
df.at_time('09:30')  # Exactly 9:30 AM

2. between_time¶

df.between_time('09:30', '16:00')  # Market hours

3. Trading Hours Filter¶

# Filter to market hours only
market_hours = df.between_time('09:30', '16:00')

Truncate Method¶

Trim data to date range.

1. Truncate Before¶

df.truncate(before='2025-03-01')

2. Truncate After¶

df.truncate(after='2025-09-30')

3. Both Directions¶

df.truncate(before='2025-03-01', after='2025-09-30')

Practical Examples¶

Common time selection patterns.

1. Last 30 Days¶

from datetime import datetime, timedelta

end = df.index.max()
start = end - timedelta(days=30)
recent = df.loc[start:end]

2. Quarter Selection¶

q1 = df.loc['2025-01':'2025-03']  # Q1
q2 = df.loc['2025-04':'2025-06']  # Q2

3. Year-over-Year Comparison¶

jan_2024 = df.loc['2024-01']
jan_2025 = df.loc['2025-01']